/**
 * This file was auto-generated by Fern from our API Definition.
 */
import * as environments from "../../../../environments";
import * as core from "../../../../core";
import * as Vectara from "../../../index";
export declare namespace Corpora {
    interface Options {
        environment?: core.Supplier<environments.VectaraEnvironment | environments.VectaraEnvironmentUrls>;
        token?: core.Supplier<core.BearerToken | undefined>;
        /** Override the x-api-key header */
        apiKey?: core.Supplier<string | undefined>;
        fetcher?: core.FetchFunction;
    }
    interface RequestOptions {
        /** The maximum time to wait for a response in seconds. */
        timeoutInSeconds?: number;
        /** The number of times to retry the request. Defaults to 2. */
        maxRetries?: number;
        /** A hook to abort the request. */
        abortSignal?: AbortSignal;
        /** Override the x-api-key header */
        apiKey?: string | undefined;
        /** Additional headers to include in the request. */
        headers?: Record<string, string>;
    }
}
/**
 * Create, manage, and update corpora and their associated settings
 */
export declare class Corpora {
    protected readonly _options: Corpora.Options;
    constructor(_options?: Corpora.Options);
    /**
     * List corpora in the account. The returned corpus objects contain less
     * detail compared to those retrieved the direct corpus retrieval operation.
     *
     * @param {Vectara.CorporaListRequest} request
     * @param {Corpora.RequestOptions} requestOptions - Request-specific configuration.
     *
     * @throws {@link Vectara.ForbiddenError}
     *
     * @example
     *     await client.corpora.list()
     */
    list(request?: Vectara.CorporaListRequest, requestOptions?: Corpora.RequestOptions): Promise<core.Page<Vectara.Corpus>>;
    /**
     * Create a corpus, which is a container to store documents and associated metadata. Here, you
     * define the unique `corpus_key` that identifies the corpus. The `corpus_key` can be custom-defined
     * following your preferred naming convention, allowing you to easily manage the corpus's data and
     * reference it in queries. For more information, see
     * [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).
     *
     * @param {Vectara.CreateCorpusRequest} request
     * @param {Corpora.RequestOptions} requestOptions - Request-specific configuration.
     *
     * @throws {@link Vectara.BadRequestError}
     * @throws {@link Vectara.ForbiddenError}
     *
     * @example
     *     await client.corpora.create({
     *         key: "my-corpus"
     *     })
     */
    create(request: Vectara.CreateCorpusRequest, requestOptions?: Corpora.RequestOptions): Promise<Vectara.Corpus>;
    /**
     * Get metadata about a corpus. This operation does not search the corpus contents.
     * Specify the `corpus_key` to identify the corpus whose metadata you want to
     * retrieve. The `corpus_key` is created when the corpus is set up, either through
     * the Vectara Console UI or the Create Corpus API. For more information,
     * see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).
     *
     * @param {Vectara.CorpusKey} corpusKey - The unique key identifying the corpus to retrieve.
     * @param {Vectara.CorporaGetRequest} request
     * @param {Corpora.RequestOptions} requestOptions - Request-specific configuration.
     *
     * @throws {@link Vectara.ForbiddenError}
     * @throws {@link Vectara.NotFoundError}
     *
     * @example
     *     await client.corpora.get("my-corpus")
     */
    get(corpusKey: Vectara.CorpusKey, request?: Vectara.CorporaGetRequest, requestOptions?: Corpora.RequestOptions): Promise<Vectara.Corpus>;
    /**
     * Permanently delete a corpus and all its associated data. The `corpus_key` uniquely identifies
     * the corpus. For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).
     *
     * @param {Vectara.CorpusKey} corpusKey - The unique key identifying the corpus to delete.
     * @param {Vectara.CorporaDeleteRequest} request
     * @param {Corpora.RequestOptions} requestOptions - Request-specific configuration.
     *
     * @throws {@link Vectara.ForbiddenError}
     * @throws {@link Vectara.NotFoundError}
     *
     * @example
     *     await client.corpora.delete("my-corpus")
     */
    delete(corpusKey: Vectara.CorpusKey, request?: Vectara.CorporaDeleteRequest, requestOptions?: Corpora.RequestOptions): Promise<void>;
    /**
     * Enable, disable, or update the name and description of a corpus. This lets you
     * manage data availability without deleting the corpus, which is useful for
     * maintenance and security purposes. The `corpus_key` uniquely identifies the corpus.
     * For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).
     * Consider updating the name and description of a corpus dynamically to help keep your data
     * aligned with changing business needs.
     *
     * @param {Vectara.CorpusKey} corpusKey - The unique key identifying the corpus to update.
     * @param {Vectara.UpdateCorpusRequest} request
     * @param {Corpora.RequestOptions} requestOptions - Request-specific configuration.
     *
     * @throws {@link Vectara.ForbiddenError}
     * @throws {@link Vectara.NotFoundError}
     *
     * @example
     *     await client.corpora.update("my-corpus")
     */
    update(corpusKey: Vectara.CorpusKey, request?: Vectara.UpdateCorpusRequest, requestOptions?: Corpora.RequestOptions): Promise<Vectara.Corpus>;
    /**
     * Resets a corpus, which removes all documents and data from the specified corpus,
     * while keeping the corpus itself. The `corpus_key` uniquely identifies the corpus.
     * For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).
     *
     * @param {Vectara.CorpusKey} corpusKey - The unique key identifying the corpus to reset.
     * @param {Vectara.CorporaResetRequest} request
     * @param {Corpora.RequestOptions} requestOptions - Request-specific configuration.
     *
     * @throws {@link Vectara.ForbiddenError}
     * @throws {@link Vectara.NotFoundError}
     *
     * @example
     *     await client.corpora.reset("my-corpus")
     */
    reset(corpusKey: Vectara.CorpusKey, request?: Vectara.CorporaResetRequest, requestOptions?: Corpora.RequestOptions): Promise<void>;
    /**
     * Replace the filter attributes of a corpus. This does not happen immediately, as
     * this operation creates a job that completes asynchronously. These new filter
     * attributes will not work until the job completes.
     *
     * You can monitor the status of the filter change using the returned job ID. The
     * `corpus_key` uniquely identifies the corpus. For more information, see
     * [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).
     *
     * @param {Vectara.CorpusKey} corpusKey - The unique key identifying the corpus having its filters replaced.
     * @param {Vectara.ReplaceFilterAttributesRequest} request
     * @param {Corpora.RequestOptions} requestOptions - Request-specific configuration.
     *
     * @throws {@link Vectara.ForbiddenError}
     * @throws {@link Vectara.NotFoundError}
     *
     * @example
     *     await client.corpora.replaceFilterAttributes("my-corpus", {
     *         filterAttributes: [{
     *                 name: "Title",
     *                 level: "document",
     *                 type: "integer"
     *             }]
     *     })
     */
    replaceFilterAttributes(corpusKey: Vectara.CorpusKey, request: Vectara.ReplaceFilterAttributesRequest, requestOptions?: Corpora.RequestOptions): Promise<Vectara.ReplaceFilterAttributesResponse>;
    /**
     * Search a single corpus with a straightforward query request, specifying the corpus key and query parameters.
     * * Specify the unique `corpus_key` identifying the corpus to query. The `corpus_key` is
     * [created in the Vectara Console UI](https://docs.vectara.com/docs/console-ui/creating-a-corpus) or the [Create Corpus API definition](https://docs.vectara.com/docs/api-reference/admin-apis/create-corpus). When creating a new corpus, you have the option to assign a custom `corpus_key` following your preferred naming convention. This key serves as a unique identifier for the corpus, allowing it to be referenced in search requests. For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).
     * * Enter the search `query` string for the corpus, which is the question you want to ask.
     * * Set the maximum number of results (`limit`) to return. **Default**: 10, **minimum**: 1
     * * Define the `offset` position from which to start in the result set.
     *
     * For more detailed information, see this [Query API guide](https://docs.vectara.com/docs/api-reference/search-apis/search).
     *
     * @param {Vectara.CorpusKey} corpusKey - The unique key identifying the corpus to query.
     * @param {Vectara.CorporaSearchRequest} request
     * @param {Corpora.RequestOptions} requestOptions - Request-specific configuration.
     *
     * @throws {@link Vectara.BadRequestError}
     * @throws {@link Vectara.ForbiddenError}
     * @throws {@link Vectara.NotFoundError}
     *
     * @example
     *     await client.corpora.search("my-corpus", {
     *         query: "query"
     *     })
     */
    search(corpusKey: Vectara.CorpusKey, request: Vectara.CorporaSearchRequest, requestOptions?: Corpora.RequestOptions): Promise<Vectara.QueryFullResponse>;
    /**
     * Perform an advanced query on a specific corpus to find relevant results, highlight relevant snippets, and use Retrieval Augmented Generation:
     *
     * * Specify the unique `corpus_key` identifying the corpus to query. The `corpus_key` is [created in the Vectara Console UI](https://docs.vectara.com/docs/console-ui/creating-a-corpus) or the [Create Corpus API definition](https://docs.vectara.com/docs/api-reference/admin-apis/create-corpus). When creating a new corpus, you have the option to assign a custom `corpus_key` following your preferred naming convention. This key serves as a unique identifier for the corpus, allowing it to be referenced in search requests. For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).
     * * Customize your search by specifying the query text (`query`), pagination details (`offset` and `limit`), and metadata filters (`metadata_filter`) to tailor your search results. [Learn more](https://docs.vectara.com/docs/api-reference/search-apis/search#query-definition)
     * * Leverage advanced search capabilities like reranking (`reranker`) and Retrieval Augmented Generation (RAG) (`generation`) for enhanced query performance. Generation is opt in by setting the `generation` property. By excluding the property or by setting it to null, the response
     * will not include generation. [Learn more](https://docs.vectara.com/docs/learn/grounded-generation/configure-query-summarization).
     * * Use hybrid search to achieve optimal results by setting different values for `lexical_interpolation` (e.g., `0.025`). [Learn more](https://docs.vectara.com/docs/learn/hybrid-search)
     * * Specify Vectara's RAG-focused LLM (Mockingbird) for the `generation_preset_name`. [Learn more](https://docs.vectara.com/docs/learn/mockingbird-llm)
     * * Use advanced summarization options that utilize detailed summarization parameters such as `max_response_characters`, `temperature`, and `frequency_penalty` for generating precise and relevant summaries. [Learn more](https://docs.vectara.com/docs/api-reference/search-apis/search#advanced-summarization-options)
     *
     * For more detailed information, see [Query API guide](https://docs.vectara.com/docs/api-reference/search-apis/search).
     */
    queryStream(corpusKey: Vectara.CorpusKey, request: Vectara.CorporaQueryStreamRequest, requestOptions?: Corpora.RequestOptions): Promise<core.Stream<Vectara.QueryStreamedResponse>>;
    /**
     * Perform an advanced query on a specific corpus to find relevant results, highlight relevant snippets, and use Retrieval Augmented Generation:
     *
     * * Specify the unique `corpus_key` identifying the corpus to query. The `corpus_key` is [created in the Vectara Console UI](https://docs.vectara.com/docs/console-ui/creating-a-corpus) or the [Create Corpus API definition](https://docs.vectara.com/docs/api-reference/admin-apis/create-corpus). When creating a new corpus, you have the option to assign a custom `corpus_key` following your preferred naming convention. This key serves as a unique identifier for the corpus, allowing it to be referenced in search requests. For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).
     * * Customize your search by specifying the query text (`query`), pagination details (`offset` and `limit`), and metadata filters (`metadata_filter`) to tailor your search results. [Learn more](https://docs.vectara.com/docs/api-reference/search-apis/search#query-definition)
     * * Leverage advanced search capabilities like reranking (`reranker`) and Retrieval Augmented Generation (RAG) (`generation`) for enhanced query performance. Generation is opt in by setting the `generation` property. By excluding the property or by setting it to null, the response
     * will not include generation. [Learn more](https://docs.vectara.com/docs/learn/grounded-generation/configure-query-summarization).
     * * Use hybrid search to achieve optimal results by setting different values for `lexical_interpolation` (e.g., `0.025`). [Learn more](https://docs.vectara.com/docs/learn/hybrid-search)
     * * Specify Vectara's RAG-focused LLM (Mockingbird) for the `generation_preset_name`. [Learn more](https://docs.vectara.com/docs/learn/mockingbird-llm)
     * * Use advanced summarization options that utilize detailed summarization parameters such as `max_response_characters`, `temperature`, and `frequency_penalty` for generating precise and relevant summaries. [Learn more](https://docs.vectara.com/docs/api-reference/search-apis/search#advanced-summarization-options)
     *
     * For more detailed information, see [Query API guide](https://docs.vectara.com/docs/api-reference/search-apis/search).
     *
     * @param {Vectara.CorpusKey} corpusKey - The unique key identifying the corpus to query.
     * @param {Vectara.CorporaQueryRequest} request
     * @param {Corpora.RequestOptions} requestOptions - Request-specific configuration.
     *
     * @throws {@link Vectara.BadRequestError}
     * @throws {@link Vectara.ForbiddenError}
     * @throws {@link Vectara.NotFoundError}
     *
     * @example
     *     await client.corpora.query("my-corpus", {
     *         query: "query"
     *     })
     */
    query(corpusKey: Vectara.CorpusKey, request: Vectara.CorporaQueryRequest, requestOptions?: Corpora.RequestOptions): Promise<Vectara.QueryFullResponse>;
    protected _getAuthorizationHeader(): Promise<string | undefined>;
}