{"version":3,"file":"azure_cosmosdb_nosql.cjs","names":["VectorStore","CosmosClient","DefaultAzureCredential","Document"],"sources":["../src/azure_cosmosdb_nosql.ts"],"sourcesContent":["import type { EmbeddingsInterface } from \"@langchain/core/embeddings\";\nimport {\n MaxMarginalRelevanceSearchOptions,\n VectorStore,\n} from \"@langchain/core/vectorstores\";\nimport { Document, DocumentInterface } from \"@langchain/core/documents\";\nimport { maximalMarginalRelevance } from \"@langchain/core/utils/math\";\nimport { getEnvironmentVariable } from \"@langchain/core/utils/env\";\nimport {\n Container,\n ContainerRequest,\n CosmosClient,\n CosmosClientOptions,\n DatabaseRequest,\n FullTextPolicy,\n IndexingPolicy,\n SqlParameter,\n SqlQuerySpec,\n VectorEmbedding,\n VectorEmbeddingPolicy,\n VectorIndex,\n} from \"@azure/cosmos\";\nimport { DefaultAzureCredential, TokenCredential } from \"@azure/identity\";\n\n/** Azure Cosmos DB for NoSQL search types. */\nexport const AzureCosmosDBNoSQLSearchType = {\n /** Vector similarity search. */\n Vector: \"vector\",\n /** Vector search with score threshold filtering. */\n VectorScoreThreshold: \"vector_score_threshold\",\n /** Full-text search only (preview feature). */\n FullTextSearch: \"full_text_search\",\n /** Full-text search with BM25 ranking (preview feature). */\n FullTextRanking: \"full_text_ranking\",\n /** Hybrid search combining vector and full-text search with RRF (preview feature). */\n Hybrid: \"hybrid\",\n /** Hybrid search with score threshold filtering (preview feature). */\n HybridScoreThreshold: \"hybrid_score_threshold\",\n} as const;\n\n/** Azure Cosmos DB for NoSQL search type. */\nexport type AzureCosmosDBNoSQLSearchType =\n (typeof AzureCosmosDBNoSQLSearchType)[keyof typeof AzureCosmosDBNoSQLSearchType];\n\n/** Re-export FullTextPolicy and VectorIndex types from @azure/cosmos for convenience. */\nexport type { FullTextPolicy, VectorIndex } from \"@azure/cosmos\";\n\n/**\n * Full-text rank filter item for full-text ranking queries.\n * Each item specifies a field to search and the search text.\n */\nexport interface AzureCosmosDBNoSQLFullTextRankFilter {\n /** The field to search. */\n searchField: string;\n /** The search text. */\n searchText: string;\n}\n\n/**\n * Projection mapping for custom field selection.\n * Maps field names to their aliases in the query results.\n */\nexport type ProjectionMapping = Record;\n\n/** Azure Cosmos DB for NoSQL query filter. */\nexport type AzureCosmosDBNoSQLQueryFilter = string | SqlQuerySpec;\n\n/** Azure Cosmos DB for NoSQL filter type. */\nexport type AzureCosmosDBNoSQLFilterType = {\n /**\n * SQL filter clause to add to the search query.\n * @example 'WHERE c.category = \"cars\"'\n */\n filterClause?: AzureCosmosDBNoSQLQueryFilter;\n /** Determines whether or not to include the embeddings in the search results. */\n includeEmbeddings?: boolean;\n /** Search type override for this query. Takes precedence over the default search type. */\n searchType?: AzureCosmosDBNoSQLSearchType;\n /**\n * Full-text rank filter for full-text ranking queries.\n * Each item specifies a field to search and the search text.\n */\n fullTextRankFilter?: AzureCosmosDBNoSQLFullTextRankFilter[];\n /**\n * Projection mapping for custom field selection.\n * Maps field names to their paths in the document.\n */\n projectionMapping?: ProjectionMapping;\n /**\n * Offset and limit clause for pagination.\n * @example 'OFFSET 10 LIMIT 20'\n */\n offsetLimit?: string;\n /**\n * Weights for hybrid search RRF (Reciprocal Rank Fusion).\n * Typically two values: [vectorWeight, fullTextWeight].\n */\n weights?: number[];\n /**\n * Score threshold for filtering results.\n * Only results with a score >= threshold are returned.\n */\n threshold?: number;\n};\n\n/** Azure Cosmos DB for NoSQL Delete Parameters. */\nexport type AzureCosmosDBNoSqlDeleteParams = {\n /** List of IDs for the documents to be removed. */\n readonly ids?: string | string[];\n /** SQL query to select the documents to be removed. */\n readonly filter?: AzureCosmosDBNoSQLQueryFilter;\n};\n\n/** Azure Cosmos DB for NoSQL database creation options. */\nexport type AzureCosmosDBNoSqlCreateDatabaseOptions = Partial<\n Omit\n>;\n/** Azure Cosmos DB for NoSQL container creation options. */\nexport type AzureCosmosDBNoSqlCreateContainerOptions = Partial<\n Omit\n>;\n\n/**\n * Initialization options for the Azure CosmosDB for NoSQL database and container.\n *\n * Note that if you provide multiple vector embeddings in the vectorEmbeddingPolicy,\n * the first one will be used for creating documents and searching.\n */\nexport interface AzureCosmosDBNoSQLInitOptions {\n readonly vectorEmbeddingPolicy?: VectorEmbeddingPolicy;\n readonly indexingPolicy?: IndexingPolicy;\n /**\n * Full-text policy for the container (preview feature).\n * Required when fullTextSearchEnabled is true.\n */\n readonly fullTextPolicy?: FullTextPolicy;\n readonly createContainerOptions?: AzureCosmosDBNoSqlCreateContainerOptions;\n readonly createDatabaseOptions?: AzureCosmosDBNoSqlCreateDatabaseOptions;\n}\n\n/**\n * Configuration options for the `AzureCosmosDBNoSQLVectorStore` constructor.\n */\nexport interface AzureCosmosDBNoSQLConfig extends AzureCosmosDBNoSQLInitOptions {\n /** Pre-configured CosmosClient instance. If provided, connectionString and endpoint are ignored. */\n readonly client?: CosmosClient;\n /** Cosmos DB connection string. */\n readonly connectionString?: string;\n /** Cosmos DB endpoint URL (used with credentials for Azure AD authentication). */\n readonly endpoint?: string;\n /** Azure credential for authentication (defaults to DefaultAzureCredential). */\n readonly credentials?: TokenCredential;\n /** Database name. Defaults to \"vectorSearchDB\". */\n readonly databaseName?: string;\n /** Container name. Defaults to \"vectorSearchContainer\". */\n readonly containerName?: string;\n /** Document field name for the text content. Defaults to \"text\". */\n readonly textKey?: string;\n /** Document field name for the metadata. Defaults to \"metadata\". */\n readonly metadataKey?: string;\n /**\n * Enable full-text search capabilities (preview feature).\n * When enabled, fullTextPolicy and appropriate indexingPolicy must be configured.\n */\n readonly fullTextSearchEnabled?: boolean;\n /** Table alias used in Cosmos DB SQL queries. Defaults to \"c\". */\n readonly tableAlias?: string;\n /**\n * Default search type to use when no search type is specified in the filter.\n * Defaults to \"vector\".\n */\n readonly defaultSearchType?: AzureCosmosDBNoSQLSearchType;\n}\n\nconst USER_AGENT_SUFFIX = \"langchainjs-cdbnosql-vectorstore-javascript\";\n\n/**\n * Azure Cosmos DB for NoSQL vector store.\n * To use this, you should have both:\n * - the `@azure/cosmos` NPM package installed\n * - a connection string associated with a NoSQL instance\n *\n * You do not need to create a database or container, it will be created\n * automatically.\n *\n * @example\n * ```typescript\n * const vectorStore = new AzureCosmosDBNoSQLVectorStore(\n * new OpenAIEmbeddings(),\n * {\n * databaseName: \"mydb\",\n * containerName: \"vectors\",\n * }\n * );\n * await vectorStore.addDocuments(docs);\n * const results = await vectorStore.similaritySearch(\"query\", 4);\n * ```\n */\nexport class AzureCosmosDBNoSQLVectorStore extends VectorStore {\n declare FilterType: AzureCosmosDBNoSQLFilterType;\n\n get lc_secrets(): { [key: string]: string } {\n return {\n connectionString: \"AZURE_COSMOSDB_NOSQL_CONNECTION_STRING\",\n };\n }\n\n private initPromise?: Promise;\n\n private readonly client: CosmosClient;\n\n private container: Container;\n\n private readonly textKey: string;\n\n private readonly metadataKey: string;\n\n private embeddingKey: string;\n\n private readonly fullTextSearchEnabled: boolean;\n\n private readonly tableAlias: string;\n\n private readonly fullTextPolicy?: FullTextPolicy;\n\n private readonly defaultSearchType: AzureCosmosDBNoSQLSearchType;\n\n /**\n * Initializes the AzureCosmosDBNoSQLVectorStore.\n * Connects the client to the database and creates the container if needed.\n * @returns A promise that resolves when the AzureCosmosDBNoSQLVectorStore has been initialized.\n */\n initialize: () => Promise;\n\n _vectorstoreType(): string {\n return \"azure_cosmosdb_nosql\";\n }\n\n constructor(\n embeddings: EmbeddingsInterface,\n dbConfig: AzureCosmosDBNoSQLConfig\n ) {\n super(embeddings, dbConfig);\n\n const connectionString =\n dbConfig.connectionString ??\n getEnvironmentVariable(\"AZURE_COSMOSDB_NOSQL_CONNECTION_STRING\");\n\n const endpoint =\n dbConfig.endpoint ??\n getEnvironmentVariable(\"AZURE_COSMOSDB_NOSQL_ENDPOINT\");\n\n if (!dbConfig.client && !connectionString && !endpoint) {\n throw new Error(\n \"AzureCosmosDBNoSQLVectorStore client, connection string or endpoint must be set.\"\n );\n }\n\n if (!dbConfig.client) {\n if (connectionString) {\n this.client = new CosmosClient({\n connectionString,\n userAgentSuffix: USER_AGENT_SUFFIX,\n });\n } else {\n // Use managed identity\n this.client = new CosmosClient({\n endpoint,\n aadCredentials: dbConfig.credentials ?? new DefaultAzureCredential(),\n userAgentSuffix: USER_AGENT_SUFFIX,\n } as CosmosClientOptions);\n }\n }\n\n const client = dbConfig.client || this.client;\n const databaseName = dbConfig.databaseName ?? \"vectorSearchDB\";\n const containerName = dbConfig.containerName ?? \"vectorSearchContainer\";\n this.textKey = dbConfig.textKey ?? \"text\";\n this.metadataKey = dbConfig.metadataKey ?? \"metadata\";\n this.fullTextSearchEnabled = dbConfig.fullTextSearchEnabled ?? false;\n this.tableAlias = dbConfig.tableAlias ?? \"c\";\n this.fullTextPolicy = dbConfig.fullTextPolicy;\n this.defaultSearchType =\n dbConfig.defaultSearchType ?? AzureCosmosDBNoSQLSearchType.Vector;\n\n const vectorEmbeddingPolicy = dbConfig.vectorEmbeddingPolicy ?? {\n vectorEmbeddings: [],\n };\n const indexingPolicy = dbConfig.indexingPolicy ?? {\n indexingMode: \"consistent\",\n automatic: true,\n includedPaths: [{ path: \"/*\" }],\n excludedPaths: [{ path: \"/_etag/?\" }],\n };\n\n if (vectorEmbeddingPolicy.vectorEmbeddings.length === 0) {\n vectorEmbeddingPolicy.vectorEmbeddings = [\n {\n path: \"/vector\",\n dataType: \"float32\",\n distanceFunction: \"cosine\",\n // Will be determined automatically during initialization\n dimensions: 0,\n } as VectorEmbedding,\n ];\n }\n\n if (!indexingPolicy.vectorIndexes?.length) {\n indexingPolicy.vectorIndexes = [\n {\n path: \"/vector\",\n type: \"quantizedFlat\",\n } as VectorIndex,\n ];\n }\n\n this.embeddingKey = vectorEmbeddingPolicy.vectorEmbeddings[0].path.slice(1);\n if (!this.embeddingKey) {\n throw new Error(\n \"AzureCosmosDBNoSQLVectorStore requires a valid vectorEmbeddings path\"\n );\n }\n\n // Validate full-text search configuration\n if (this.fullTextSearchEnabled) {\n if (\n !indexingPolicy.fullTextIndexes ||\n indexingPolicy.fullTextIndexes.length === 0\n ) {\n throw new Error(\n \"fullTextIndexes cannot be null or empty in the indexingPolicy if full text search is enabled.\"\n );\n }\n if (\n !this.fullTextPolicy ||\n !this.fullTextPolicy.fullTextPaths ||\n this.fullTextPolicy.fullTextPaths.length === 0\n ) {\n throw new Error(\n \"fullTextPolicy with fullTextPaths cannot be null or empty if full text search is enabled.\"\n );\n }\n }\n\n // Deferring initialization to the first call to `initialize`\n this.initialize = () => {\n if (this.initPromise === undefined) {\n this.initPromise = this.init(client, databaseName, containerName, {\n vectorEmbeddingPolicy,\n indexingPolicy,\n fullTextPolicy: this.fullTextPolicy,\n createContainerOptions: dbConfig.createContainerOptions,\n createDatabaseOptions: dbConfig.createDatabaseOptions,\n }).catch((error) => {\n console.error(\n \"Error during AzureCosmosDBNoSQLVectorStore initialization:\",\n error\n );\n });\n }\n\n return this.initPromise;\n };\n }\n\n /**\n * Removes specified documents from the AzureCosmosDBNoSQLVectorStore.\n * If no IDs or filter are specified, all documents will be removed.\n * @param params Parameters for the delete operation.\n * @returns A promise that resolves when the documents have been removed.\n */\n async delete(params: AzureCosmosDBNoSqlDeleteParams = {}): Promise {\n await this.initialize();\n\n if (params.ids && params.filter) {\n throw new Error(\n `AzureCosmosDBNoSQLVectorStore delete requires either \"ids\" or \"filter\" to be set in the params object, not both`\n );\n }\n\n let ids: string[];\n let query: AzureCosmosDBNoSQLQueryFilter | undefined = params.filter;\n\n // Delete all documents\n if (!params.ids && !params.filter) {\n query = \"SELECT c.id FROM c\";\n }\n\n if (query) {\n const { resources } = await this.container.items.query(query).fetchAll();\n ids = resources.map((item) => item.id);\n } else {\n ids = (Array.isArray(params.ids) ? params.ids : [params.ids]) as string[];\n }\n\n if (ids.length === 0) {\n return;\n }\n\n await Promise.all(ids.map((id) => this.container.item(id, id).delete()));\n }\n\n /**\n * Gets the underlying Cosmos DB container.\n * Useful for performing direct operations on the container.\n * @returns The Cosmos DB container instance.\n */\n getContainer(): Container {\n return this.container;\n }\n\n /**\n * Method for adding vectors to the AzureCosmosDBNoSQLVectorStore.\n * @param vectors Vectors to be added.\n * @param documents Corresponding documents to be added.\n * @returns A promise that resolves to the added documents IDs.\n */\n async addVectors(\n vectors: number[][],\n documents: DocumentInterface[]\n ): Promise {\n await this.initialize();\n const docs = vectors.map((embedding, idx) => ({\n [this.textKey]: documents[idx].pageContent,\n [this.embeddingKey]: embedding,\n [this.metadataKey]: documents[idx].metadata,\n ...(documents[idx].id ? { id: documents[idx].id } : {}),\n }));\n\n const ids: string[] = [];\n const results = await Promise.all(\n docs.map((doc) => this.container.items.create(doc))\n );\n\n for (const result of results) {\n ids.push(result.resource?.id ?? \"error: could not create item\");\n }\n\n return ids;\n }\n\n /**\n * Method for adding documents to the AzureCosmosDBNoSQLVectorStore. It first converts\n * the documents to texts and then adds them as vectors.\n * @param documents The documents to add.\n * @returns A promise that resolves to the added documents IDs.\n */\n async addDocuments(documents: DocumentInterface[]): Promise {\n const texts = documents.map(({ pageContent }) => pageContent);\n return this.addVectors(\n await this.embeddings.embedDocuments(texts),\n documents\n );\n }\n\n /**\n * Performs a similarity search on the vectors stored in the container.\n * Routes to the appropriate search implementation based on the search type\n * specified in the filter or the default search type configured for the vector store.\n * @param queryVector Query vector for the similarity search.\n * @param k Number of nearest neighbors to return. Defaults to 4.\n * @param filter Optional filter options for the documents.\n * @returns Promise that resolves to a list of documents and their corresponding similarity scores.\n */\n async similaritySearchVectorWithScore(\n queryVector: number[],\n k = 4,\n filter: this[\"FilterType\"] | undefined = undefined\n ): Promise<[Document, number][]> {\n const searchType = filter?.searchType ?? this.defaultSearchType;\n\n if (searchType === AzureCosmosDBNoSQLSearchType.Vector) {\n return this.vectorSearchWithScore(queryVector, k, filter);\n } else if (\n searchType === AzureCosmosDBNoSQLSearchType.VectorScoreThreshold\n ) {\n return this.vectorSearchWithScoreThreshold(\n queryVector,\n k,\n filter,\n filter?.threshold\n );\n } else if (searchType === AzureCosmosDBNoSQLSearchType.Hybrid) {\n if (\n !filter?.fullTextRankFilter ||\n filter.fullTextRankFilter.length === 0\n ) {\n throw new Error(\n `fullTextRankFilter is required for ${searchType} search type`\n );\n }\n return this.hybridSearchWithScore(\n queryVector,\n k,\n filter,\n filter.fullTextRankFilter\n );\n } else if (\n searchType === AzureCosmosDBNoSQLSearchType.HybridScoreThreshold\n ) {\n if (\n !filter?.fullTextRankFilter ||\n filter.fullTextRankFilter.length === 0\n ) {\n throw new Error(\n `fullTextRankFilter is required for ${searchType} search type`\n );\n }\n return this.hybridSearchWithScoreThreshold(\n queryVector,\n k,\n filter,\n filter.fullTextRankFilter,\n filter?.threshold\n );\n } else if (searchType === AzureCosmosDBNoSQLSearchType.FullTextSearch) {\n return this.fullTextSearch(k, filter);\n } else if (searchType === AzureCosmosDBNoSQLSearchType.FullTextRanking) {\n if (!filter?.fullTextRankFilter) {\n throw new Error(\n `fullTextRankFilter is required for ${searchType} search type`\n );\n }\n return this.fullTextRanking(k, filter, filter.fullTextRankFilter);\n }\n throw new Error(`Unrecognized search type '${searchType}'`);\n }\n\n /**\n * Return documents selected using the maximal marginal relevance.\n * Maximal marginal relevance optimizes for similarity to the query AND\n * diversity among selected documents.\n * @param query Text to look up documents similar to.\n * @param options.k Number of documents to return.\n * @param options.fetchK Number of documents to fetch before passing to\n * the MMR algorithm. Defaults to 20.\n * @param options.lambda Number between 0 and 1 that determines the\n * degree of diversity among the results, where 0 corresponds to maximum\n * diversity and 1 to minimum diversity. Defaults to 0.5.\n * @returns List of documents selected by maximal marginal relevance.\n */\n async maxMarginalRelevanceSearch(\n query: string,\n options: MaxMarginalRelevanceSearchOptions\n ): Promise {\n const queryEmbedding = await this.embeddings.embedQuery(query);\n return this.maxMarginalRelevanceSearchByVector(queryEmbedding, options);\n }\n\n /**\n * Return documents selected using the maximal marginal relevance from a vector.\n * Maximal marginal relevance optimizes for similarity to the query AND\n * diversity among selected documents.\n * @param queryEmbedding Query embedding vector.\n * @param options.k Number of documents to return.\n * @param options.fetchK Number of documents to fetch before passing to\n * the MMR algorithm. Defaults to 20.\n * @param options.lambda Number between 0 and 1 that determines the\n * degree of diversity among the results, where 0 corresponds to maximum\n * diversity and 1 to minimum diversity. Defaults to 0.5.\n * @returns List of documents selected by maximal marginal relevance.\n */\n async maxMarginalRelevanceSearchByVector(\n queryEmbedding: number[],\n options: MaxMarginalRelevanceSearchOptions\n ): Promise {\n const { k, fetchK = 20, lambda = 0.5, filter } = options;\n const includeEmbeddingsFlag = filter?.includeEmbeddings || false;\n\n const docs = await this.similaritySearchVectorWithScore(\n queryEmbedding,\n fetchK,\n {\n ...filter,\n includeEmbeddings: true,\n }\n );\n const embeddingList = docs.map((doc) => doc[0].metadata[this.embeddingKey]);\n\n // Re-rank the results using MMR\n const mmrIndexes = maximalMarginalRelevance(\n queryEmbedding,\n embeddingList,\n lambda,\n k\n );\n\n return mmrIndexes.map((index) => {\n const doc = docs[index][0];\n\n // Remove embeddings if they were not requested originally\n if (!includeEmbeddingsFlag) {\n delete doc.metadata[this.embeddingKey];\n }\n return doc;\n });\n }\n\n /**\n * Initializes the AzureCosmosDBNoSQLVectorStore by connecting to the database.\n * @param client The CosmosClient to use for connecting to the database.\n * @param databaseName The name of the database to use.\n * @param containerName The name of the container to use.\n * @param initOptions Initialization options for the database and container.\n * @returns A promise that resolves when the AzureCosmosDBNoSQLVectorStore has been initialized.\n */\n private async init(\n client: CosmosClient,\n databaseName: string,\n containerName: string,\n initOptions: AzureCosmosDBNoSQLInitOptions\n ): Promise {\n // Determine vector dimensions if not provided\n const vectorEmbeddingPolicy = initOptions.vectorEmbeddingPolicy!;\n const needDimensions = vectorEmbeddingPolicy.vectorEmbeddings.some(\n (v) => !v.dimensions\n );\n if (needDimensions) {\n const queryEmbedding = await this.embeddings.embedQuery(\"test\");\n for (const v of vectorEmbeddingPolicy.vectorEmbeddings) {\n if (!v.dimensions) {\n v.dimensions = queryEmbedding.length;\n }\n }\n }\n\n const { database } = await client.databases.createIfNotExists({\n ...(initOptions?.createDatabaseOptions ?? {}),\n id: databaseName,\n });\n\n const containerOptions = initOptions?.createContainerOptions ?? {};\n const { container } = await database.containers.createIfNotExists({\n ...containerOptions,\n // Default partition key to /id if not specified\n partitionKey: containerOptions.partitionKey ?? \"/id\",\n indexingPolicy: initOptions?.indexingPolicy,\n vectorEmbeddingPolicy,\n fullTextPolicy: initOptions?.fullTextPolicy,\n id: containerName,\n });\n this.container = container;\n }\n\n /**\n * Performs a plain vector similarity search.\n */\n private async vectorSearchWithScore(\n queryVector: number[],\n k = 4,\n filter: this[\"FilterType\"] | undefined = undefined\n ): Promise<[Document, number][]> {\n await this.initialize();\n\n const { query, parameters } = this.constructQuery(\n k,\n AzureCosmosDBNoSQLSearchType.Vector,\n {\n embeddings: queryVector,\n filterClause: filter?.filterClause,\n offsetLimit: filter?.offsetLimit,\n projectionMapping: filter?.projectionMapping,\n withEmbedding: filter?.includeEmbeddings,\n }\n );\n\n return this.executeQuery(\n query,\n AzureCosmosDBNoSQLSearchType.Vector,\n parameters,\n {\n withEmbedding: filter?.includeEmbeddings,\n projectionMapping: filter?.projectionMapping,\n }\n );\n }\n\n /**\n * Performs hybrid search combining vector similarity and full-text search using RRF.\n */\n private async hybridSearchWithScore(\n embeddings: number[],\n k = 4,\n filter: this[\"FilterType\"] | undefined = undefined,\n fullTextRankFilter: AzureCosmosDBNoSQLFullTextRankFilter[]\n ): Promise<[Document, number][]> {\n const { query, parameters } = this.constructQuery(\n k,\n AzureCosmosDBNoSQLSearchType.Hybrid,\n {\n embeddings,\n fullTextRankFilter,\n offsetLimit: filter?.offsetLimit,\n projectionMapping: filter?.projectionMapping,\n withEmbedding: filter?.includeEmbeddings,\n filterClause: filter?.filterClause,\n weights: filter?.weights,\n }\n );\n\n return this.executeQuery(\n query,\n AzureCosmosDBNoSQLSearchType.Hybrid,\n parameters,\n {\n withEmbedding: filter?.includeEmbeddings,\n projectionMapping: filter?.projectionMapping,\n }\n );\n }\n\n /**\n * Performs hybrid search with score threshold filtering.\n */\n private async hybridSearchWithScoreThreshold(\n embeddings: number[],\n k = 4,\n filter: this[\"FilterType\"] | undefined = undefined,\n fullTextRankFilter: AzureCosmosDBNoSQLFullTextRankFilter[],\n threshold: number = 0.5\n ): Promise<[Document, number][]> {\n const { query, parameters } = this.constructQuery(\n k,\n AzureCosmosDBNoSQLSearchType.HybridScoreThreshold,\n {\n embeddings,\n fullTextRankFilter,\n offsetLimit: filter?.offsetLimit,\n projectionMapping: filter?.projectionMapping,\n withEmbedding: filter?.includeEmbeddings,\n filterClause: filter?.filterClause,\n weights: filter?.weights,\n }\n );\n\n return this.executeQuery(\n query,\n AzureCosmosDBNoSQLSearchType.HybridScoreThreshold,\n parameters,\n {\n withEmbedding: filter?.includeEmbeddings,\n projectionMapping: filter?.projectionMapping,\n threshold,\n }\n );\n }\n\n /**\n * Performs vector similarity search with score threshold filtering.\n */\n private async vectorSearchWithScoreThreshold(\n embeddings: number[],\n k = 4,\n filter: this[\"FilterType\"] | undefined = undefined,\n threshold: number = 0.5\n ): Promise<[Document, number][]> {\n const { query, parameters } = this.constructQuery(\n k,\n AzureCosmosDBNoSQLSearchType.VectorScoreThreshold,\n {\n embeddings,\n filterClause: filter?.filterClause,\n offsetLimit: filter?.offsetLimit,\n projectionMapping: filter?.projectionMapping,\n withEmbedding: filter?.includeEmbeddings,\n }\n );\n\n return this.executeQuery(\n query,\n AzureCosmosDBNoSQLSearchType.VectorScoreThreshold,\n parameters,\n {\n withEmbedding: filter?.includeEmbeddings,\n projectionMapping: filter?.projectionMapping,\n threshold,\n }\n );\n }\n\n /**\n * Performs full-text search with BM25 ranking using RRF.\n * Note: Full-text ranking is a preview feature.\n */\n private async fullTextRanking(\n k = 4,\n filter: this[\"FilterType\"] | undefined = undefined,\n fullTextRankFilter: AzureCosmosDBNoSQLFullTextRankFilter[]\n ): Promise<[Document, number][]> {\n const { query, parameters } = this.constructQuery(\n k,\n AzureCosmosDBNoSQLSearchType.FullTextRanking,\n {\n offsetLimit: filter?.offsetLimit,\n projectionMapping: filter?.projectionMapping,\n fullTextRankFilter,\n filterClause: filter?.filterClause,\n }\n );\n\n return this.executeQuery(\n query,\n AzureCosmosDBNoSQLSearchType.FullTextRanking,\n parameters,\n {\n withEmbedding: false,\n projectionMapping: filter?.projectionMapping,\n }\n );\n }\n\n /**\n * Performs basic full-text search without vector similarity.\n * Note: Full-text search is a preview feature.\n */\n private async fullTextSearch(\n k = 4,\n filter: this[\"FilterType\"] | undefined = undefined\n ): Promise<[Document, number][]> {\n const { query, parameters } = this.constructQuery(\n k,\n AzureCosmosDBNoSQLSearchType.FullTextSearch,\n {\n offsetLimit: filter?.offsetLimit,\n projectionMapping: filter?.projectionMapping,\n filterClause: filter?.filterClause,\n }\n );\n\n return this.executeQuery(\n query,\n AzureCosmosDBNoSQLSearchType.FullTextSearch,\n parameters,\n {\n withEmbedding: false,\n projectionMapping: filter?.projectionMapping,\n }\n );\n }\n\n /**\n * Constructs a Cosmos DB SQL query string and parameters based on the search type and options.\n * Builds the complete SQL query including SELECT clause, projections, FROM clause,\n * WHERE conditions, ORDER BY clauses (with RRF for hybrid/full-text), and pagination.\n *\n * @param k The maximum number of results to return.\n * @param searchType The type of search to perform.\n * @param options Configuration options including embeddings, filters, projections, and search-specific parameters.\n * @returns An object containing the constructed SQL query string and an array of parameters.\n */\n private constructQuery(\n k: number,\n searchType: AzureCosmosDBNoSQLSearchType,\n options: {\n embeddings?: number[];\n fullTextRankFilter?: AzureCosmosDBNoSQLFullTextRankFilter[];\n offsetLimit?: string;\n projectionMapping?: ProjectionMapping;\n withEmbedding?: boolean;\n filterClause?: AzureCosmosDBNoSQLQueryFilter;\n weights?: number[];\n }\n ): { query: string; parameters: SqlParameter[] } {\n const table = this.tableAlias;\n\n let query = `SELECT ${!options.offsetLimit ? \"TOP @limit \" : \"\"}`;\n\n // Add projection fields\n query += this.generateProjectionFields(\n searchType,\n options.projectionMapping,\n options.fullTextRankFilter,\n options.withEmbedding\n );\n\n // Add FROM clause\n query += ` FROM ${table}`;\n\n // Add filterClause (WHERE clause) if provided\n if (options.filterClause) {\n if (typeof options.filterClause === \"string\") {\n query += ` ${options.filterClause}`;\n } else {\n query += ` ${options.filterClause.query}`;\n }\n }\n\n // Add ORDER BY clause based on search type\n if (searchType === AzureCosmosDBNoSQLSearchType.FullTextRanking) {\n if (options?.fullTextRankFilter?.length === 1) {\n const item = options.fullTextRankFilter[0];\n const terms = item.searchText\n .split(\" \")\n .map((_, termIndex) => `@${item.searchField}_0_term_${termIndex}`)\n .join(\", \");\n query += ` ORDER BY RANK FullTextScore(${table}[@${item.searchField}], ${terms})`;\n } else {\n const rankComponents = options?.fullTextRankFilter?.map(\n (item, filterIndex) => {\n const terms = item.searchText\n .split(\" \")\n .map(\n (_, termIndex) =>\n `@${item.searchField}_${filterIndex}_term_${termIndex}`\n )\n .join(\", \");\n return `FullTextScore(${table}[@${item.searchField}], ${terms})`;\n }\n );\n query += ` ORDER BY RANK RRF(${rankComponents?.join(\", \")})`;\n }\n } else if (\n searchType === AzureCosmosDBNoSQLSearchType.Vector ||\n searchType === AzureCosmosDBNoSQLSearchType.VectorScoreThreshold\n ) {\n query += ` ORDER BY VectorDistance(${table}[@embeddingKey], @embeddings)`;\n } else if (\n searchType === AzureCosmosDBNoSQLSearchType.Hybrid ||\n searchType === AzureCosmosDBNoSQLSearchType.HybridScoreThreshold\n ) {\n const rankComponents = options?.fullTextRankFilter?.map(\n (item, filterIndex) => {\n const terms = item.searchText\n .split(\" \")\n .map(\n (_, termIndex) =>\n `@${item.searchField}_${filterIndex}_term_${termIndex}`\n )\n .join(\", \");\n return `FullTextScore(${table}[@${item.searchField}], ${terms})`;\n }\n );\n\n query += ` ORDER BY RANK RRF(${rankComponents?.join(\n \", \"\n )}, VectorDistance(${table}[@embeddingKey], @embeddings)`;\n if (options.weights) {\n query += \", @weights)\";\n } else {\n query += \")\";\n }\n }\n\n // Add offset/limit if provided\n if (options.offsetLimit) {\n query += ` ${options.offsetLimit}`;\n }\n\n const parameters = this.buildParameters(\n k,\n searchType,\n options.embeddings,\n options.projectionMapping,\n options.fullTextRankFilter,\n options.weights,\n options.filterClause\n );\n\n return { query, parameters };\n }\n\n /**\n * Generates the SELECT clause projection fields for the SQL query.\n */\n private generateProjectionFields(\n searchType: AzureCosmosDBNoSQLSearchType,\n projectionMapping?: ProjectionMapping,\n fullTextRankFilter?: AzureCosmosDBNoSQLFullTextRankFilter[],\n withEmbedding?: boolean\n ): string {\n const table = this.tableAlias;\n let projection = \"\";\n let isDefaultProjectionRequired = true;\n\n if (projectionMapping) {\n const fields = Object.entries(projectionMapping).map(\n ([key, alias]) => `${table}[@${key}] as ${alias}`\n );\n\n if (fields.length > 0) {\n projection += fields.join(\", \");\n isDefaultProjectionRequired = false;\n }\n }\n\n if (fullTextRankFilter) {\n if (isDefaultProjectionRequired) {\n projection = `${table}.id, ${table}[@metadataKey] as ${this.metadataKey}`;\n }\n const fields: string[] = [];\n const addedSearchFields = new Set();\n fullTextRankFilter.forEach((item) => {\n if (\n !projectionMapping ||\n !Object.keys(projectionMapping).includes(item.searchField)\n ) {\n if (!addedSearchFields.has(item.searchField)) {\n fields.push(\n `${table}[@${item.searchField}] as ${item.searchField}`\n );\n addedSearchFields.add(item.searchField);\n }\n }\n });\n if (fields.length > 0) {\n projection += `, ${fields.join(\", \")}`;\n }\n } else {\n if (isDefaultProjectionRequired) {\n projection = `${table}.id, ${table}[@textKey] as ${this.textKey}, ${table}[@metadataKey] as ${this.metadataKey}`;\n }\n }\n\n if (\n searchType === AzureCosmosDBNoSQLSearchType.Vector ||\n searchType === AzureCosmosDBNoSQLSearchType.VectorScoreThreshold ||\n searchType === AzureCosmosDBNoSQLSearchType.Hybrid ||\n searchType === AzureCosmosDBNoSQLSearchType.HybridScoreThreshold\n ) {\n if (withEmbedding) {\n projection += `, ${table}[@embeddingKey] as ${this.embeddingKey}`;\n }\n projection += `, VectorDistance(${table}[@embeddingKey], @embeddings) as SimilarityScore`;\n }\n\n return projection;\n }\n\n /**\n * Builds the parameter array for the SQL query.\n */\n private buildParameters(\n k: number,\n searchType: AzureCosmosDBNoSQLSearchType,\n embeddings?: number[],\n projectionMapping?: ProjectionMapping,\n fullTextRankFilter?: AzureCosmosDBNoSQLFullTextRankFilter[],\n weights?: number[],\n filterClause?: AzureCosmosDBNoSQLQueryFilter\n ): SqlParameter[] {\n const parameters: SqlParameter[] = [{ name: \"@limit\", value: k }];\n let isDefaultParamRequired = true;\n\n // Add filterClause parameters if it's a SqlQuerySpec\n if (filterClause && typeof filterClause !== \"string\") {\n if (filterClause.parameters) {\n parameters.push(...filterClause.parameters);\n }\n }\n\n const addedFieldParams = new Set();\n\n if (projectionMapping) {\n isDefaultParamRequired = false;\n Object.keys(projectionMapping).forEach((key) => {\n if (!addedFieldParams.has(key)) {\n parameters.push({ name: `@${key}`, value: key });\n addedFieldParams.add(key);\n }\n });\n }\n\n if (\n searchType === AzureCosmosDBNoSQLSearchType.Vector ||\n searchType === AzureCosmosDBNoSQLSearchType.VectorScoreThreshold ||\n searchType === AzureCosmosDBNoSQLSearchType.Hybrid ||\n searchType === AzureCosmosDBNoSQLSearchType.HybridScoreThreshold\n ) {\n parameters.push({\n name: \"@embeddingKey\",\n value: this.embeddingKey,\n });\n if (embeddings) {\n parameters.push({ name: \"@embeddings\", value: embeddings });\n }\n if (weights) {\n parameters.push({ name: \"@weights\", value: weights });\n }\n }\n\n if (fullTextRankFilter) {\n if (isDefaultParamRequired) {\n parameters.push({ name: \"@metadataKey\", value: this.metadataKey });\n }\n isDefaultParamRequired = false;\n fullTextRankFilter.forEach((item, filterIndex) => {\n if (!addedFieldParams.has(item.searchField)) {\n parameters.push({\n name: `@${item.searchField}`,\n value: item.searchField,\n });\n addedFieldParams.add(item.searchField);\n }\n item.searchText.split(\" \").forEach((term, termIndex) => {\n parameters.push({\n name: `@${item.searchField}_${filterIndex}_term_${termIndex}`,\n value: term,\n });\n });\n });\n }\n\n if (isDefaultParamRequired) {\n parameters.push({\n name: \"@textKey\",\n value: this.textKey,\n });\n parameters.push({ name: \"@metadataKey\", value: this.metadataKey });\n }\n\n return parameters;\n }\n\n /**\n * Extracts the text content from a query result item.\n * Uses custom projection mapping alias if provided, otherwise falls back to the default textKey.\n */\n private extractTextFromItem(\n item: Record,\n projectionMapping?: ProjectionMapping\n ): string {\n if (projectionMapping && this.textKey in projectionMapping) {\n const textKey = projectionMapping[this.textKey];\n return item[textKey] as string;\n }\n return item[this.textKey] as string;\n }\n\n /**\n * Populates metadata object from query result item fields.\n * Adds projected field aliases to metadata, or defaults to adding the document id.\n */\n private populateMetadataFromItem(\n item: Record,\n baseMetadata: Record,\n projectionMapping?: ProjectionMapping\n ): Record {\n if (projectionMapping) {\n for (const [key, alias] of Object.entries(projectionMapping)) {\n if (key !== this.textKey) {\n baseMetadata[alias] = item[alias];\n }\n }\n } else {\n baseMetadata.id = item.id;\n }\n return baseMetadata;\n }\n\n /**\n * Executes a Cosmos DB SQL query and transforms the results into Documents with scores.\n * Runs the query against the container, processes the returned items,\n * applies threshold filtering if specified, and constructs Document objects with metadata.\n *\n * @param query The SQL query string to execute.\n * @param searchType The type of search being performed, which affects result processing.\n * @param parameters The query parameters to bind to the SQL query.\n * @param options Execution options including threshold filtering, embedding inclusion, and projection mapping.\n * @returns A promise that resolves to an array of tuples containing Documents and their scores.\n */\n private async executeQuery(\n query: string,\n searchType: AzureCosmosDBNoSQLSearchType,\n parameters: SqlParameter[],\n options: {\n withEmbedding?: boolean;\n projectionMapping?: ProjectionMapping;\n threshold?: number;\n }\n ): Promise<[Document, number][]> {\n await this.initialize();\n\n const { resources: items } = await this.container.items\n .query({ query, parameters }, { forceQueryPlan: true })\n .fetchAll();\n\n const threshold = options.threshold ?? 0;\n const isVectorSearch =\n searchType === AzureCosmosDBNoSQLSearchType.Vector ||\n searchType === AzureCosmosDBNoSQLSearchType.Hybrid ||\n searchType === AzureCosmosDBNoSQLSearchType.VectorScoreThreshold ||\n searchType === AzureCosmosDBNoSQLSearchType.HybridScoreThreshold;\n\n const isThresholdSearch =\n searchType === AzureCosmosDBNoSQLSearchType.VectorScoreThreshold ||\n searchType === AzureCosmosDBNoSQLSearchType.HybridScoreThreshold;\n\n const docsAndScores: [Document, number][] = [];\n\n for (const item of items) {\n const score = isVectorSearch ? item.SimilarityScore : 0;\n\n // Skip items below threshold for threshold-based searches\n if (isThresholdSearch && score <= threshold) {\n continue;\n }\n\n const metadata: Record = {\n ...(item[this.metadataKey] || {}),\n };\n\n // Include embeddings if requested\n if (isVectorSearch && options.withEmbedding) {\n metadata[this.embeddingKey] = item[this.embeddingKey];\n }\n\n const text = this.extractTextFromItem(item, options.projectionMapping);\n this.populateMetadataFromItem(item, metadata, options.projectionMapping);\n\n docsAndScores.push([\n new Document({\n id: item.id,\n pageContent: text,\n metadata,\n }),\n score,\n ]);\n }\n\n return docsAndScores;\n }\n\n /**\n * Static method to create an instance of AzureCosmosDBNoSQLVectorStore from a\n * list of texts. It first converts the texts to vectors and then adds\n * them to the collection.\n * @param texts List of texts to be converted to vectors.\n * @param metadatas Metadata for the texts.\n * @param embeddings Embeddings to be used for conversion.\n * @param dbConfig Database configuration for Azure Cosmos DB for NoSQL.\n * @returns Promise that resolves to a new instance of AzureCosmosDBNoSQLVectorStore.\n */\n static async fromTexts(\n texts: string[],\n metadatas: object[] | object,\n embeddings: EmbeddingsInterface,\n dbConfig: AzureCosmosDBNoSQLConfig\n ): Promise {\n const docs: Document[] = [];\n for (let i = 0; i < texts.length; i += 1) {\n const metadata = Array.isArray(metadatas) ? metadatas[i] : metadatas;\n const newDoc = new Document({\n pageContent: texts[i],\n metadata,\n });\n docs.push(newDoc);\n }\n return AzureCosmosDBNoSQLVectorStore.fromDocuments(\n docs,\n embeddings,\n dbConfig\n );\n }\n\n /**\n * Static method to create an instance of AzureCosmosDBNoSQLVectorStore from a\n * list of documents. It first converts the documents to vectors and then\n * adds them to the collection.\n * @param docs List of documents to be converted to vectors.\n * @param embeddings Embeddings to be used for conversion.\n * @param dbConfig Database configuration for Azure Cosmos DB for NoSQL.\n * @returns Promise that resolves to a new instance of AzureCosmosDBNoSQLVectorStore.\n */\n static async fromDocuments(\n docs: Document[],\n embeddings: EmbeddingsInterface,\n dbConfig: AzureCosmosDBNoSQLConfig\n ): Promise {\n const instance = new this(embeddings, dbConfig);\n await instance.addDocuments(docs);\n return instance;\n 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