interface definitions {
    [id: string]: any;
}
/**
 * This file was auto-generated by openapi-typescript.
 * Do not make direct changes to the file.
 */
export interface paths {
    "/deployables/create": {
        /**
         * Create a private deployable.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "deployables:write",
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "body": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["CreateDeployable"];
    };
    "/deployables/{deployable_id}/get": {
        /**
         * Get a deployable.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "deployables:read"
         *     ],
         *     "deployables": [
         *       {
         *         "params": "deployable_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["GetDeployable"];
    };
    "/deployables/{deployable_id}/update": {
        /**
         * Update a deployable
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "deployables:write"
         *     ],
         *     "deployables": [
         *       {
         *         "params": "deployable_id"
         *       }
         *     ]
         *   },
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "body": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["UpdateDeployable"];
    };
    "/deployables/delete": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "deployables:write"
         *     ],
         *     "deployables": [
         *       {
         *         "body": "id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteDeployable"];
    };
    "/deployables/{deployable_id}/share": {
        /**
         * Share a private deployable.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "sharing:write",
         *       "deployables:read"
         *     ],
         *     "deployables": [
         *       {
         *         "params": "deployable_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["CreateDeployableKey"];
    };
    "/deployables/{deployable_id}/invite": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "users:write",
         *       "deployables:read"
         *     ],
         *     "deployables": [
         *       {
         *         "params": "deployable_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["CreateDeployableInvite"];
    };
    "/deployables/{deployable_id}/users/{user_id}/update": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "users:write",
         *       "deployables:read"
         *     ],
         *     "deployables": [
         *       {
         *         "params": "deployable_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["UpdateUsersDeployablePermissions"];
    };
    "/deployables/{deployable_id}/private": {
        /**
         * Unshare a shared deployable, making it private.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "sharing:write",
         *       "deployables:read"
         *     ],
         *     "deployables": [
         *       {
         *         "params": "deployable_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteDeployableKey"];
    };
    "/deployables/list": {
        /**
         * List all deployables.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": []
         *   }
         * ]
         */
        get: operations["ListDeployables"];
    };
    "/deployablegroups/{deployablegroup_id}/delete": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["DeleteDeployableGroup"];
    };
    "/deployablegroups/list": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["ListDeployableGroups"];
    };
    "/deployablegroups/create": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["CreateDeployableGroup"];
    };
    "/deployablegroups/{deployablegroup_id}/get": {
        /**
         * ### Required permissions
         * > []
         */
        get: operations["GetDeployableGroup"];
    };
    "/deployablegroups/{deployablegroup_id}/update": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["UpdateDeployableGroup"];
    };
    "/deployablegroups/{deployablegroup_id}/share": {
        /**
         * Share a private deployable group. Required read access to all deployables that are shared, and the datasets deployables are tied to.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "sharing:write"
         *     ]
         *   }
         * ]
         */
        post: operations["CreateDeployableGroupKey"];
    };
    "/deployablegroups/{deployablegroup_id}/private": {
        /**
         * Unshare a shared deployable group, making it private.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "sharing:write"
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteDeployableGroupKey"];
    };
    "/projects/create": {
        /**
         * Create a new Project that your user will have access to
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "organizations:write"
         *     ],
         *     "organizations": [
         *       {
         *         "body": "organization_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["CreateProject"];
    };
    "/projects/update": {
        /**
         * Update metadata of a Project
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "admin"
         *     ]
         *   }
         * ]
         */
        post: operations["UpdateProject"];
    };
    "/projects/transfer_to_organization": {
        /**
         * Tie a project to an organization. This requires admin privileges over project and organization.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "admin"
         *     ],
         *     "projects": [
         *       {
         *         "user_info": "username"
         *       }
         *     ]
         *   },
         *   {
         *     "actions": [
         *       "organizations:write"
         *     ],
         *     "organizations": [
         *       {
         *         "body": "organization_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["TransferProjectToOrganization"];
    };
    "/projects/list": {
        /**
         * List all projects and their metadata
         *
         * ### Required permissions
         * > []
         */
        get: operations["ListProjects"];
    };
    "/auth/users/create": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "users:write"
         *     ]
         *   }
         * ]
         */
        post: operations["CreateUser"];
    };
    "/auth/users/list": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": []
         *   }
         * ]
         */
        post: operations["ListUsers"];
    };
    "/auth/is_authorized": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["IsUserAuthorized"];
    };
    "/auth/info": {
        /**
         * Get user_id, key_id and permissions from an auth header.
         *
         * ### Required permissions
         * > []
         */
        get: operations["GetAuthHeaderInfo"];
    };
    "/auth/invite/create": {
        /**
         * Invite a user to a project using either their email or their user_id
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "users:write"
         *     ]
         *   }
         * ]
         */
        post: operations["CreateProjectInvite"];
    };
    "/auth/invite/list": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["ListProjectInvites"];
    };
    "/auth/invite/accept": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["AcceptProjectInvite"];
    };
    "/auth/invite/delete": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "users:write"
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteProjectInvite"];
    };
    "/auth/invite/resend": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "users:write"
         *     ]
         *   }
         * ]
         */
        post: operations["ResendProjectInvite"];
    };
    "/auth/users/{user_id}": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "users": [
         *       {
         *         "params": "user_id"
         *       }
         *     ],
         *     "actions": [
         *       "users:read"
         *     ]
         *   }
         * ]
         */
        get: operations["GetUser"];
    };
    "/auth/users/{user_id}/update": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "users": [
         *       {
         *         "params": "user_id"
         *       }
         *     ],
         *     "actions": [
         *       "users:write"
         *     ]
         *   }
         * ]
         */
        post: operations["UpdateUser"];
    };
    "/auth/users/{user_id}/delete": {
        /**
         * Deletes a User from ONLY your project. If they have permissions for other projects, they will still have acces to them.
         *
         * ### Required permissions
         * > [
         *   {
         *     "users": [
         *       {
         *         "params": "user_id"
         *       }
         *     ],
         *     "actions": [
         *       "users:write"
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteUser"];
    };
    "/auth/users/{user_id}/keys/create": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "users": [
         *       {
         *         "params": "user_id"
         *       }
         *     ],
         *     "actions": [
         *       "users:write"
         *     ]
         *   }
         * ]
         */
        post: operations["CreateUserKey"];
    };
    "/auth/users/{user_id}/keys/list": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "users": [
         *       {
         *         "params": "user_id"
         *       }
         *     ],
         *     "actions": [
         *       "users:read"
         *     ]
         *   }
         * ]
         */
        post: operations["ListUserKeys"];
    };
    "/auth/users/{user_id}/keys/delete": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "users": [
         *       {
         *         "params": "user_id"
         *       }
         *     ],
         *     "actions": [
         *       "users:write"
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteUserKey"];
    };
    "/datasets/{dataset_id}/cluster/centroids/insert": {
        /**
         * Insert your own cluster centroids for it to be used in approximate search settings and cluster aggregations.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["InsertClusterCentroids"];
    };
    "/datasets/{dataset_id}/cluster/centroids/update": {
        /**
         * Update your own cluster centroids for it to be used in approximate search settings and cluster aggregations. Will merge rather than replace centroids.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["UpdateClusterCentroids"];
    };
    "/datasets/{dataset_id}/cluster/centroids/compare_centroids": {
        /**
         * For each centroid in a list of centroids, find the closest centroids in another set of centroids.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       },
         *       {
         *         "body": "comparison_centroids.dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["CompareClusterCentroids"];
    };
    "/datasets/{dataset_id}/aggregate": {
        /**
         * Aggregation/Groupby of a collection using an aggregation query.
         * The aggregation query is a json body that follows the schema of:
         *
         *     {
         *         "groupby" : [
         *             {"name": <alias>, "field": <field in the collection>, "agg": "category"},
         *             {"name": <alias>, "field": <another groupby field in the collection>, "agg": "numeric"}
         *         ],
         *         "metrics" : [
         *             {"name": <alias>, "field": <numeric field in the collection>, "agg": "avg"}
         *             {"name": <alias>, "field": <another numeric field in the collection>, "agg": "max"}
         *             {"name": <alias>, "fields": [<numeric field in the collection>, <another numeric field in the collection>], "agg": "correlation"}
         *         ]
         *     }
         *     For example, one can use the following aggregations to group score based on region and player name.
         *     {
         *         "groupby" : [
         *             {"name": "region", "field": "player_region", "agg": "category"},
         *             {"name": "player_name", "field": "name", "agg": "category"}
         *         ],
         *         "metrics" : [
         *             {"name": "average_score", "field": "final_score", "agg": "avg"},
         *             {"name": "max_score", "field": "final_score", "agg": "max"},
         *             {'name':'total_score','field':"final_score", 'agg':'sum'},
         *             {'name':'average_deaths','field':"final_deaths", 'agg':'avg'},
         *             {'name':'highest_deaths','field':"final_deaths", 'agg':'max'},
         *             {'name':'score_death_correlation', 'fields':['final_deaths', 'final_score'], 'agg': 'correlation'},
         *         ]
         *     }
         * - "groupby" is the fields you want to split the data into. These are the available groupby types:
         *     - category" : groupby a field that is a category
         *     - numeric: groupby a field that is a numeric
         * - "metrics" is the fields you want to metrics you want to calculate in each of those, every aggregation includes a frequency metric. These are the available metric types:
         *     - For single fields: "avg"/"average"/"mean", "cardinality", "count", "kurtosis", "max", "min", "percentiles", "kurtosis", "std_deviation", "std_deviation_bounds", "sum", "sum_of_squares", "variance"
         *     - For multiple fields (the attribute "fields" must be used instead of "field"): "correlation", "covariance", "kurtosis", "mean", "skewness", "variance"
         *
         * The response returned has the following in descending order.
         *
         * IF you want to return documents, specify a "group_size" parameter and a "select_fields" parameter if you want to limit the specific fields chosen.
         * This looks as such:
         *
         *     {
         *       'groupby':[
         *         {'name':'Manufacturer','field':'manufacturer','agg':'category',
         *         'group_size': 10, 'select_fields': ["name"]},
         *       ],
         *       'metrics':[
         *         {'name':'Price Average','field':'price','agg':'avg'},
         *       ],
         *     }
         *
         *     {"title": {"title": "books", "frequency": 200, "documents": [{...}, {...}]}, {"title": "books", "frequency": 100, "documents": [{...}, {...}]}}
         *
         * For array-aggregations, you can add "agg": "array" into the aggregation query.
         *
         *
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["Aggregate"];
    };
    "/datasets/{dataset_id}/cluster/aggregate": {
        /**
         * Takes an aggregation query and gets the aggregate of each cluster in a collection. This helps you interpret each cluster and what is in them.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["AggregateClusters"];
    };
    "/datasets/{dataset_id}/cluster/facets": {
        /**
         * Takes a high level aggregation of every field and every cluster in a collection. This helps you interpret each cluster and what is in them.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["ListClusterFacets"];
    };
    "/datasets/{dataset_id}/cluster/centroids/list_closest_to_center": {
        /**
         * List documents with vector fields closest to centroids.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["ListClosestToCentroids"];
    };
    "/datasets/{dataset_id}/cluster/centroids/list_furthest_from_center": {
        /**
         * List documents with vector fields furthest from centroids.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["ListFurthestFromCentroids"];
    };
    "/datasets/{dataset_id}/cluster/centroids/documents": {
        /**
         * List centroids, optionally filtering by their ids.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["ListCentroids"];
    };
    "/datasets/{dataset_id}/cluster/centroids/configs/list": {
        /**
         * List centroids configs for a dataset.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["ListCentroidConfigs"];
    };
    "/datasets/{dataset_id}/cluster/centroids/{centroid_id}/delete": {
        /**
         * Delete a centroid by ID
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteCentroid"];
    };
    "/datasets/{dataset_id}/cluster/realtime": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "body": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["RealtimeClustering"];
    };
    "/datasets/{dataset_id}/cluster/merge": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["MergeClusters"];
    };
    "/datasets/{dataset_id}/cluster/centroids/summaries/create": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read",
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["CreateClusterSummaries"];
    };
    "/datasets/{dataset_id}/cluster/centroids/labels/create": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read",
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["CreateClusterLabels"];
    };
    "/datasets/{dataset_id}/cluster/centroids/summaries/list": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["ListClusterSummaries"];
    };
    "/datasets/{dataset_id}/cluster/hierarchical/create": {
        /**
         * Endpoint for creating Hierarchical Clusters from a given dataset on top of existing clusters.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read",
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["CreateHierarchicalClusters"];
    };
    "/datasets/{dataset_id}/cluster/centroids/summaries/bulk_delete": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteClusterSummaries"];
    };
    "/organizations/create": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["CreateOrganization"];
    };
    "/organizations/{organization_id}/update": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "organizations:write"
         *     ],
         *     "organizations": [
         *       {
         *         "params": "organization_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["UpdateOrganization"];
    };
    "/admin/organizations/{organization_id}/update": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "support:write"
         *     ],
         *     "organizations": [
         *       {
         *         "params": "organization_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["UpdateOrganizationAdmin"];
    };
    "/organizations/list": {
        /**
         * List all organizations and their metadata
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "organizations:read"
         *     ]
         *   }
         * ]
         */
        get: operations["ListOrganizations"];
    };
    "/organizations/{organization_id}/delete": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "organizations:write"
         *     ],
         *     "organizations": [
         *       {
         *         "params": "organization_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteOrganization"];
    };
    "/organizations/{organization_id}/get": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "organizations:read"
         *     ],
         *     "organizations": [
         *       {
         *         "params": "organization_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["GetOrganization"];
    };
    "/organizations/{organization_id}/usage/get": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "organizations:read"
         *     ],
         *     "organizations": [
         *       {
         *         "params": "organization_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["GetOrganizationUsage"];
    };
    "/organizations/{organization_id}/users/list": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "organizations:read"
         *     ],
         *     "organizations": [
         *       {
         *         "params": "organization_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["ListUsersInOrganization"];
    };
    "/organizations/{organization_id}/projects/list": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "organizations:read"
         *     ],
         *     "organizations": [
         *       {
         *         "params": "organization_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["ListProjectsInOrganization"];
    };
    "/organizations/{organization_id}/metrics/aggregate": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "organizations:read"
         *     ],
         *     "organizations": [
         *       {
         *         "params": "organization_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["AggregateOrganizationMetrics"];
    };
    "/connectors/create": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write",
         *       "connectors:write"
         *     ],
         *     "datasets": [
         *       {
         *         "body": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["CreateConnector"];
    };
    "/connectors/check_connection": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["CheckConnection"];
    };
    "/connectors/{connector_id}/get": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "connectors:read"
         *     ],
         *     "connectors": [
         *       {
         *         "params": "connector_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["GetConnector"];
    };
    "/connectors/{connector_id}/update": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "connectors:write",
         *       "datasets:write"
         *     ],
         *     "connectors": [
         *       {
         *         "params": "connector_id"
         *       }
         *     ],
         *     "datasets": [
         *       {
         *         "body": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["UpdateConnector"];
    };
    "/connectors/{connector_id}/trigger": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "connectors:write"
         *     ],
         *     "connectors": [
         *       {
         *         "params": "connector_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["TriggerConnector"];
    };
    "/connectors/{connector_id}/delete": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "connectors:write"
         *     ],
         *     "connectors": [
         *       {
         *         "params": "connector_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteConnector"];
    };
    "/connectors/list": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": []
         *   }
         * ]
         */
        get: operations["ListConnectors"];
    };
    "/connectors/types/list": {
        /**
         * ### Required permissions
         * > []
         */
        get: operations["ListConnectorTypes"];
    };
    "/connectors/types/{connector_id}/get": {
        /**
         * ### Required permissions
         * > []
         */
        get: operations["GetConnectorType"];
    };
    "/connectors/{connector_id}/jobs/list": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "connectors:read"
         *     ],
         *     "connectors": [
         *       {
         *         "params": "connector_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["ListConnectorJobs"];
    };
    "/connectors/{connector_id}/jobs/{job_id}/get": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "connectors:read"
         *     ],
         *     "connectors": [
         *       {
         *         "params": "connector_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["GetConnectorJob"];
    };
    "/workflows/trigger": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read",
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "body": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["TriggerWorkflow"];
    };
    "/workflows/list": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ]
         *   }
         * ]
         */
        get: operations["ListWorkflows"];
    };
    "/workflows/{workflow_id}/get": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ]
         *   }
         * ]
         */
        post: operations["GetWorkflowStatus"];
    };
    "/workflows/{workflow_id}/delete": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteWorkflowStatus"];
    };
    "/workflows/{workflow_id}/terminate": {
        /**
         * Terminate a workflow
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ]
         *   }
         * ]
         */
        post: operations["TerminateWorkflow"];
    };
    "/workflows/{workflow_id}/metadata": {
        /**
         * Update metadata for a workflow run
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ]
         *   }
         * ]
         */
        post: operations["UpsertWorkflowMetadata"];
    };
    "/workflows/{workflow_id}/progress": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ]
         *   }
         * ]
         */
        post: operations["UpsertWorkflowProgress"];
    };
    "/workflows/{workflow_id}/status": {
        /**
         * Update status for a workflow run
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ]
         *   }
         * ]
         */
        post: operations["UpsertWorkflowStatus"];
    };
    "/workflows/types/list": {
        /**
         * ### Required permissions
         * > []
         */
        get: operations["ListWorkflowTypes"];
    };
    "/workflows/types/{workflow_id}/get": {
        /**
         * ### Required permissions
         * > []
         */
        get: operations["GetWorkflowType"];
    };
    "/workflows/types/{workflow_id}/validate": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["ValidateWorkflowParams"];
    };
    "/datasets/{dataset_id}/documents/insert": {
        /**
         * When inserting the document you can specify your own id for a document by using the field name **"\_id"**.
         *
         *     For specifying your own vector use the suffix (ends with)  **"\_vector\_"** for the field name.
         * e.g. "product\_description\_vector\_"
         *
         *
         *     If inserting many items in a short timespan, use bulkInsert, or set update_schema to false.
         *     Every insert api call temporarily locks and updates the schema, so will slow down with many inserts in quick succession.
         *
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["Insert"];
    };
    "/datasets/{dataset_id}/documents/bulk_insert": {
        /**
         * Insert one or more documents.
         *
         * This method is the same as [`/insert`](/reference/insert) but provides support for more than one document to be inserted at a time.
         *
         * For each insertion, if an `_id` is provided and a document with that ID already exists, all existing field values will be deleted and the new ones will be stored. If no `_id` field is provided, one will automatically be added to the document.
         *
         * This method will entirely replace the documents. If you would like to only partially update a document, use [`/update_where`](/reference/updatewhere).
         *
         * If the document with the provided `_id` doesn't exist, one will be automatically created.
         * If the provided dataset doesn't exist, one will be automatically created.
         *
         * Limit the payload of documents to a maximum 100MB for optimal performance.
         *
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["BulkInsert"];
    };
    "/datasets/{dataset_id}/get_file_upload_urls": {
        /**
         * specify a list of file paths. For each file path, a url upload_url is returned. files can be POSTed on upload_url to upload them. They can then be accessed on url. Upon dataset deletion, these files will be deleted.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["GetFileUploadUrlsForDataset"];
    };
    "/datasets/{dataset_id}/list_file_uploads": {
        /**
         * Return up to 1000 files uploaded for a dataset.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["ListFileUploadsForDataset"];
    };
    "/datasets/{dataset_id}/parse_blob": {
        /**
         * Bulk insert a large number of documents by downloading a file using "download url". This bypasses the need to directly send documents to the api, as happens in BulkInsert.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["ParseBlob"];
    };
    "/admin/copy_foreign_dataset": {
        /**
         * Copy a dataset from another users projects into your project. This is considered a project job.
         *
         * ### Required permissions
         * > []
         */
        post: operations["CopyForeignDataset"];
    };
    "/admin/request_read_api_key": {
        /**
         * Creates a read only key for your project. Make sure to save the api key somewhere safe. When doing a search the admin username should still be used.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "users:write",
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "raw": "*"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["CreateProjectReadKey"];
    };
    "/datasets/{dataset_id}/delete": {
        /**
         * Delete a dataset.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteDataset"];
    };
    "/datasets/create": {
        /**
         * A dataset can store documents to be **searched, retrieved, filtered and aggregated** (similar to Collections in MongoDB, Tables in SQL, Indexes in ElasticSearch).
         *
         * A powerful and core feature of VecDB is that you can store both your metadata and vectors in the same document.
         * When specifying the schema of a dataset and inserting your own vector use the suffix (ends with) **"\_vector\_"** for the field name, and specify the length of the vector in dataset_schema.
         *
         * For example:
         *
         *     {
         *         "product_image_vector_": 1024,
         *         "product_text_description_vector_" : 128
         *     }
         *
         * These are the field types supported in our datasets: **["text", "numeric", "date", "dict", "chunks", "vector", "chunkvector"]**.
         *
         * For example:
         *
         *     {
         *         "product_text_description" : "text",
         *         "price" : "numeric",
         *         "created_date" : "date",
         *         "product_texts_chunk_": "chunks",
         *         "product_text_chunkvector_" : 1024
         *     }
         *
         * You don't have to specify the schema of every single field when creating a dataset, as VecDB will automatically detect the appropriate data type for each field (vectors will be automatically identified by its **"\_vector\_"** suffix). Infact you also don't always have to use this endpoint to create a dataset as **\/datasets/bulk_insert** will infer and create the dataset and schema as you insert new documents.
         *
         * Note:
         *  * A dataset name/id can only contain undercase letters, dash, underscore and numbers.
         *  * "\_id" is reserved as the key and id of a document.
         *  * Once a schema is set for a dataset it cannot be altered. If it has to be altered, utlise the copy dataset endpoint.
         *
         * For more information about vectors check out the 'Vectorizing' section, **\/services/search/vector** or out blog at [https://relevance.ai/blog](https://relevance.ai/blog).
         * For more information about chunks and chunk vectors check out **\/services/search/chunk**.
         *
         *
         *
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "body": "id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["CreateDataset"];
    };
    "/datasets/{dataset_id}/schema": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["GetSchema"];
    };
    "/datasets/list": {
        /**
         * List all datasets in a project that you are authorized to read/write.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": []
         *   }
         * ]
         */
        get: operations["ListDatasets"];
    };
    "/datasets/combine": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "body": "dataset_ids"
         *       }
         *     ]
         *   },
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "body": "new_dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["CombineDatasets"];
    };
    "/datasets/combine/{job_id}/get": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["GetCombineJobStatus"];
    };
    "/datasets/search": {
        /**
         * Search all datasets in a project that you are authorized to read/write.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": []
         *   }
         * ]
         */
        get: operations["SearchDatasets"];
    };
    "/datasets/{dataset_id}/monitor/health": {
        /**
         * Gives you a summary of the health of your vectors, e.g. how many documents with vectors are missing, how many documents with zero vectors
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["GetFieldHealth"];
    };
    "/datasets/{dataset_id}/monitor/stats": {
        /**
         * View the usage statistics of a dataset.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["GetDatasetStats"];
    };
    "/datasets/{dataset_id}/monitor/usage": {
        /**
         * View api call usage statistics for a dataset.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["GetDatasetUsage"];
    };
    "/datasets/{dataset_id}/vector_mappings": {
        /**
         * DEPRECATED !!!! Retrieve the mapping of vectors generated through fields, dictionary, array, etc
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["GetVectorMappings"];
    };
    "/datasets/{dataset_id}/details": {
        /**
         * Returns a page of datasets and in detail the dataset's associated information that you are authorized to read/write. The information includes:
         *
         * | Information      | Description |
         * | ----------- | ----------- |
         * | schema      | Data schema of a dataset. (returns same data as **\/dataset/{dataset_id}/schema**) |
         * | metadata   | Metadata of a dataset. (returns same data as **\/dataset/{dataset_id}/metadata**) |
         * | stats   | Statistics of number of documents and size of a dataset. (returns same data as **\/dataset/{dataset_id}/monitor/stats**) |
         * | vector_health   | Number of zero vectors stored. (returns same data as **\/dataset/{dataset_id}/monitor/health**) |
         * | schema_stats   | Fields and number of documents missing/not missing for that field. (returns same data as **\/dataset/{dataset_id}/monitor/stats**) |
         * | active_jobs   | All active jobs/tasks on the dataset. (returns same data as **\/dataset/{dataset_id}/tasks/list**) |
         *
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["GetDatasetDetails"];
    };
    "/datasets/{dataset_id}/documents/get": {
        /**
         * Retrieve a document by its `_id`.
         *
         * This is the most performant method to get a document or check if it exists, faster than filtering a dataset for by an `_id`.
         *
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["GetDocument"];
    };
    "/datasets/{dataset_id}/documents/delete": {
        /**
         * Delete a document by its `_id` and remove it from the dataset.
         *
         * Support for deleting many documents at once is available through [`/bulk_delete`](/reference/bulk_delete_api_datasets__dataset_id__documents_bulk_delete_post).
         *
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteDocument"];
    };
    "/datasets/{dataset_id}/settings": {
        /**
         * Retreives settings for dataset.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["GetDatasetSettings"];
        /**
         * Add and overwrite settings about a dataset. Notably description, data source, etc.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["UpsertDatasetSettings"];
    };
    "/datasets/{dataset_id}/metadata": {
        /**
         * Retreives metadata about a dataset. Notably description, data source, etc
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["GetDatasetMetadata"];
        /**
         * Retreives metadata about a dataset. Notably description, data source, etc
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["UpsertDatasetMetadata"];
    };
    "/datasets/{dataset_id}/documents/update": {
        /**
         * Update an existing document by providing its `_id` and an object containing the new field values.
         *
         * This method supports a partial update so you may add new fields or update existing ones. It will not overwrite the entire existing document.
         *
         * If the provided `_id` has no matching document in the dataset, a new document will be created. Likewise, if the provided `dataset_id` does not match a dataset in your project a new one will be created.
         *
         * Support for updating many documents at once with a filter condition is available through [`/update_where`](/reference/updatewhere).
         *
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["Update"];
    };
    "/datasets/{dataset_id}/documents/delete_fields": {
        /**
         * Delete fields in a document in a dataset by its id.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteDocumentFields"];
    };
    "/datasets/{dataset_id}/documents/bulk_update": {
        /**
         * Edits documents by providing a key value pair of fields you are adding or changing, make sure to include the "_id" in the documents.
         *
         *     If updating many items in a short timespan, please use BulkUpdate.
         *
         *     Every update api call temporarily locks and updates the schema, so will slow down with many updates in quick succession.
         *
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["BulkUpdate"];
    };
    "/datasets/{dataset_id}/documents/update_where": {
        /**
         * Update fields in all existing documents that match a filter condition.
         *
         * This method supports a partial update so you may add new fields or update only those you need. Other fields will remain unaffected.
         *
         * If the provided `dataset_id` does not match a dataset in your project, a new one will be created.
         *
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["UpdateWhere"];
    };
    "/datasets/{dataset_id}/tags/append": {
        /**
         * Append tags to tag field.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["AppendTags"];
    };
    "/datasets/{dataset_id}/tags/delete": {
        /**
         * Delete tags from tag field by tag value. tag field can be in format ["cat"], or [{"label":"cat"}]
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteTags"];
    };
    "/datasets/{dataset_id}/tags/merge": {
        /**
         * Delete tags from tag field by tag value. tag field can be in format ["cat"], or [{"label":"cat"}]. tags_to_merge should look like {"dog":"pet","cat":"pet"}
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["MergeTags"];
    };
    "/datasets/{dataset_id}/tags/edit": {
        /**
         * Perform all tagging operations on a field in one payload.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["EditTags"];
    };
    "/datasets/{dataset_id}/facets": {
        /**
         * Takes a high level aggregation of every field, return their unique values and frequencies. This is used to help create the filter bar for search.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["ListFacets"];
    };
    "/datasets/{dataset_id}/documents/get_where": {
        /**
         * Retrieve documents with filters.
         * afterId is provided to retrieve even more documents. Loop through it to retrieve all documents in the database.
         * Filter is used to retrieve documents that match the conditions set in a filter query.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["GetWhere"];
    };
    "/datasets/{dataset_id}/documents/paginate": {
        /**
         * Retrieve documents with filters.
         * afterId is provided to retrieve even more documents. Loop through it to retrieve all documents in the database.
         * Filter is used to retrieve documents that match the conditions set in a filter query.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["PaginateDocuments"];
    };
    "/datasets/{dataset_id}/summaries/create": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read",
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["CreateDatasetSummary"];
    };
    "/datasets/{dataset_id}/summaries/history/list": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["ListDatasetSummaryHistory"];
    };
    "/datasets/{dataset_id}/documents/bulk_get": {
        /**
         * Retrieve documents by their IDs ("_id" field). This will retrieve the documents faster than a filter applied on the "_id" field. For single id lookup version of this request use /datasets/{dataset_id}/documents/get.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["BulkGetDocuments"];
    };
    "/datasets/{dataset_id}/documents/bulk_delete": {
        /**
         * Delete a list of documents by their IDs. For deleting a single document refer to /datasets/{dataset_id}/documents/delete or deleting documents by filters refer to /datasets/{dataset_id}/documents/delete_where.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["BulkDeleteDocuments"];
    };
    "/datasets/{dataset_id}/documents/list": {
        /**
         * Retrieve documents with filters.
         * afterId is provided to retrieve even more documents. Loop through it to retrieve all documents in the database.
         * Filter is used to retrieve documents that match the conditions set in a filter query.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        get: operations["ListDocuments"];
    };
    "/datasets/{dataset_id}/documents/delete_where": {
        /**
         * Delete documents that match on a filters. At least one filter must be provided.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["DeleteWhere"];
    };
    "/datasets/{dataset_id}/simple_search": {
        /**
         * SimpleSearch is an easy way to use vector search and traditional text matching to search your dataset. It also supports filtering, sorting and aggregating results.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["SimpleSearchPost"];
    };
    "/datasets/{dataset_id}/search": {
        /**
         * SimpleSearch is an easy way to use vector search and traditional text matching to search your dataset. It also supports filtering, sorting and aggregating results.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["Search"];
    };
    "/datasets/{dataset_id}/recommend": {
        /**
         * Recommend documents similar to specific documents. Specify which vector field must be used for recommendation using  the documentsToRecommend property.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["Recommend"];
    };
    "/datasets/{dataset_id}/clone": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   },
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "body": "new_dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["CloneDataset"];
    };
    "/services/prediction/regression/knn": {
        /**
         * Predict using KNN regression.
         *
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:read"
         *     ],
         *     "datasets": [
         *       {
         *         "body": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["PredictKNNRegression"];
    };
    "/services/prediction/knn_from_results": {
        /**
         * Predict using KNN regression from search results.
         *
         * ### Required permissions
         * > []
         */
        post: operations["PredictKNNFromResults"];
    };
    "/services/evaluation/bias": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["BiasEvaluation"];
    };
    "/services/vectorize": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["Vectorize"];
    };
    "/services/vectorize/{dataset_id}/vectorize_and_insert": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["VectorizeAndInsert"];
    };
    "/services/vectorize/{dataset_id}": {
        /**
         * ### Required permissions
         * > [
         *   {
         *     "actions": [
         *       "datasets:write"
         *     ],
         *     "datasets": [
         *       {
         *         "params": "dataset_id"
         *       }
         *     ]
         *   }
         * ]
         */
        post: operations["VectorizeField"];
    };
    "/services/prompt_to_image/create": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["CreateImageFromPrompt"];
    };
    "/datasets/{dataset_id}/field_children/{fieldchildren_id}/delete": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["DeleteFieldChildren"];
    };
    "/datasets/{dataset_id}/field_children/list": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["ListFieldChildrens"];
    };
    "/datasets/{dataset_id}/field_children/{fieldchildren_id}/update": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["UpdateFieldChildren"];
    };
    "/workflows/favourites/{favouriteworkflow_id}/delete": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["DeleteFavouriteWorkflow"];
    };
    "/workflows/favourites/list": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["ListFavouriteWorkflows"];
    };
    "/workflows/favourites/{favouriteworkflow_id}/update": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["UpdateFavouriteWorkflow"];
    };
    "/savedfilters/{savedfilter_id}/delete": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["DeleteSavedFilter"];
    };
    "/savedfilters/list": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["ListSavedFilters"];
    };
    "/savedfilters/{savedfilter_id}/update": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["UpdateSavedFilter"];
    };
    "/savedfilters/{savedfilter_id}/get": {
        /**
         * ### Required permissions
         * > []
         */
        get: operations["GetSavedFilter"];
    };
    "/savedfilters/create": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["CreateSavedFilter"];
    };
    "/components/{component_id}/delete": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["DeleteComponent"];
    };
    "/components/list": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["ListComponents"];
    };
    "/components/{component_id}/update": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["UpdateComponent"];
    };
    "/components/{component_id}/get": {
        /**
         * ### Required permissions
         * > []
         */
        get: operations["GetComponent"];
    };
    "/components/create": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["CreateComponent"];
    };
    "/datasets/{dataset_id}/editor/history/list": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["ListDatasetEditorHistorys"];
    };
    "/datasets/{dataset_id}/editor/history/create": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["CreateDatasetEditorHistory"];
    };
    "/datasets/{dataset_id}/editor/configuration/list": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["ListDatasetEditorConfigurations"];
    };
    "/datasets/{dataset_id}/editor/configuration/create": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["CreateDatasetEditorConfiguration"];
    };
    "/datasets/{dataset_id}/editor/configuration/{dataseteditorconfiguration_id}/update": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["UpdateDatasetEditorConfiguration"];
    };
    "/datasets/{dataset_id}/editor/configuration/{dataseteditorconfiguration_id}/delete": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["DeleteDatasetEditorConfiguration"];
    };
    "/datasets/{dataset_id}/editor/configuration/{dataseteditorconfiguration_id}/get": {
        /**
         * ### Required permissions
         * > []
         */
        get: operations["GetDatasetEditorConfiguration"];
    };
    "/datasets/{dataset_id}/fields/{field}/keyphrase/list": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["ListKeyphrases"];
    };
    "/datasets/{dataset_id}/fields/{field}/keyphrase/{keyphrase_id}/update": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["UpdateKeyphrase"];
    };
    "/datasets/{dataset_id}/fields/{field}/keyphrase/{keyphrase_id}/delete": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["DeleteKeyphrase"];
    };
    "/datasets/{dataset_id}/fields/{field}/keyphrase/{keyphrase_id}/get": {
        /**
         * ### Required permissions
         * > []
         */
        get: operations["GetKeyphrase"];
    };
    "/datasets/{dataset_id}/fields/{field}/keyphrase/bulk_update": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["BulkUpdateKeyphrases"];
    };
    "/taxonomys/list": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["ListTaxonomys"];
    };
    "/taxonomys/{taxonomy_id}/update": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["UpdateTaxonomy"];
    };
    "/taxonomys/{taxonomy_id}/delete": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["DeleteTaxonomy"];
    };
    "/taxonomys/{taxonomy_id}/get": {
        /**
         * ### Required permissions
         * > []
         */
        get: operations["GetTaxonomy"];
    };
    "/taxonomys/create": {
        /**
         * ### Required permissions
         * > []
         */
        post: operations["CreateTaxonomy"];
    };
}
export interface components {
    schemas: {
        CreateDeployableInput: {
            /** @description The deployable configuration. */
            configuration?: {
                [key: string]: unknown;
            };
            /** @description Dataset to create deployable for. */
            dataset_id?: string;
        };
        CreateDeployableOutput: {
            deployable_id: string;
            dataset_id: string;
            project_id: string;
            configuration: {
                [key: string]: unknown;
            };
            private?: boolean;
        };
        GetDeployableInput: unknown;
        GetDeployableOutput: {
            deployable_id?: string;
            dataset_id?: string;
            project_id?: string;
            api_key?: string;
            configuration?: {
                [key: string]: unknown;
            };
            private?: boolean;
            updated_at?: string;
        };
        UpdateDeployableInput: {
            /** @description The deployable configuration. */
            configuration?: {
                [key: string]: unknown;
            };
            /** @description Dataset to create deployable for. */
            dataset_id?: string;
            /**
             * @description Whether to overwrite document if it exists.
             * @default true
             */
            overwrite?: boolean;
            /**
             * @description This is prioritised over overwrite. If True, adds new fields.
             * @default true
             */
            upsert?: boolean;
        };
        UpdateDeployableOutput: {
            status: string;
            message: string;
        };
        DeleteDeployableInput: {
            /** @description The deployable ID. */
            id: string;
        };
        DeleteDeployableOutput: unknown;
        CreateDeployableKeyInput: unknown;
        CreateDeployableKeyOutput: {
            status: string;
            message: string;
        };
        CreateDeployableInviteInput: {
            email: string;
            type: "read" | "write";
        };
        CreateDeployableInviteOutput: {
            invite_code: string;
        };
        UpdateUsersDeployablePermissionsInput: {
            type: "read" | "write" | "none";
        };
        UpdateUsersDeployablePermissionsOutput: unknown;
        DeleteDeployableKeyInput: unknown;
        DeleteDeployableKeyOutput: {
            status: string;
            message: string;
        };
        ListDeployablesInput: unknown;
        ListDeployablesOutput: {
            deployables: {
                deployable_id?: string;
                dataset_id?: string;
                project_id?: string;
                api_key?: string;
                configuration?: {
                    [key: string]: unknown;
                };
                private?: boolean;
                updated_at?: string;
            }[];
            count: number;
        };
        DeleteDeployableGroupInput: unknown;
        DeleteDeployableGroupOutput: unknown;
        ListDeployableGroupsInput: {
            page?: number;
            page_size?: number;
            /** @description Fields to sort by. For each field, sort by descending or ascending. If you are using descending by datetime, it will get the most recent ones. */
            sort?: (string | {
                [key: string]: "asc" | "desc";
            })[];
        };
        ListDeployableGroupsOutput: {
            results: {
                _id?: string;
                name?: string;
                description?: string;
                api_key?: string;
                private?: boolean;
                deployables?: ({
                    id?: string;
                    type?: string;
                    text?: string;
                    name?: string;
                } | string)[];
            }[];
        };
        CreateDeployableGroupInput: {
            _id?: string;
            name?: string;
            description?: string;
            api_key?: string;
            private?: boolean;
            deployables?: ({
                id?: string;
                type?: string;
                text?: string;
                name?: string;
            } | string)[];
        };
        CreateDeployableGroupOutput: {
            id: string;
        };
        GetDeployableGroupInput: unknown;
        GetDeployableGroupOutput: {
            _id?: string;
            name?: string;
            description?: string;
            api_key?: string;
            private?: boolean;
            deployables?: ({
                id?: string;
                type?: string;
                text?: string;
                name?: string;
            } | string)[];
        };
        UpdateDeployableGroupInput: {
            _id?: string;
            name?: string;
            description?: string;
            api_key?: string;
            private?: boolean;
            deployables?: ({
                id?: string;
                type?: string;
                text?: string;
                name?: string;
            } | string)[];
        };
        UpdateDeployableGroupOutput: unknown;
        CreateDeployableGroupKeyInput: unknown;
        CreateDeployableGroupKeyOutput: unknown;
        DeleteDeployableGroupKeyInput: unknown;
        DeleteDeployableGroupKeyOutput: unknown;
        CreateProjectInput: {
            /** @description The name of the project */
            name?: string;
            /** @description The description of the project */
            description?: string;
            organization_id: string;
        };
        CreateProjectOutput: {
            project_id: string;
        };
        UpdateProjectInput: {
            /** @description The name of the project */
            name?: string;
            /** @description The description of the project */
            description?: string;
        };
        UpdateProjectOutput: {
            status: string;
            message: string;
        };
        TransferProjectToOrganizationInput: {
            /** @description The ID of the organization. */
            organization_id: string;
        };
        TransferProjectToOrganizationOutput: unknown;
        ListProjectsInput: unknown;
        ListProjectsOutput: {
            projects: {
                project_id?: string;
                name?: string;
                description?: string;
                permissions?: {
                    [key: string]: unknown;
                };
                updated_at?: string;
            }[];
            count: number;
        };
        permissions: {
            items: {
                [key: string]: {
                    resources?: {
                        datasets?: {
                            [key: string]: boolean;
                        };
                        deployables?: {
                            [key: string]: boolean;
                        };
                        users?: {
                            [key: string]: boolean;
                        };
                    };
                    actions?: {
                        [key: string]: boolean;
                    };
                };
            };
        };
        CreateUserInput: {
            profile_picture_url?: string;
            onboarded?: boolean;
            first_name?: string;
            last_name?: string;
            role?: string;
            company?: string;
            label?: string;
            notes?: string;
            permissions?: components["schemas"]["permissions"];
            organization_permissions?: {
                [key: string]: components["schemas"]["permissions"];
            };
            referral_code?: string;
            organization_name?: string;
            project_name?: string;
            /** @description The name of the project. This will contain all your datasets. */
            project?: string;
            /** @description The id token for a signed in account. This attaches the sign in account to the user. */
            id_token?: string;
        };
        CreateUserOutput: {
            user_id: string;
            api_key: string;
            project: string;
            organization_id?: string;
        };
        ListUsersInput: {
            /** @default 1 */
            page?: number;
            /** @default 20 */
            page_size?: number;
            required_permissions?: {
                items: {
                    [key: string]: {
                        resources?: {
                            datasets?: {
                                [key: string]: boolean;
                            };
                            deployables?: {
                                [key: string]: boolean;
                            };
                            users?: {
                                [key: string]: boolean;
                            };
                        };
                        actions?: {
                            [key: string]: boolean;
                        };
                    };
                };
            };
            /** @description Currently limited to exact_match, ids, exists, regexp filter_type */
            filters?: {
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }[];
        };
        ListUsersOutput: {
            results: {
                profile_picture_url?: string;
                onboarded?: boolean;
                first_name?: string;
                last_name?: string;
                role?: string;
                company?: string;
                label?: string;
                notes?: string;
                permissions: {
                    projects: {
                        [key: string]: {
                            items: {
                                [key: string]: {
                                    resources?: {
                                        datasets?: {
                                            [key: string]: boolean;
                                        };
                                        deployables?: {
                                            [key: string]: boolean;
                                        };
                                        users?: {
                                            [key: string]: boolean;
                                        };
                                    };
                                    actions?: {
                                        [key: string]: boolean;
                                    };
                                };
                            };
                        };
                    };
                    organizations?: {
                        [key: string]: {
                            items: {
                                [key: string]: {
                                    resources?: {
                                        datasets?: {
                                            [key: string]: boolean;
                                        };
                                        deployables?: {
                                            [key: string]: boolean;
                                        };
                                        users?: {
                                            [key: string]: boolean;
                                        };
                                    };
                                    actions?: {
                                        [key: string]: boolean;
                                    };
                                };
                            };
                        };
                    };
                };
                type: "user" | "machine";
                _id: string;
                email?: string;
                creator_user_id?: string;
            }[];
        };
        IsUserAuthorizedInput: {
            permissions?: components["schemas"]["permissions"];
            organization_permissions?: {
                [key: string]: components["schemas"]["permissions"];
            };
        };
        IsUserAuthorizedOutput: {
            valid: boolean;
            message: string;
        };
        GetAuthHeaderInfoInput: unknown;
        fullUserPermissions: {
            projects: {
                [key: string]: {
                    items: {
                        [key: string]: {
                            resources?: {
                                datasets?: {
                                    [key: string]: boolean;
                                };
                                deployables?: {
                                    [key: string]: boolean;
                                };
                                users?: {
                                    [key: string]: boolean;
                                };
                            };
                            actions?: {
                                [key: string]: boolean;
                            };
                        };
                    };
                };
            };
            organizations?: {
                [key: string]: {
                    items: {
                        [key: string]: {
                            resources?: {
                                datasets?: {
                                    [key: string]: boolean;
                                };
                                deployables?: {
                                    [key: string]: boolean;
                                };
                                users?: {
                                    [key: string]: boolean;
                                };
                            };
                            actions?: {
                                [key: string]: boolean;
                            };
                        };
                    };
                };
            };
        };
        GetAuthHeaderInfoOutput: {
            profile_picture_url?: string;
            onboarded?: boolean;
            first_name?: string;
            last_name?: string;
            role?: string;
            company?: string;
            label?: string;
            notes?: string;
            user_id: string;
            key_id: string;
            permissions: components["schemas"]["fullUserPermissions"];
            email?: string;
        };
        CreateProjectInviteInput: {
            permissions?: components["schemas"]["permissions"];
            organization_permissions?: {
                [key: string]: components["schemas"]["permissions"];
            };
            email?: string;
            user_id?: string;
            type?: "deployable" | "project";
        };
        CreateProjectInviteOutput: {
            invite_code: string;
        };
        ListProjectInvitesInput: unknown;
        ListProjectInvitesOutput: {
            results: {
                _id: string;
                user_id?: string;
                permissions?: {
                    items: {
                        [key: string]: {
                            resources?: {
                                datasets?: {
                                    [key: string]: boolean;
                                };
                                deployables?: {
                                    [key: string]: boolean;
                                };
                                users?: {
                                    [key: string]: boolean;
                                };
                            };
                            actions?: {
                                [key: string]: boolean;
                            };
                        };
                    };
                };
                email?: string;
                project?: string;
            }[];
        };
        AcceptProjectInviteInput: {
            invite_code: string;
        };
        AcceptProjectInviteOutput: {
            [key: string]: unknown;
        };
        DeleteProjectInviteInput: {
            invite_code: string;
        };
        DeleteProjectInviteOutput: {
            [key: string]: unknown;
        };
        ResendProjectInviteInput: {
            permissions?: {
                items: {
                    [key: string]: {
                        resources?: {
                            datasets?: {
                                [key: string]: boolean;
                            };
                            deployables?: {
                                [key: string]: boolean;
                            };
                            users?: {
                                [key: string]: boolean;
                            };
                        };
                        actions?: {
                            [key: string]: boolean;
                        };
                    };
                };
            };
            organization_permissions?: {
                [key: string]: components["schemas"]["permissions"];
            };
            invite_code: string;
        };
        ResendProjectInviteOutput: {
            invite_code: string;
        };
        GetUserInput: {
            [key: string]: unknown;
        };
        GetUserOutput: {
            profile_picture_url?: string;
            onboarded?: boolean;
            first_name?: string;
            last_name?: string;
            role?: string;
            company?: string;
            label?: string;
            notes?: string;
            permissions: {
                projects: {
                    [key: string]: {
                        items: {
                            [key: string]: {
                                resources?: {
                                    datasets?: {
                                        [key: string]: boolean;
                                    };
                                    deployables?: {
                                        [key: string]: boolean;
                                    };
                                    users?: {
                                        [key: string]: boolean;
                                    };
                                };
                                actions?: {
                                    [key: string]: boolean;
                                };
                            };
                        };
                    };
                };
                organizations?: {
                    [key: string]: {
                        items: {
                            [key: string]: {
                                resources?: {
                                    datasets?: {
                                        [key: string]: boolean;
                                    };
                                    deployables?: {
                                        [key: string]: boolean;
                                    };
                                    users?: {
                                        [key: string]: boolean;
                                    };
                                };
                                actions?: {
                                    [key: string]: boolean;
                                };
                            };
                        };
                    };
                };
            };
            type: "user" | "machine";
            _id: string;
            email?: string;
            creator_user_id?: string;
        };
        UpdateUserInput: {
            profile_picture_url?: string;
            onboarded?: boolean;
            first_name?: string;
            last_name?: string;
            role?: string;
            company?: string;
            label?: string;
            notes?: string;
            permissions?: components["schemas"]["permissions"];
            organization_permissions?: {
                [key: string]: components["schemas"]["permissions"];
            };
            /** @description The id token for a signed in account. This attaches the sign in account to the user. */
            id_token?: string;
            /** @description Set this to true to replace users permissions for project, rather than upserting */
            overwrite?: boolean;
        };
        UpdateUserOutput: {
            [key: string]: unknown;
        };
        DeleteUserInput: {
            [key: string]: unknown;
        };
        DeleteUserOutput: {
            [key: string]: unknown;
        };
        CreateUserKeyInput: unknown;
        CreateUserKeyOutput: {
            api_key: string;
            id: string;
        };
        ListUserKeysInput: unknown;
        ListUserKeysOutput: {
            results: {
                _id: string;
                label?: string;
                notes?: string;
            }[];
        };
        DeleteUserKeyInput: {
            id: string;
        };
        DeleteUserKeyOutput: {
            [key: string]: unknown;
        };
        InsertClusterCentroidsInput: {
            /** @description The vector fields that these centroids are associated with. */
            vector_fields?: string[];
            centroid_vector_fields?: string[];
            /** @description Alias is used to name a cluster. */
            alias?: string;
            dataset_id?: string;
            /**
             * @description Insert cluster centers.
             *   Cluster center documents needs to be the following:
             *   [
             *       {
             *           "_id": "cluster_0",
             *           "centroid_vector_": [0.1, 0.2, 0.1]
             *       }
             *   ]
             */
            cluster_centers: {
                _id: string;
            }[];
        };
        InsertClusterCentroidsOutput: {
            /** @description Number of successfully processed documents. */
            inserted: number;
            /** @description Information about documents that were not processed successfully. */
            failed_documents: {
                /** @description _id field of unprocessed document. */
                _id?: unknown;
                error?: {
                    /** @description http status code of individual document insertion operation. */
                    status?: number;
                    /** @description body response of individual document insertion operation. */
                    body?: string;
                };
            }[];
        };
        UpdateClusterCentroidsInput: {
            /** @description The vector fields that these centroids are associated with. */
            vector_fields?: string[];
            centroid_vector_fields?: string[];
            /** @description Alias is used to name a cluster. */
            alias?: string;
            dataset_id?: string;
            /**
             * @description Insert cluster centers.
             *   Cluster center documents needs to be the following:
             *   [
             *       {
             *           "_id": "cluster_0",
             *           "centroid_vector_": [0.1, 0.2, 0.1]
             *       }
             *   ]
             */
            cluster_centers: {
                _id: string;
            }[];
        };
        UpdateClusterCentroidsOutput: {
            /** @description Number of successfully processed documents. */
            inserted: number;
            /** @description Information about documents that were not processed successfully. */
            failed_documents: {
                /** @description _id field of unprocessed document. */
                _id?: unknown;
                error?: {
                    /** @description http status code of individual document insertion operation. */
                    status?: number;
                    /** @description body response of individual document insertion operation. */
                    body?: string;
                };
            }[];
        };
        CompareClusterCentroidsInput: {
            /** @description The vector fields that these centroids are associated with. */
            vector_fields?: string[];
            centroid_vector_fields?: string[];
            /** @description Alias is used to name a cluster. */
            alias?: string;
            dataset_id?: string;
            comparison_centroids: {
                /** @description The vector fields that these centroids are associated with. */
                vector_fields?: string[];
                centroid_vector_fields?: string[];
                /** @description Alias is used to name a cluster. */
                alias?: string;
                dataset_id?: string;
            };
        };
        CompareClusterCentroidsOutput: {
            results: {
                [key: string]: {
                    results?: unknown[];
                };
            };
        };
        simpleSearchAndFlatFilterItem: Partial<{
            /** @description Match where document[field] is in value list. */
            match?: {
                /** @description If matching on text, match even if there are extra words / case insensitivity */
                fuzzy?: boolean;
                /** @description Field to match on. */
                field?: string;
                /** @description Can be either a single item or a list of items to match on. */
                value: string | boolean | number | unknown[];
            };
            /** @description Match where document._id is in value list. */
            matchIds?: {
                /** @description Can be either a single item or a list of items to match on. */
                value: string | boolean | number | unknown[];
            };
            /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
            range?: {
                /** @description Field to match on. */
                field?: string;
                greaterThan?: unknown;
                lessThan?: unknown;
                greaterThanOrEqualTo?: unknown;
                lessThanOrEqualTo?: unknown;
            };
            /**
             * @description
             * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
             *
             * Possible matching patterns:
             *
             * * - documents where the field has any value
             * ? - documents where a single character from the field matches the provided values
             *
             * Example:
             *
             * {
             *   wildcard: {
             *      field: "title",
             *      value: [ "Avenger*" ]
             *   }
             * }
             */
            wildcard?: {
                /** @description Field to match on. */
                field?: string;
                /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                value: string | string[];
            };
            /** @description hybrid search query that must reach a certain value to filter */
            search?: {
                /**
                 * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                 *
                 *     "tele" matches "Television", "This television is an excellent product…"
                 *
                 *     Example: "tele"
                 */
                query?: string;
                /** @description Configuration for traditional search query. */
                queryConfig?: {
                    /**
                     * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                     * @default 1
                     */
                    weight?: number;
                    /**
                     * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                     *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                     * @default 1
                     */
                    sentenceWeight?: number;
                    /**
                     * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                     *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                     * @default 1
                     */
                    fuzzyWeight?: number;
                };
                /**
                 * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                 *
                 *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                 *
                 *     It can be an object or a list of objects.
                 *
                 *
                 *
                 *     Example payloads:
                 *
                 *     {"field":"animaltype_vector_","query":"kitten"}
                 *
                 *     [
                 *
                 *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                 *
                 *     ]
                 */
                vectorSearchQuery?: {
                    /** @description Vector name to search on. For example, title_vector_ */
                    field: string;
                    /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                    query?: string;
                    /**
                     * @description Model name to generate the vector with.
                     * @default text
                     */
                    model?: string;
                    /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                    model_url?: string;
                    /**
                     * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                     * @default 1
                     */
                    weight?: number;
                    /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                    vector?: number[];
                    chunkConfig?: {
                        chunkField: string;
                        page?: number;
                        pageSize?: number;
                    };
                } | {
                    /** @description Vector name to search on. For example, title_vector_ */
                    field: string;
                    /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                    query?: string;
                    /**
                     * @description Model name to generate the vector with.
                     * @default text
                     */
                    model?: string;
                    /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                    model_url?: string;
                    /**
                     * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                     * @default 1
                     */
                    weight?: number;
                    /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                    vector?: number[];
                    chunkConfig?: {
                        chunkField: string;
                        page?: number;
                        pageSize?: number;
                    };
                }[];
                realtimeClustering?: {
                    /** @default 4 */
                    nClusters?: number;
                    /** @default kmeans-realtime-4 */
                    alias?: string;
                    vectorField: string;
                    /** @default _cluster_.{vector_field}.{alias} */
                    outputField?: string;
                };
                /**
                 * @description A list of fields to search using the "query" parameter.
                 *
                 *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                 *
                 *     Default behaviour is to search all fields.
                 *
                 *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                 */
                fieldsToSearch?: (string | {
                    /** @description Field name to search. */
                    field?: string;
                    /**
                     * @description Multiply the relevance contribution of a specific field when using traditional search.
                     * @default 1
                     */
                    weight?: number;
                    /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                    chunkField?: string;
                })[];
                /**
                 * @description Only return documents with a _relevance above this threshold.
                 *
                 *     Example: 0.3
                 */
                minimumRelevance: number;
            };
            /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
            fieldExists?: {
                /** @description Field that must exist. */
                field?: string;
            };
            /** @description Match documents where doc[field] % module == value */
            matchModulo?: {
                /** @default _id */
                field?: string;
                modulo: number;
                value: number;
            };
            /** @description Filter down to one document for each value of selected field. */
            dedupeByValue?: {
                /** @description Field to filter on. */
                field: string;
            };
            /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
            selfreference?: {
                /** @description First field in comparison. */
                a: string;
                /** @description Second field in comparison. */
                b: string;
                /** @description Operator used to compare a and b. */
                operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
            };
            /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
            wordCount?: {
                /** @description Field to match on. */
                field: string;
                /** @description Minimum word count. */
                greaterThan?: number;
                /** @description Maximum word count. */
                lessThan?: number;
            };
            /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
            characterCount?: {
                /** @description Field to match on. */
                field: string;
                /** @description Minimum character count. */
                greaterThan?: number;
                /** @description Maximum character count. */
                lessThan?: number;
            };
            /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
            or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
            /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
            not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
            /** @description Filter based on data within a _chunk_ field. */
            chunk?: {
                /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                path: string;
                filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
            };
        }> & Partial<{
            strict?: "must" | "should" | "must_or";
            condition?: string;
            field?: string;
            filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
            condition_value?: unknown;
            fuzzy?: number;
            join?: boolean;
        }>;
        AggregateInput: {
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            /** @description Aggregation query to aggregate data */
            aggregation_query?: {
                groupby?: {
                    agg?: "texts" | "categories" | "classes" | "text" | "category" | "class" | "terms" | "number" | "numeric" | "bool" | "wordcloud" | "array" | "boolean_filter" | "histogram";
                    size?: number;
                    field?: string;
                    name?: string;
                    boolean_filter?: Partial<{
                        /** @description Match where document[field] is in value list. */
                        match?: {
                            /** @description If matching on text, match even if there are extra words / case insensitivity */
                            fuzzy?: boolean;
                            /** @description Field to match on. */
                            field?: string;
                            /** @description Can be either a single item or a list of items to match on. */
                            value: string | boolean | number | unknown[];
                        };
                        /** @description Match where document._id is in value list. */
                        matchIds?: {
                            /** @description Can be either a single item or a list of items to match on. */
                            value: string | boolean | number | unknown[];
                        };
                        /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                        range?: {
                            /** @description Field to match on. */
                            field?: string;
                            greaterThan?: unknown;
                            lessThan?: unknown;
                            greaterThanOrEqualTo?: unknown;
                            lessThanOrEqualTo?: unknown;
                        };
                        /**
                         * @description
                         * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                         *
                         * Possible matching patterns:
                         *
                         * * - documents where the field has any value
                         * ? - documents where a single character from the field matches the provided values
                         *
                         * Example:
                         *
                         * {
                         *   wildcard: {
                         *      field: "title",
                         *      value: [ "Avenger*" ]
                         *   }
                         * }
                         */
                        wildcard?: {
                            /** @description Field to match on. */
                            field?: string;
                            /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                            value: string | string[];
                        };
                        /** @description hybrid search query that must reach a certain value to filter */
                        search?: {
                            /**
                             * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                             *
                             *     "tele" matches "Television", "This television is an excellent product…"
                             *
                             *     Example: "tele"
                             */
                            query?: string;
                            /** @description Configuration for traditional search query. */
                            queryConfig?: {
                                /**
                                 * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                                 * @default 1
                                 */
                                weight?: number;
                                /**
                                 * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                                 *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                                 * @default 1
                                 */
                                sentenceWeight?: number;
                                /**
                                 * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                                 *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                                 * @default 1
                                 */
                                fuzzyWeight?: number;
                            };
                            /**
                             * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                             *
                             *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                             *
                             *     It can be an object or a list of objects.
                             *
                             *
                             *
                             *     Example payloads:
                             *
                             *     {"field":"animaltype_vector_","query":"kitten"}
                             *
                             *     [
                             *
                             *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                             *
                             *     ]
                             */
                            vectorSearchQuery?: {
                                /** @description Vector name to search on. For example, title_vector_ */
                                field: string;
                                /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                                query?: string;
                                /**
                                 * @description Model name to generate the vector with.
                                 * @default text
                                 */
                                model?: string;
                                /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                                model_url?: string;
                                /**
                                 * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                                 * @default 1
                                 */
                                weight?: number;
                                /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                                vector?: number[];
                                chunkConfig?: {
                                    chunkField: string;
                                    page?: number;
                                    pageSize?: number;
                                };
                            } | {
                                /** @description Vector name to search on. For example, title_vector_ */
                                field: string;
                                /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                                query?: string;
                                /**
                                 * @description Model name to generate the vector with.
                                 * @default text
                                 */
                                model?: string;
                                /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                                model_url?: string;
                                /**
                                 * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                                 * @default 1
                                 */
                                weight?: number;
                                /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                                vector?: number[];
                                chunkConfig?: {
                                    chunkField: string;
                                    page?: number;
                                    pageSize?: number;
                                };
                            }[];
                            realtimeClustering?: {
                                /** @default 4 */
                                nClusters?: number;
                                /** @default kmeans-realtime-4 */
                                alias?: string;
                                vectorField: string;
                                /** @default _cluster_.{vector_field}.{alias} */
                                outputField?: string;
                            };
                            /**
                             * @description A list of fields to search using the "query" parameter.
                             *
                             *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                             *
                             *     Default behaviour is to search all fields.
                             *
                             *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                             */
                            fieldsToSearch?: (string | {
                                /** @description Field name to search. */
                                field?: string;
                                /**
                                 * @description Multiply the relevance contribution of a specific field when using traditional search.
                                 * @default 1
                                 */
                                weight?: number;
                                /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                                chunkField?: string;
                            })[];
                            /**
                             * @description Only return documents with a _relevance above this threshold.
                             *
                             *     Example: 0.3
                             */
                            minimumRelevance: number;
                        };
                        /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                        fieldExists?: {
                            /** @description Field that must exist. */
                            field?: string;
                        };
                        /** @description Match documents where doc[field] % module == value */
                        matchModulo?: {
                            /** @default _id */
                            field?: string;
                            modulo: number;
                            value: number;
                        };
                        /** @description Filter down to one document for each value of selected field. */
                        dedupeByValue?: {
                            /** @description Field to filter on. */
                            field: string;
                        };
                        /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                        selfreference?: {
                            /** @description First field in comparison. */
                            a: string;
                            /** @description Second field in comparison. */
                            b: string;
                            /** @description Operator used to compare a and b. */
                            operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                        };
                        /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                        wordCount?: {
                            /** @description Field to match on. */
                            field: string;
                            /** @description Minimum word count. */
                            greaterThan?: number;
                            /** @description Maximum word count. */
                            lessThan?: number;
                        };
                        /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                        characterCount?: {
                            /** @description Field to match on. */
                            field: string;
                            /** @description Minimum character count. */
                            greaterThan?: number;
                            /** @description Maximum character count. */
                            lessThan?: number;
                        };
                        /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                        or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                        /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                        not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                        /** @description Filter based on data within a _chunk_ field. */
                        chunk?: {
                            /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                            path: string;
                            filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                        };
                    }> & Partial<{
                        strict?: "must" | "should" | "must_or";
                        condition?: string;
                        field?: string;
                        filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                        condition_value?: unknown;
                        fuzzy?: number;
                        join?: boolean;
                    }>;
                    /** @description Control the number of results for this aggregation level. This will default to the page_size provided in the root body if not provided. */
                    page_size?: number;
                    group_size?: number;
                    select_fields?: string[];
                    remove_words?: string[];
                    /** @default monthly */
                    date_interval?: "monthly" | "daily" | "yearly" | "hourly" | "minutely" | "weekly";
                    /** @description If provided, group by field into n buckets in date range. */
                    n_date_buckets?: number;
                    buckets?: Partial<{
                        histogram?: {
                            interval?: number;
                            min_doc_count?: number;
                        };
                    }> & Partial<{
                        from?: number;
                        to?: number;
                    }[]>;
                }[];
                metrics?: {
                    field?: string;
                    fields?: string[];
                    agg?: "avg" | "average" | "cardinality" | "max" | "mean" | "min" | "sum" | "percentiles" | "sum_of_squares" | "variance" | "std_deviation" | "std_deviation_bounds" | "kurtosis" | "covariance" | "skewness" | "character_count" | "count" | "correlation" | "missing";
                    name?: string;
                }[];
                sort?: (string | {
                    [key: string]: "asc" | "desc";
                })[];
            };
            /**
             * @description Size of each page of results
             * @default 20
             */
            page_size?: number;
            /**
             * @description Page of the results
             * @default 1
             */
            page?: number;
            /** @description Whether to sort results by ascending or descending order. */
            asc?: boolean;
            dataset_ids?: string[];
            dataset_id?: string;
        };
        AggregateOutput: {
            results?: unknown[];
        };
        AggregateClustersInput: {
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            /** @description Aggregation query to aggregate data */
            aggregation_query?: {
                groupby?: {
                    agg?: "texts" | "categories" | "classes" | "text" | "category" | "class" | "terms" | "number" | "numeric" | "bool" | "wordcloud" | "array" | "boolean_filter" | "histogram";
                    size?: number;
                    field?: string;
                    name?: string;
                    boolean_filter?: Partial<{
                        /** @description Match where document[field] is in value list. */
                        match?: {
                            /** @description If matching on text, match even if there are extra words / case insensitivity */
                            fuzzy?: boolean;
                            /** @description Field to match on. */
                            field?: string;
                            /** @description Can be either a single item or a list of items to match on. */
                            value: string | boolean | number | unknown[];
                        };
                        /** @description Match where document._id is in value list. */
                        matchIds?: {
                            /** @description Can be either a single item or a list of items to match on. */
                            value: string | boolean | number | unknown[];
                        };
                        /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                        range?: {
                            /** @description Field to match on. */
                            field?: string;
                            greaterThan?: unknown;
                            lessThan?: unknown;
                            greaterThanOrEqualTo?: unknown;
                            lessThanOrEqualTo?: unknown;
                        };
                        /**
                         * @description
                         * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                         *
                         * Possible matching patterns:
                         *
                         * * - documents where the field has any value
                         * ? - documents where a single character from the field matches the provided values
                         *
                         * Example:
                         *
                         * {
                         *   wildcard: {
                         *      field: "title",
                         *      value: [ "Avenger*" ]
                         *   }
                         * }
                         */
                        wildcard?: {
                            /** @description Field to match on. */
                            field?: string;
                            /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                            value: string | string[];
                        };
                        /** @description hybrid search query that must reach a certain value to filter */
                        search?: {
                            /**
                             * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                             *
                             *     "tele" matches "Television", "This television is an excellent product…"
                             *
                             *     Example: "tele"
                             */
                            query?: string;
                            /** @description Configuration for traditional search query. */
                            queryConfig?: {
                                /**
                                 * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                                 * @default 1
                                 */
                                weight?: number;
                                /**
                                 * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                                 *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                                 * @default 1
                                 */
                                sentenceWeight?: number;
                                /**
                                 * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                                 *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                                 * @default 1
                                 */
                                fuzzyWeight?: number;
                            };
                            /**
                             * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                             *
                             *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                             *
                             *     It can be an object or a list of objects.
                             *
                             *
                             *
                             *     Example payloads:
                             *
                             *     {"field":"animaltype_vector_","query":"kitten"}
                             *
                             *     [
                             *
                             *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                             *
                             *     ]
                             */
                            vectorSearchQuery?: {
                                /** @description Vector name to search on. For example, title_vector_ */
                                field: string;
                                /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                                query?: string;
                                /**
                                 * @description Model name to generate the vector with.
                                 * @default text
                                 */
                                model?: string;
                                /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                                model_url?: string;
                                /**
                                 * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                                 * @default 1
                                 */
                                weight?: number;
                                /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                                vector?: number[];
                                chunkConfig?: {
                                    chunkField: string;
                                    page?: number;
                                    pageSize?: number;
                                };
                            } | {
                                /** @description Vector name to search on. For example, title_vector_ */
                                field: string;
                                /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                                query?: string;
                                /**
                                 * @description Model name to generate the vector with.
                                 * @default text
                                 */
                                model?: string;
                                /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                                model_url?: string;
                                /**
                                 * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                                 * @default 1
                                 */
                                weight?: number;
                                /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                                vector?: number[];
                                chunkConfig?: {
                                    chunkField: string;
                                    page?: number;
                                    pageSize?: number;
                                };
                            }[];
                            realtimeClustering?: {
                                /** @default 4 */
                                nClusters?: number;
                                /** @default kmeans-realtime-4 */
                                alias?: string;
                                vectorField: string;
                                /** @default _cluster_.{vector_field}.{alias} */
                                outputField?: string;
                            };
                            /**
                             * @description A list of fields to search using the "query" parameter.
                             *
                             *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                             *
                             *     Default behaviour is to search all fields.
                             *
                             *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                             */
                            fieldsToSearch?: (string | {
                                /** @description Field name to search. */
                                field?: string;
                                /**
                                 * @description Multiply the relevance contribution of a specific field when using traditional search.
                                 * @default 1
                                 */
                                weight?: number;
                                /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                                chunkField?: string;
                            })[];
                            /**
                             * @description Only return documents with a _relevance above this threshold.
                             *
                             *     Example: 0.3
                             */
                            minimumRelevance: number;
                        };
                        /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                        fieldExists?: {
                            /** @description Field that must exist. */
                            field?: string;
                        };
                        /** @description Match documents where doc[field] % module == value */
                        matchModulo?: {
                            /** @default _id */
                            field?: string;
                            modulo: number;
                            value: number;
                        };
                        /** @description Filter down to one document for each value of selected field. */
                        dedupeByValue?: {
                            /** @description Field to filter on. */
                            field: string;
                        };
                        /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                        selfreference?: {
                            /** @description First field in comparison. */
                            a: string;
                            /** @description Second field in comparison. */
                            b: string;
                            /** @description Operator used to compare a and b. */
                            operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                        };
                        /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                        wordCount?: {
                            /** @description Field to match on. */
                            field: string;
                            /** @description Minimum word count. */
                            greaterThan?: number;
                            /** @description Maximum word count. */
                            lessThan?: number;
                        };
                        /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                        characterCount?: {
                            /** @description Field to match on. */
                            field: string;
                            /** @description Minimum character count. */
                            greaterThan?: number;
                            /** @description Maximum character count. */
                            lessThan?: number;
                        };
                        /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                        or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                        /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                        not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                        /** @description Filter based on data within a _chunk_ field. */
                        chunk?: {
                            /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                            path: string;
                            filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                        };
                    }> & Partial<{
                        strict?: "must" | "should" | "must_or";
                        condition?: string;
                        field?: string;
                        filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                        condition_value?: unknown;
                        fuzzy?: number;
                        join?: boolean;
                    }>;
                    /** @description Control the number of results for this aggregation level. This will default to the page_size provided in the root body if not provided. */
                    page_size?: number;
                    group_size?: number;
                    select_fields?: string[];
                    remove_words?: string[];
                    /** @default monthly */
                    date_interval?: "monthly" | "daily" | "yearly" | "hourly" | "minutely" | "weekly";
                    /** @description If provided, group by field into n buckets in date range. */
                    n_date_buckets?: number;
                    buckets?: Partial<{
                        histogram?: {
                            interval?: number;
                            min_doc_count?: number;
                        };
                    }> & Partial<{
                        from?: number;
                        to?: number;
                    }[]>;
                }[];
                metrics?: {
                    field?: string;
                    fields?: string[];
                    agg?: "avg" | "average" | "cardinality" | "max" | "mean" | "min" | "sum" | "percentiles" | "sum_of_squares" | "variance" | "std_deviation" | "std_deviation_bounds" | "kurtosis" | "covariance" | "skewness" | "character_count" | "count" | "correlation" | "missing";
                    name?: string;
                }[];
                sort?: (string | {
                    [key: string]: "asc" | "desc";
                })[];
            };
            /**
             * @description Size of each page of results
             * @default 20
             */
            page_size?: number;
            /**
             * @description Page of the results
             * @default 1
             */
            page?: number;
            /** @description Whether to sort results by ascending or descending order. */
            asc?: boolean;
            dataset_ids?: string[];
            dataset_id?: string;
            alias?: string;
            vector_fields?: string[];
            /** @description Field to cluster on */
            cluster_field?: string;
            /** @description List of cluster IDs to aggregate on */
            cluster_ids?: unknown[];
            /** @description Filter if clusters with certain characteristics should be hidden in results. */
            cluster_properties_filter?: {
                /** @description Clusters with less than this many items will be hidden from results. */
                minimum_cluster_size?: number;
                /**
                 * @description Whether to add to list of cluster to show (or), or filter clusters to show based on minimum_cluster_size (and)
                 * @default and
                 */
                minimum_cluster_size_filter_type?: "or" | "and";
                cluster_page?: number;
                cluster_page_size?: number;
                sort_metrics?: {
                    field?: string;
                    fields?: string[];
                    agg?: "avg" | "average" | "cardinality" | "max" | "mean" | "min" | "sum" | "percentiles" | "sum_of_squares" | "variance" | "std_deviation" | "std_deviation_bounds" | "kurtosis" | "covariance" | "skewness" | "character_count" | "count" | "correlation" | "missing";
                    name?: string;
                }[];
                /** @description Whether to sort results by ascending or descending order. */
                asc?: boolean;
            };
            /** @description For each frequency in results, also return expected_frequency as cluster_size*(count(groupbyattribute)/count(dataset)). Useful for knowing if an attribute is occurring more or less than expected. */
            include_expected_frequency?: boolean;
            /** @description Return the number of clusters in the dataset in the field "count", and the number of documents belonging to at least one cluster in "document_count". */
            include_counts?: boolean;
        };
        AggregateClustersOutput: {
            results: {
                [key: string]: unknown[];
            };
            cluster_order: string[];
            cluster_stats?: {
                [key: string]: {
                    percentage: number;
                    count: number;
                };
            };
            /** @description Total number of clusters in the dataset. */
            count?: number;
            /** @description Total number of documents that are part of at least one cluster. */
            document_count?: number;
        };
        ListClusterFacetsInput: {
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            /** @description Aggregation query to aggregate data */
            aggregation_query?: {
                groupby?: {
                    agg?: "texts" | "categories" | "classes" | "text" | "category" | "class" | "terms" | "number" | "numeric" | "bool" | "wordcloud" | "array" | "boolean_filter" | "histogram";
                    size?: number;
                    field?: string;
                    name?: string;
                    boolean_filter?: Partial<{
                        /** @description Match where document[field] is in value list. */
                        match?: {
                            /** @description If matching on text, match even if there are extra words / case insensitivity */
                            fuzzy?: boolean;
                            /** @description Field to match on. */
                            field?: string;
                            /** @description Can be either a single item or a list of items to match on. */
                            value: string | boolean | number | unknown[];
                        };
                        /** @description Match where document._id is in value list. */
                        matchIds?: {
                            /** @description Can be either a single item or a list of items to match on. */
                            value: string | boolean | number | unknown[];
                        };
                        /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                        range?: {
                            /** @description Field to match on. */
                            field?: string;
                            greaterThan?: unknown;
                            lessThan?: unknown;
                            greaterThanOrEqualTo?: unknown;
                            lessThanOrEqualTo?: unknown;
                        };
                        /**
                         * @description
                         * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                         *
                         * Possible matching patterns:
                         *
                         * * - documents where the field has any value
                         * ? - documents where a single character from the field matches the provided values
                         *
                         * Example:
                         *
                         * {
                         *   wildcard: {
                         *      field: "title",
                         *      value: [ "Avenger*" ]
                         *   }
                         * }
                         */
                        wildcard?: {
                            /** @description Field to match on. */
                            field?: string;
                            /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                            value: string | string[];
                        };
                        /** @description hybrid search query that must reach a certain value to filter */
                        search?: {
                            /**
                             * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                             *
                             *     "tele" matches "Television", "This television is an excellent product…"
                             *
                             *     Example: "tele"
                             */
                            query?: string;
                            /** @description Configuration for traditional search query. */
                            queryConfig?: {
                                /**
                                 * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                                 * @default 1
                                 */
                                weight?: number;
                                /**
                                 * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                                 *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                                 * @default 1
                                 */
                                sentenceWeight?: number;
                                /**
                                 * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                                 *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                                 * @default 1
                                 */
                                fuzzyWeight?: number;
                            };
                            /**
                             * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                             *
                             *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                             *
                             *     It can be an object or a list of objects.
                             *
                             *
                             *
                             *     Example payloads:
                             *
                             *     {"field":"animaltype_vector_","query":"kitten"}
                             *
                             *     [
                             *
                             *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                             *
                             *     ]
                             */
                            vectorSearchQuery?: {
                                /** @description Vector name to search on. For example, title_vector_ */
                                field: string;
                                /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                                query?: string;
                                /**
                                 * @description Model name to generate the vector with.
                                 * @default text
                                 */
                                model?: string;
                                /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                                model_url?: string;
                                /**
                                 * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                                 * @default 1
                                 */
                                weight?: number;
                                /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                                vector?: number[];
                                chunkConfig?: {
                                    chunkField: string;
                                    page?: number;
                                    pageSize?: number;
                                };
                            } | {
                                /** @description Vector name to search on. For example, title_vector_ */
                                field: string;
                                /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                                query?: string;
                                /**
                                 * @description Model name to generate the vector with.
                                 * @default text
                                 */
                                model?: string;
                                /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                                model_url?: string;
                                /**
                                 * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                                 * @default 1
                                 */
                                weight?: number;
                                /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                                vector?: number[];
                                chunkConfig?: {
                                    chunkField: string;
                                    page?: number;
                                    pageSize?: number;
                                };
                            }[];
                            realtimeClustering?: {
                                /** @default 4 */
                                nClusters?: number;
                                /** @default kmeans-realtime-4 */
                                alias?: string;
                                vectorField: string;
                                /** @default _cluster_.{vector_field}.{alias} */
                                outputField?: string;
                            };
                            /**
                             * @description A list of fields to search using the "query" parameter.
                             *
                             *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                             *
                             *     Default behaviour is to search all fields.
                             *
                             *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                             */
                            fieldsToSearch?: (string | {
                                /** @description Field name to search. */
                                field?: string;
                                /**
                                 * @description Multiply the relevance contribution of a specific field when using traditional search.
                                 * @default 1
                                 */
                                weight?: number;
                                /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                                chunkField?: string;
                            })[];
                            /**
                             * @description Only return documents with a _relevance above this threshold.
                             *
                             *     Example: 0.3
                             */
                            minimumRelevance: number;
                        };
                        /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                        fieldExists?: {
                            /** @description Field that must exist. */
                            field?: string;
                        };
                        /** @description Match documents where doc[field] % module == value */
                        matchModulo?: {
                            /** @default _id */
                            field?: string;
                            modulo: number;
                            value: number;
                        };
                        /** @description Filter down to one document for each value of selected field. */
                        dedupeByValue?: {
                            /** @description Field to filter on. */
                            field: string;
                        };
                        /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                        selfreference?: {
                            /** @description First field in comparison. */
                            a: string;
                            /** @description Second field in comparison. */
                            b: string;
                            /** @description Operator used to compare a and b. */
                            operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                        };
                        /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                        wordCount?: {
                            /** @description Field to match on. */
                            field: string;
                            /** @description Minimum word count. */
                            greaterThan?: number;
                            /** @description Maximum word count. */
                            lessThan?: number;
                        };
                        /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                        characterCount?: {
                            /** @description Field to match on. */
                            field: string;
                            /** @description Minimum character count. */
                            greaterThan?: number;
                            /** @description Maximum character count. */
                            lessThan?: number;
                        };
                        /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                        or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                        /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                        not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                        /** @description Filter based on data within a _chunk_ field. */
                        chunk?: {
                            /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                            path: string;
                            filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                        };
                    }> & Partial<{
                        strict?: "must" | "should" | "must_or";
                        condition?: string;
                        field?: string;
                        filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                        condition_value?: unknown;
                        fuzzy?: number;
                        join?: boolean;
                    }>;
                    /** @description Control the number of results for this aggregation level. This will default to the page_size provided in the root body if not provided. */
                    page_size?: number;
                    group_size?: number;
                    select_fields?: string[];
                    remove_words?: string[];
                    /** @default monthly */
                    date_interval?: "monthly" | "daily" | "yearly" | "hourly" | "minutely" | "weekly";
                    /** @description If provided, group by field into n buckets in date range. */
                    n_date_buckets?: number;
                    buckets?: Partial<{
                        histogram?: {
                            interval?: number;
                            min_doc_count?: number;
                        };
                    }> & Partial<{
                        from?: number;
                        to?: number;
                    }[]>;
                }[];
                metrics?: {
                    field?: string;
                    fields?: string[];
                    agg?: "avg" | "average" | "cardinality" | "max" | "mean" | "min" | "sum" | "percentiles" | "sum_of_squares" | "variance" | "std_deviation" | "std_deviation_bounds" | "kurtosis" | "covariance" | "skewness" | "character_count" | "count" | "correlation" | "missing";
                    name?: string;
                }[];
                sort?: (string | {
                    [key: string]: "asc" | "desc";
                })[];
            };
            /**
             * @description Size of facet page
             * @default 1000
             */
            page_size?: number;
            /**
             * @description Page of the results
             * @default 1
             */
            page?: number;
            /** @description Whether to sort results by ascending or descending frequency */
            asc?: boolean;
            dataset_ids?: string[];
            dataset_id?: string;
            alias?: string;
            vector_fields?: string[];
            /** @description Field to cluster on */
            cluster_field?: string;
            /** @description List of cluster IDs to aggregate on */
            cluster_ids?: unknown[];
            /** @description Filter if clusters with certain characteristics should be hidden in results. */
            cluster_properties_filter?: {
                /** @description Clusters with less than this many items will be hidden from results. */
                minimum_cluster_size?: number;
                /**
                 * @description Whether to add to list of cluster to show (or), or filter clusters to show based on minimum_cluster_size (and)
                 * @default and
                 */
                minimum_cluster_size_filter_type?: "or" | "and";
                cluster_page?: number;
                cluster_page_size?: number;
                sort_metrics?: {
                    field?: string;
                    fields?: string[];
                    agg?: "avg" | "average" | "cardinality" | "max" | "mean" | "min" | "sum" | "percentiles" | "sum_of_squares" | "variance" | "std_deviation" | "std_deviation_bounds" | "kurtosis" | "covariance" | "skewness" | "character_count" | "count" | "correlation" | "missing";
                    name?: string;
                }[];
                /** @description Whether to sort results by ascending or descending order. */
                asc?: boolean;
            };
            /** @description Fields to include in the facets, if [] then all */
            facet_fields?: string[];
            /** @default monthly */
            date_interval?: "monthly" | "daily" | "yearly" | "hourly" | "minutely" | "weekly";
        };
        ListClusterFacetsOutput: {
            results?: {
                [key: string]: {
                    results: {
                        [key: string]: ({
                            avg: number;
                            max: number;
                            min: number;
                            sum: number;
                            date?: ({
                                value: unknown;
                                frequency: number;
                            } & {
                                [key: string]: unknown;
                            })[];
                        } & {
                            [key: string]: unknown;
                        }) | ({
                            value: unknown;
                            frequency: number;
                        } & {
                            [key: string]: unknown;
                        })[];
                    };
                };
            };
        };
        ListClosestToCentroidsInput: {
            /** @description The vector fields that these centroids are associated with. */
            vector_fields?: string[];
            centroid_vector_fields?: string[];
            /** @description Alias is used to name a cluster. */
            alias?: string;
            dataset_id?: string;
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            /** @description List of cluster IDs to retrieve */
            cluster_ids?: unknown[];
            /** @description Field to cluster on */
            cluster_field?: string;
            select_fields?: string[];
            approx?: number;
            sum_fields?: boolean;
            /**
             * @description Size of each page of results
             * @default 20
             */
            page_size?: number;
            /**
             * @description Page of the results
             * @default 1
             */
            page?: number;
            similarity_metric?: "cosine" | "l2" | "l1" | "dp";
            min_score?: number;
            /** @description Include vectors in the search results */
            include_vector?: boolean;
            include_count?: boolean;
            /** @description Filter if clusters with certain characteristics should be hidden in results. */
            cluster_properties_filter?: {
                /** @description Clusters with less than this many items will be hidden from results. */
                minimum_cluster_size?: number;
                /**
                 * @description Whether to add to list of cluster to show (or), or filter clusters to show based on minimum_cluster_size (and)
                 * @default and
                 */
                minimum_cluster_size_filter_type?: "or" | "and";
                cluster_page?: number;
                cluster_page_size?: number;
                sort_metrics?: {
                    field?: string;
                    fields?: string[];
                    agg?: "avg" | "average" | "cardinality" | "max" | "mean" | "min" | "sum" | "percentiles" | "sum_of_squares" | "variance" | "std_deviation" | "std_deviation_bounds" | "kurtosis" | "covariance" | "skewness" | "character_count" | "count" | "correlation" | "missing";
                    name?: string;
                }[];
                /** @description Whether to sort results by ascending or descending order. */
                asc?: boolean;
            };
            /** @description Whether to include relevance scores for each item in each cluster. */
            include_relevance?: boolean;
        };
        ListClosestToCentroidsOutput: {
            results: {
                [key: string]: {
                    results?: unknown[];
                };
            };
        };
        ListFurthestFromCentroidsInput: {
            /** @description The vector fields that these centroids are associated with. */
            vector_fields?: string[];
            centroid_vector_fields?: string[];
            /** @description Alias is used to name a cluster. */
            alias?: string;
            dataset_id?: string;
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            /** @description List of cluster IDs to retrieve */
            cluster_ids?: unknown[];
            /** @description Field to cluster on */
            cluster_field?: string;
            select_fields?: string[];
            approx?: number;
            sum_fields?: boolean;
            /**
             * @description Size of each page of results
             * @default 20
             */
            page_size?: number;
            /**
             * @description Page of the results
             * @default 1
             */
            page?: number;
            similarity_metric?: "cosine" | "l2" | "l1" | "dp";
            min_score?: number;
            /** @description Include vectors in the search results */
            include_vector?: boolean;
            include_count?: boolean;
            /** @description Filter if clusters with certain characteristics should be hidden in results. */
            cluster_properties_filter?: {
                /** @description Clusters with less than this many items will be hidden from results. */
                minimum_cluster_size?: number;
                /**
                 * @description Whether to add to list of cluster to show (or), or filter clusters to show based on minimum_cluster_size (and)
                 * @default and
                 */
                minimum_cluster_size_filter_type?: "or" | "and";
                cluster_page?: number;
                cluster_page_size?: number;
                sort_metrics?: {
                    field?: string;
                    fields?: string[];
                    agg?: "avg" | "average" | "cardinality" | "max" | "mean" | "min" | "sum" | "percentiles" | "sum_of_squares" | "variance" | "std_deviation" | "std_deviation_bounds" | "kurtosis" | "covariance" | "skewness" | "character_count" | "count" | "correlation" | "missing";
                    name?: string;
                }[];
                /** @description Whether to sort results by ascending or descending order. */
                asc?: boolean;
            };
            /** @description Whether to include relevance scores for each item in each cluster. */
            include_relevance?: boolean;
        };
        ListFurthestFromCentroidsOutput: {
            results: {
                [key: string]: {
                    results?: unknown[];
                };
            };
        };
        ListCentroidsInput: {
            /** @description The vector fields that these centroids are associated with. */
            vector_fields?: string[];
            centroid_vector_fields?: string[];
            /** @description Alias is used to name a cluster. */
            alias?: string;
            dataset_id?: string;
            /** @description List of cluster IDs to retrieve */
            cluster_ids?: unknown[];
            /** @description Size of each page of results */
            page_size?: number;
            /** @description Page of the results */
            page?: number;
            /** @description Include vectors in the search results */
            include_vector?: boolean;
        };
        ListCentroidsOutput: {
            /**
             * @description List of documents. List items are affected by page, pageSize, query, filters. Items order is affected by vectorSeachQuery, sort, textSort.
             *
             *     Example: [{"_id":"abcd","animal":"cat","price":10}, {"_id":"abcd","price":13}]
             */
            results: {
                /** @description Measures how closely a document matches on query and vectorSearchQuery. */
                _relevance?: number;
                _chunk_results?: {
                    [key: string]: {
                        results: {
                            _relevance?: number;
                        }[];
                        _relevance: number;
                    };
                };
            }[];
        };
        ListCentroidConfigsInput: unknown;
        ListCentroidConfigsOutput: {
            results: {
                count: number;
                /** @description The vector fields that these centroids are associated with. */
                vector_fields: string[];
                /** @description Alias is used to name a cluster. */
                alias: string;
            }[];
        };
        DeleteCentroidInput: {
            /** @description The vector fields that these centroids are associated with. */
            vector_fields?: string[];
            centroid_vector_fields?: string[];
            /** @description Alias is used to name a cluster. */
            alias?: string;
            dataset_id?: string;
        };
        DeleteCentroidOutput: unknown;
        RealtimeClusteringInput: {
            /** @description The vector field to cluster in real-time. */
            vector_field: string;
            /** @description The keyword query to apply during vector retrieval */
            keyword_query: string;
            /** @description The number of documents to get. */
            number_of_documents?: number;
            /** @description The number of clusters to cluster the documents over. */
            number_of_clusters?: number;
        };
        RealtimeClusteringOutput: {
            results?: unknown[];
        };
        MergeClustersInput: {
            /** @description The vector fields that these centroids are associated with. */
            vector_fields?: string[];
            centroid_vector_fields?: string[];
            /** @description Alias is used to name a cluster. */
            alias?: string;
            dataset_id?: string;
            cluster_ids: unknown[];
        };
        MergeClustersOutput: {
            results?: string;
        };
        CreateClusterSummariesInput: {
            /** @description The vector fields that these centroids are associated with. */
            vector_fields?: string[];
            centroid_vector_fields?: string[];
            /** @description Alias is used to name a cluster. */
            alias?: string;
            dataset_id?: string;
            cluster_ids?: unknown[];
            /** @description Set to true to only return summaries, not save them. */
            dont_save_summaries?: boolean;
            questions: {
                cluster_ids?: unknown[];
                /**
                 * @description Summarise the top results in a sentence. Fields will form a string like:
                 *   ```
                 *   {question_prefix}
                 *   {item_prefix} 1:{field_value}
                 *   {item_prefix} 2: {field_value_2}
                 *   {question_suffix}}
                 *   ```
                 *   To create a summary on a field name 'feedback' for each cluster, An example payload could be:
                 *
                 *   ```
                 *   "cluster_text_summary":[
                 *     {"item_prefix":"Feedback","question_suffix":"Summarise the feedback in one short sentence.","field":"feedback"}
                 *   ]
                 *   ```
                 */
                config: {
                    examples?: {
                        /** @description Embedded in prompt as example text that should predict label. */
                        items: string[];
                        /** @description Will embedded in the prompt as the expected target label of the items. */
                        label: string;
                    }[];
                    /** @description Text field of clusters to summarize */
                    field: string;
                    question_prefix?: string;
                    question_suffix?: string;
                    item_prefix?: string;
                    /**
                     * @description Add 1:,2:,3:... to each item. This makes it more likely output will be numbered.
                     * @default true
                     */
                    items_numbered?: boolean;
                    /**
                     * @description Cluster items provide context to the question. Limiting cluster item char length reduces cost, but may miss end of sentences with context.
                     * @default 200
                     */
                    max_item_length?: number;
                    /**
                     * @description Cluster items provide context to the question. Limiting cluster item count reduces cost, but may miss items with context.
                     * @default 10
                     */
                    max_item_count?: number;
                    /**
                     * @description Controls answer quality. Higher quality answers have exponentially higher cost.
                     * @default 1
                     */
                    accuracy?: 1 | 2 | 3 | 4;
                    /** @default 0.7 */
                    temperature?: number;
                    /**
                     * @description Maximum number of words that will be generated.
                     * @default 60
                     */
                    max_answer_length?: number;
                    /**
                     * @description To avoid overloading our summary API, you can increase / decrease the ms delay between questions.
                     * @default 1000
                     */
                    delay_between_summary_generation?: number;
                };
            }[];
        };
        CreateClusterSummariesOutput: {
            results: {
                [key: string]: {
                    answers: {
                        answer: string;
                        question: string;
                        /**
                         * @description Summarise the top results in a sentence. Fields will form a string like:
                         *   ```
                         *   {question_prefix}
                         *   {item_prefix} 1:{field_value}
                         *   {item_prefix} 2: {field_value_2}
                         *   {question_suffix}}
                         *   ```
                         *   To create a summary on a field name 'feedback' for each cluster, An example payload could be:
                         *
                         *   ```
                         *   "cluster_text_summary":[
                         *     {"item_prefix":"Feedback","question_suffix":"Summarise the feedback in one short sentence.","field":"feedback"}
                         *   ]
                         *   ```
                         */
                        config: {
                            examples?: {
                                /** @description Embedded in prompt as example text that should predict label. */
                                items: string[];
                                /** @description Will embedded in the prompt as the expected target label of the items. */
                                label: string;
                            }[];
                            /** @description Text field of clusters to summarize */
                            field: string;
                            question_prefix?: string;
                            question_suffix?: string;
                            item_prefix?: string;
                            /**
                             * @description Add 1:,2:,3:... to each item. This makes it more likely output will be numbered.
                             * @default true
                             */
                            items_numbered?: boolean;
                            /**
                             * @description Cluster items provide context to the question. Limiting cluster item char length reduces cost, but may miss end of sentences with context.
                             * @default 200
                             */
                            max_item_length?: number;
                            /**
                             * @description Cluster items provide context to the question. Limiting cluster item count reduces cost, but may miss items with context.
                             * @default 10
                             */
                            max_item_count?: number;
                            /**
                             * @description Controls answer quality. Higher quality answers have exponentially higher cost.
                             * @default 1
                             */
                            accuracy?: 1 | 2 | 3 | 4;
                            /** @default 0.7 */
                            temperature?: number;
                            /**
                             * @description Maximum number of words that will be generated.
                             * @default 60
                             */
                            max_answer_length?: number;
                            /**
                             * @description To avoid overloading our summary API, you can increase / decrease the ms delay between questions.
                             * @default 1000
                             */
                            delay_between_summary_generation?: number;
                        };
                        confidence?: number;
                        retry_count?: number;
                    }[];
                };
            };
        };
        CreateClusterLabelsInput: {
            /** @description The vector fields that these centroids are associated with. */
            vector_fields?: string[];
            centroid_vector_fields?: string[];
            /** @description Alias is used to name a cluster. */
            alias?: string;
            dataset_id?: string;
            cluster_ids?: unknown[];
            /** @description Set to true to only return summaries, not save them. */
            dont_save_summaries?: boolean;
            questions: {
                cluster_ids?: unknown[];
                /**
                 * @description Summarise the top results in a sentence. Fields will form a string like:
                 *   ```
                 *   {question_prefix}
                 *   {item_prefix} 1:{field_value}
                 *   {item_prefix} 2: {field_value_2}
                 *   {question_suffix}}
                 *   ```
                 *   To create a summary on a field name 'feedback' for each cluster, An example payload could be:
                 *
                 *   ```
                 *   "cluster_text_summary":[
                 *     {"item_prefix":"Feedback","question_suffix":"Summarise the feedback in one short sentence.","field":"feedback"}
                 *   ]
                 *   ```
                 */
                config: {
                    examples?: {
                        /** @description Embedded in prompt as example text that should predict label. */
                        items: string[];
                        /** @description Will embedded in the prompt as the expected target label of the items. */
                        label: string;
                    }[];
                    /** @description Text field of clusters to summarize */
                    field: string;
                    question_prefix?: string;
                    question_suffix?: string;
                    item_prefix?: string;
                    /**
                     * @description Add 1:,2:,3:... to each item. This makes it more likely output will be numbered.
                     * @default true
                     */
                    items_numbered?: boolean;
                    /**
                     * @description Cluster items provide context to the question. Limiting cluster item char length reduces cost, but may miss end of sentences with context.
                     * @default 200
                     */
                    max_item_length?: number;
                    /**
                     * @description Cluster items provide context to the question. Limiting cluster item count reduces cost, but may miss items with context.
                     * @default 10
                     */
                    max_item_count?: number;
                    /**
                     * @description Controls answer quality. Higher quality answers have exponentially higher cost.
                     * @default 1
                     */
                    accuracy?: 1 | 2 | 3 | 4;
                    /** @default 0.7 */
                    temperature?: number;
                    /**
                     * @description Maximum number of words that will be generated.
                     * @default 60
                     */
                    max_answer_length?: number;
                    /**
                     * @description To avoid overloading our summary API, you can increase / decrease the ms delay between questions.
                     * @default 1000
                     */
                    delay_between_summary_generation?: number;
                };
            }[];
        };
        CreateClusterLabelsOutput: {
            results: {
                [key: string]: {
                    answers: {
                        answer: string;
                        question: string;
                        /**
                         * @description Summarise the top results in a sentence. Fields will form a string like:
                         *   ```
                         *   {question_prefix}
                         *   {item_prefix} 1:{field_value}
                         *   {item_prefix} 2: {field_value_2}
                         *   {question_suffix}}
                         *   ```
                         *   To create a summary on a field name 'feedback' for each cluster, An example payload could be:
                         *
                         *   ```
                         *   "cluster_text_summary":[
                         *     {"item_prefix":"Feedback","question_suffix":"Summarise the feedback in one short sentence.","field":"feedback"}
                         *   ]
                         *   ```
                         */
                        config: {
                            examples?: {
                                /** @description Embedded in prompt as example text that should predict label. */
                                items: string[];
                                /** @description Will embedded in the prompt as the expected target label of the items. */
                                label: string;
                            }[];
                            /** @description Text field of clusters to summarize */
                            field: string;
                            question_prefix?: string;
                            question_suffix?: string;
                            item_prefix?: string;
                            /**
                             * @description Add 1:,2:,3:... to each item. This makes it more likely output will be numbered.
                             * @default true
                             */
                            items_numbered?: boolean;
                            /**
                             * @description Cluster items provide context to the question. Limiting cluster item char length reduces cost, but may miss end of sentences with context.
                             * @default 200
                             */
                            max_item_length?: number;
                            /**
                             * @description Cluster items provide context to the question. Limiting cluster item count reduces cost, but may miss items with context.
                             * @default 10
                             */
                            max_item_count?: number;
                            /**
                             * @description Controls answer quality. Higher quality answers have exponentially higher cost.
                             * @default 1
                             */
                            accuracy?: 1 | 2 | 3 | 4;
                            /** @default 0.7 */
                            temperature?: number;
                            /**
                             * @description Maximum number of words that will be generated.
                             * @default 60
                             */
                            max_answer_length?: number;
                            /**
                             * @description To avoid overloading our summary API, you can increase / decrease the ms delay between questions.
                             * @default 1000
                             */
                            delay_between_summary_generation?: number;
                        };
                        confidence?: number;
                        retry_count?: number;
                    }[];
                };
            };
        };
        ListClusterSummariesInput: {
            /** @description The vector fields that these centroids are associated with. */
            vector_fields?: string[];
            centroid_vector_fields?: string[];
            /** @description Alias is used to name a cluster. */
            alias?: string;
            dataset_id?: string;
        };
        ListClusterSummariesOutput: {
            results: {
                [key: string]: {
                    answers: {
                        answer: string;
                        question: string;
                        /**
                         * @description Summarise the top results in a sentence. Fields will form a string like:
                         *   ```
                         *   {question_prefix}
                         *   {item_prefix} 1:{field_value}
                         *   {item_prefix} 2: {field_value_2}
                         *   {question_suffix}}
                         *   ```
                         *   To create a summary on a field name 'feedback' for each cluster, An example payload could be:
                         *
                         *   ```
                         *   "cluster_text_summary":[
                         *     {"item_prefix":"Feedback","question_suffix":"Summarise the feedback in one short sentence.","field":"feedback"}
                         *   ]
                         *   ```
                         */
                        config: {
                            examples?: {
                                /** @description Embedded in prompt as example text that should predict label. */
                                items: string[];
                                /** @description Will embedded in the prompt as the expected target label of the items. */
                                label: string;
                            }[];
                            /** @description Text field of clusters to summarize */
                            field: string;
                            question_prefix?: string;
                            question_suffix?: string;
                            item_prefix?: string;
                            /**
                             * @description Add 1:,2:,3:... to each item. This makes it more likely output will be numbered.
                             * @default true
                             */
                            items_numbered?: boolean;
                            /**
                             * @description Cluster items provide context to the question. Limiting cluster item char length reduces cost, but may miss end of sentences with context.
                             * @default 200
                             */
                            max_item_length?: number;
                            /**
                             * @description Cluster items provide context to the question. Limiting cluster item count reduces cost, but may miss items with context.
                             * @default 10
                             */
                            max_item_count?: number;
                            /**
                             * @description Controls answer quality. Higher quality answers have exponentially higher cost.
                             * @default 1
                             */
                            accuracy?: 1 | 2 | 3 | 4;
                            /** @default 0.7 */
                            temperature?: number;
                            /**
                             * @description Maximum number of words that will be generated.
                             * @default 60
                             */
                            max_answer_length?: number;
                            /**
                             * @description To avoid overloading our summary API, you can increase / decrease the ms delay between questions.
                             * @default 1000
                             */
                            delay_between_summary_generation?: number;
                        };
                        confidence?: number;
                        retry_count?: number;
                    }[];
                };
            };
        };
        CreateHierarchicalClustersInput: {
            /** @description The vector fields that these centroids are associated with. */
            vector_fields?: string[];
            centroid_vector_fields?: string[];
            /** @description Alias is used to name a cluster. */
            alias?: string;
            dataset_id?: string;
            /** @default 10 */
            n_clusters?: number;
        };
        CreateHierarchicalClustersOutput: {
            alias: string;
        };
        DeleteClusterSummariesInput: {
            /** @description The vector fields that these centroids are associated with. */
            vector_fields?: string[];
            centroid_vector_fields?: string[];
            /** @description Alias is used to name a cluster. */
            alias?: string;
            dataset_id?: string;
            cluster_ids: unknown[];
            /** @description Whether to optionally only delete summaries matching the question suffix. */
            question_suffix?: string;
            /** @description Whether to optionally only delete summaries matching the question prefix. */
            question_prefix?: string;
            /** @description Whether to optionally only delete summaries matching the field. */
            field?: string;
        };
        DeleteClusterSummariesOutput: unknown;
        CreateOrganizationInput: {
            name?: string;
        };
        CreateOrganizationOutput: {
            organization_id: string;
        };
        UpdateOrganizationInput: {
            name?: string;
        };
        UpdateOrganizationOutput: unknown;
        UpdateOrganizationAdminInput: {
            name?: string;
            entitlements?: {
                users?: {
                    limit?: number;
                };
                workflows_gpu?: {
                    enabled?: boolean;
                };
            };
            entitlement_plan?: "Starter" | "Business" | "Enterprise";
        };
        UpdateOrganizationAdminOutput: unknown;
        ListOrganizationsInput: unknown;
        ListOrganizationsOutput: {
            results: {
                organization_id?: string;
                name?: string;
                entitlements?: {
                    users?: {
                        limit?: number;
                    };
                    workflows_gpu?: {
                        enabled?: boolean;
                    };
                };
                entitlement_plan?: "Starter" | "Business" | "Enterprise";
            }[];
        };
        DeleteOrganizationInput: unknown;
        DeleteOrganizationOutput: unknown;
        GetOrganizationInput: unknown;
        GetOrganizationOutput: {
            organization_id?: string;
            name?: string;
            entitlements?: {
                users?: {
                    limit?: number;
                };
                workflows_gpu?: {
                    enabled?: boolean;
                };
            };
            entitlement_plan?: "Starter" | "Business" | "Enterprise";
        };
        GetOrganizationUsageInput: unknown;
        GetOrganizationUsageOutput: {
            usage: {
                users?: {
                    limit?: number;
                };
                workflows_gpu?: {
                    enabled?: boolean;
                };
            };
            limit: {
                users?: {
                    limit?: number;
                };
                workflows_gpu?: {
                    enabled?: boolean;
                };
            };
        };
        ListUsersInOrganizationInput: {
            /** @description Currently limited to exact_match, ids, exists, regexp filter_type */
            filters?: {
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }[];
        };
        ListUsersInOrganizationOutput: {
            results: {
                _id?: string;
                profile_picture_url?: string;
                onboarded?: boolean;
                first_name?: string;
                last_name?: string;
                role?: string;
                company?: string;
                label?: string;
                notes?: string;
            }[];
        };
        ListProjectsInOrganizationInput: unknown;
        ListProjectsInOrganizationOutput: {
            results: {
                _id?: string;
                project_id?: string;
                name?: string;
                description?: string;
                permissions?: {
                    [key: string]: unknown;
                };
                updated_at?: string;
            }[];
        };
        AggregateOrganizationMetricsInput: {
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            /** @description Aggregation query to aggregate data */
            aggregation_query?: {
                groupby?: {
                    agg?: "texts" | "categories" | "classes" | "text" | "category" | "class" | "terms" | "number" | "numeric" | "bool" | "wordcloud" | "array" | "boolean_filter" | "histogram";
                    size?: number;
                    field?: string;
                    name?: string;
                    boolean_filter?: Partial<{
                        /** @description Match where document[field] is in value list. */
                        match?: {
                            /** @description If matching on text, match even if there are extra words / case insensitivity */
                            fuzzy?: boolean;
                            /** @description Field to match on. */
                            field?: string;
                            /** @description Can be either a single item or a list of items to match on. */
                            value: string | boolean | number | unknown[];
                        };
                        /** @description Match where document._id is in value list. */
                        matchIds?: {
                            /** @description Can be either a single item or a list of items to match on. */
                            value: string | boolean | number | unknown[];
                        };
                        /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                        range?: {
                            /** @description Field to match on. */
                            field?: string;
                            greaterThan?: unknown;
                            lessThan?: unknown;
                            greaterThanOrEqualTo?: unknown;
                            lessThanOrEqualTo?: unknown;
                        };
                        /**
                         * @description
                         * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                         *
                         * Possible matching patterns:
                         *
                         * * - documents where the field has any value
                         * ? - documents where a single character from the field matches the provided values
                         *
                         * Example:
                         *
                         * {
                         *   wildcard: {
                         *      field: "title",
                         *      value: [ "Avenger*" ]
                         *   }
                         * }
                         */
                        wildcard?: {
                            /** @description Field to match on. */
                            field?: string;
                            /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                            value: string | string[];
                        };
                        /** @description hybrid search query that must reach a certain value to filter */
                        search?: {
                            /**
                             * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                             *
                             *     "tele" matches "Television", "This television is an excellent product…"
                             *
                             *     Example: "tele"
                             */
                            query?: string;
                            /** @description Configuration for traditional search query. */
                            queryConfig?: {
                                /**
                                 * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                                 * @default 1
                                 */
                                weight?: number;
                                /**
                                 * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                                 *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                                 * @default 1
                                 */
                                sentenceWeight?: number;
                                /**
                                 * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                                 *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                                 * @default 1
                                 */
                                fuzzyWeight?: number;
                            };
                            /**
                             * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                             *
                             *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                             *
                             *     It can be an object or a list of objects.
                             *
                             *
                             *
                             *     Example payloads:
                             *
                             *     {"field":"animaltype_vector_","query":"kitten"}
                             *
                             *     [
                             *
                             *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                             *
                             *     ]
                             */
                            vectorSearchQuery?: {
                                /** @description Vector name to search on. For example, title_vector_ */
                                field: string;
                                /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                                query?: string;
                                /**
                                 * @description Model name to generate the vector with.
                                 * @default text
                                 */
                                model?: string;
                                /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                                model_url?: string;
                                /**
                                 * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                                 * @default 1
                                 */
                                weight?: number;
                                /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                                vector?: number[];
                                chunkConfig?: {
                                    chunkField: string;
                                    page?: number;
                                    pageSize?: number;
                                };
                            } | {
                                /** @description Vector name to search on. For example, title_vector_ */
                                field: string;
                                /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                                query?: string;
                                /**
                                 * @description Model name to generate the vector with.
                                 * @default text
                                 */
                                model?: string;
                                /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                                model_url?: string;
                                /**
                                 * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                                 * @default 1
                                 */
                                weight?: number;
                                /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                                vector?: number[];
                                chunkConfig?: {
                                    chunkField: string;
                                    page?: number;
                                    pageSize?: number;
                                };
                            }[];
                            realtimeClustering?: {
                                /** @default 4 */
                                nClusters?: number;
                                /** @default kmeans-realtime-4 */
                                alias?: string;
                                vectorField: string;
                                /** @default _cluster_.{vector_field}.{alias} */
                                outputField?: string;
                            };
                            /**
                             * @description A list of fields to search using the "query" parameter.
                             *
                             *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                             *
                             *     Default behaviour is to search all fields.
                             *
                             *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                             */
                            fieldsToSearch?: (string | {
                                /** @description Field name to search. */
                                field?: string;
                                /**
                                 * @description Multiply the relevance contribution of a specific field when using traditional search.
                                 * @default 1
                                 */
                                weight?: number;
                                /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                                chunkField?: string;
                            })[];
                            /**
                             * @description Only return documents with a _relevance above this threshold.
                             *
                             *     Example: 0.3
                             */
                            minimumRelevance: number;
                        };
                        /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                        fieldExists?: {
                            /** @description Field that must exist. */
                            field?: string;
                        };
                        /** @description Match documents where doc[field] % module == value */
                        matchModulo?: {
                            /** @default _id */
                            field?: string;
                            modulo: number;
                            value: number;
                        };
                        /** @description Filter down to one document for each value of selected field. */
                        dedupeByValue?: {
                            /** @description Field to filter on. */
                            field: string;
                        };
                        /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                        selfreference?: {
                            /** @description First field in comparison. */
                            a: string;
                            /** @description Second field in comparison. */
                            b: string;
                            /** @description Operator used to compare a and b. */
                            operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                        };
                        /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                        wordCount?: {
                            /** @description Field to match on. */
                            field: string;
                            /** @description Minimum word count. */
                            greaterThan?: number;
                            /** @description Maximum word count. */
                            lessThan?: number;
                        };
                        /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                        characterCount?: {
                            /** @description Field to match on. */
                            field: string;
                            /** @description Minimum character count. */
                            greaterThan?: number;
                            /** @description Maximum character count. */
                            lessThan?: number;
                        };
                        /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                        or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                        /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                        not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                        /** @description Filter based on data within a _chunk_ field. */
                        chunk?: {
                            /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                            path: string;
                            filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                        };
                    }> & Partial<{
                        strict?: "must" | "should" | "must_or";
                        condition?: string;
                        field?: string;
                        filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                        condition_value?: unknown;
                        fuzzy?: number;
                        join?: boolean;
                    }>;
                    /** @description Control the number of results for this aggregation level. This will default to the page_size provided in the root body if not provided. */
                    page_size?: number;
                    group_size?: number;
                    select_fields?: string[];
                    remove_words?: string[];
                    /** @default monthly */
                    date_interval?: "monthly" | "daily" | "yearly" | "hourly" | "minutely" | "weekly";
                    /** @description If provided, group by field into n buckets in date range. */
                    n_date_buckets?: number;
                    buckets?: Partial<{
                        histogram?: {
                            interval?: number;
                            min_doc_count?: number;
                        };
                    }> & Partial<{
                        from?: number;
                        to?: number;
                    }[]>;
                }[];
                metrics?: {
                    field?: string;
                    fields?: string[];
                    agg?: "avg" | "average" | "cardinality" | "max" | "mean" | "min" | "sum" | "percentiles" | "sum_of_squares" | "variance" | "std_deviation" | "std_deviation_bounds" | "kurtosis" | "covariance" | "skewness" | "character_count" | "count" | "correlation" | "missing";
                    name?: string;
                }[];
                sort?: (string | {
                    [key: string]: "asc" | "desc";
                })[];
            };
            /**
             * @description Size of each page of results
             * @default 20
             */
            page_size?: number;
            /**
             * @description Page of the results
             * @default 1
             */
            page?: number;
            /** @description Whether to sort results by ascending or descending order. */
            asc?: boolean;
            dataset_ids?: string[];
            dataset_id?: string;
            /** @default monthly */
            date_interval?: "monthly" | "daily" | "yearly" | "hourly" | "minutely" | "weekly";
        };
        AggregateOrganizationMetricsOutput: {
            results?: unknown[];
        };
        CreateConnectorInput: {
            source_type: "Snowflake";
            source_database?: string;
            source_collection: string;
            /** @description If provided, use this field as the _id value in documents. */
            id_field?: string;
            source_settings: {
                custom?: {
                    [key: string]: unknown;
                };
                Snowflake?: {
                    /** Authorization Method */
                    credentials?: {
                        /** @default OAuth */
                        auth_type: string;
                        /**
                         * Client ID
                         * @description The Client ID of your Snowflake developer application.
                         */
                        client_id: string;
                        /**
                         * Client Secret
                         * @description The Client Secret of your Snowflake developer application.
                         */
                        client_secret: string;
                        /**
                         * Access Token
                         * @description Access Token for making authenticated requests.
                         */
                        access_token?: string;
                        /**
                         * Refresh Token
                         * @description Refresh Token for making authenticated requests.
                         */
                        refresh_token?: string;
                    } | {
                        /** @default username/password */
                        auth_type: string;
                        /**
                         * Username
                         * @description The username you created to allow Airbyte to access the database.
                         */
                        username: string;
                        /**
                         * Password
                         * @description The password associated with the username.
                         */
                        password: string;
                    };
                    /**
                     * Account Name
                     * @description The host domain of the snowflake instance (must include the account, region, cloud environment, and end with snowflakecomputing.com).
                     */
                    host?: string;
                    /**
                     * Role
                     * @description The role you created for Airbyte to access Snowflake.
                     */
                    role?: string;
                    /**
                     * Warehouse
                     * @description The warehouse you created for Airbyte to access data.
                     */
                    warehouse?: string;
                    /**
                     * Database
                     * @description The database you created for Airbyte to access data.
                     */
                    database?: string;
                    /** Schema */
                    schema?: string;
                };
            };
            schedule?: {
                date_interval: "minutes" | "hours" | "days" | "weeks" | "months";
                units: number;
            };
            dataset_id: string;
            name?: string;
        };
        ConnectorSchema: {
            source_type: "Snowflake";
            source_database?: string;
            source_collection: string;
            /** @description If provided, use this field as the _id value in documents. */
            id_field?: string;
            source_settings: {
                custom?: {
                    [key: string]: unknown;
                };
                Snowflake?: {
                    /** Authorization Method */
                    credentials?: {
                        /** @default OAuth */
                        auth_type: string;
                        /**
                         * Client ID
                         * @description The Client ID of your Snowflake developer application.
                         */
                        client_id: string;
                        /**
                         * Client Secret
                         * @description The Client Secret of your Snowflake developer application.
                         */
                        client_secret: string;
                        /**
                         * Access Token
                         * @description Access Token for making authenticated requests.
                         */
                        access_token?: string;
                        /**
                         * Refresh Token
                         * @description Refresh Token for making authenticated requests.
                         */
                        refresh_token?: string;
                    } | {
                        /** @default username/password */
                        auth_type: string;
                        /**
                         * Username
                         * @description The username you created to allow Airbyte to access the database.
                         */
                        username: string;
                        /**
                         * Password
                         * @description The password associated with the username.
                         */
                        password: string;
                    };
                    /**
                     * Account Name
                     * @description The host domain of the snowflake instance (must include the account, region, cloud environment, and end with snowflakecomputing.com).
                     */
                    host?: string;
                    /**
                     * Role
                     * @description The role you created for Airbyte to access Snowflake.
                     */
                    role?: string;
                    /**
                     * Warehouse
                     * @description The warehouse you created for Airbyte to access data.
                     */
                    warehouse?: string;
                    /**
                     * Database
                     * @description The database you created for Airbyte to access data.
                     */
                    database?: string;
                    /** Schema */
                    schema?: string;
                };
            };
            schedule?: {
                date_interval: "minutes" | "hours" | "days" | "weeks" | "months";
                units: number;
            };
            dataset_id: string;
            name?: string;
        };
        CreateConnectorOutput: {
            _id: string;
        };
        CheckConnectionInput: {
            source_type: "Snowflake";
            source_settings: {
                custom?: {
                    [key: string]: unknown;
                };
                Snowflake?: {
                    /** Authorization Method */
                    credentials?: {
                        /** @default OAuth */
                        auth_type: string;
                        /**
                         * Client ID
                         * @description The Client ID of your Snowflake developer application.
                         */
                        client_id: string;
                        /**
                         * Client Secret
                         * @description The Client Secret of your Snowflake developer application.
                         */
                        client_secret: string;
                        /**
                         * Access Token
                         * @description Access Token for making authenticated requests.
                         */
                        access_token?: string;
                        /**
                         * Refresh Token
                         * @description Refresh Token for making authenticated requests.
                         */
                        refresh_token?: string;
                    } | {
                        /** @default username/password */
                        auth_type: string;
                        /**
                         * Username
                         * @description The username you created to allow Airbyte to access the database.
                         */
                        username: string;
                        /**
                         * Password
                         * @description The password associated with the username.
                         */
                        password: string;
                    };
                    /**
                     * Account Name
                     * @description The host domain of the snowflake instance (must include the account, region, cloud environment, and end with snowflakecomputing.com).
                     */
                    host?: string;
                    /**
                     * Role
                     * @description The role you created for Airbyte to access Snowflake.
                     */
                    role?: string;
                    /**
                     * Warehouse
                     * @description The warehouse you created for Airbyte to access data.
                     */
                    warehouse?: string;
                    /**
                     * Database
                     * @description The database you created for Airbyte to access data.
                     */
                    database?: string;
                    /** Schema */
                    schema?: string;
                };
            };
        };
        CheckConnectionOutput: {
            status: "succeeded" | "failed";
            message: string;
        };
        GetConnectorInput: unknown;
        GetConnectorOutput: {
            source_type: "Snowflake";
            source_database?: string;
            source_collection: string;
            /** @description If provided, use this field as the _id value in documents. */
            id_field?: string;
            source_settings: {
                custom?: {
                    [key: string]: unknown;
                };
                Snowflake?: {
                    /** Authorization Method */
                    credentials?: {
                        /** @default OAuth */
                        auth_type: string;
                        /**
                         * Client ID
                         * @description The Client ID of your Snowflake developer application.
                         */
                        client_id: string;
                        /**
                         * Client Secret
                         * @description The Client Secret of your Snowflake developer application.
                         */
                        client_secret: string;
                        /**
                         * Access Token
                         * @description Access Token for making authenticated requests.
                         */
                        access_token?: string;
                        /**
                         * Refresh Token
                         * @description Refresh Token for making authenticated requests.
                         */
                        refresh_token?: string;
                    } | {
                        /** @default username/password */
                        auth_type: string;
                        /**
                         * Username
                         * @description The username you created to allow Airbyte to access the database.
                         */
                        username: string;
                        /**
                         * Password
                         * @description The password associated with the username.
                         */
                        password: string;
                    };
                    /**
                     * Account Name
                     * @description The host domain of the snowflake instance (must include the account, region, cloud environment, and end with snowflakecomputing.com).
                     */
                    host?: string;
                    /**
                     * Role
                     * @description The role you created for Airbyte to access Snowflake.
                     */
                    role?: string;
                    /**
                     * Warehouse
                     * @description The warehouse you created for Airbyte to access data.
                     */
                    warehouse?: string;
                    /**
                     * Database
                     * @description The database you created for Airbyte to access data.
                     */
                    database?: string;
                    /** Schema */
                    schema?: string;
                };
            };
            schedule?: {
                date_interval: "minutes" | "hours" | "days" | "weeks" | "months";
                units: number;
            };
            dataset_id: string;
            name?: string;
            _id?: string;
        };
        UpdateConnectorInput: {
            source_type: "Snowflake";
            source_database?: string;
            source_collection: string;
            /** @description If provided, use this field as the _id value in documents. */
            id_field?: string;
            source_settings: {
                custom?: {
                    [key: string]: unknown;
                };
                Snowflake?: {
                    /** Authorization Method */
                    credentials?: {
                        /** @default OAuth */
                        auth_type: string;
                        /**
                         * Client ID
                         * @description The Client ID of your Snowflake developer application.
                         */
                        client_id: string;
                        /**
                         * Client Secret
                         * @description The Client Secret of your Snowflake developer application.
                         */
                        client_secret: string;
                        /**
                         * Access Token
                         * @description Access Token for making authenticated requests.
                         */
                        access_token?: string;
                        /**
                         * Refresh Token
                         * @description Refresh Token for making authenticated requests.
                         */
                        refresh_token?: string;
                    } | {
                        /** @default username/password */
                        auth_type: string;
                        /**
                         * Username
                         * @description The username you created to allow Airbyte to access the database.
                         */
                        username: string;
                        /**
                         * Password
                         * @description The password associated with the username.
                         */
                        password: string;
                    };
                    /**
                     * Account Name
                     * @description The host domain of the snowflake instance (must include the account, region, cloud environment, and end with snowflakecomputing.com).
                     */
                    host?: string;
                    /**
                     * Role
                     * @description The role you created for Airbyte to access Snowflake.
                     */
                    role?: string;
                    /**
                     * Warehouse
                     * @description The warehouse you created for Airbyte to access data.
                     */
                    warehouse?: string;
                    /**
                     * Database
                     * @description The database you created for Airbyte to access data.
                     */
                    database?: string;
                    /** Schema */
                    schema?: string;
                };
            };
            schedule?: {
                date_interval: "minutes" | "hours" | "days" | "weeks" | "months";
                units: number;
            };
            dataset_id: string;
            name?: string;
        };
        UpdateConnectorOutput: unknown;
        TriggerConnectorInput: unknown;
        TriggerConnectorOutput: unknown;
        DeleteConnectorInput: unknown;
        DeleteConnectorOutput: unknown;
        ListConnectorsInput: unknown;
        ListConnectorsOutput: {
            results: {
                source_type: "Snowflake";
                source_database?: string;
                source_collection: string;
                /** @description If provided, use this field as the _id value in documents. */
                id_field?: string;
                source_settings: {
                    custom?: {
                        [key: string]: unknown;
                    };
                    Snowflake?: {
                        /** Authorization Method */
                        credentials?: {
                            /** @default OAuth */
                            auth_type: string;
                            /**
                             * Client ID
                             * @description The Client ID of your Snowflake developer application.
                             */
                            client_id: string;
                            /**
                             * Client Secret
                             * @description The Client Secret of your Snowflake developer application.
                             */
                            client_secret: string;
                            /**
                             * Access Token
                             * @description Access Token for making authenticated requests.
                             */
                            access_token?: string;
                            /**
                             * Refresh Token
                             * @description Refresh Token for making authenticated requests.
                             */
                            refresh_token?: string;
                        } | {
                            /** @default username/password */
                            auth_type: string;
                            /**
                             * Username
                             * @description The username you created to allow Airbyte to access the database.
                             */
                            username: string;
                            /**
                             * Password
                             * @description The password associated with the username.
                             */
                            password: string;
                        };
                        /**
                         * Account Name
                         * @description The host domain of the snowflake instance (must include the account, region, cloud environment, and end with snowflakecomputing.com).
                         */
                        host?: string;
                        /**
                         * Role
                         * @description The role you created for Airbyte to access Snowflake.
                         */
                        role?: string;
                        /**
                         * Warehouse
                         * @description The warehouse you created for Airbyte to access data.
                         */
                        warehouse?: string;
                        /**
                         * Database
                         * @description The database you created for Airbyte to access data.
                         */
                        database?: string;
                        /** Schema */
                        schema?: string;
                    };
                };
                schedule?: {
                    date_interval: "minutes" | "hours" | "days" | "weeks" | "months";
                    units: number;
                };
                dataset_id: string;
                name?: string;
                _id?: string;
            }[];
        };
        ListConnectorTypesInput: unknown;
        ListConnectorTypesOutput: {
            results: {
                image: string;
                _id: "Snowflake";
                name: string;
                tags: "Data Warehouse"[];
                description: string;
            }[];
        };
        GetConnectorTypeInput: unknown;
        GetConnectorTypeOutput: {
            image: string;
            _id: "Snowflake";
            name: string;
            tags: "Data Warehouse"[];
            description: string;
            schema: {
                [key: string]: unknown;
            };
        };
        ListConnectorJobsInput: unknown;
        ListConnectorJobsOutput: {
            results: {
                status: "pending" | "running" | "incomplete" | "failed" | "succeeded" | "cancelled";
                created_at: number;
                error_message?: string;
                _id: number;
            }[];
        };
        GetConnectorJobInput: unknown;
        GetConnectorJobOutput: {
            status: "pending" | "running" | "incomplete" | "failed" | "succeeded" | "cancelled";
            created_at: number;
            error_message?: string;
            _id: number;
        };
        TriggerWorkflowInput: {
            /**
             * @description the workflows version to execute. It could be a tag like production or a version like v1.1.5
             * @default production
             */
            version?: string;
            params: {
                [key: string]: unknown;
            };
            notebook_path?: string;
            instance_type?: string;
            dataset_id: string;
            workflow_id?: string;
            template?: {
                transformations: {
                    [key: string]: {
                        properties: {
                            params: {
                                [key: string]: unknown;
                            };
                            workflow_id: string;
                        };
                        depends_on?: string[];
                        if?: string;
                    };
                };
            };
        };
        TriggerWorkflowOutput: {
            job_id: string;
        };
        ListWorkflowsInput: unknown;
        ListWorkflowsOutput: {
            results: ({
                creation_time?: string;
                completion_time?: string;
                notebook_path?: string;
                instance_type?: string;
                dataset_id?: string;
                params?: {
                    [key: string]: unknown;
                };
                _id?: string;
                workflow_id?: string;
                parent_job_id?: string;
                metadata?: {
                    [key: string]: unknown;
                };
                /** @description Status of the workflow. Used for knowing when to send an email notification. */
                status?: "complete" | "inprogress" | "failed" | "cancelled";
                job_status?: "InProgress" | "Completed" | "Failed" | "Cancelled";
                worker_status?: {
                    [key: string]: {
                        status?: "complete" | "inprogress" | "failed" | "cancelled";
                        completion_time?: string;
                        creation_time?: string;
                    };
                };
                n_workers?: number;
                template?: {
                    transformations: {
                        [key: string]: {
                            properties: {
                                params: {
                                    [key: string]: unknown;
                                };
                                workflow_id: string;
                            };
                            depends_on?: string[];
                            if?: string;
                        };
                    };
                };
                steps?: {
                    [key: string]: {
                        worker_progress?: {
                            [key: string]: {
                                n_processed?: number;
                                n_total?: number;
                            };
                        };
                        n_processed?: number;
                        n_total?: number;
                        job_id?: string;
                        error_message?: string;
                    };
                };
            } & {
                [key: string]: unknown;
            })[];
        };
        GetWorkflowStatusInput: unknown;
        GetWorkflowStatusOutput: {
            creation_time?: string;
            completion_time?: string;
            notebook_path?: string;
            instance_type?: string;
            dataset_id?: string;
            params?: {
                [key: string]: unknown;
            };
            _id?: string;
            workflow_id?: string;
            parent_job_id?: string;
            metadata?: {
                [key: string]: unknown;
            };
            /** @description Status of the workflow. Used for knowing when to send an email notification. */
            status?: "complete" | "inprogress" | "failed" | "cancelled";
            job_status?: "InProgress" | "Completed" | "Failed" | "Cancelled";
            worker_status?: {
                [key: string]: {
                    status?: "complete" | "inprogress" | "failed" | "cancelled";
                    completion_time?: string;
                    creation_time?: string;
                };
            };
            n_workers?: number;
            template?: {
                transformations: {
                    [key: string]: {
                        properties: {
                            params: {
                                [key: string]: unknown;
                            };
                            workflow_id: string;
                        };
                        depends_on?: string[];
                        if?: string;
                    };
                };
            };
            steps?: {
                [key: string]: {
                    worker_progress?: {
                        [key: string]: {
                            n_processed?: number;
                            n_total?: number;
                        };
                    };
                    n_processed?: number;
                    n_total?: number;
                    job_id?: string;
                    error_message?: string;
                };
            };
        } & {
            [key: string]: unknown;
        };
        DeleteWorkflowStatusInput: unknown;
        DeleteWorkflowStatusOutput: unknown;
        TerminateWorkflowInput: unknown;
        TerminateWorkflowOutput: unknown;
        UpsertWorkflowMetadataInput: {
            /** @description Edit and add metadata for the workflow. */
            metadata: {
                [key: string]: unknown;
            };
        };
        UpsertWorkflowMetadataOutput: unknown;
        UpsertWorkflowProgressInput: {
            worker_number: number;
            step: string;
            n_processed?: number;
            n_total?: number;
        };
        UpsertWorkflowProgressOutput: unknown;
        UpsertWorkflowStatusInput: {
            /** @description Metadata of the workflow. Can be an object including any information you want to store. */
            metadata?: {
                [key: string]: unknown;
            };
            /** @description Status of the workflow. Used for knowing when to send an email notification. */
            status: "complete" | "inprogress" | "failed" | "cancelled";
            /** @description Workflow name that is passed into the email. */
            workflow_name?: string;
            /** @description Additional information of the workflow that is passed into the email. */
            additional_information?: string;
            /**
             * @description Whether to send an email on workflow completion.
             * @default true
             */
            send_email?: boolean;
            worker_number?: number;
        };
        UpsertWorkflowStatusOutput: {
            creation_time?: string;
            completion_time?: string;
            notebook_path?: string;
            instance_type?: string;
            dataset_id?: string;
            params?: {
                [key: string]: unknown;
            };
            _id?: string;
            workflow_id?: string;
            parent_job_id?: string;
            metadata?: {
                [key: string]: unknown;
            };
            /** @description Status of the workflow. Used for knowing when to send an email notification. */
            status?: "complete" | "inprogress" | "failed" | "cancelled";
            job_status?: "InProgress" | "Completed" | "Failed" | "Cancelled";
            worker_status?: {
                [key: string]: {
                    status?: "complete" | "inprogress" | "failed" | "cancelled";
                    completion_time?: string;
                    creation_time?: string;
                };
            };
            n_workers?: number;
            template?: {
                transformations: {
                    [key: string]: {
                        properties: {
                            params: {
                                [key: string]: unknown;
                            };
                            workflow_id: string;
                        };
                        depends_on?: string[];
                        if?: string;
                    };
                };
            };
            steps?: {
                [key: string]: {
                    worker_progress?: {
                        [key: string]: {
                            n_processed?: number;
                            n_total?: number;
                        };
                    };
                    n_processed?: number;
                    n_total?: number;
                    job_id?: string;
                    error_message?: string;
                };
            };
        } & {
            [key: string]: unknown;
        };
        ListWorkflowTypesInput: unknown;
        ListWorkflowTypesOutput: {
            results: ({
                workflow_id: string;
                workflow_path?: string;
                template_raw_?: {
                    [key: string]: unknown;
                };
                parameters_raw_?: {
                    [key: string]: unknown;
                };
            } & {
                [key: string]: unknown;
            })[];
        };
        GetWorkflowTypeInput: unknown;
        GetWorkflowTypeOutput: {
            workflow_id: string;
            workflow_path?: string;
            template_raw_?: {
                [key: string]: unknown;
            };
            parameters_raw_?: {
                [key: string]: unknown;
            };
        } & {
            [key: string]: unknown;
        };
        ValidateWorkflowParamsInput: {
            /**
             * @description the workflows version to execute. It could be a tag like production or a version like v1.1.5
             * @default production
             */
            version?: string;
            parameters: {
                [key: string]: unknown;
            };
        };
        ValidateWorkflowParamsOutput: {
            status: string;
            message: string;
        };
        InsertInput: {
            /** @description Each document to upsert must have an _id field matching an existing document. */
            document?: {
                _id?: unknown;
            } & {
                [key: string]: unknown;
            };
            /** @description Return immediately after storing data in a spot where it can be ingested in the background. Best for large datasets. */
            ingest_in_background?: boolean;
            /**
             * @description Whether to include insert date as a field 'insert_date_'.
             * @default true
             */
            insert_date?: boolean;
            /**
             * @description Whether to transform fields containing dates to a valid Relevance date format.
             * @default true
             */
            transform_date_format?: boolean;
            /**
             * @description Whether to remove fields with the value 'N/A' from documents. Required to process pandas style CSVs.
             * @default true
             */
            remove_na?: boolean;
            /**
             * @description Whether to overwrite document if it exists.
             * @default true
             */
            overwrite?: boolean;
            /**
             * @description Whether the api should check the documents for vector datatype to update the schema.
             * @default true
             */
            update_schema?: boolean;
            /**
             * @description Whether to return before all documents have finished updating.
             * @default true
             */
            wait_for_update?: boolean;
            encoders?: {
                /** @description The model url to use */
                model_url?: string;
                /** @description The model name to use. */
                model_name?: "image_text" | "text_image" | "all-mpnet-base-v2" | "clip-vit-b-32-image" | "clip-vit-b-32-text" | "clip-vit-l-14-image" | "clip-vit-l-14-text" | "sentence-transformers" | "similarity-all-mpnet-base-v2";
                /** @description Can be 'url' or 'text'. Use 'url' for image models and 'text' for text models. */
                body?: string;
                /** @description The document field to encode. */
                field: string;
                /** @description Add 'alias' to a created vector field name. Output format is {field}_{alias}_vector_. */
                alias?: string;
            }[];
            /** @description Before insertion, fields of documents will be transformed according to this array of rules. If transformation fails, the output field will not be generated. */
            field_transformers?: {
                /** @description Field to transform. to transform a nested field, use a.b */
                field: string;
                /** @description If specified, place result in this field. To place in a nested field, use format a.b */
                output_field?: string;
                /** @description Whether to strip html tags from field. */
                remove_html?: boolean;
                /** @description Whether transform a text field into an array of text by splitting sentences. */
                split_sentences?: boolean;
                /** @description If specified, documents with the vector fields will be clustered according to the clustering alias */
                assign_to_cluster?: {
                    /** @description The alias of the clustering. */
                    alias?: string;
                };
            }[];
            /** @description A list of jobs to run once all documents are processed. */
            jobs_to_trigger?: {
                /** @description Specify this to run an encoding job after documents are processed. Supports chunk field encoding, encoding many fields, and storing results in a new field specified by alias. */
                encode?: {
                    /** @description Chunk field to encode. Will produce a field with _chunkvector_ suffix. */
                    chunk_field?: string;
                    /** @description Regular fields to encode. Will produce a field with _vector suffix. */
                    fields?: string[];
                    /** @description If provided, this string will be added to the output field name. */
                    alias?: string;
                    /** @description Model name to use for encoding. */
                    model_name?: string;
                };
            }[];
        };
        InsertOutput: unknown;
        BulkInsertInput: {
            documents?: ({
                _id?: unknown;
            } & {
                [key: string]: unknown;
            })[];
            /** @description Return immediately after storing data in a spot where it can be ingested in the background. Best for large datasets. */
            ingest_in_background?: boolean;
            /**
             * @description Whether to include insert date as a field 'insert_date_'.
             * @default true
             */
            insert_date?: boolean;
            /**
             * @description Whether to transform fields containing dates to a valid Relevance date format.
             * @default true
             */
            transform_date_format?: boolean;
            /**
             * @description Whether to remove fields with the value 'N/A' from documents. Required to process pandas style CSVs.
             * @default true
             */
            remove_na?: boolean;
            /**
             * @description Whether to overwrite document if it exists.
             * @default true
             */
            overwrite?: boolean;
            /**
             * @description Whether the api should check the documents for vector datatype to update the schema.
             * @default true
             */
            update_schema?: boolean;
            /**
             * @description Whether to return before all documents have finished updating.
             * @default true
             */
            wait_for_update?: boolean;
            encoders?: {
                /** @description The model url to use */
                model_url?: string;
                /** @description The model name to use. */
                model_name?: "image_text" | "text_image" | "all-mpnet-base-v2" | "clip-vit-b-32-image" | "clip-vit-b-32-text" | "clip-vit-l-14-image" | "clip-vit-l-14-text" | "sentence-transformers" | "similarity-all-mpnet-base-v2";
                /** @description Can be 'url' or 'text'. Use 'url' for image models and 'text' for text models. */
                body?: string;
                /** @description The document field to encode. */
                field: string;
                /** @description Add 'alias' to a created vector field name. Output format is {field}_{alias}_vector_. */
                alias?: string;
            }[];
            /** @description Before insertion, fields of documents will be transformed according to this array of rules. If transformation fails, the output field will not be generated. */
            field_transformers?: {
                /** @description Field to transform. to transform a nested field, use a.b */
                field: string;
                /** @description If specified, place result in this field. To place in a nested field, use format a.b */
                output_field?: string;
                /** @description Whether to strip html tags from field. */
                remove_html?: boolean;
                /** @description Whether transform a text field into an array of text by splitting sentences. */
                split_sentences?: boolean;
                /** @description If specified, documents with the vector fields will be clustered according to the clustering alias */
                assign_to_cluster?: {
                    /** @description The alias of the clustering. */
                    alias?: string;
                };
            }[];
            /** @description A list of jobs to run once all documents are processed. */
            jobs_to_trigger?: {
                /** @description Specify this to run an encoding job after documents are processed. Supports chunk field encoding, encoding many fields, and storing results in a new field specified by alias. */
                encode?: {
                    /** @description Chunk field to encode. Will produce a field with _chunkvector_ suffix. */
                    chunk_field?: string;
                    /** @description Regular fields to encode. Will produce a field with _vector suffix. */
                    fields?: string[];
                    /** @description If provided, this string will be added to the output field name. */
                    alias?: string;
                    /** @description Model name to use for encoding. */
                    model_name?: string;
                };
            }[];
        };
        BulkInsertOutput: {
            /** @description Number of successfully processed documents. */
            inserted: number;
            /** @description Information about documents that were not processed successfully. */
            failed_documents: {
                /** @description _id field of unprocessed document. */
                _id?: unknown;
                error?: {
                    /** @description http status code of individual document insertion operation. */
                    status?: number;
                    /** @description body response of individual document insertion operation. */
                    body?: string;
                };
            }[];
        };
        GetFileUploadUrlsForDatasetInput: {
            /** @description files we be accessible under a url that will be returned in the output. */
            files: string[];
            include_file_path_in_url?: boolean;
        };
        GetFileUploadUrlsForDatasetOutput: {
            files: {
                upload_url: string;
                url: string;
            }[];
        };
        ListFileUploadsForDatasetInput: unknown;
        ListFileUploadsForDatasetOutput: {
            files: {
                url: string;
            }[];
        };
        ParseBlobInput: {
            /** @description Used to download the blob of documents. */
            blob_url: string;
            /** @description Whether the blob is in csv or json format. */
            format: "csv" | "json";
            /** @description replace _id with (_id||id)['$oid'], or JSON.stringify(_id||id) */
            process_id?: boolean;
            /** @description Only process rows of length skip_rows. */
            skip_rows?: number;
            /**
             * @description Whether to return quickly and then wait for ingestion to complete in the background. This will speed up document ingestion significantly.
             * @default true
             */
            ingest_in_background?: boolean;
            /** @description Whether to parse this blob in a workflow for csv files only. Required for csv files larger than 30mb. */
            run_csv_workflow?: boolean;
        };
        ParseBlobOutput: {
            status: string;
            /** @description Response message or error message. */
            message: string;
            /** @description Number of successfully inserted documents. */
            inserted?: number;
            /** @description Failed documents. */
            failed_documents?: unknown[];
        };
        CopyForeignDatasetInput: {
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            /** @description Dataset name to copy into */
            dataset_id: string;
            /** @description project name you want to copy the dataset into */
            project: string;
            /** @description api key of the project you want to copy the dataset into */
            api_key: string;
            /** @description Dataset to copy from */
            source_dataset_id: string;
            /** @description Source project name of whom the dataset belongs to */
            source_project: string;
            /** @description Api key to access the source project name */
            source_api_key: string;
        };
        CopyForeignDatasetOutput: unknown;
        CreateProjectReadKeyInput: {
            /** @description Username for read only key */
            read_username: string | number;
        };
        CreateProjectReadKeyOutput: unknown;
        DeleteDatasetInput: {
            [key: string]: unknown;
        };
        DeleteDatasetOutput: {
            /** @description Completion status. */
            status: string;
            /** @description Response message for completion state. */
            message: string;
        };
        CreateDatasetInput: {
            id?: string;
            schema?: {
                [key: string]: ("binary" | "bool" | "date" | "numeric" | "text" | "dict" | "chunks" | "base64" | "boolean" | "raw") | {
                    chunkvector?: number;
                    vector?: number;
                } | number;
            };
            /**
             * @description If true, creating a dataset that already exists will not error. If false, it will error.
             * @default true
             */
            upsert?: boolean;
        };
        CreateDatasetOutput: unknown;
        GetSchemaInput: unknown;
        GetSchemaOutput: {
            [key: string]: ("binary" | "bool" | "date" | "numeric" | "text" | "dict" | "chunks" | "base64" | "boolean" | "raw") | {
                chunkvector?: number;
                vector?: number;
            } | number;
        };
        ListDatasetsInput: unknown;
        ListDatasetsOutput: {
            datasets: string[];
            count: number;
        };
        CombineDatasetsInput: {
            dataset_ids: string[];
            dataset_to_copy_metadata_from?: string;
            new_dataset_id: string;
            rename_fields?: {
                [key: string]: string;
            };
            include_fields?: string[];
            remove_fields?: string[];
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
        };
        CombineDatasetsOutput: {
            job_id: string;
        };
        GetCombineJobStatusInput: unknown;
        GetCombineJobStatusOutput: {
            job_message: string;
            job_status: "Completed" | "InProgress";
        };
        SearchDatasetsInput: unknown;
        SearchDatasetsOutput: {
            datasets: string[];
            count: number;
        };
        GetFieldHealthInput: unknown;
        GetFieldHealthOutput: {
            [key: string]: {
                exists: number;
                missing: number;
            };
        };
        GetDatasetStatsInput: unknown;
        GetDatasetStatsOutput: {
            [key: string]: unknown;
        };
        GetDatasetUsageInput: unknown;
        GetDatasetUsageOutput: {
            insert_date_: number;
            frequency: number;
        }[];
        GetVectorMappingsInput: unknown;
        GetVectorMappingsOutput: {
            [key: string]: unknown;
        };
        GetDatasetDetailsInput: {
            /**
             * @description Whether to return schema
             * @default true
             */
            include_schema?: boolean;
            /**
             * @description Whether to return stats
             * @default true
             */
            include_stats?: boolean;
            /**
             * @description Whether to return metadata
             * @default true
             */
            include_metadata?: boolean;
            /** @description Whether to return schema stats */
            include_schema_stats?: boolean;
            /** @description Whether to return vector health */
            include_vector_health?: boolean;
            /** @description Whether to return active jobs */
            include_active_jobs?: boolean;
            /** @description Whether to retrieve the datasets dashboard settings. */
            include_settings?: boolean;
        };
        GetDatasetDetailsOutput: {
            schema?: unknown;
            stats?: unknown;
            metadata?: unknown;
            schema_stats?: unknown;
            vector_health?: unknown;
            active_jobs?: unknown;
            settings?: unknown;
        };
        GetDocumentInput: unknown;
        GetDocumentOutput: {
            document: {
                [key: string]: unknown;
            };
        };
        DeleteDocumentInput: {
            /** @description Delete document with matching _id. */
            id: string;
        };
        DeleteDocumentOutput: unknown;
        UpsertDatasetSettingsInput: {
            settings: {
                [key: string]: unknown;
            };
        };
        UpsertDatasetSettingsOutput: unknown;
        UpsertDatasetMetadataInput: {
            /** @description Edit and add metadata about a dataset. Notably description, data source, etc */
            metadata: {
                [key: string]: unknown;
            };
        };
        UpsertDatasetMetadataOutput: unknown;
        GetDatasetMetadataInput: unknown;
        GetDatasetMetadataOutput: {
            results: {
                [key: string]: unknown;
            };
        };
        GetDatasetSettingsInput: unknown;
        GetDatasetSettingsOutput: {
            results: {
                [key: string]: unknown;
            };
        };
        UpdateInput: {
            /** @description _id of document to update. */
            id: unknown;
            /** @description Each document to upsert must have an _id field matching an existing document. */
            updates: {
                _id?: unknown;
            } & {
                [key: string]: unknown;
            };
            /** @description Return immediately after storing data in a spot where it can be ingested in the background. Best for large datasets. */
            ingest_in_background?: boolean;
            /**
             * @description Whether to include insert date as a field 'insert_date_'.
             * @default true
             */
            insert_date?: boolean;
            /**
             * @description Whether to transform fields containing dates to a valid Relevance date format.
             * @default true
             */
            transform_date_format?: boolean;
            /**
             * @description Whether to remove fields with the value 'N/A' from documents. Required to process pandas style CSVs.
             * @default true
             */
            remove_na?: boolean;
            /**
             * @description Whether to overwrite document if it exists.
             * @default true
             */
            overwrite?: boolean;
            /**
             * @description Whether the api should check the documents for vector datatype to update the schema.
             * @default true
             */
            update_schema?: boolean;
            /**
             * @description Whether to return before all documents have finished updating.
             * @default true
             */
            wait_for_update?: boolean;
            encoders?: {
                /** @description The model url to use */
                model_url?: string;
                /** @description The model name to use. */
                model_name?: "image_text" | "text_image" | "all-mpnet-base-v2" | "clip-vit-b-32-image" | "clip-vit-b-32-text" | "clip-vit-l-14-image" | "clip-vit-l-14-text" | "sentence-transformers" | "similarity-all-mpnet-base-v2";
                /** @description Can be 'url' or 'text'. Use 'url' for image models and 'text' for text models. */
                body?: string;
                /** @description The document field to encode. */
                field: string;
                /** @description Add 'alias' to a created vector field name. Output format is {field}_{alias}_vector_. */
                alias?: string;
            }[];
            /** @description Before insertion, fields of documents will be transformed according to this array of rules. If transformation fails, the output field will not be generated. */
            field_transformers?: {
                /** @description Field to transform. to transform a nested field, use a.b */
                field: string;
                /** @description If specified, place result in this field. To place in a nested field, use format a.b */
                output_field?: string;
                /** @description Whether to strip html tags from field. */
                remove_html?: boolean;
                /** @description Whether transform a text field into an array of text by splitting sentences. */
                split_sentences?: boolean;
                /** @description If specified, documents with the vector fields will be clustered according to the clustering alias */
                assign_to_cluster?: {
                    /** @description The alias of the clustering. */
                    alias?: string;
                };
            }[];
            /** @description A list of jobs to run once all documents are processed. */
            jobs_to_trigger?: {
                /** @description Specify this to run an encoding job after documents are processed. Supports chunk field encoding, encoding many fields, and storing results in a new field specified by alias. */
                encode?: {
                    /** @description Chunk field to encode. Will produce a field with _chunkvector_ suffix. */
                    chunk_field?: string;
                    /** @description Regular fields to encode. Will produce a field with _vector suffix. */
                    fields?: string[];
                    /** @description If provided, this string will be added to the output field name. */
                    alias?: string;
                    /** @description Model name to use for encoding. */
                    model_name?: string;
                };
            }[];
        };
        UpdateOutput: {
            /** @description Completion status. */
            status: string;
            /** @description Response message for completion state. */
            message: string;
        };
        DeleteDocumentFieldsInput: {
            /** @description _id of document to delete fields of. */
            id: string;
            /** @description List of fields to delete in a document */
            fields: string[];
        };
        DeleteDocumentFieldsOutput: unknown;
        BulkUpdateInput: {
            /** @description List of updates to apply to documents. Each update item must contain an _id. */
            updates: ({
                _id?: unknown;
            } & {
                [key: string]: unknown;
            })[];
            /** @description Return immediately after storing data in a spot where it can be ingested in the background. Best for large datasets. */
            ingest_in_background?: boolean;
            /**
             * @description Whether to include insert date as a field 'insert_date_'.
             * @default true
             */
            insert_date?: boolean;
            /**
             * @description Whether to transform fields containing dates to a valid Relevance date format.
             * @default true
             */
            transform_date_format?: boolean;
            /**
             * @description Whether to remove fields with the value 'N/A' from documents. Required to process pandas style CSVs.
             * @default true
             */
            remove_na?: boolean;
            /**
             * @description Whether to overwrite document if it exists.
             * @default true
             */
            overwrite?: boolean;
            /**
             * @description Whether the api should check the documents for vector datatype to update the schema.
             * @default true
             */
            update_schema?: boolean;
            /**
             * @description Whether to return before all documents have finished updating.
             * @default true
             */
            wait_for_update?: boolean;
            encoders?: {
                /** @description The model url to use */
                model_url?: string;
                /** @description The model name to use. */
                model_name?: "image_text" | "text_image" | "all-mpnet-base-v2" | "clip-vit-b-32-image" | "clip-vit-b-32-text" | "clip-vit-l-14-image" | "clip-vit-l-14-text" | "sentence-transformers" | "similarity-all-mpnet-base-v2";
                /** @description Can be 'url' or 'text'. Use 'url' for image models and 'text' for text models. */
                body?: string;
                /** @description The document field to encode. */
                field: string;
                /** @description Add 'alias' to a created vector field name. Output format is {field}_{alias}_vector_. */
                alias?: string;
            }[];
            /** @description Before insertion, fields of documents will be transformed according to this array of rules. If transformation fails, the output field will not be generated. */
            field_transformers?: {
                /** @description Field to transform. to transform a nested field, use a.b */
                field: string;
                /** @description If specified, place result in this field. To place in a nested field, use format a.b */
                output_field?: string;
                /** @description Whether to strip html tags from field. */
                remove_html?: boolean;
                /** @description Whether transform a text field into an array of text by splitting sentences. */
                split_sentences?: boolean;
                /** @description If specified, documents with the vector fields will be clustered according to the clustering alias */
                assign_to_cluster?: {
                    /** @description The alias of the clustering. */
                    alias?: string;
                };
            }[];
            /** @description A list of jobs to run once all documents are processed. */
            jobs_to_trigger?: {
                /** @description Specify this to run an encoding job after documents are processed. Supports chunk field encoding, encoding many fields, and storing results in a new field specified by alias. */
                encode?: {
                    /** @description Chunk field to encode. Will produce a field with _chunkvector_ suffix. */
                    chunk_field?: string;
                    /** @description Regular fields to encode. Will produce a field with _vector suffix. */
                    fields?: string[];
                    /** @description If provided, this string will be added to the output field name. */
                    alias?: string;
                    /** @description Model name to use for encoding. */
                    model_name?: string;
                };
            }[];
        };
        BulkUpdateOutput: {
            /** @description Number of successfully processed documents. */
            inserted: number;
            /** @description Information about documents that were not processed successfully. */
            failed_documents: {
                /** @description _id field of unprocessed document. */
                _id?: unknown;
                error?: {
                    /** @description http status code of individual document insertion operation. */
                    status?: number;
                    /** @description body response of individual document insertion operation. */
                    body?: string;
                };
            }[];
        };
        UpdateWhereInput: {
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            /** @description Updates to make to the documents. It should be specified in a format of {"field_name": "value"}. e.g. {"item.status" : "Sold Out"} */
            updates: {
                [key: string]: unknown;
            };
            /** @description Updates to apply conditionally based on a fields value. For example, if a field category exists, to add categoryUppercase use conditional_updates:[{field:"category",output_field:"categoryUppercase",condition_map:{"nonfiction":"NONFICTION"}}] */
            conditional_updates?: {
                field: string;
                output_field: string;
                condition_map: {
                    [key: string]: unknown;
                };
            }[];
        };
        UpdateWhereOutput: {
            /** @description Completion status. */
            status: string;
            /** @description Response message for completion state. */
            message: string;
        };
        AppendTagsInput: {
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            field: string;
            tags_to_add: string[];
        };
        AppendTagsOutput: unknown;
        DeleteTagsInput: {
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            field: string;
            tags_to_delete: string[];
        };
        DeleteTagsOutput: unknown;
        MergeTagsInput: {
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            field: string;
            tags_to_merge: {
                [key: string]: string;
            };
        };
        MergeTagsOutput: unknown;
        EditTagsInput: {
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            field: string;
            tags_to_merge?: {
                [key: string]: string;
            };
            tags_to_delete?: string[];
            tags_to_add?: string[];
        };
        EditTagsOutput: unknown;
        ListFacetsInput: {
            /** @description Fields to include in the facets, if [] then all */
            fields?: string[];
            /** @default monthly */
            date_interval?: "monthly" | "daily" | "yearly" | "hourly" | "minutely" | "weekly";
            /**
             * @description Size of facet page
             * @default 1000
             */
            page_size?: number;
            /** @description Whether to sort results by ascending or descending frequency */
            asc?: boolean;
        };
        ListFacetsOutput: {
            results: {
                [key: string]: ({
                    avg: number;
                    max: number;
                    min: number;
                    sum: number;
                    date?: ({
                        value: unknown;
                        frequency: number;
                    } & {
                        [key: string]: unknown;
                    })[];
                } & {
                    [key: string]: unknown;
                }) | ({
                    value: unknown;
                    frequency: number;
                } & {
                    [key: string]: unknown;
                })[];
            };
        };
        GetWhereInput: {
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            /** @description Fields to include in the search results, empty array/list means all fields. */
            select_fields?: string[];
            /** @description Size of each page of results. */
            page_size?: number;
            /** @description Size of each page of results. */
            page?: number;
            /** @description Fields to sort by. For each field, sort by descending or ascending. If you are using descending by datetime, it will get the most recent ones. */
            sort?: (string | {
                [key: string]: "asc" | "desc";
            })[];
            /** @description Include vectors in the search results. */
            include_vector?: boolean;
            /** @description (NOT IMPLEMENTED - not functional) If true, retrieves documents randomly. */
            is_random?: boolean;
            /** @description (NOT IMPLEMENTED - not functional)Random Seed for retrieving random documents. */
            random_state?: number;
            /** @description Retrieve items after these sort values in the sort order. */
            after_id?: unknown[];
        };
        GetWhereOutput: {
            count: number;
            /**
             * @description List of documents. List items are affected by page, pageSize, query, filters. Items order is affected by vectorSeachQuery, sort, textSort.
             *
             *     Example: [{"_id":"abcd","animal":"cat","price":10}, {"_id":"abcd","price":13}]
             */
            documents: {
                /** @description Measures how closely a document matches on query and vectorSearchQuery. */
                _relevance?: number;
                _chunk_results?: {
                    [key: string]: {
                        results: {
                            _relevance?: number;
                        }[];
                        _relevance: number;
                    };
                };
            }[];
            /** @description Used in future requests to retrieve items after these values in the sort order. */
            after_id: unknown[];
        };
        PaginateDocumentsInput: {
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            /** @description Fields to include in the search results, empty array/list means all fields. */
            select_fields?: string[];
            /** @description Size of each page of results. */
            page_size?: number;
            /** @description Size of each page of results. */
            page?: number;
            /** @description Fields to sort by. For each field, sort by descending or ascending. If you are using descending by datetime, it will get the most recent ones. */
            sort?: (string | {
                [key: string]: "asc" | "desc";
            })[];
            /** @description Include vectors in the search results. */
            include_vector?: boolean;
            /** @description (NOT IMPLEMENTED - not functional) If true, retrieves documents randomly. */
            is_random?: boolean;
            /** @description (NOT IMPLEMENTED - not functional)Random Seed for retrieving random documents. */
            random_state?: number;
            /** @description Retrieve items after these sort values in the sort order. */
            after_id?: unknown[];
        };
        PaginateDocumentsOutput: {
            count: number;
            /**
             * @description List of documents. List items are affected by page, pageSize, query, filters. Items order is affected by vectorSeachQuery, sort, textSort.
             *
             *     Example: [{"_id":"abcd","animal":"cat","price":10}, {"_id":"abcd","price":13}]
             */
            documents: {
                /** @description Measures how closely a document matches on query and vectorSearchQuery. */
                _relevance?: number;
                _chunk_results?: {
                    [key: string]: {
                        results: {
                            _relevance?: number;
                        }[];
                        _relevance: number;
                    };
                };
            }[];
            /** @description Used in future requests to retrieve items after these values in the sort order. */
            after_id: unknown[];
        };
        CreateDatasetSummaryInput: {
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            /** @description Fields to include in the search results, empty array/list means all fields. */
            select_fields?: string[];
            /** @description Size of each page of results. */
            page_size?: number;
            /** @description Size of each page of results. */
            page?: number;
            /** @description Fields to sort by. For each field, sort by descending or ascending. If you are using descending by datetime, it will get the most recent ones. */
            sort?: (string | {
                [key: string]: "asc" | "desc";
            })[];
            /** @description Include vectors in the search results. */
            include_vector?: boolean;
            /** @description (NOT IMPLEMENTED - not functional) If true, retrieves documents randomly. */
            is_random?: boolean;
            /** @description (NOT IMPLEMENTED - not functional)Random Seed for retrieving random documents. */
            random_state?: number;
            /** @description Retrieve items after these sort values in the sort order. */
            after_id?: unknown[];
            question: string;
            field: string;
        };
        CreateDatasetSummaryOutput: {
            question: string;
            answer: string;
        };
        ListDatasetSummaryHistoryInput: unknown;
        ListDatasetSummaryHistoryOutput: {
            results: {
                question?: string;
                prompt?: string;
                field?: string;
                answer?: string;
                dataset?: string;
            }[];
        };
        BulkGetDocumentsInput: {
            /** @description IDs of documents to retrieve */
            ids: string[];
            /** @description Fields to include in the search results, empty array/list means all fields. */
            select_fields?: string[];
            /** @description Include vectors in the search results */
            include_vector?: boolean;
        };
        BulkGetDocumentsOutput: {
            /** @description A list of documents. Document is a JSON-like data that we store our metadata and vectors with. For specifying id of the document use the field '_id', for specifying vector field use the suffix of '_vector_' */
            documents: unknown[];
        };
        BulkDeleteDocumentsInput: {
            /** @description IDs of documents to delete */
            ids: string[];
        };
        BulkDeleteDocumentsOutput: unknown;
        ListDocumentsInput: unknown;
        ListDocumentsOutput: {
            count: number;
            /**
             * @description List of documents. List items are affected by page, pageSize, query, filters. Items order is affected by vectorSeachQuery, sort, textSort.
             *
             *     Example: [{"_id":"abcd","animal":"cat","price":10}, {"_id":"abcd","price":13}]
             */
            documents: {
                /** @description Measures how closely a document matches on query and vectorSearchQuery. */
                _relevance?: number;
                _chunk_results?: {
                    [key: string]: {
                        results: {
                            _relevance?: number;
                        }[];
                        _relevance: number;
                    };
                };
            }[];
            /** @description Used in future requests to retrieve items after these values in the sort order. */
            after_id: unknown[];
        };
        DeleteWhereInput: {
            [key: string]: unknown;
        } & {
            filters: unknown;
        };
        DeleteWhereOutput: {
            /** @description Completion status. */
            status: string;
            /** @description Response message for completion state. */
            message: string;
        };
        /**
         * @description Control which fields aggregate data will be generated for. Aggregate data will appear in the "aggregates" property of the response body.
         *
         *     Each list element can be a string like "name" or a dictionary like {"field":"name","resultsSize":11}
         *
         *     Default: []
         *
         *     Example: ["name","price","likes_cats",{"field":"color","resultsSize":20}]
         */
        fieldsToAggregate: (string | {
            /** @description Field to aggregate. */
            field?: string;
            /** @description Controls which field the aggregate output will be placed into. */
            outputField?: string;
            /**
             * @description Number of top aggregate results to return.
             * @default 10
             */
            resultsSize?: number;
            /**
             * @description Sort aggregation results by count ascending or descending.
             * @default desc
             */
            sort?: "asc" | "desc";
            page?: number;
            fieldsToAggregate?: components["schemas"]["fieldsToAggregate"];
            /** @description Whether to retrieve min, max, avg and sum stats for the field as well */
            getStats?: boolean;
            dateAggregate?: {
                /** @default monthly */
                date_interval?: "monthly" | "daily" | "yearly" | "hourly" | "minutely" | "weekly";
            };
            /** @description Retrieve documents for each aggregated category. use 'pageSize' to control the number of documents to retrieve, and 'includeFields' to only return certain fields. */
            getDocuments?: {
                pageSize?: number;
                includeFields?: string[];
            };
            chunkAggregate?: {
                chunkFieldName: string;
                filters?: components["schemas"]["simpleSearchAndFlatFilterItem"][];
            };
        })[];
        /**
         * @description Control which fields aggregate stats data will be generated for. This includes, min value, max value, average value, and document counts per interval within the dataset.
         *
         *     Aggregate data will appear in the "aggregateStats" property of the response body.
         *
         *     Each list item can be:
         *
         *      a string stating which field to aggregate stats on
         *
         *     An object containing "field" to pick a field, and "interval" to control the range of each bucket when counting documents:
         *
         *     For example, if we had products {"price":50},{"price:"150"},{price:"180"},{"field":"price","interval":100}  would split results into 2 buckets.
         *
         *     Default: []
         *     Example: ["postcode",{"field":"price","interval":10}]
         */
        fieldsToAggregateStats: (string | {
            /** @description Field to return stats for in aggregateStats. */
            field?: string;
            /** @description Interval gap when building a histogram. An interval of 2 will count documents in range 0-2,2-4,4-6... */
            interval?: number;
            /** @description Controls which field the aggregate output will be placed into. */
            outputField?: string;
        })[];
        simpleSearchFilterItem: {
            /** @description Match where document[field] is in value list. */
            match?: {
                /** @description If matching on text, match even if there are extra words / case insensitivity */
                fuzzy?: boolean;
                /** @description Field to match on. */
                field?: string;
                /** @description Can be either a single item or a list of items to match on. */
                value: string | boolean | number | unknown[];
            };
            /** @description Match where document._id is in value list. */
            matchIds?: {
                /** @description Can be either a single item or a list of items to match on. */
                value: string | boolean | number | unknown[];
            };
            /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
            range?: {
                /** @description Field to match on. */
                field?: string;
                greaterThan?: unknown;
                lessThan?: unknown;
                greaterThanOrEqualTo?: unknown;
                lessThanOrEqualTo?: unknown;
            };
            /**
             * @description
             * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
             *
             * Possible matching patterns:
             *
             * * - documents where the field has any value
             * ? - documents where a single character from the field matches the provided values
             *
             * Example:
             *
             * {
             *   wildcard: {
             *      field: "title",
             *      value: [ "Avenger*" ]
             *   }
             * }
             */
            wildcard?: {
                /** @description Field to match on. */
                field?: string;
                /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                value: string | string[];
            };
            /** @description hybrid search query that must reach a certain value to filter */
            search?: {
                /**
                 * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                 *
                 *     "tele" matches "Television", "This television is an excellent product…"
                 *
                 *     Example: "tele"
                 */
                query?: string;
                /** @description Configuration for traditional search query. */
                queryConfig?: {
                    /**
                     * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                     * @default 1
                     */
                    weight?: number;
                    /**
                     * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                     *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                     * @default 1
                     */
                    sentenceWeight?: number;
                    /**
                     * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                     *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                     * @default 1
                     */
                    fuzzyWeight?: number;
                };
                /**
                 * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                 *
                 *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                 *
                 *     It can be an object or a list of objects.
                 *
                 *
                 *
                 *     Example payloads:
                 *
                 *     {"field":"animaltype_vector_","query":"kitten"}
                 *
                 *     [
                 *
                 *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                 *
                 *     ]
                 */
                vectorSearchQuery?: {
                    /** @description Vector name to search on. For example, title_vector_ */
                    field: string;
                    /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                    query?: string;
                    /**
                     * @description Model name to generate the vector with.
                     * @default text
                     */
                    model?: string;
                    /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                    model_url?: string;
                    /**
                     * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                     * @default 1
                     */
                    weight?: number;
                    /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                    vector?: number[];
                    chunkConfig?: {
                        chunkField: string;
                        page?: number;
                        pageSize?: number;
                    };
                } | {
                    /** @description Vector name to search on. For example, title_vector_ */
                    field: string;
                    /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                    query?: string;
                    /**
                     * @description Model name to generate the vector with.
                     * @default text
                     */
                    model?: string;
                    /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                    model_url?: string;
                    /**
                     * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                     * @default 1
                     */
                    weight?: number;
                    /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                    vector?: number[];
                    chunkConfig?: {
                        chunkField: string;
                        page?: number;
                        pageSize?: number;
                    };
                }[];
                realtimeClustering?: {
                    /** @default 4 */
                    nClusters?: number;
                    /** @default kmeans-realtime-4 */
                    alias?: string;
                    vectorField: string;
                    /** @default _cluster_.{vector_field}.{alias} */
                    outputField?: string;
                };
                /**
                 * @description A list of fields to search using the "query" parameter.
                 *
                 *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                 *
                 *     Default behaviour is to search all fields.
                 *
                 *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                 */
                fieldsToSearch?: (string | {
                    /** @description Field name to search. */
                    field?: string;
                    /**
                     * @description Multiply the relevance contribution of a specific field when using traditional search.
                     * @default 1
                     */
                    weight?: number;
                    /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                    chunkField?: string;
                })[];
                /**
                 * @description Only return documents with a _relevance above this threshold.
                 *
                 *     Example: 0.3
                 */
                minimumRelevance: number;
            };
            /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
            fieldExists?: {
                /** @description Field that must exist. */
                field?: string;
            };
            /** @description Match documents where doc[field] % module == value */
            matchModulo?: {
                /** @default _id */
                field?: string;
                modulo: number;
                value: number;
            };
            /** @description Filter down to one document for each value of selected field. */
            dedupeByValue?: {
                /** @description Field to filter on. */
                field: string;
            };
            /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
            selfreference?: {
                /** @description First field in comparison. */
                a: string;
                /** @description Second field in comparison. */
                b: string;
                /** @description Operator used to compare a and b. */
                operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
            };
            /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
            wordCount?: {
                /** @description Field to match on. */
                field: string;
                /** @description Minimum word count. */
                greaterThan?: number;
                /** @description Maximum word count. */
                lessThan?: number;
            };
            /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
            characterCount?: {
                /** @description Field to match on. */
                field: string;
                /** @description Minimum character count. */
                greaterThan?: number;
                /** @description Maximum character count. */
                lessThan?: number;
            };
            /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
            or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
            /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
            not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
            /** @description Filter based on data within a _chunk_ field. */
            chunk?: {
                /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                path: string;
                filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
            };
        };
        flatFilterItem: {
            strict?: "must" | "should" | "must_or";
            condition?: string;
            field?: string;
            filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
            condition_value?: unknown;
            fuzzy?: number;
            join?: boolean;
        };
        relevanceBoosterItem: {
            strict?: "must" | "should" | "must_or";
            condition?: string;
            field?: string;
            filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
            condition_value?: unknown;
            fuzzy?: number;
            join?: boolean;
            /**
             * @description Multiplies the relevance contribution of this condition.
             * @default 1
             */
            weight?: number;
        };
        SimpleSearchPostInput: {
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            /**
             * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
             *
             *     "tele" matches "Television", "This television is an excellent product…"
             *
             *     Example: "tele"
             */
            query?: string;
            /** @description Configuration for traditional search query. */
            queryConfig?: {
                /**
                 * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                 * @default 1
                 */
                weight?: number;
                /**
                 * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                 *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                 * @default 1
                 */
                sentenceWeight?: number;
                /**
                 * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                 *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                 * @default 1
                 */
                fuzzyWeight?: number;
            };
            /**
             * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
             *
             *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
             *
             *     It can be an object or a list of objects.
             *
             *
             *
             *     Example payloads:
             *
             *     {"field":"animaltype_vector_","query":"kitten"}
             *
             *     [
             *
             *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
             *
             *     ]
             */
            vectorSearchQuery?: {
                /** @description Vector name to search on. For example, title_vector_ */
                field: string;
                /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                query?: string;
                /**
                 * @description Model name to generate the vector with.
                 * @default text
                 */
                model?: string;
                /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                model_url?: string;
                /**
                 * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                 * @default 1
                 */
                weight?: number;
                /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                vector?: number[];
                chunkConfig?: {
                    chunkField: string;
                    page?: number;
                    pageSize?: number;
                };
            } | {
                /** @description Vector name to search on. For example, title_vector_ */
                field: string;
                /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                query?: string;
                /**
                 * @description Model name to generate the vector with.
                 * @default text
                 */
                model?: string;
                /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                model_url?: string;
                /**
                 * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                 * @default 1
                 */
                weight?: number;
                /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                vector?: number[];
                chunkConfig?: {
                    chunkField: string;
                    page?: number;
                    pageSize?: number;
                };
            }[];
            realtimeClustering?: {
                /** @default 4 */
                nClusters?: number;
                /** @default kmeans-realtime-4 */
                alias?: string;
                vectorField: string;
                /** @default _cluster_.{vector_field}.{alias} */
                outputField?: string;
            };
            /**
             * @description A list of fields to search using the "query" parameter.
             *
             *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
             *
             *     Default behaviour is to search all fields.
             *
             *     Example: ["name",{"field":"favourite_color","weight":0.2}]
             */
            fieldsToSearch?: (string | {
                /** @description Field name to search. */
                field?: string;
                /**
                 * @description Multiply the relevance contribution of a specific field when using traditional search.
                 * @default 1
                 */
                weight?: number;
                /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                chunkField?: string;
            })[];
            /**
             * @description Only return documents with a _relevance above this threshold.
             *
             *     Example: 0.3
             */
            minimumRelevance?: number;
            instantAnswerQuery?: {
                query: string;
                field: string;
            };
            /**
             * @description Page of results to return.
             *     Returns results from position page*pageSize to (page+1)*pageSize
             *
             *     Default: 0
             *
             *     Example: 3
             */
            page?: number;
            /**
             * @description Page size of results to return.
             *     Returns results from position page*pageSize to (page+1)*pageSize
             *
             *     Default: 10
             *
             *     Example: 25
             */
            pageSize?: number;
            /**
             * @description Add _relevance field to each document containing the documents relevance based on the search criteria.
             *
             *     Example: true
             * @default true
             */
            includeRelevance?: boolean;
            /**
             * @description Add _dataset field to each document containing the documents parent dataset. Useful when searching multiple datasets at once.
             *
             *     Example: false
             */
            includeDataset?: boolean;
            /**
             * @description Use the datasets schema to remove parameters that are unsafe before making a request.
             *
             *     For example, fieldsToAggregateStats only supports numeric fields. if this flag is set, all non numeric fields will be removed from fieldsToAggregateStats.
             *
             *     Example: true
             */
            cleanPayloadUsingSchema?: boolean;
            /**
             * @description Prioritise results based on integer, float and boolean fields values. Can sort ascending or descending.
             *
             *     Example 1: {"on_sale":"desc"}
             *
             *     Example 2: {"price":"asc"}
             */
            sort?: {
                [key: string]: "asc" | "desc";
            };
            /**
             * @description Only return fields of documents listed in this array.
             *
             *     Example: ["name","description_vector_"]
             */
            includeFields?: string[];
            /**
             * @description Don't return fields of documents listed in this array.
             *
             *     Example: ["name","description_vector"]
             */
            excludeFields?: string[];
            /**
             * @description Set to true to return all vector fields. includeFields / excludeFields has priority over this rule.
             *
             *     Example: true / false
             */
            includeVectors?: boolean;
            /**
             * @description Prioritise results based on integer, float and boolean fields values. Can sort ascending or descending.
             *
             *     Example 1: {"on_sale":"desc"}
             *
             *     Example 2: {"price":"asc"}
             */
            textSort?: {
                [key: string]: "asc" | "desc";
            };
            fieldsToAggregate?: components["schemas"]["fieldsToAggregate"];
            fieldsToAggregateStats?: components["schemas"]["fieldsToAggregateStats"];
            /** @description Add relevance to documents if they match conditions in this list. configure 'weight' on each condition to modify the relevance contribution. */
            relevanceBoosters?: components["schemas"]["relevanceBoosterItem"][];
            /** @description Used in future requests to retrieve items after these values in the sort order. */
            afterId?: unknown[];
        };
        /** @description Aggregate output. Recursive aggregate output is returned here. */
        outputAggregations: {
            [key: string]: {
                /** @description Most common field values and their counts. */
                results: {
                    [key: string]: number;
                };
                aggregates: {
                    [key: string]: components["schemas"]["outputAggregations"];
                };
                aggregateStats?: {
                    /** @description Maximum value of field. */
                    max: number;
                    /** @description Minimum value of field. */
                    min: number;
                    /** @description Average value of field. */
                    avg: number;
                    /** @description Sum of all field values. */
                    sum: number;
                };
                dateAggregate?: {
                    value: number;
                    frequency: number;
                }[];
                /** @description Documents retrieved by the 'getDocuments' parameter. */
                documents?: unknown[];
            };
        };
        /**
         * @description Dictionary of field stats.
         *
         *     Fields to summarise is specified in "fieldsToAggregateStats" of request body.
         *
         *     Example: {"price":{"min":0.5,"max":2000,"avg":100,sum:"40230032","results":{"0":202,"100":43,"200":16}}}
         */
        aggregateStats: {
            [key: string]: {
                /** @description Maximum value of field. */
                max: number;
                /** @description Minimum value of field. */
                min: number;
                /** @description Average value of field. */
                avg: number;
                /** @description Sum of all field values. */
                sum: number;
            };
        };
        SimpleSearchPostOutput: {
            /**
             * @description List of documents. List items are affected by page, pageSize, query, filters. Items order is affected by vectorSeachQuery, sort, textSort.
             *
             *     Example: [{"_id":"abcd","animal":"cat","price":10}, {"_id":"abcd","price":13}]
             */
            results: {
                /** @description Measures how closely a document matches on query and vectorSearchQuery. */
                _relevance?: number;
                _chunk_results?: {
                    [key: string]: {
                        results: {
                            _relevance?: number;
                        }[];
                        _relevance: number;
                    };
                };
            }[];
            instantAnswerResults?: {
                answer?: string;
                status?: string;
                message?: string;
                question?: string;
            };
            /**
             * @description Total number of documents matched in the dataset.
             *
             *     Example: 100
             */
            resultsSize: number;
            aggregates: components["schemas"]["outputAggregations"];
            aggregateStats: components["schemas"]["aggregateStats"];
            /** @description Used in future requests to retrieve items after these values in the sort order. */
            afterId: unknown[];
        } & {
            aggregations: unknown;
        };
        SearchInput: {
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
            /**
             * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
             *
             *     "tele" matches "Television", "This television is an excellent product…"
             *
             *     Example: "tele"
             */
            query?: string;
            /** @description Configuration for traditional search query. */
            queryConfig?: {
                /**
                 * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                 * @default 1
                 */
                weight?: number;
                /**
                 * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                 *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                 * @default 1
                 */
                sentenceWeight?: number;
                /**
                 * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                 *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                 * @default 1
                 */
                fuzzyWeight?: number;
            };
            /**
             * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
             *
             *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
             *
             *     It can be an object or a list of objects.
             *
             *
             *
             *     Example payloads:
             *
             *     {"field":"animaltype_vector_","query":"kitten"}
             *
             *     [
             *
             *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
             *
             *     ]
             */
            vectorSearchQuery?: {
                /** @description Vector name to search on. For example, title_vector_ */
                field: string;
                /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                query?: string;
                /**
                 * @description Model name to generate the vector with.
                 * @default text
                 */
                model?: string;
                /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                model_url?: string;
                /**
                 * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                 * @default 1
                 */
                weight?: number;
                /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                vector?: number[];
                chunkConfig?: {
                    chunkField: string;
                    page?: number;
                    pageSize?: number;
                };
            } | {
                /** @description Vector name to search on. For example, title_vector_ */
                field: string;
                /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                query?: string;
                /**
                 * @description Model name to generate the vector with.
                 * @default text
                 */
                model?: string;
                /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                model_url?: string;
                /**
                 * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                 * @default 1
                 */
                weight?: number;
                /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                vector?: number[];
                chunkConfig?: {
                    chunkField: string;
                    page?: number;
                    pageSize?: number;
                };
            }[];
            realtimeClustering?: {
                /** @default 4 */
                nClusters?: number;
                /** @default kmeans-realtime-4 */
                alias?: string;
                vectorField: string;
                /** @default _cluster_.{vector_field}.{alias} */
                outputField?: string;
            };
            /**
             * @description A list of fields to search using the "query" parameter.
             *
             *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
             *
             *     Default behaviour is to search all fields.
             *
             *     Example: ["name",{"field":"favourite_color","weight":0.2}]
             */
            fieldsToSearch?: (string | {
                /** @description Field name to search. */
                field?: string;
                /**
                 * @description Multiply the relevance contribution of a specific field when using traditional search.
                 * @default 1
                 */
                weight?: number;
                /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                chunkField?: string;
            })[];
            /**
             * @description Only return documents with a _relevance above this threshold.
             *
             *     Example: 0.3
             */
            minimumRelevance?: number;
            instantAnswerQuery?: {
                query: string;
                field: string;
            };
            /**
             * @description Page of results to return.
             *     Returns results from position page*pageSize to (page+1)*pageSize
             *
             *     Default: 0
             *
             *     Example: 3
             */
            page?: number;
            /**
             * @description Page size of results to return.
             *     Returns results from position page*pageSize to (page+1)*pageSize
             *
             *     Default: 10
             *
             *     Example: 25
             */
            pageSize?: number;
            /**
             * @description Add _relevance field to each document containing the documents relevance based on the search criteria.
             *
             *     Example: true
             * @default true
             */
            includeRelevance?: boolean;
            /**
             * @description Add _dataset field to each document containing the documents parent dataset. Useful when searching multiple datasets at once.
             *
             *     Example: false
             */
            includeDataset?: boolean;
            /**
             * @description Use the datasets schema to remove parameters that are unsafe before making a request.
             *
             *     For example, fieldsToAggregateStats only supports numeric fields. if this flag is set, all non numeric fields will be removed from fieldsToAggregateStats.
             *
             *     Example: true
             */
            cleanPayloadUsingSchema?: boolean;
            /**
             * @description Prioritise results based on integer, float and boolean fields values. Can sort ascending or descending.
             *
             *     Example 1: {"on_sale":"desc"}
             *
             *     Example 2: {"price":"asc"}
             */
            sort?: {
                [key: string]: "asc" | "desc";
            };
            /**
             * @description Only return fields of documents listed in this array.
             *
             *     Example: ["name","description_vector_"]
             */
            includeFields?: string[];
            /**
             * @description Don't return fields of documents listed in this array.
             *
             *     Example: ["name","description_vector"]
             */
            excludeFields?: string[];
            /**
             * @description Set to true to return all vector fields. includeFields / excludeFields has priority over this rule.
             *
             *     Example: true / false
             */
            includeVectors?: boolean;
            /**
             * @description Prioritise results based on integer, float and boolean fields values. Can sort ascending or descending.
             *
             *     Example 1: {"on_sale":"desc"}
             *
             *     Example 2: {"price":"asc"}
             */
            textSort?: {
                [key: string]: "asc" | "desc";
            };
            fieldsToAggregate?: components["schemas"]["fieldsToAggregate"];
            fieldsToAggregateStats?: components["schemas"]["fieldsToAggregateStats"];
            /** @description Add relevance to documents if they match conditions in this list. configure 'weight' on each condition to modify the relevance contribution. */
            relevanceBoosters?: components["schemas"]["relevanceBoosterItem"][];
            /** @description Used in future requests to retrieve items after these values in the sort order. */
            afterId?: unknown[];
        };
        SearchOutput: {
            /**
             * @description List of documents. List items are affected by page, pageSize, query, filters. Items order is affected by vectorSeachQuery, sort, textSort.
             *
             *     Example: [{"_id":"abcd","animal":"cat","price":10}, {"_id":"abcd","price":13}]
             */
            results: {
                /** @description Measures how closely a document matches on query and vectorSearchQuery. */
                _relevance?: number;
                _chunk_results?: {
                    [key: string]: {
                        results: {
                            _relevance?: number;
                        }[];
                        _relevance: number;
                    };
                };
            }[];
            instantAnswerResults?: {
                answer?: string;
                status?: string;
                message?: string;
                question?: string;
            };
            /**
             * @description Total number of documents matched in the dataset.
             *
             *     Example: 100
             */
            resultsSize: number;
            aggregates: components["schemas"]["outputAggregations"];
            aggregateStats: components["schemas"]["aggregateStats"];
            /** @description Used in future requests to retrieve items after these values in the sort order. */
            afterId: unknown[];
        } & {
            aggregations: unknown;
        };
        RecommendInput: {
            /**
             * @description This takes a list of objects. Each object must specify the id of the document to generate recommendations for, and the vector field that will be compared.
             *     Weight can be changed to increase or decrease how much a document contributes to the recommendation. A negative weight will make a document less likely to be recommended.
             */
            documentsToRecommend: {
                /** @description The vector field used for recommendation. */
                field: string;
                /** @description The id of the document used for recommendation. */
                id: string;
                /** @description Influences how much a document affects recommendation results. A negative weight causes documents like this to show up less. */
                weight?: number;
            }[];
            /**
             * @description Set to true to return all vector fields. includeFields / excludeFields has priority over this rule.
             *
             *     Example: true / false
             */
            includeVectors?: boolean;
            /**
             * @description Add _relevance field to each document containing the documents relevance based on the search criteria.
             *
             *     Example: true
             * @default true
             */
            includeRelevance?: boolean;
            /**
             * @description Only return fields of documents listed in this array.
             *
             *     Example: ["name","description_vector_"]
             */
            includeFields?: string[];
        };
        RecommendOutput: {
            /**
             * @description List of documents. List items are affected by page, pageSize, query, filters. Items order is affected by vectorSeachQuery, sort, textSort.
             *
             *     Example: [{"_id":"abcd","animal":"cat","price":10}, {"_id":"abcd","price":13}]
             */
            results: {
                /** @description Measures how closely a document matches on query and vectorSearchQuery. */
                _relevance?: number;
                _chunk_results?: {
                    [key: string]: {
                        results: {
                            _relevance?: number;
                        }[];
                        _relevance: number;
                    };
                };
            }[];
        };
        CloneDatasetInput: {
            new_dataset_id: string;
            /** @default [object Object] */
            rename_fields?: {
                [key: string]: string;
            };
            /** @default */
            remove_fields?: string[];
            filters?: (Partial<{
                /** @description Match where document[field] is in value list. */
                match?: {
                    /** @description If matching on text, match even if there are extra words / case insensitivity */
                    fuzzy?: boolean;
                    /** @description Field to match on. */
                    field?: string;
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match where document._id is in value list. */
                matchIds?: {
                    /** @description Can be either a single item or a list of items to match on. */
                    value: string | boolean | number | unknown[];
                };
                /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                range?: {
                    /** @description Field to match on. */
                    field?: string;
                    greaterThan?: unknown;
                    lessThan?: unknown;
                    greaterThanOrEqualTo?: unknown;
                    lessThanOrEqualTo?: unknown;
                };
                /**
                 * @description
                 * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                 *
                 * Possible matching patterns:
                 *
                 * * - documents where the field has any value
                 * ? - documents where a single character from the field matches the provided values
                 *
                 * Example:
                 *
                 * {
                 *   wildcard: {
                 *      field: "title",
                 *      value: [ "Avenger*" ]
                 *   }
                 * }
                 */
                wildcard?: {
                    /** @description Field to match on. */
                    field?: string;
                    /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                    value: string | string[];
                };
                /** @description hybrid search query that must reach a certain value to filter */
                search?: {
                    /**
                     * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                     *
                     *     "tele" matches "Television", "This television is an excellent product…"
                     *
                     *     Example: "tele"
                     */
                    query?: string;
                    /** @description Configuration for traditional search query. */
                    queryConfig?: {
                        /**
                         * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                         * @default 1
                         */
                        weight?: number;
                        /**
                         * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                         *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                         * @default 1
                         */
                        sentenceWeight?: number;
                        /**
                         * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                         *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                         * @default 1
                         */
                        fuzzyWeight?: number;
                    };
                    /**
                     * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                     *
                     *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                     *
                     *     It can be an object or a list of objects.
                     *
                     *
                     *
                     *     Example payloads:
                     *
                     *     {"field":"animaltype_vector_","query":"kitten"}
                     *
                     *     [
                     *
                     *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                     *
                     *     ]
                     */
                    vectorSearchQuery?: {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    } | {
                        /** @description Vector name to search on. For example, title_vector_ */
                        field: string;
                        /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                        query?: string;
                        /**
                         * @description Model name to generate the vector with.
                         * @default text
                         */
                        model?: string;
                        /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                        model_url?: string;
                        /**
                         * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                        vector?: number[];
                        chunkConfig?: {
                            chunkField: string;
                            page?: number;
                            pageSize?: number;
                        };
                    }[];
                    realtimeClustering?: {
                        /** @default 4 */
                        nClusters?: number;
                        /** @default kmeans-realtime-4 */
                        alias?: string;
                        vectorField: string;
                        /** @default _cluster_.{vector_field}.{alias} */
                        outputField?: string;
                    };
                    /**
                     * @description A list of fields to search using the "query" parameter.
                     *
                     *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                     *
                     *     Default behaviour is to search all fields.
                     *
                     *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                     */
                    fieldsToSearch?: (string | {
                        /** @description Field name to search. */
                        field?: string;
                        /**
                         * @description Multiply the relevance contribution of a specific field when using traditional search.
                         * @default 1
                         */
                        weight?: number;
                        /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                        chunkField?: string;
                    })[];
                    /**
                     * @description Only return documents with a _relevance above this threshold.
                     *
                     *     Example: 0.3
                     */
                    minimumRelevance: number;
                };
                /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                fieldExists?: {
                    /** @description Field that must exist. */
                    field?: string;
                };
                /** @description Match documents where doc[field] % module == value */
                matchModulo?: {
                    /** @default _id */
                    field?: string;
                    modulo: number;
                    value: number;
                };
                /** @description Filter down to one document for each value of selected field. */
                dedupeByValue?: {
                    /** @description Field to filter on. */
                    field: string;
                };
                /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                selfreference?: {
                    /** @description First field in comparison. */
                    a: string;
                    /** @description Second field in comparison. */
                    b: string;
                    /** @description Operator used to compare a and b. */
                    operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                };
                /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                wordCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum word count. */
                    greaterThan?: number;
                    /** @description Maximum word count. */
                    lessThan?: number;
                };
                /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                characterCount?: {
                    /** @description Field to match on. */
                    field: string;
                    /** @description Minimum character count. */
                    greaterThan?: number;
                    /** @description Maximum character count. */
                    lessThan?: number;
                };
                /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                or?: (components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"])[];
                /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                not?: components["schemas"]["simpleSearchAndFlatFilterItem"][] | components["schemas"]["simpleSearchAndFlatFilterItem"];
                /** @description Filter based on data within a _chunk_ field. */
                chunk?: {
                    /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                    path: string;
                    filters: components["schemas"]["simpleSearchAndFlatFilterItem"][];
                };
            }> & Partial<{
                strict?: "must" | "should" | "must_or";
                condition?: string;
                field?: string;
                filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                condition_value?: unknown;
                fuzzy?: number;
                join?: boolean;
            }>)[];
        };
        CloneDatasetOutput: {
            job_id: string;
        };
        PredictKNNRegressionInput: {
            /** @description The unique name of dataset. */
            dataset_id: string;
            /** @description An array of floats that represents data. */
            vector: number[];
            /** @description The vector field to search in. */
            vector_field: string;
            /** @description The field to perform regression on. */
            target_field: string;
            /** @description The number of results for KNN. */
            k?: number;
            /** @description The weighting for each prediction. Can either be a boolean or an array of weights. */
            weighting?: boolean | unknown[];
            /** @description The value used to fill in the document when the data is missing. */
            impute_value?: number;
            /** @description How to predict using the vectors. Can be either "most_frequent" or "sum_scores". */
            predict_operation?: "mean" | "sum" | "weighted_mean";
            /** @description Whether to include search results. */
            include_search_results?: boolean;
        };
        PredictKNNRegressionOutput: {
            results: number;
            search_results?: unknown[];
        };
        PredictKNNFromResultsInput: {
            /** @description The field in "results" to use for prediction. */
            field: string;
            /** @description A list of documents */
            results: unknown[];
            /** @description The weighting for each prediction. Can either be a boolean or an array of weights. */
            weighting?: boolean | unknown[];
            /** @description The value used to fill in the document when the data is missing. */
            impute_value?: number | string;
            /** @description How to predict using the vectors. Can be either "most_frequent" or "sum_scores". */
            predict_operation?: "most_frequent" | "score_sum";
        };
        PredictKNNFromResultsOutput: {
            results?: string | number;
        };
        BiasEvaluationInput: {
            /** @description Two keywords to compare. */
            query_list: unknown[];
            /** @description A list of words to compare the keywords against. */
            word_list: unknown[];
            /**
             * @description
             *   To encode a field 'description' with a sentence-transformers model, use:
             *   ```
             *   {field:'description',model_name:'sentence-transformers',model_url:'sentence-t5-base'}
             *   ```
             */
            encoder?: {
                /** @description The model url to use */
                model_url?: string;
                /** @description The model name to use. */
                model_name?: "image_text" | "text_image" | "all-mpnet-base-v2" | "clip-vit-b-32-image" | "clip-vit-b-32-text" | "clip-vit-l-14-image" | "clip-vit-l-14-text" | "sentence-transformers" | "similarity-all-mpnet-base-v2";
                /** @description Can be 'url' or 'text'. Use 'url' for image models and 'text' for text models. */
                body?: string;
                /** @description The document field to encode. */
                field: string;
                /** @description Add 'alias' to a created vector field name. Output format is {field}_{alias}_vector_. */
                alias?: string;
            };
        };
        BiasEvaluationOutput: {
            results?: {
                [key: string]: unknown;
            };
        };
        VectorizeInput: {
            /** @description The model url to use */
            model_url?: string;
            /** @description The model name to use. */
            model_name?: "image_text" | "text_image" | "all-mpnet-base-v2" | "clip-vit-b-32-image" | "clip-vit-b-32-text" | "clip-vit-l-14-image" | "clip-vit-l-14-text" | "sentence-transformers" | "similarity-all-mpnet-base-v2";
            /** @description Can be 'url' or 'text'. Use 'url' for image models and 'text' for text models. */
            body?: string;
            /** @description The document field to encode. */
            field: string;
            /** @description Add 'alias' to a created vector field name. Output format is {field}_{alias}_vector_. */
            alias?: string;
            /** @description The documents that will be inserted */
            documents: ({
                _id?: unknown;
            } & {
                [key: string]: unknown;
            })[];
        };
        VectorizeOutput: {
            /** @description The documents that will be vectorized */
            results: ({
                _id?: unknown;
            } & {
                [key: string]: unknown;
            })[];
            errors: unknown[];
        };
        VectorizeAndInsertInput: {
            /** @description The model url to use */
            model_url?: string;
            /** @description The model name to use. */
            model_name?: "image_text" | "text_image" | "all-mpnet-base-v2" | "clip-vit-b-32-image" | "clip-vit-b-32-text" | "clip-vit-l-14-image" | "clip-vit-l-14-text" | "sentence-transformers" | "similarity-all-mpnet-base-v2";
            /** @description Can be 'url' or 'text'. Use 'url' for image models and 'text' for text models. */
            body?: string;
            /** @description The document field to encode. */
            field: string;
            /** @description Add 'alias' to a created vector field name. Output format is {field}_{alias}_vector_. */
            alias?: string;
            /** @description The documents that will be inserted */
            documents: ({
                _id?: unknown;
            } & {
                [key: string]: unknown;
            })[];
        };
        VectorizeAndInsertOutput: {
            /** @description Number of successfully processed documents. */
            inserted: number;
            /** @description Information about documents that were not processed successfully. */
            failed_documents: {
                /** @description _id field of unprocessed document. */
                _id?: unknown;
                error?: {
                    /** @description http status code of individual document insertion operation. */
                    status?: number;
                    /** @description body response of individual document insertion operation. */
                    body?: string;
                };
            }[];
        };
        /**
         * @description
         *   To encode a field 'description' with a sentence-transformers model, use:
         *   ```
         *   {field:'description',model_name:'sentence-transformers',model_url:'sentence-t5-base'}
         *   ```
         */
        VectorizeFieldInput: {
            /** @description The model url to use */
            model_url?: string;
            /** @description The model name to use. */
            model_name?: "image_text" | "text_image" | "all-mpnet-base-v2" | "clip-vit-b-32-image" | "clip-vit-b-32-text" | "clip-vit-l-14-image" | "clip-vit-l-14-text" | "sentence-transformers" | "similarity-all-mpnet-base-v2";
            /** @description Can be 'url' or 'text'. Use 'url' for image models and 'text' for text models. */
            body?: string;
            /** @description The document field to encode. */
            field: string;
            /** @description Add 'alias' to a created vector field name. Output format is {field}_{alias}_vector_. */
            alias?: string;
        };
        VectorizeFieldOutput: {
            /** @description Number of successfully processed documents. */
            inserted: number;
            /** @description Information about documents that were not processed successfully. */
            failed_documents: {
                /** @description _id field of unprocessed document. */
                _id?: unknown;
                error?: {
                    /** @description http status code of individual document insertion operation. */
                    status?: number;
                    /** @description body response of individual document insertion operation. */
                    body?: string;
                };
            }[];
        };
        CreateImageFromPromptInput: {
            prompt: string;
            /** @default 512 */
            width?: number;
            /** @default 512 */
            height?: number;
            /** @default 50 */
            num_inference_steps?: number;
        };
        CreateImageFromPromptOutput: {
            url: string;
        };
        DeleteFieldChildrenInput: unknown;
        DeleteFieldChildrenOutput: unknown;
        ListFieldChildrensInput: {
            page?: number;
            page_size?: number;
            /** @description Fields to sort by. For each field, sort by descending or ascending. If you are using descending by datetime, it will get the most recent ones. */
            sort?: (string | {
                [key: string]: "asc" | "desc";
            })[];
        };
        ListFieldChildrensOutput: {
            results: {
                _id?: string;
                field?: string;
                field_children?: string[];
                metadata?: {
                    [key: string]: unknown;
                };
                category?: string;
            }[];
        };
        UpdateFieldChildrenInput: {
            _id?: string;
            field?: string;
            field_children?: string[];
            metadata?: {
                [key: string]: unknown;
            };
            category?: string;
        };
        UpdateFieldChildrenOutput: unknown;
        DeleteFavouriteWorkflowInput: unknown;
        DeleteFavouriteWorkflowOutput: unknown;
        ListFavouriteWorkflowsInput: {
            page?: number;
            page_size?: number;
            /** @description Fields to sort by. For each field, sort by descending or ascending. If you are using descending by datetime, it will get the most recent ones. */
            sort?: (string | {
                [key: string]: "asc" | "desc";
            })[];
        };
        ListFavouriteWorkflowsOutput: {
            results: {
                _id?: string;
            }[];
        };
        UpdateFavouriteWorkflowInput: {
            _id?: string;
        };
        UpdateFavouriteWorkflowOutput: unknown;
        DeleteSavedFilterInput: unknown;
        DeleteSavedFilterOutput: unknown;
        ListSavedFiltersInput: {
            page?: number;
            page_size?: number;
            /** @description Fields to sort by. For each field, sort by descending or ascending. If you are using descending by datetime, it will get the most recent ones. */
            sort?: (string | {
                [key: string]: "asc" | "desc";
            })[];
        };
        ListSavedFiltersOutput: {
            results: {
                _id?: string;
                filters?: {
                    [key: string]: unknown;
                };
                label?: string;
                name?: string;
            }[];
        };
        UpdateSavedFilterInput: {
            _id?: string;
            filters?: {
                [key: string]: unknown;
            };
            label?: string;
            name?: string;
        };
        UpdateSavedFilterOutput: unknown;
        GetSavedFilterInput: unknown;
        GetSavedFilterOutput: {
            _id?: string;
            filters?: {
                [key: string]: unknown;
            };
            label?: string;
            name?: string;
        };
        CreateSavedFilterInput: {
            _id?: string;
            filters?: {
                [key: string]: unknown;
            };
            label?: string;
            name?: string;
        };
        CreateSavedFilterOutput: {
            id: string;
        };
        DeleteComponentInput: unknown;
        DeleteComponentOutput: unknown;
        ListComponentsInput: {
            page?: number;
            page_size?: number;
            /** @description Fields to sort by. For each field, sort by descending or ascending. If you are using descending by datetime, it will get the most recent ones. */
            sort?: (string | {
                [key: string]: "asc" | "desc";
            })[];
        };
        ListComponentsOutput: {
            results: {
                _id?: string;
                configuration?: {
                    [key: string]: unknown;
                };
            }[];
        };
        UpdateComponentInput: {
            _id?: string;
            configuration?: {
                [key: string]: unknown;
            };
        };
        UpdateComponentOutput: unknown;
        GetComponentInput: unknown;
        GetComponentOutput: {
            _id?: string;
            configuration?: {
                [key: string]: unknown;
            };
        };
        CreateComponentInput: {
            _id?: string;
            configuration?: {
                [key: string]: unknown;
            };
        };
        CreateComponentOutput: {
            id: string;
        };
        ListDatasetEditorHistorysInput: {
            page?: number;
            page_size?: number;
            /** @description Fields to sort by. For each field, sort by descending or ascending. If you are using descending by datetime, it will get the most recent ones. */
            sort?: (string | {
                [key: string]: "asc" | "desc";
            })[];
        };
        ListDatasetEditorHistorysOutput: {
            results: {
                _id?: string;
                metadata?: {
                    [key: string]: unknown;
                };
                items?: {
                    [key: string]: {
                        updates?: unknown[];
                        filters?: unknown[];
                    };
                };
            }[];
        };
        CreateDatasetEditorHistoryInput: {
            _id?: string;
            metadata?: {
                [key: string]: unknown;
            };
            items?: {
                [key: string]: {
                    updates?: unknown[];
                    filters?: unknown[];
                };
            };
        };
        CreateDatasetEditorHistoryOutput: {
            id: string;
        };
        ListDatasetEditorConfigurationsInput: {
            page?: number;
            page_size?: number;
            /** @description Fields to sort by. For each field, sort by descending or ascending. If you are using descending by datetime, it will get the most recent ones. */
            sort?: (string | {
                [key: string]: "asc" | "desc";
            })[];
        };
        ListDatasetEditorConfigurationsOutput: {
            results: {
                _id?: string;
                metadata?: {
                    [key: string]: unknown;
                };
                items?: {
                    [key: string]: {
                        updates?: unknown[];
                        filters?: unknown[];
                    };
                };
            }[];
        };
        CreateDatasetEditorConfigurationInput: {
            _id?: string;
            metadata?: {
                [key: string]: unknown;
            };
            items?: {
                [key: string]: {
                    updates?: unknown[];
                    filters?: unknown[];
                };
            };
        };
        CreateDatasetEditorConfigurationOutput: {
            id: string;
        };
        UpdateDatasetEditorConfigurationInput: {
            _id?: string;
            metadata?: {
                [key: string]: unknown;
            };
            items?: {
                [key: string]: {
                    updates?: unknown[];
                    filters?: unknown[];
                };
            };
        };
        UpdateDatasetEditorConfigurationOutput: unknown;
        DeleteDatasetEditorConfigurationInput: unknown;
        DeleteDatasetEditorConfigurationOutput: unknown;
        GetDatasetEditorConfigurationInput: unknown;
        GetDatasetEditorConfigurationOutput: {
            _id?: string;
            metadata?: {
                [key: string]: unknown;
            };
            items?: {
                [key: string]: {
                    updates?: unknown[];
                    filters?: unknown[];
                };
            };
        };
        ListKeyphrasesInput: {
            page?: number;
            page_size?: number;
            /** @description Fields to sort by. For each field, sort by descending or ascending. If you are using descending by datetime, it will get the most recent ones. */
            sort?: (string | {
                [key: string]: "asc" | "desc";
            })[];
        };
        ListKeyphrasesOutput: {
            results: {
                _id?: string;
                keyphrase?: string;
                ancestors?: string[];
                parents?: string[];
                metadata?: {
                    [key: string]: unknown;
                };
                score?: number;
            }[];
        };
        UpdateKeyphraseInput: {
            _id?: string;
            keyphrase?: string;
            ancestors?: string[];
            parents?: string[];
            metadata?: {
                [key: string]: unknown;
            };
            score?: number;
        };
        UpdateKeyphraseOutput: unknown;
        DeleteKeyphraseInput: unknown;
        DeleteKeyphraseOutput: unknown;
        GetKeyphraseInput: unknown;
        GetKeyphraseOutput: {
            _id?: string;
            keyphrase?: string;
            ancestors?: string[];
            parents?: string[];
            metadata?: {
                [key: string]: unknown;
            };
            score?: number;
        };
        BulkUpdateKeyphrasesInput: {
            updates?: {
                _id?: string;
                keyphrase?: string;
                ancestors?: string[];
                parents?: string[];
                metadata?: {
                    [key: string]: unknown;
                };
                score?: number;
            }[];
        };
        BulkUpdateKeyphrasesOutput: unknown;
        ListTaxonomysInput: {
            page?: number;
            page_size?: number;
            /** @description Fields to sort by. For each field, sort by descending or ascending. If you are using descending by datetime, it will get the most recent ones. */
            sort?: (string | {
                [key: string]: "asc" | "desc";
            })[];
        };
        ListTaxonomysOutput: {
            results: {
                _id?: string;
                name?: string;
                taxonomy?: string[];
                description?: string;
                metadata?: {
                    [key: string]: unknown;
                };
            }[];
        };
        UpdateTaxonomyInput: {
            _id?: string;
            name?: string;
            taxonomy?: string[];
            description?: string;
            metadata?: {
                [key: string]: unknown;
            };
        };
        UpdateTaxonomyOutput: unknown;
        DeleteTaxonomyInput: unknown;
        DeleteTaxonomyOutput: unknown;
        GetTaxonomyInput: unknown;
        GetTaxonomyOutput: {
            _id?: string;
            name?: string;
            taxonomy?: string[];
            description?: string;
            metadata?: {
                [key: string]: unknown;
            };
        };
        CreateTaxonomyInput: {
            _id?: string;
            name?: string;
            taxonomy?: string[];
            description?: string;
            metadata?: {
                [key: string]: unknown;
            };
        };
        CreateTaxonomyOutput: {
            id: string;
        };
    };
}
export interface operations {
    /**
     * Create a private deployable.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "deployables:write",
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "body": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    CreateDeployable: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateDeployableOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateDeployableInput"];
            };
        };
    };
    /**
     * Get a deployable.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "deployables:read"
     *     ],
     *     "deployables": [
     *       {
     *         "params": "deployable_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetDeployable: {
        parameters: {
            path: {
                /** ID of deployable */
                deployable_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetDeployableOutput"];
                };
            };
        };
    };
    /**
     * Update a deployable
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "deployables:write"
     *     ],
     *     "deployables": [
     *       {
     *         "params": "deployable_id"
     *       }
     *     ]
     *   },
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "body": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    UpdateDeployable: {
        parameters: {
            path: {
                /** ID of deployable */
                deployable_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateDeployableOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateDeployableInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "deployables:write"
     *     ],
     *     "deployables": [
     *       {
     *         "body": "id"
     *       }
     *     ]
     *   }
     * ]
     */
    DeleteDeployable: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteDeployableOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteDeployableInput"];
            };
        };
    };
    /**
     * Share a private deployable.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "sharing:write",
     *       "deployables:read"
     *     ],
     *     "deployables": [
     *       {
     *         "params": "deployable_id"
     *       }
     *     ]
     *   }
     * ]
     */
    CreateDeployableKey: {
        parameters: {
            path: {
                /** ID of deployable */
                deployable_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateDeployableKeyOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateDeployableKeyInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "users:write",
     *       "deployables:read"
     *     ],
     *     "deployables": [
     *       {
     *         "params": "deployable_id"
     *       }
     *     ]
     *   }
     * ]
     */
    CreateDeployableInvite: {
        parameters: {
            path: {
                /** ID of deployable */
                deployable_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateDeployableInviteOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateDeployableInviteInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "users:write",
     *       "deployables:read"
     *     ],
     *     "deployables": [
     *       {
     *         "params": "deployable_id"
     *       }
     *     ]
     *   }
     * ]
     */
    UpdateUsersDeployablePermissions: {
        parameters: {
            path: {
                /** ID of deployable */
                deployable_id: string;
                /** ID of user */
                user_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateUsersDeployablePermissionsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateUsersDeployablePermissionsInput"];
            };
        };
    };
    /**
     * Unshare a shared deployable, making it private.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "sharing:write",
     *       "deployables:read"
     *     ],
     *     "deployables": [
     *       {
     *         "params": "deployable_id"
     *       }
     *     ]
     *   }
     * ]
     */
    DeleteDeployableKey: {
        parameters: {
            path: {
                /** ID of deployable */
                deployable_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteDeployableKeyOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteDeployableKeyInput"];
            };
        };
    };
    /**
     * List all deployables.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": []
     *   }
     * ]
     */
    ListDeployables: {
        parameters: {
            query: {
                filters?: (Partial<{
                    /** @description Match where document[field] is in value list. */
                    match?: {
                        /** @description If matching on text, match even if there are extra words / case insensitivity */
                        fuzzy?: boolean;
                        /** @description Field to match on. */
                        key?: string;
                        /** @description Field to match on. */
                        field?: string;
                        /** @description Can be either a single item or a list of items to match on. */
                        value: string | boolean | number | unknown[];
                    };
                    /** @description Match where document._id is in value list. */
                    matchIds?: {
                        /** @description Can be either a single item or a list of items to match on. */
                        value: string | boolean | number | unknown[];
                    };
                    /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                    range?: {
                        /** @description Field to match on. */
                        key?: string;
                        /** @description Field to match on. */
                        field?: string;
                        greaterThan?: unknown;
                        lessThan?: unknown;
                        greaterThanOrEqualTo?: unknown;
                        lessThanOrEqualTo?: unknown;
                    };
                    /**
                     * @description
                     * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                     *
                     * Possible matching patterns:
                     *
                     * * - documents where the field has any value
                     * ? - documents where a single character from the field matches the provided values
                     *
                     * Example:
                     *
                     * {
                     *   wildcard: {
                     *      field: "title",
                     *      value: [ "Avenger*" ]
                     *   }
                     * }
                     */
                    wildcard?: {
                        /** @description Field to match on. */
                        key?: string;
                        /** @description Field to match on. */
                        field?: string;
                        /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                        value: string | string[];
                    };
                    /** @description hybrid search query that must reach a certain value to filter */
                    search?: {
                        /**
                         * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                         *
                         *     "tele" matches "Television", "This television is an excellent product…"
                         *
                         *     Example: "tele"
                         */
                        query?: string;
                        /** @description Configuration for traditional search query. */
                        queryConfig?: {
                            /**
                             * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                             * @default 1
                             */
                            weight?: number;
                            /**
                             * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                             *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                             * @default 1
                             */
                            sentenceWeight?: number;
                            /**
                             * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                             *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                             * @default 1
                             */
                            fuzzyWeight?: number;
                        };
                        /**
                         * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                         *
                         *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                         *
                         *     It can be an object or a list of objects.
                         *
                         *
                         *
                         *     Example payloads:
                         *
                         *     {"field":"animaltype_vector_","query":"kitten"}
                         *
                         *     [
                         *
                         *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                         *
                         *     ]
                         */
                        vectorSearchQuery?: {
                            /** @description Vector name to search on. For example, title_vector_ */
                            field: string;
                            /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                            query?: string;
                            /**
                             * @description Model name to generate the vector with.
                             * @default text
                             */
                            model?: string;
                            /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                            model_url?: string;
                            /**
                             * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                             * @default 1
                             */
                            weight?: number;
                            /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                            vector?: number[];
                            chunkConfig?: {
                                chunkField: string;
                                page?: number;
                                pageSize?: number;
                            };
                        } | {
                            /** @description Vector name to search on. For example, title_vector_ */
                            field: string;
                            /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                            query?: string;
                            /**
                             * @description Model name to generate the vector with.
                             * @default text
                             */
                            model?: string;
                            /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                            model_url?: string;
                            /**
                             * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                             * @default 1
                             */
                            weight?: number;
                            /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                            vector?: number[];
                            chunkConfig?: {
                                chunkField: string;
                                page?: number;
                                pageSize?: number;
                            };
                        }[];
                        realtimeClustering?: {
                            /** @default 4 */
                            nClusters?: number;
                            /** @default kmeans-realtime-4 */
                            alias?: string;
                            vectorField: string;
                            /** @default _cluster_.{vector_field}.{alias} */
                            outputField?: string;
                        };
                        /**
                         * @description A list of fields to search using the "query" parameter.
                         *
                         *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                         *
                         *     Default behaviour is to search all fields.
                         *
                         *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                         */
                        fieldsToSearch?: (string | {
                            /** @description Field name to search. */
                            key?: string;
                            /** @description Field name to search. */
                            field?: string;
                            /**
                             * @description Multiply the relevance contribution of a specific field when using traditional search.
                             * @default 1
                             */
                            weight?: number;
                            /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                            chunkField?: string;
                        })[];
                        /**
                         * @description Only return documents with a _relevance above this threshold.
                         *
                         *     Example: 0.3
                         */
                        minimumRelevance: number;
                    };
                    /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                    fieldExists?: {
                        /** @description Field that must exist. */
                        key?: string;
                        /** @description Field that must exist. */
                        field?: string;
                    };
                    /** @description Match documents where doc[field] % module == value */
                    matchModulo?: {
                        /** @default _id */
                        field?: string;
                        modulo: number;
                        value: number;
                    };
                    /** @description Filter down to one document for each value of selected field. */
                    dedupeByValue?: {
                        /** @description Field to filter on. */
                        field: string;
                    };
                    /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                    selfreference?: {
                        /** @description First field in comparison. */
                        a: string;
                        /** @description Second field in comparison. */
                        b: string;
                        /** @description Operator used to compare a and b. */
                        operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                    };
                    /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                    wordCount?: {
                        /** @description Field to match on. */
                        field: string;
                        /** @description Minimum word count. */
                        greaterThan?: number;
                        /** @description Maximum word count. */
                        lessThan?: number;
                    };
                    /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                    characterCount?: {
                        /** @description Field to match on. */
                        field: string;
                        /** @description Minimum character count. */
                        greaterThan?: number;
                        /** @description Maximum character count. */
                        lessThan?: number;
                    };
                    /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                    or?: (definitions["simpleSearchAndFlatFilterItem"][] | definitions["simpleSearchAndFlatFilterItem"])[];
                    /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                    not?: definitions["simpleSearchAndFlatFilterItem"][] | definitions["simpleSearchAndFlatFilterItem"];
                    /** @description Filter based on data within a _chunk_ field. */
                    chunk?: {
                        /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                        path: string;
                        filters: definitions["simpleSearchAndFlatFilterItem"][];
                    };
                }> & Partial<{
                    strict?: "must" | "should" | "must_or";
                    condition?: string;
                    field?: string;
                    filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                    condition_value?: unknown;
                    fuzzy?: number;
                    join?: boolean;
                }>)[];
                /** Fields to include in the search results, empty array/list means all fields. */
                select_fields?: string[];
                /** Size of each page of results. */
                page_size?: number;
                /** Size of each page of results. */
                page?: number;
                /** Fields to sort by. For each field, sort by descending or ascending. If you are using descending by datetime, it will get the most recent ones. */
                sort?: (string | {
                    [key: string]: "asc" | "desc";
                })[];
                /** Include vectors in the search results. */
                include_vector?: boolean;
                /** (NOT IMPLEMENTED - not functional) If true, retrieves documents randomly. */
                is_random?: boolean;
                /** (NOT IMPLEMENTED - not functional)Random Seed for retrieving random documents. */
                random_state?: number;
                /** Retrieve items after these sort values in the sort order. */
                after_id?: unknown[];
                /** Unique name of the dataset */
                dataset_id?: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListDeployablesOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    DeleteDeployableGroup: {
        parameters: {
            path: {
                /** ID of deployablegroup */
                deployablegroup_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteDeployableGroupOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteDeployableGroupInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    ListDeployableGroups: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListDeployableGroupsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListDeployableGroupsInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    CreateDeployableGroup: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateDeployableGroupOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateDeployableGroupInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    GetDeployableGroup: {
        parameters: {
            path: {
                /** ID of deployablegroup */
                deployablegroup_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetDeployableGroupOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    UpdateDeployableGroup: {
        parameters: {
            path: {
                /** ID of deployablegroup */
                deployablegroup_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateDeployableGroupOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateDeployableGroupInput"];
            };
        };
    };
    /**
     * Share a private deployable group. Required read access to all deployables that are shared, and the datasets deployables are tied to.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "sharing:write"
     *     ]
     *   }
     * ]
     */
    CreateDeployableGroupKey: {
        parameters: {
            path: {
                /** ID of deployablegroup */
                deployablegroup_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateDeployableGroupKeyOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateDeployableGroupKeyInput"];
            };
        };
    };
    /**
     * Unshare a shared deployable group, making it private.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "sharing:write"
     *     ]
     *   }
     * ]
     */
    DeleteDeployableGroupKey: {
        parameters: {
            path: {
                /** ID of deployablegroup */
                deployablegroup_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteDeployableGroupKeyOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteDeployableGroupKeyInput"];
            };
        };
    };
    /**
     * Create a new Project that your user will have access to
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "organizations:write"
     *     ],
     *     "organizations": [
     *       {
     *         "body": "organization_id"
     *       }
     *     ]
     *   }
     * ]
     */
    CreateProject: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateProjectOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateProjectInput"];
            };
        };
    };
    /**
     * Update metadata of a Project
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "admin"
     *     ]
     *   }
     * ]
     */
    UpdateProject: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateProjectOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateProjectInput"];
            };
        };
    };
    /**
     * Tie a project to an organization. This requires admin privileges over project and organization.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "admin"
     *     ],
     *     "projects": [
     *       {
     *         "user_info": "username"
     *       }
     *     ]
     *   },
     *   {
     *     "actions": [
     *       "organizations:write"
     *     ],
     *     "organizations": [
     *       {
     *         "body": "organization_id"
     *       }
     *     ]
     *   }
     * ]
     */
    TransferProjectToOrganization: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["TransferProjectToOrganizationOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["TransferProjectToOrganizationInput"];
            };
        };
    };
    /**
     * List all projects and their metadata
     *
     * ### Required permissions
     * > []
     */
    ListProjects: {
        parameters: {
            query: {
                page?: number;
                page_size?: number;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListProjectsOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "users:write"
     *     ]
     *   }
     * ]
     */
    CreateUser: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateUserOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateUserInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": []
     *   }
     * ]
     */
    ListUsers: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListUsersOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListUsersInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    IsUserAuthorized: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["IsUserAuthorizedOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["IsUserAuthorizedInput"];
            };
        };
    };
    /**
     * Get user_id, key_id and permissions from an auth header.
     *
     * ### Required permissions
     * > []
     */
    GetAuthHeaderInfo: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetAuthHeaderInfoOutput"];
                };
            };
        };
    };
    /**
     * Invite a user to a project using either their email or their user_id
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "users:write"
     *     ]
     *   }
     * ]
     */
    CreateProjectInvite: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateProjectInviteOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateProjectInviteInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    ListProjectInvites: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListProjectInvitesOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListProjectInvitesInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    AcceptProjectInvite: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["AcceptProjectInviteOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["AcceptProjectInviteInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "users:write"
     *     ]
     *   }
     * ]
     */
    DeleteProjectInvite: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteProjectInviteOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteProjectInviteInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "users:write"
     *     ]
     *   }
     * ]
     */
    ResendProjectInvite: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ResendProjectInviteOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ResendProjectInviteInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "users": [
     *       {
     *         "params": "user_id"
     *       }
     *     ],
     *     "actions": [
     *       "users:read"
     *     ]
     *   }
     * ]
     */
    GetUser: {
        parameters: {
            path: {
                /** ID of user */
                user_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetUserOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "users": [
     *       {
     *         "params": "user_id"
     *       }
     *     ],
     *     "actions": [
     *       "users:write"
     *     ]
     *   }
     * ]
     */
    UpdateUser: {
        parameters: {
            path: {
                /** ID of user */
                user_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateUserOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateUserInput"];
            };
        };
    };
    /**
     * Deletes a User from ONLY your project. If they have permissions for other projects, they will still have acces to them.
     *
     * ### Required permissions
     * > [
     *   {
     *     "users": [
     *       {
     *         "params": "user_id"
     *       }
     *     ],
     *     "actions": [
     *       "users:write"
     *     ]
     *   }
     * ]
     */
    DeleteUser: {
        parameters: {
            path: {
                /** ID of user */
                user_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteUserOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteUserInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "users": [
     *       {
     *         "params": "user_id"
     *       }
     *     ],
     *     "actions": [
     *       "users:write"
     *     ]
     *   }
     * ]
     */
    CreateUserKey: {
        parameters: {
            path: {
                /** ID of user */
                user_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateUserKeyOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateUserKeyInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "users": [
     *       {
     *         "params": "user_id"
     *       }
     *     ],
     *     "actions": [
     *       "users:read"
     *     ]
     *   }
     * ]
     */
    ListUserKeys: {
        parameters: {
            path: {
                /** ID of user */
                user_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListUserKeysOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListUserKeysInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "users": [
     *       {
     *         "params": "user_id"
     *       }
     *     ],
     *     "actions": [
     *       "users:write"
     *     ]
     *   }
     * ]
     */
    DeleteUserKey: {
        parameters: {
            path: {
                /** ID of user */
                user_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteUserKeyOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteUserKeyInput"];
            };
        };
    };
    /**
     * Insert your own cluster centroids for it to be used in approximate search settings and cluster aggregations.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    InsertClusterCentroids: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["InsertClusterCentroidsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["InsertClusterCentroidsInput"];
            };
        };
    };
    /**
     * Update your own cluster centroids for it to be used in approximate search settings and cluster aggregations. Will merge rather than replace centroids.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    UpdateClusterCentroids: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateClusterCentroidsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateClusterCentroidsInput"];
            };
        };
    };
    /**
     * For each centroid in a list of centroids, find the closest centroids in another set of centroids.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       },
     *       {
     *         "body": "comparison_centroids.dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    CompareClusterCentroids: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CompareClusterCentroidsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CompareClusterCentroidsInput"];
            };
        };
    };
    /**
     * Aggregation/Groupby of a collection using an aggregation query.
     * The aggregation query is a json body that follows the schema of:
     *
     *     {
     *         "groupby" : [
     *             {"name": <alias>, "field": <field in the collection>, "agg": "category"},
     *             {"name": <alias>, "field": <another groupby field in the collection>, "agg": "numeric"}
     *         ],
     *         "metrics" : [
     *             {"name": <alias>, "field": <numeric field in the collection>, "agg": "avg"}
     *             {"name": <alias>, "field": <another numeric field in the collection>, "agg": "max"}
     *             {"name": <alias>, "fields": [<numeric field in the collection>, <another numeric field in the collection>], "agg": "correlation"}
     *         ]
     *     }
     *     For example, one can use the following aggregations to group score based on region and player name.
     *     {
     *         "groupby" : [
     *             {"name": "region", "field": "player_region", "agg": "category"},
     *             {"name": "player_name", "field": "name", "agg": "category"}
     *         ],
     *         "metrics" : [
     *             {"name": "average_score", "field": "final_score", "agg": "avg"},
     *             {"name": "max_score", "field": "final_score", "agg": "max"},
     *             {'name':'total_score','field':"final_score", 'agg':'sum'},
     *             {'name':'average_deaths','field':"final_deaths", 'agg':'avg'},
     *             {'name':'highest_deaths','field':"final_deaths", 'agg':'max'},
     *             {'name':'score_death_correlation', 'fields':['final_deaths', 'final_score'], 'agg': 'correlation'},
     *         ]
     *     }
     * - "groupby" is the fields you want to split the data into. These are the available groupby types:
     *     - category" : groupby a field that is a category
     *     - numeric: groupby a field that is a numeric
     * - "metrics" is the fields you want to metrics you want to calculate in each of those, every aggregation includes a frequency metric. These are the available metric types:
     *     - For single fields: "avg"/"average"/"mean", "cardinality", "count", "kurtosis", "max", "min", "percentiles", "kurtosis", "std_deviation", "std_deviation_bounds", "sum", "sum_of_squares", "variance"
     *     - For multiple fields (the attribute "fields" must be used instead of "field"): "correlation", "covariance", "kurtosis", "mean", "skewness", "variance"
     *
     * The response returned has the following in descending order.
     *
     * IF you want to return documents, specify a "group_size" parameter and a "select_fields" parameter if you want to limit the specific fields chosen.
     * This looks as such:
     *
     *     {
     *       'groupby':[
     *         {'name':'Manufacturer','field':'manufacturer','agg':'category',
     *         'group_size': 10, 'select_fields': ["name"]},
     *       ],
     *       'metrics':[
     *         {'name':'Price Average','field':'price','agg':'avg'},
     *       ],
     *     }
     *
     *     {"title": {"title": "books", "frequency": 200, "documents": [{...}, {...}]}, {"title": "books", "frequency": 100, "documents": [{...}, {...}]}}
     *
     * For array-aggregations, you can add "agg": "array" into the aggregation query.
     *
     *
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    Aggregate: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["AggregateOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["AggregateInput"];
            };
        };
    };
    /**
     * Takes an aggregation query and gets the aggregate of each cluster in a collection. This helps you interpret each cluster and what is in them.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    AggregateClusters: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["AggregateClustersOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["AggregateClustersInput"];
            };
        };
    };
    /**
     * Takes a high level aggregation of every field and every cluster in a collection. This helps you interpret each cluster and what is in them.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    ListClusterFacets: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListClusterFacetsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListClusterFacetsInput"];
            };
        };
    };
    /**
     * List documents with vector fields closest to centroids.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    ListClosestToCentroids: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListClosestToCentroidsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListClosestToCentroidsInput"];
            };
        };
    };
    /**
     * List documents with vector fields furthest from centroids.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    ListFurthestFromCentroids: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListFurthestFromCentroidsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListFurthestFromCentroidsInput"];
            };
        };
    };
    /**
     * List centroids, optionally filtering by their ids.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    ListCentroids: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListCentroidsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListCentroidsInput"];
            };
        };
    };
    /**
     * List centroids configs for a dataset.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    ListCentroidConfigs: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListCentroidConfigsOutput"];
                };
            };
        };
    };
    /**
     * Delete a centroid by ID
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    DeleteCentroid: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
                /** ID of centroid */
                centroid_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteCentroidOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteCentroidInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "body": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    RealtimeClustering: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["RealtimeClusteringOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["RealtimeClusteringInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    MergeClusters: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["MergeClustersOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["MergeClustersInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read",
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    CreateClusterSummaries: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateClusterSummariesOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateClusterSummariesInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read",
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    CreateClusterLabels: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateClusterLabelsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateClusterLabelsInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    ListClusterSummaries: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListClusterSummariesOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListClusterSummariesInput"];
            };
        };
    };
    /**
     * Endpoint for creating Hierarchical Clusters from a given dataset on top of existing clusters.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read",
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    CreateHierarchicalClusters: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateHierarchicalClustersOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateHierarchicalClustersInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    DeleteClusterSummaries: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteClusterSummariesOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteClusterSummariesInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    CreateOrganization: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateOrganizationOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateOrganizationInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "organizations:write"
     *     ],
     *     "organizations": [
     *       {
     *         "params": "organization_id"
     *       }
     *     ]
     *   }
     * ]
     */
    UpdateOrganization: {
        parameters: {
            path: {
                /** ID of organization */
                organization_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateOrganizationOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateOrganizationInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "support:write"
     *     ],
     *     "organizations": [
     *       {
     *         "params": "organization_id"
     *       }
     *     ]
     *   }
     * ]
     */
    UpdateOrganizationAdmin: {
        parameters: {
            path: {
                /** ID of organization */
                organization_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateOrganizationAdminOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateOrganizationAdminInput"];
            };
        };
    };
    /**
     * List all organizations and their metadata
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "organizations:read"
     *     ]
     *   }
     * ]
     */
    ListOrganizations: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListOrganizationsOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "organizations:write"
     *     ],
     *     "organizations": [
     *       {
     *         "params": "organization_id"
     *       }
     *     ]
     *   }
     * ]
     */
    DeleteOrganization: {
        parameters: {
            path: {
                /** ID of organization */
                organization_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteOrganizationOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteOrganizationInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "organizations:read"
     *     ],
     *     "organizations": [
     *       {
     *         "params": "organization_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetOrganization: {
        parameters: {
            path: {
                /** ID of organization */
                organization_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetOrganizationOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "organizations:read"
     *     ],
     *     "organizations": [
     *       {
     *         "params": "organization_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetOrganizationUsage: {
        parameters: {
            path: {
                /** ID of organization */
                organization_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetOrganizationUsageOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "organizations:read"
     *     ],
     *     "organizations": [
     *       {
     *         "params": "organization_id"
     *       }
     *     ]
     *   }
     * ]
     */
    ListUsersInOrganization: {
        parameters: {
            path: {
                /** ID of organization */
                organization_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListUsersInOrganizationOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "organizations:read"
     *     ],
     *     "organizations": [
     *       {
     *         "params": "organization_id"
     *       }
     *     ]
     *   }
     * ]
     */
    ListProjectsInOrganization: {
        parameters: {
            path: {
                /** ID of organization */
                organization_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListProjectsInOrganizationOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "organizations:read"
     *     ],
     *     "organizations": [
     *       {
     *         "params": "organization_id"
     *       }
     *     ]
     *   }
     * ]
     */
    AggregateOrganizationMetrics: {
        parameters: {
            path: {
                /** ID of organization */
                organization_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["AggregateOrganizationMetricsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["AggregateOrganizationMetricsInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write",
     *       "connectors:write"
     *     ],
     *     "datasets": [
     *       {
     *         "body": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    CreateConnector: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateConnectorOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateConnectorInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    CheckConnection: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CheckConnectionOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CheckConnectionInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "connectors:read"
     *     ],
     *     "connectors": [
     *       {
     *         "params": "connector_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetConnector: {
        parameters: {
            path: {
                /** ID of connector */
                connector_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetConnectorOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "connectors:write",
     *       "datasets:write"
     *     ],
     *     "connectors": [
     *       {
     *         "params": "connector_id"
     *       }
     *     ],
     *     "datasets": [
     *       {
     *         "body": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    UpdateConnector: {
        parameters: {
            path: {
                /** ID of connector */
                connector_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateConnectorOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateConnectorInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "connectors:write"
     *     ],
     *     "connectors": [
     *       {
     *         "params": "connector_id"
     *       }
     *     ]
     *   }
     * ]
     */
    TriggerConnector: {
        parameters: {
            path: {
                /** ID of connector */
                connector_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["TriggerConnectorOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["TriggerConnectorInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "connectors:write"
     *     ],
     *     "connectors": [
     *       {
     *         "params": "connector_id"
     *       }
     *     ]
     *   }
     * ]
     */
    DeleteConnector: {
        parameters: {
            path: {
                /** ID of connector */
                connector_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteConnectorOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteConnectorInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": []
     *   }
     * ]
     */
    ListConnectors: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListConnectorsOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    ListConnectorTypes: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListConnectorTypesOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    GetConnectorType: {
        parameters: {
            path: {
                /** ID of connector */
                connector_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetConnectorTypeOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "connectors:read"
     *     ],
     *     "connectors": [
     *       {
     *         "params": "connector_id"
     *       }
     *     ]
     *   }
     * ]
     */
    ListConnectorJobs: {
        parameters: {
            path: {
                /** ID of connector */
                connector_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListConnectorJobsOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "connectors:read"
     *     ],
     *     "connectors": [
     *       {
     *         "params": "connector_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetConnectorJob: {
        parameters: {
            path: {
                /** ID of connector */
                connector_id: string;
                /** ID of job */
                job_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetConnectorJobOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read",
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "body": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    TriggerWorkflow: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["TriggerWorkflowOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["TriggerWorkflowInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ]
     *   }
     * ]
     */
    ListWorkflows: {
        parameters: {
            query: {
                /** Only retrieve workflows of this status */
                job_status?: string;
                /** Currently limited to exact_match, ids, exists, regexp filter_type */
                filters?: {
                    strict?: "must" | "should" | "must_or";
                    condition?: string;
                    field?: string;
                    filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                    condition_value?: unknown;
                    fuzzy?: number;
                    join?: boolean;
                }[];
                /** Size of each page of results. */
                page?: number;
                /** Size of each page of results. */
                page_size?: number;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListWorkflowsOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ]
     *   }
     * ]
     */
    GetWorkflowStatus: {
        parameters: {
            path: {
                /** ID of workflow */
                workflow_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetWorkflowStatusOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["GetWorkflowStatusInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ]
     *   }
     * ]
     */
    DeleteWorkflowStatus: {
        parameters: {
            path: {
                /** ID of workflow */
                workflow_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteWorkflowStatusOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteWorkflowStatusInput"];
            };
        };
    };
    /**
     * Terminate a workflow
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ]
     *   }
     * ]
     */
    TerminateWorkflow: {
        parameters: {
            path: {
                /** ID of workflow */
                workflow_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["TerminateWorkflowOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["TerminateWorkflowInput"];
            };
        };
    };
    /**
     * Update metadata for a workflow run
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ]
     *   }
     * ]
     */
    UpsertWorkflowMetadata: {
        parameters: {
            path: {
                /** ID of workflow */
                workflow_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpsertWorkflowMetadataOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpsertWorkflowMetadataInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ]
     *   }
     * ]
     */
    UpsertWorkflowProgress: {
        parameters: {
            path: {
                /** ID of workflow */
                workflow_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpsertWorkflowProgressOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpsertWorkflowProgressInput"];
            };
        };
    };
    /**
     * Update status for a workflow run
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ]
     *   }
     * ]
     */
    UpsertWorkflowStatus: {
        parameters: {
            path: {
                /** ID of workflow */
                workflow_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpsertWorkflowStatusOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpsertWorkflowStatusInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    ListWorkflowTypes: {
        parameters: {
            query: {
                filters?: (Partial<{
                    /** @description Match where document[field] is in value list. */
                    match?: {
                        /** @description If matching on text, match even if there are extra words / case insensitivity */
                        fuzzy?: boolean;
                        /** @description Field to match on. */
                        key?: string;
                        /** @description Field to match on. */
                        field?: string;
                        /** @description Can be either a single item or a list of items to match on. */
                        value: string | boolean | number | unknown[];
                    };
                    /** @description Match where document._id is in value list. */
                    matchIds?: {
                        /** @description Can be either a single item or a list of items to match on. */
                        value: string | boolean | number | unknown[];
                    };
                    /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                    range?: {
                        /** @description Field to match on. */
                        key?: string;
                        /** @description Field to match on. */
                        field?: string;
                        greaterThan?: unknown;
                        lessThan?: unknown;
                        greaterThanOrEqualTo?: unknown;
                        lessThanOrEqualTo?: unknown;
                    };
                    /**
                     * @description
                     * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                     *
                     * Possible matching patterns:
                     *
                     * * - documents where the field has any value
                     * ? - documents where a single character from the field matches the provided values
                     *
                     * Example:
                     *
                     * {
                     *   wildcard: {
                     *      field: "title",
                     *      value: [ "Avenger*" ]
                     *   }
                     * }
                     */
                    wildcard?: {
                        /** @description Field to match on. */
                        key?: string;
                        /** @description Field to match on. */
                        field?: string;
                        /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                        value: string | string[];
                    };
                    /** @description hybrid search query that must reach a certain value to filter */
                    search?: {
                        /**
                         * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                         *
                         *     "tele" matches "Television", "This television is an excellent product…"
                         *
                         *     Example: "tele"
                         */
                        query?: string;
                        /** @description Configuration for traditional search query. */
                        queryConfig?: {
                            /**
                             * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                             * @default 1
                             */
                            weight?: number;
                            /**
                             * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                             *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                             * @default 1
                             */
                            sentenceWeight?: number;
                            /**
                             * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                             *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                             * @default 1
                             */
                            fuzzyWeight?: number;
                        };
                        /**
                         * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                         *
                         *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                         *
                         *     It can be an object or a list of objects.
                         *
                         *
                         *
                         *     Example payloads:
                         *
                         *     {"field":"animaltype_vector_","query":"kitten"}
                         *
                         *     [
                         *
                         *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                         *
                         *     ]
                         */
                        vectorSearchQuery?: {
                            /** @description Vector name to search on. For example, title_vector_ */
                            field: string;
                            /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                            query?: string;
                            /**
                             * @description Model name to generate the vector with.
                             * @default text
                             */
                            model?: string;
                            /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                            model_url?: string;
                            /**
                             * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                             * @default 1
                             */
                            weight?: number;
                            /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                            vector?: number[];
                            chunkConfig?: {
                                chunkField: string;
                                page?: number;
                                pageSize?: number;
                            };
                        } | {
                            /** @description Vector name to search on. For example, title_vector_ */
                            field: string;
                            /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                            query?: string;
                            /**
                             * @description Model name to generate the vector with.
                             * @default text
                             */
                            model?: string;
                            /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                            model_url?: string;
                            /**
                             * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                             * @default 1
                             */
                            weight?: number;
                            /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                            vector?: number[];
                            chunkConfig?: {
                                chunkField: string;
                                page?: number;
                                pageSize?: number;
                            };
                        }[];
                        realtimeClustering?: {
                            /** @default 4 */
                            nClusters?: number;
                            /** @default kmeans-realtime-4 */
                            alias?: string;
                            vectorField: string;
                            /** @default _cluster_.{vector_field}.{alias} */
                            outputField?: string;
                        };
                        /**
                         * @description A list of fields to search using the "query" parameter.
                         *
                         *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                         *
                         *     Default behaviour is to search all fields.
                         *
                         *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                         */
                        fieldsToSearch?: (string | {
                            /** @description Field name to search. */
                            key?: string;
                            /** @description Field name to search. */
                            field?: string;
                            /**
                             * @description Multiply the relevance contribution of a specific field when using traditional search.
                             * @default 1
                             */
                            weight?: number;
                            /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                            chunkField?: string;
                        })[];
                        /**
                         * @description Only return documents with a _relevance above this threshold.
                         *
                         *     Example: 0.3
                         */
                        minimumRelevance: number;
                    };
                    /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                    fieldExists?: {
                        /** @description Field that must exist. */
                        key?: string;
                        /** @description Field that must exist. */
                        field?: string;
                    };
                    /** @description Match documents where doc[field] % module == value */
                    matchModulo?: {
                        /** @default _id */
                        field?: string;
                        modulo: number;
                        value: number;
                    };
                    /** @description Filter down to one document for each value of selected field. */
                    dedupeByValue?: {
                        /** @description Field to filter on. */
                        field: string;
                    };
                    /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                    selfreference?: {
                        /** @description First field in comparison. */
                        a: string;
                        /** @description Second field in comparison. */
                        b: string;
                        /** @description Operator used to compare a and b. */
                        operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                    };
                    /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                    wordCount?: {
                        /** @description Field to match on. */
                        field: string;
                        /** @description Minimum word count. */
                        greaterThan?: number;
                        /** @description Maximum word count. */
                        lessThan?: number;
                    };
                    /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                    characterCount?: {
                        /** @description Field to match on. */
                        field: string;
                        /** @description Minimum character count. */
                        greaterThan?: number;
                        /** @description Maximum character count. */
                        lessThan?: number;
                    };
                    /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                    or?: (definitions["simpleSearchAndFlatFilterItem"][] | definitions["simpleSearchAndFlatFilterItem"])[];
                    /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                    not?: definitions["simpleSearchAndFlatFilterItem"][] | definitions["simpleSearchAndFlatFilterItem"];
                    /** @description Filter based on data within a _chunk_ field. */
                    chunk?: {
                        /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                        path: string;
                        filters: definitions["simpleSearchAndFlatFilterItem"][];
                    };
                }> & Partial<{
                    strict?: "must" | "should" | "must_or";
                    condition?: string;
                    field?: string;
                    filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                    condition_value?: unknown;
                    fuzzy?: number;
                    join?: boolean;
                }>)[];
                /** the workflows version to execute. It could be a tag like production or a version like v1.1.5 */
                version?: string;
                show_hidden?: boolean;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListWorkflowTypesOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    GetWorkflowType: {
        parameters: {
            path: {
                /** ID of workflow */
                workflow_id: string;
            };
            query: {
                /** the workflows version to execute. It could be a tag like production or a version like v1.1.5 */
                version?: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetWorkflowTypeOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    ValidateWorkflowParams: {
        parameters: {
            path: {
                /** ID of workflow */
                workflow_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ValidateWorkflowParamsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ValidateWorkflowParamsInput"];
            };
        };
    };
    /**
     * When inserting the document you can specify your own id for a document by using the field name **"\_id"**.
     *
     *     For specifying your own vector use the suffix (ends with)  **"\_vector\_"** for the field name.
     * e.g. "product\_description\_vector\_"
     *
     *
     *     If inserting many items in a short timespan, use bulkInsert, or set update_schema to false.
     *     Every insert api call temporarily locks and updates the schema, so will slow down with many inserts in quick succession.
     *
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    Insert: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["InsertOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["InsertInput"];
            };
        };
    };
    /**
     * Insert one or more documents.
     *
     * This method is the same as [`/insert`](/reference/insert) but provides support for more than one document to be inserted at a time.
     *
     * For each insertion, if an `_id` is provided and a document with that ID already exists, all existing field values will be deleted and the new ones will be stored. If no `_id` field is provided, one will automatically be added to the document.
     *
     * This method will entirely replace the documents. If you would like to only partially update a document, use [`/update_where`](/reference/updatewhere).
     *
     * If the document with the provided `_id` doesn't exist, one will be automatically created.
     * If the provided dataset doesn't exist, one will be automatically created.
     *
     * Limit the payload of documents to a maximum 100MB for optimal performance.
     *
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    BulkInsert: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["BulkInsertOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["BulkInsertInput"];
            };
        };
    };
    /**
     * specify a list of file paths. For each file path, a url upload_url is returned. files can be POSTed on upload_url to upload them. They can then be accessed on url. Upon dataset deletion, these files will be deleted.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetFileUploadUrlsForDataset: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetFileUploadUrlsForDatasetOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["GetFileUploadUrlsForDatasetInput"];
            };
        };
    };
    /**
     * Return up to 1000 files uploaded for a dataset.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    ListFileUploadsForDataset: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListFileUploadsForDatasetOutput"];
                };
            };
        };
    };
    /**
     * Bulk insert a large number of documents by downloading a file using "download url". This bypasses the need to directly send documents to the api, as happens in BulkInsert.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    ParseBlob: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ParseBlobOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ParseBlobInput"];
            };
        };
    };
    /**
     * Copy a dataset from another users projects into your project. This is considered a project job.
     *
     * ### Required permissions
     * > []
     */
    CopyForeignDataset: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CopyForeignDatasetOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CopyForeignDatasetInput"];
            };
        };
    };
    /**
     * Creates a read only key for your project. Make sure to save the api key somewhere safe. When doing a search the admin username should still be used.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "users:write",
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "raw": "*"
     *       }
     *     ]
     *   }
     * ]
     */
    CreateProjectReadKey: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateProjectReadKeyOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateProjectReadKeyInput"];
            };
        };
    };
    /**
     * Delete a dataset.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    DeleteDataset: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteDatasetOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteDatasetInput"];
            };
        };
    };
    /**
     * A dataset can store documents to be **searched, retrieved, filtered and aggregated** (similar to Collections in MongoDB, Tables in SQL, Indexes in ElasticSearch).
     *
     * A powerful and core feature of VecDB is that you can store both your metadata and vectors in the same document.
     * When specifying the schema of a dataset and inserting your own vector use the suffix (ends with) **"\_vector\_"** for the field name, and specify the length of the vector in dataset_schema.
     *
     * For example:
     *
     *     {
     *         "product_image_vector_": 1024,
     *         "product_text_description_vector_" : 128
     *     }
     *
     * These are the field types supported in our datasets: **["text", "numeric", "date", "dict", "chunks", "vector", "chunkvector"]**.
     *
     * For example:
     *
     *     {
     *         "product_text_description" : "text",
     *         "price" : "numeric",
     *         "created_date" : "date",
     *         "product_texts_chunk_": "chunks",
     *         "product_text_chunkvector_" : 1024
     *     }
     *
     * You don't have to specify the schema of every single field when creating a dataset, as VecDB will automatically detect the appropriate data type for each field (vectors will be automatically identified by its **"\_vector\_"** suffix). Infact you also don't always have to use this endpoint to create a dataset as **\/datasets/bulk_insert** will infer and create the dataset and schema as you insert new documents.
     *
     * Note:
     *  * A dataset name/id can only contain undercase letters, dash, underscore and numbers.
     *  * "\_id" is reserved as the key and id of a document.
     *  * Once a schema is set for a dataset it cannot be altered. If it has to be altered, utlise the copy dataset endpoint.
     *
     * For more information about vectors check out the 'Vectorizing' section, **\/services/search/vector** or out blog at [https://relevance.ai/blog](https://relevance.ai/blog).
     * For more information about chunks and chunk vectors check out **\/services/search/chunk**.
     *
     *
     *
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "body": "id"
     *       }
     *     ]
     *   }
     * ]
     */
    CreateDataset: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateDatasetOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateDatasetInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetSchema: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetSchemaOutput"];
                };
            };
        };
    };
    /**
     * List all datasets in a project that you are authorized to read/write.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": []
     *   }
     * ]
     */
    ListDatasets: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListDatasetsOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "body": "dataset_ids"
     *       }
     *     ]
     *   },
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "body": "new_dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    CombineDatasets: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CombineDatasetsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CombineDatasetsInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    GetCombineJobStatus: {
        parameters: {
            path: {
                /** ID of job */
                job_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetCombineJobStatusOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["GetCombineJobStatusInput"];
            };
        };
    };
    /**
     * Search all datasets in a project that you are authorized to read/write.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": []
     *   }
     * ]
     */
    SearchDatasets: {
        parameters: {
            query: {
                /** Any string that belongs to part of a dataset name. */
                query?: string;
                /** DEPRECATED  - this wont work Sort by created at date. By default shows the newest datasets. Set asc=False to get oldest dataset. */
                sort_by_created_date?: boolean;
                /** DEPRECATED - this wont work Whether to sort results by ascending or descending order */
                asc?: boolean;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["SearchDatasetsOutput"];
                };
            };
        };
    };
    /**
     * Gives you a summary of the health of your vectors, e.g. how many documents with vectors are missing, how many documents with zero vectors
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetFieldHealth: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetFieldHealthOutput"];
                };
            };
        };
    };
    /**
     * View the usage statistics of a dataset.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetDatasetStats: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetDatasetStatsOutput"];
                };
            };
        };
    };
    /**
     * View api call usage statistics for a dataset.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetDatasetUsage: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetDatasetUsageOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["GetDatasetUsageInput"];
            };
        };
    };
    /**
     * DEPRECATED !!!! Retrieve the mapping of vectors generated through fields, dictionary, array, etc
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetVectorMappings: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetVectorMappingsOutput"];
                };
            };
        };
    };
    /**
     * Returns a page of datasets and in detail the dataset's associated information that you are authorized to read/write. The information includes:
     *
     * | Information      | Description |
     * | ----------- | ----------- |
     * | schema      | Data schema of a dataset. (returns same data as **\/dataset/{dataset_id}/schema**) |
     * | metadata   | Metadata of a dataset. (returns same data as **\/dataset/{dataset_id}/metadata**) |
     * | stats   | Statistics of number of documents and size of a dataset. (returns same data as **\/dataset/{dataset_id}/monitor/stats**) |
     * | vector_health   | Number of zero vectors stored. (returns same data as **\/dataset/{dataset_id}/monitor/health**) |
     * | schema_stats   | Fields and number of documents missing/not missing for that field. (returns same data as **\/dataset/{dataset_id}/monitor/stats**) |
     * | active_jobs   | All active jobs/tasks on the dataset. (returns same data as **\/dataset/{dataset_id}/tasks/list**) |
     *
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetDatasetDetails: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetDatasetDetailsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["GetDatasetDetailsInput"];
            };
        };
    };
    /**
     * Retrieve a document by its `_id`.
     *
     * This is the most performant method to get a document or check if it exists, faster than filtering a dataset for by an `_id`.
     *
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetDocument: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
            query: {
                /** Retrieve document with matching _id. */
                id?: string;
                /** Include vectors in the search results */
                include_vector?: boolean;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetDocumentOutput"];
                };
            };
        };
    };
    /**
     * Delete a document by its `_id` and remove it from the dataset.
     *
     * Support for deleting many documents at once is available through [`/bulk_delete`](/reference/bulk_delete_api_datasets__dataset_id__documents_bulk_delete_post).
     *
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    DeleteDocument: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteDocumentOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteDocumentInput"];
            };
        };
    };
    /**
     * Retreives settings for dataset.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetDatasetSettings: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetDatasetSettingsOutput"];
                };
            };
        };
    };
    /**
     * Add and overwrite settings about a dataset. Notably description, data source, etc.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    UpsertDatasetSettings: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpsertDatasetSettingsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpsertDatasetSettingsInput"];
            };
        };
    };
    /**
     * Retreives metadata about a dataset. Notably description, data source, etc
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetDatasetMetadata: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetDatasetMetadataOutput"];
                };
            };
        };
    };
    /**
     * Retreives metadata about a dataset. Notably description, data source, etc
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    UpsertDatasetMetadata: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpsertDatasetMetadataOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpsertDatasetMetadataInput"];
            };
        };
    };
    /**
     * Update an existing document by providing its `_id` and an object containing the new field values.
     *
     * This method supports a partial update so you may add new fields or update existing ones. It will not overwrite the entire existing document.
     *
     * If the provided `_id` has no matching document in the dataset, a new document will be created. Likewise, if the provided `dataset_id` does not match a dataset in your project a new one will be created.
     *
     * Support for updating many documents at once with a filter condition is available through [`/update_where`](/reference/updatewhere).
     *
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    Update: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateInput"];
            };
        };
    };
    /**
     * Delete fields in a document in a dataset by its id.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    DeleteDocumentFields: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteDocumentFieldsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteDocumentFieldsInput"];
            };
        };
    };
    /**
     * Edits documents by providing a key value pair of fields you are adding or changing, make sure to include the "_id" in the documents.
     *
     *     If updating many items in a short timespan, please use BulkUpdate.
     *
     *     Every update api call temporarily locks and updates the schema, so will slow down with many updates in quick succession.
     *
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    BulkUpdate: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["BulkUpdateOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["BulkUpdateInput"];
            };
        };
    };
    /**
     * Update fields in all existing documents that match a filter condition.
     *
     * This method supports a partial update so you may add new fields or update only those you need. Other fields will remain unaffected.
     *
     * If the provided `dataset_id` does not match a dataset in your project, a new one will be created.
     *
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    UpdateWhere: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateWhereOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateWhereInput"];
            };
        };
    };
    /**
     * Append tags to tag field.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    AppendTags: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["AppendTagsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["AppendTagsInput"];
            };
        };
    };
    /**
     * Delete tags from tag field by tag value. tag field can be in format ["cat"], or [{"label":"cat"}]
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    DeleteTags: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteTagsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteTagsInput"];
            };
        };
    };
    /**
     * Delete tags from tag field by tag value. tag field can be in format ["cat"], or [{"label":"cat"}]. tags_to_merge should look like {"dog":"pet","cat":"pet"}
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    MergeTags: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["MergeTagsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["MergeTagsInput"];
            };
        };
    };
    /**
     * Perform all tagging operations on a field in one payload.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    EditTags: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["EditTagsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["EditTagsInput"];
            };
        };
    };
    /**
     * Takes a high level aggregation of every field, return their unique values and frequencies. This is used to help create the filter bar for search.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    ListFacets: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListFacetsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListFacetsInput"];
            };
        };
    };
    /**
     * Retrieve documents with filters.
     * afterId is provided to retrieve even more documents. Loop through it to retrieve all documents in the database.
     * Filter is used to retrieve documents that match the conditions set in a filter query.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    GetWhere: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetWhereOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["GetWhereInput"];
            };
        };
    };
    /**
     * Retrieve documents with filters.
     * afterId is provided to retrieve even more documents. Loop through it to retrieve all documents in the database.
     * Filter is used to retrieve documents that match the conditions set in a filter query.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    PaginateDocuments: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["PaginateDocumentsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["PaginateDocumentsInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read",
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    CreateDatasetSummary: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateDatasetSummaryOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateDatasetSummaryInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    ListDatasetSummaryHistory: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
            query: {
                filters?: (Partial<{
                    /** @description Match where document[field] is in value list. */
                    match?: {
                        /** @description If matching on text, match even if there are extra words / case insensitivity */
                        fuzzy?: boolean;
                        /** @description Field to match on. */
                        key?: string;
                        /** @description Field to match on. */
                        field?: string;
                        /** @description Can be either a single item or a list of items to match on. */
                        value: string | boolean | number | unknown[];
                    };
                    /** @description Match where document._id is in value list. */
                    matchIds?: {
                        /** @description Can be either a single item or a list of items to match on. */
                        value: string | boolean | number | unknown[];
                    };
                    /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                    range?: {
                        /** @description Field to match on. */
                        key?: string;
                        /** @description Field to match on. */
                        field?: string;
                        greaterThan?: unknown;
                        lessThan?: unknown;
                        greaterThanOrEqualTo?: unknown;
                        lessThanOrEqualTo?: unknown;
                    };
                    /**
                     * @description
                     * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                     *
                     * Possible matching patterns:
                     *
                     * * - documents where the field has any value
                     * ? - documents where a single character from the field matches the provided values
                     *
                     * Example:
                     *
                     * {
                     *   wildcard: {
                     *      field: "title",
                     *      value: [ "Avenger*" ]
                     *   }
                     * }
                     */
                    wildcard?: {
                        /** @description Field to match on. */
                        key?: string;
                        /** @description Field to match on. */
                        field?: string;
                        /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                        value: string | string[];
                    };
                    /** @description hybrid search query that must reach a certain value to filter */
                    search?: {
                        /**
                         * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                         *
                         *     "tele" matches "Television", "This television is an excellent product…"
                         *
                         *     Example: "tele"
                         */
                        query?: string;
                        /** @description Configuration for traditional search query. */
                        queryConfig?: {
                            /**
                             * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                             * @default 1
                             */
                            weight?: number;
                            /**
                             * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                             *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                             * @default 1
                             */
                            sentenceWeight?: number;
                            /**
                             * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                             *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                             * @default 1
                             */
                            fuzzyWeight?: number;
                        };
                        /**
                         * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                         *
                         *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                         *
                         *     It can be an object or a list of objects.
                         *
                         *
                         *
                         *     Example payloads:
                         *
                         *     {"field":"animaltype_vector_","query":"kitten"}
                         *
                         *     [
                         *
                         *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                         *
                         *     ]
                         */
                        vectorSearchQuery?: {
                            /** @description Vector name to search on. For example, title_vector_ */
                            field: string;
                            /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                            query?: string;
                            /**
                             * @description Model name to generate the vector with.
                             * @default text
                             */
                            model?: string;
                            /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                            model_url?: string;
                            /**
                             * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                             * @default 1
                             */
                            weight?: number;
                            /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                            vector?: number[];
                            chunkConfig?: {
                                chunkField: string;
                                page?: number;
                                pageSize?: number;
                            };
                        } | {
                            /** @description Vector name to search on. For example, title_vector_ */
                            field: string;
                            /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                            query?: string;
                            /**
                             * @description Model name to generate the vector with.
                             * @default text
                             */
                            model?: string;
                            /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                            model_url?: string;
                            /**
                             * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                             * @default 1
                             */
                            weight?: number;
                            /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                            vector?: number[];
                            chunkConfig?: {
                                chunkField: string;
                                page?: number;
                                pageSize?: number;
                            };
                        }[];
                        realtimeClustering?: {
                            /** @default 4 */
                            nClusters?: number;
                            /** @default kmeans-realtime-4 */
                            alias?: string;
                            vectorField: string;
                            /** @default _cluster_.{vector_field}.{alias} */
                            outputField?: string;
                        };
                        /**
                         * @description A list of fields to search using the "query" parameter.
                         *
                         *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                         *
                         *     Default behaviour is to search all fields.
                         *
                         *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                         */
                        fieldsToSearch?: (string | {
                            /** @description Field name to search. */
                            key?: string;
                            /** @description Field name to search. */
                            field?: string;
                            /**
                             * @description Multiply the relevance contribution of a specific field when using traditional search.
                             * @default 1
                             */
                            weight?: number;
                            /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                            chunkField?: string;
                        })[];
                        /**
                         * @description Only return documents with a _relevance above this threshold.
                         *
                         *     Example: 0.3
                         */
                        minimumRelevance: number;
                    };
                    /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                    fieldExists?: {
                        /** @description Field that must exist. */
                        key?: string;
                        /** @description Field that must exist. */
                        field?: string;
                    };
                    /** @description Match documents where doc[field] % module == value */
                    matchModulo?: {
                        /** @default _id */
                        field?: string;
                        modulo: number;
                        value: number;
                    };
                    /** @description Filter down to one document for each value of selected field. */
                    dedupeByValue?: {
                        /** @description Field to filter on. */
                        field: string;
                    };
                    /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                    selfreference?: {
                        /** @description First field in comparison. */
                        a: string;
                        /** @description Second field in comparison. */
                        b: string;
                        /** @description Operator used to compare a and b. */
                        operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                    };
                    /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                    wordCount?: {
                        /** @description Field to match on. */
                        field: string;
                        /** @description Minimum word count. */
                        greaterThan?: number;
                        /** @description Maximum word count. */
                        lessThan?: number;
                    };
                    /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                    characterCount?: {
                        /** @description Field to match on. */
                        field: string;
                        /** @description Minimum character count. */
                        greaterThan?: number;
                        /** @description Maximum character count. */
                        lessThan?: number;
                    };
                    /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                    or?: (definitions["simpleSearchAndFlatFilterItem"][] | definitions["simpleSearchAndFlatFilterItem"])[];
                    /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                    not?: definitions["simpleSearchAndFlatFilterItem"][] | definitions["simpleSearchAndFlatFilterItem"];
                    /** @description Filter based on data within a _chunk_ field. */
                    chunk?: {
                        /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                        path: string;
                        filters: definitions["simpleSearchAndFlatFilterItem"][];
                    };
                }> & Partial<{
                    strict?: "must" | "should" | "must_or";
                    condition?: string;
                    field?: string;
                    filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                    condition_value?: unknown;
                    fuzzy?: number;
                    join?: boolean;
                }>)[];
                /** Fields to include in the search results, empty array/list means all fields. */
                select_fields?: string[];
                /** Size of each page of results. */
                page_size?: number;
                /** Size of each page of results. */
                page?: number;
                /** Fields to sort by. For each field, sort by descending or ascending. If you are using descending by datetime, it will get the most recent ones. */
                sort?: (string | {
                    [key: string]: "asc" | "desc";
                })[];
                /** Include vectors in the search results. */
                include_vector?: boolean;
                /** (NOT IMPLEMENTED - not functional) If true, retrieves documents randomly. */
                is_random?: boolean;
                /** (NOT IMPLEMENTED - not functional)Random Seed for retrieving random documents. */
                random_state?: number;
                /** Retrieve items after these sort values in the sort order. */
                after_id?: unknown[];
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListDatasetSummaryHistoryOutput"];
                };
            };
        };
    };
    /**
     * Retrieve documents by their IDs ("_id" field). This will retrieve the documents faster than a filter applied on the "_id" field. For single id lookup version of this request use /datasets/{dataset_id}/documents/get.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    BulkGetDocuments: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["BulkGetDocumentsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["BulkGetDocumentsInput"];
            };
        };
    };
    /**
     * Delete a list of documents by their IDs. For deleting a single document refer to /datasets/{dataset_id}/documents/delete or deleting documents by filters refer to /datasets/{dataset_id}/documents/delete_where.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    BulkDeleteDocuments: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["BulkDeleteDocumentsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["BulkDeleteDocumentsInput"];
            };
        };
    };
    /**
     * Retrieve documents with filters.
     * afterId is provided to retrieve even more documents. Loop through it to retrieve all documents in the database.
     * Filter is used to retrieve documents that match the conditions set in a filter query.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    ListDocuments: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
            query: {
                filters?: (Partial<{
                    /** @description Match where document[field] is in value list. */
                    match?: {
                        /** @description If matching on text, match even if there are extra words / case insensitivity */
                        fuzzy?: boolean;
                        /** @description Field to match on. */
                        key?: string;
                        /** @description Field to match on. */
                        field?: string;
                        /** @description Can be either a single item or a list of items to match on. */
                        value: string | boolean | number | unknown[];
                    };
                    /** @description Match where document._id is in value list. */
                    matchIds?: {
                        /** @description Can be either a single item or a list of items to match on. */
                        value: string | boolean | number | unknown[];
                    };
                    /** @description Match documents where greaterThan < document[field] < lessThan. Supports numbers and date strings. */
                    range?: {
                        /** @description Field to match on. */
                        key?: string;
                        /** @description Field to match on. */
                        field?: string;
                        greaterThan?: unknown;
                        lessThan?: unknown;
                        greaterThanOrEqualTo?: unknown;
                        lessThanOrEqualTo?: unknown;
                    };
                    /**
                     * @description
                     * Match documents where the field either contains the value as a substring, or matches a provided matching pattern.
                     *
                     * Possible matching patterns:
                     *
                     * * - documents where the field has any value
                     * ? - documents where a single character from the field matches the provided values
                     *
                     * Example:
                     *
                     * {
                     *   wildcard: {
                     *      field: "title",
                     *      value: [ "Avenger*" ]
                     *   }
                     * }
                     */
                    wildcard?: {
                        /** @description Field to match on. */
                        key?: string;
                        /** @description Field to match on. */
                        field?: string;
                        /** @description single string or array of valid wildcard strings to match on, for example ['tele*'] */
                        value: string | string[];
                    };
                    /** @description hybrid search query that must reach a certain value to filter */
                    search?: {
                        /**
                         * @description Search for documents that contain this query string in your dataset. Use fieldsToSearch parameter to restrict which fields are searched.
                         *
                         *     "tele" matches "Television", "This television is an excellent product…"
                         *
                         *     Example: "tele"
                         */
                        query?: string;
                        /** @description Configuration for traditional search query. */
                        queryConfig?: {
                            /**
                             * @description Increases or decreases the impact of all traditional search matching when calculating a documents _relevance.
                             * @default 1
                             */
                            weight?: number;
                            /**
                             * @description Increases or decreases the impact of sentence matching when calculating a documents _relevance.
                             *         For example, with query "A fox jumped", with high sentence weight, 'A fox jumped over the' would match before 'jumped a fox over fox'
                             * @default 1
                             */
                            sentenceWeight?: number;
                            /**
                             * @description Increases or decreases the impact of fuzzy matching when calculating a documents _relevance.
                             *         For example, with query "rain", with 0 fuzzy weight, 'brain' would not match.
                             * @default 1
                             */
                            fuzzyWeight?: number;
                        };
                        /**
                         * @description Prioritise the result list of documents based on semantic similarity to "query" provided here.
                         *
                         *     For example if field "animaltype_vector_" contains encoded vector values for "cat", lion, "dog","bird", and "query" is set to "kitten", documents with "cat", "lion" will be returned first in the results list.
                         *
                         *     It can be an object or a list of objects.
                         *
                         *
                         *
                         *     Example payloads:
                         *
                         *     {"field":"animaltype_vector_","query":"kitten"}
                         *
                         *     [
                         *
                         *     {"field":"animaltype_vector_","query":"kitten","weight":1","model":"text"}, {"field":"animaltype_vector_","query":"https://www.dogimage.com/dogimage.png","model":"image","weight":2}
                         *
                         *     ]
                         */
                        vectorSearchQuery?: {
                            /** @description Vector name to search on. For example, title_vector_ */
                            field: string;
                            /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                            query?: string;
                            /**
                             * @description Model name to generate the vector with.
                             * @default text
                             */
                            model?: string;
                            /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                            model_url?: string;
                            /**
                             * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                             * @default 1
                             */
                            weight?: number;
                            /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                            vector?: number[];
                            chunkConfig?: {
                                chunkField: string;
                                page?: number;
                                pageSize?: number;
                            };
                        } | {
                            /** @description Vector name to search on. For example, title_vector_ */
                            field: string;
                            /** @description Query to transform to a vector and then search with. Default to query in the root body if not provided. */
                            query?: string;
                            /**
                             * @description Model name to generate the vector with.
                             * @default text
                             */
                            model?: string;
                            /** @description Model url to use for encoding. If model and model_url are both set, model_url will override it. */
                            model_url?: string;
                            /**
                             * @description Increases or decreases the impact of this vector fields match on documents relevance score.
                             * @default 1
                             */
                            weight?: number;
                            /** @description Instead of generating a vector by vectorising the query, use the vector provided here for vector search. */
                            vector?: number[];
                            chunkConfig?: {
                                chunkField: string;
                                page?: number;
                                pageSize?: number;
                            };
                        }[];
                        realtimeClustering?: {
                            /** @default 4 */
                            nClusters?: number;
                            /** @default kmeans-realtime-4 */
                            alias?: string;
                            vectorField: string;
                            /** @default _cluster_.{vector_field}.{alias} */
                            outputField?: string;
                        };
                        /**
                         * @description A list of fields to search using the "query" parameter.
                         *
                         *     Each item can be field name as a string, or an object with 'field' as field name and optional parameters such as field weight.
                         *
                         *     Default behaviour is to search all fields.
                         *
                         *     Example: ["name",{"field":"favourite_color","weight":0.2}]
                         */
                        fieldsToSearch?: (string | {
                            /** @description Field name to search. */
                            key?: string;
                            /** @description Field name to search. */
                            field?: string;
                            /**
                             * @description Multiply the relevance contribution of a specific field when using traditional search.
                             * @default 1
                             */
                            weight?: number;
                            /** @description Enabled text search on chunk fields. Scoring will take the max similarity of any subitem within the chunk. */
                            chunkField?: string;
                        })[];
                        /**
                         * @description Only return documents with a _relevance above this threshold.
                         *
                         *     Example: 0.3
                         */
                        minimumRelevance: number;
                    };
                    /** @description Match documents where field specified by 'field' exists in the document. for nested fields, use syntax: a.b.c */
                    fieldExists?: {
                        /** @description Field that must exist. */
                        key?: string;
                        /** @description Field that must exist. */
                        field?: string;
                    };
                    /** @description Match documents where doc[field] % module == value */
                    matchModulo?: {
                        /** @default _id */
                        field?: string;
                        modulo: number;
                        value: number;
                    };
                    /** @description Filter down to one document for each value of selected field. */
                    dedupeByValue?: {
                        /** @description Field to filter on. */
                        field: string;
                    };
                    /** @description Match documents where document[a] <=/>=/</>/==/!=/stringEquals document[b]. Use stringEquals to compare strings. */
                    selfreference?: {
                        /** @description First field in comparison. */
                        a: string;
                        /** @description Second field in comparison. */
                        b: string;
                        /** @description Operator used to compare a and b. */
                        operation: "<=" | ">=" | "<" | ">" | "==" | "!=" | "stringEquals";
                    };
                    /** @description Match documents where greaterThan < wordCount(document[field]) < lessThan. */
                    wordCount?: {
                        /** @description Field to match on. */
                        field: string;
                        /** @description Minimum word count. */
                        greaterThan?: number;
                        /** @description Maximum word count. */
                        lessThan?: number;
                    };
                    /** @description Match documents where greaterThan < characterCount(document[field]) < lessThan. */
                    characterCount?: {
                        /** @description Field to match on. */
                        field: string;
                        /** @description Minimum character count. */
                        greaterThan?: number;
                        /** @description Maximum character count. */
                        lessThan?: number;
                    };
                    /** @description Used to perform a logical OR of filters. each element of the OR list can itself be a list  to perform a nested AND. {or:[[A,B],C]} is equivalent to (A AND B) OR C */
                    or?: (definitions["simpleSearchAndFlatFilterItem"][] | definitions["simpleSearchAndFlatFilterItem"])[];
                    /** @description Used to perform NOT filter. Can be a single filter or a list of filters to perform a !(AND). {not:[A,B]} is equivalent to !(A AND B) */
                    not?: definitions["simpleSearchAndFlatFilterItem"][] | definitions["simpleSearchAndFlatFilterItem"];
                    /** @description Filter based on data within a _chunk_ field. */
                    chunk?: {
                        /** @description The path of the chunk field to filter on. For example: description_sentences_chunk_ */
                        path: string;
                        filters: definitions["simpleSearchAndFlatFilterItem"][];
                    };
                }> & Partial<{
                    strict?: "must" | "should" | "must_or";
                    condition?: string;
                    field?: string;
                    filter_type?: "text_match" | "word_match" | "term" | "terms" | "text" | "texts" | "match" | "contains" | "substring" | "class" | "category" | "exact_match" | "classes" | "categories" | "exists" | "traditional" | "fuzzy" | "regexp" | "ids" | "date" | "numeric" | "search" | "or" | "word_count" | "character_count" | "dedupe_by_value";
                    condition_value?: unknown;
                    fuzzy?: number;
                    join?: boolean;
                }>)[];
                /** Fields to include in the search results, empty array/list means all fields. */
                select_fields?: string[];
                /** Size of each page of results. */
                page_size?: number;
                /** Size of each page of results. */
                page?: number;
                /** Fields to sort by. For each field, sort by descending or ascending. If you are using descending by datetime, it will get the most recent ones. */
                sort?: (string | {
                    [key: string]: "asc" | "desc";
                })[];
                /** Include vectors in the search results. */
                include_vector?: boolean;
                /** (NOT IMPLEMENTED - not functional) If true, retrieves documents randomly. */
                is_random?: boolean;
                /** (NOT IMPLEMENTED - not functional)Random Seed for retrieving random documents. */
                random_state?: number;
                /** Retrieve items after these sort values in the sort order. */
                after_id?: unknown[];
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListDocumentsOutput"];
                };
            };
        };
    };
    /**
     * Delete documents that match on a filters. At least one filter must be provided.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    DeleteWhere: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteWhereOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteWhereInput"];
            };
        };
    };
    /**
     * SimpleSearch is an easy way to use vector search and traditional text matching to search your dataset. It also supports filtering, sorting and aggregating results.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    SimpleSearchPost: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["SimpleSearchPostOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["SimpleSearchPostInput"];
            };
        };
    };
    /**
     * SimpleSearch is an easy way to use vector search and traditional text matching to search your dataset. It also supports filtering, sorting and aggregating results.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    Search: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["SearchOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["SearchInput"];
            };
        };
    };
    /**
     * Recommend documents similar to specific documents. Specify which vector field must be used for recommendation using  the documentsToRecommend property.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    Recommend: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["RecommendOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["RecommendInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   },
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "body": "new_dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    CloneDataset: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CloneDatasetOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CloneDatasetInput"];
            };
        };
    };
    /**
     * Predict using KNN regression.
     *
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:read"
     *     ],
     *     "datasets": [
     *       {
     *         "body": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    PredictKNNRegression: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["PredictKNNRegressionOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["PredictKNNRegressionInput"];
            };
        };
    };
    /**
     * Predict using KNN regression from search results.
     *
     * ### Required permissions
     * > []
     */
    PredictKNNFromResults: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["PredictKNNFromResultsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["PredictKNNFromResultsInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    BiasEvaluation: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["BiasEvaluationOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["BiasEvaluationInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    Vectorize: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["VectorizeOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["VectorizeInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    VectorizeAndInsert: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["VectorizeAndInsertOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["VectorizeAndInsertInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > [
     *   {
     *     "actions": [
     *       "datasets:write"
     *     ],
     *     "datasets": [
     *       {
     *         "params": "dataset_id"
     *       }
     *     ]
     *   }
     * ]
     */
    VectorizeField: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["VectorizeFieldOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["VectorizeFieldInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    CreateImageFromPrompt: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateImageFromPromptOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateImageFromPromptInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    DeleteFieldChildren: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
                /** ID of fieldchildren */
                fieldchildren_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteFieldChildrenOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteFieldChildrenInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    ListFieldChildrens: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListFieldChildrensOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListFieldChildrensInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    UpdateFieldChildren: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
                /** ID of fieldchildren */
                fieldchildren_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateFieldChildrenOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateFieldChildrenInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    DeleteFavouriteWorkflow: {
        parameters: {
            path: {
                /** ID of favouriteworkflow */
                favouriteworkflow_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteFavouriteWorkflowOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteFavouriteWorkflowInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    ListFavouriteWorkflows: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListFavouriteWorkflowsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListFavouriteWorkflowsInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    UpdateFavouriteWorkflow: {
        parameters: {
            path: {
                /** ID of favouriteworkflow */
                favouriteworkflow_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateFavouriteWorkflowOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateFavouriteWorkflowInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    DeleteSavedFilter: {
        parameters: {
            path: {
                /** ID of savedfilter */
                savedfilter_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteSavedFilterOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteSavedFilterInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    ListSavedFilters: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListSavedFiltersOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListSavedFiltersInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    UpdateSavedFilter: {
        parameters: {
            path: {
                /** ID of savedfilter */
                savedfilter_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateSavedFilterOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateSavedFilterInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    GetSavedFilter: {
        parameters: {
            path: {
                /** ID of savedfilter */
                savedfilter_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetSavedFilterOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    CreateSavedFilter: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateSavedFilterOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateSavedFilterInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    DeleteComponent: {
        parameters: {
            path: {
                /** ID of component */
                component_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteComponentOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteComponentInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    ListComponents: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListComponentsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListComponentsInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    UpdateComponent: {
        parameters: {
            path: {
                /** ID of component */
                component_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateComponentOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateComponentInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    GetComponent: {
        parameters: {
            path: {
                /** ID of component */
                component_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetComponentOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    CreateComponent: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateComponentOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateComponentInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    ListDatasetEditorHistorys: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListDatasetEditorHistorysOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListDatasetEditorHistorysInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    CreateDatasetEditorHistory: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateDatasetEditorHistoryOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateDatasetEditorHistoryInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    ListDatasetEditorConfigurations: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListDatasetEditorConfigurationsOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListDatasetEditorConfigurationsInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    CreateDatasetEditorConfiguration: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateDatasetEditorConfigurationOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateDatasetEditorConfigurationInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    UpdateDatasetEditorConfiguration: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
                /** ID of dataseteditorconfiguration */
                dataseteditorconfiguration_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateDatasetEditorConfigurationOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateDatasetEditorConfigurationInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    DeleteDatasetEditorConfiguration: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
                /** ID of dataseteditorconfiguration */
                dataseteditorconfiguration_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteDatasetEditorConfigurationOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteDatasetEditorConfigurationInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    GetDatasetEditorConfiguration: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
                /** ID of dataseteditorconfiguration */
                dataseteditorconfiguration_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetDatasetEditorConfigurationOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    ListKeyphrases: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
                /** ID of field */
                field: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListKeyphrasesOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListKeyphrasesInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    UpdateKeyphrase: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
                /** ID of field */
                field: string;
                /** ID of keyphrase */
                keyphrase_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateKeyphraseOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateKeyphraseInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    DeleteKeyphrase: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
                /** ID of field */
                field: string;
                /** ID of keyphrase */
                keyphrase_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteKeyphraseOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteKeyphraseInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    GetKeyphrase: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
                /** ID of field */
                field: string;
                /** ID of keyphrase */
                keyphrase_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetKeyphraseOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    BulkUpdateKeyphrases: {
        parameters: {
            path: {
                /** ID of dataset */
                dataset_id: string;
                /** ID of field */
                field: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["BulkUpdateKeyphrasesOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["BulkUpdateKeyphrasesInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    ListTaxonomys: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["ListTaxonomysOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["ListTaxonomysInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    UpdateTaxonomy: {
        parameters: {
            path: {
                /** ID of taxonomy */
                taxonomy_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["UpdateTaxonomyOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["UpdateTaxonomyInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    DeleteTaxonomy: {
        parameters: {
            path: {
                /** ID of taxonomy */
                taxonomy_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["DeleteTaxonomyOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["DeleteTaxonomyInput"];
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    GetTaxonomy: {
        parameters: {
            path: {
                /** ID of taxonomy */
                taxonomy_id: string;
            };
        };
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["GetTaxonomyOutput"];
                };
            };
        };
    };
    /**
     * ### Required permissions
     * > []
     */
    CreateTaxonomy: {
        parameters: {};
        responses: {
            /** successful operation */
            200: {
                content: {
                    "application/json": components["schemas"]["CreateTaxonomyOutput"];
                };
            };
        };
        requestBody: {
            content: {
                "application/json": components["schemas"]["CreateTaxonomyInput"];
            };
        };
    };
}
export interface external {
}
export {};