/**
 * This file was auto-generated by Fern from our API Definition.
 */
import * as Vectara from "./api/index";
import { ApiKeys } from "./api/resources/apiKeys/client/Client";
import { AppClients } from "./api/resources/appClients/client/Client";
import { Auth } from "./api/resources/auth/client/Client";
import { Chats } from "./api/resources/chats/client/Client";
import { Corpora } from "./api/resources/corpora/client/Client";
import { Documents } from "./api/resources/documents/client/Client";
import { Encoders } from "./api/resources/encoders/client/Client";
import { GenerationPresets } from "./api/resources/generationPresets/client/Client";
import { Index } from "./api/resources/index/client/Client";
import { Jobs } from "./api/resources/jobs/client/Client";
import { Llms } from "./api/resources/llms/client/Client";
import { QueryHistory } from "./api/resources/queryHistory/client/Client";
import { Rerankers } from "./api/resources/rerankers/client/Client";
import { Upload } from "./api/resources/upload/client/Client";
import { Users } from "./api/resources/users/client/Client";
import * as core from "./core";
import * as environments from "./environments";
export declare namespace VectaraClient {
    interface Options {
        environment?: core.Supplier<environments.VectaraEnvironment | environments.VectaraEnvironmentUrls>;
        clientId?: core.Supplier<string>;
        clientSecret?: core.Supplier<string>;
        /** 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>;
    }
}
export declare class VectaraClient {
    protected readonly _options: VectaraClient.Options;
    private readonly _oauthTokenProvider;
    protected _corpora: Corpora | undefined;
    protected _upload: Upload | undefined;
    protected _documents: Documents | undefined;
    protected _index: Index | undefined;
    protected _chats: Chats | undefined;
    protected _llms: Llms | undefined;
    protected _generationPresets: GenerationPresets | undefined;
    protected _encoders: Encoders | undefined;
    protected _rerankers: Rerankers | undefined;
    protected _jobs: Jobs | undefined;
    protected _users: Users | undefined;
    protected _apiKeys: ApiKeys | undefined;
    protected _appClients: AppClients | undefined;
    protected _queryHistory: QueryHistory | undefined;
    protected _auth: Auth | undefined;
    constructor(_options?: VectaraClient.Options);
    get corpora(): Corpora;
    get upload(): Upload;
    get documents(): Documents;
    get index(): Index;
    get chats(): Chats;
    get llms(): Llms;
    get generationPresets(): GenerationPresets;
    get encoders(): Encoders;
    get rerankers(): Rerankers;
    get jobs(): Jobs;
    get users(): Users;
    get apiKeys(): ApiKeys;
    get appClients(): AppClients;
    get queryHistory(): QueryHistory;
    get auth(): Auth;
    /**
     * Perform a multipurpose query across to retrieve relevant information from one or more corpora and generate a response using Retrieval Augmented Generation (RAG).
     *
     * * 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 opt-in 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)
     * * 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-customization-options)
     * * Customize citation formats in summaries using the `citations` object to include numeric, HTML, or Markdown links. [Learn more](https://docs.vectara.com/docs/api-reference/search-apis/search#citation-format-in-summary)
     *
     * For more detailed information, see this [Query API guide](https://docs.vectara.com/docs/api-reference/search-apis/search).
     */
    queryStream(request: Vectara.QueryStreamRequest, requestOptions?: VectaraClient.RequestOptions): Promise<core.Stream<Vectara.QueryStreamedResponse>>;
    /**
     * Perform a multipurpose query across to retrieve relevant information from one or more corpora and generate a response using Retrieval Augmented Generation (RAG).
     *
     * * 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 opt-in 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)
     * * 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-customization-options)
     * * Customize citation formats in summaries using the `citations` object to include numeric, HTML, or Markdown links. [Learn more](https://docs.vectara.com/docs/api-reference/search-apis/search#citation-format-in-summary)
     *
     * For more detailed information, see this [Query API guide](https://docs.vectara.com/docs/api-reference/search-apis/search).
     *
     * @param {Vectara.QueryRequest} request
     * @param {VectaraClient.RequestOptions} requestOptions - Request-specific configuration.
     *
     * @throws {@link Vectara.BadRequestError}
     * @throws {@link Vectara.ForbiddenError}
     * @throws {@link Vectara.NotFoundError}
     *
     * @example
     *     await client.query({
     *         query: "What is a hallucination?",
     *         search: {
     *             corpora: [{
     *                     corpusKey: "corpus_key",
     *                     metadataFilter: "",
     *                     lexicalInterpolation: 0.005
     *                 }],
     *             contextConfiguration: {
     *                 sentencesBefore: 2,
     *                 sentencesAfter: 2
     *             },
     *             reranker: {
     *                 type: "customer_reranker",
     *                 rerankerId: "rnk_272725719"
     *             }
     *         },
     *         generation: {
     *             responseLanguage: "eng",
     *             enableFactualConsistencyScore: true
     *         }
     *     })
     */
    query(request: Vectara.QueryRequest, requestOptions?: VectaraClient.RequestOptions): Promise<Vectara.QueryFullResponse>;
    /**
     * Create a chat while specifying the default retrieval parameters used by the prompt.
     */
    chatStream(request: Vectara.ChatStreamRequest, requestOptions?: VectaraClient.RequestOptions): Promise<core.Stream<Vectara.ChatStreamedResponse>>;
    /**
     * Create a chat while specifying the default retrieval parameters used by the prompt.
     *
     * @param {Vectara.ChatRequest} request
     * @param {VectaraClient.RequestOptions} requestOptions - Request-specific configuration.
     *
     * @throws {@link Vectara.BadRequestError}
     * @throws {@link Vectara.ForbiddenError}
     * @throws {@link Vectara.NotFoundError}
     *
     * @example
     *     await client.chat({
     *         query: "What is a hallucination?",
     *         search: {
     *             corpora: [{
     *                     corpusKey: "corpus_key",
     *                     metadataFilter: "",
     *                     lexicalInterpolation: 0.005
     *                 }],
     *             contextConfiguration: {
     *                 sentencesBefore: 2,
     *                 sentencesAfter: 2
     *             },
     *             reranker: {
     *                 type: "customer_reranker",
     *                 rerankerId: "rnk_272725719"
     *             }
     *         },
     *         generation: {
     *             responseLanguage: "eng",
     *             enableFactualConsistencyScore: true,
     *             citations: {
     *                 style: "none"
     *             }
     *         },
     *         chat: {
     *             store: true
     *         }
     *     })
     */
    chat(request: Vectara.ChatRequest, requestOptions?: VectaraClient.RequestOptions): Promise<Vectara.ChatFullResponse>;
    protected _getAuthorizationHeader(): Promise<string | undefined>;
}