import { SearchClient, SearchIndexClient, SearchIndex, VectorFilterMode } from "@azure/search-documents";
import type { KeyCredential, TokenCredential } from "@azure/core-auth";
import { MaxMarginalRelevanceSearchOptions, VectorStore } from "@langchain/core/vectorstores";
import type { EmbeddingsInterface } from "@langchain/core/embeddings";
import { Document } from "@langchain/core/documents";
/**
 * Azure AI Search query type.
 */
export declare const AzureAISearchQueryType: {
    /** Vector search. */
    readonly Similarity: "similarity";
    /** Hybrid full text and vector search. */
    readonly SimilarityHybrid: "similarity_hybrid";
    /** Hybrid full text and vector search with semantic ranking. */
    readonly SemanticHybrid: "semantic_hybrid";
};
/**
 * Azure AI Search query type.
 */
export type AzureAISearchQueryType = (typeof AzureAISearchQueryType)[keyof typeof AzureAISearchQueryType];
/**
 * Azure AI Search settings.
 */
export interface AzureAISearchQueryOptions {
    readonly type?: AzureAISearchQueryType;
    readonly semanticConfigurationName?: string;
}
/**
 * Configuration options for the `AzureAISearchStore` constructor.
 */
export interface AzureAISearchConfig {
    readonly client?: SearchClient<AzureAISearchDocument>;
    readonly indexName?: string;
    readonly endpoint?: string;
    readonly key?: string;
    readonly credentials?: KeyCredential | TokenCredential;
    readonly search?: AzureAISearchQueryOptions;
}
/**
 * Azure AI Search options metadata schema.
 * If yout want to add custom data, use the attributes property.
 */
export type AzureAISearchDocumentMetadata = {
    source: string;
    attributes?: Array<{
        key: string;
        value: string;
    }>;
    embedding?: number[];
};
/**
 * Azure AI Search indexed document.
 */
export type AzureAISearchDocument = {
    id: string;
    content: string;
    content_vector: number[];
    metadata: AzureAISearchDocumentMetadata;
};
/**
 * Azure AI Search options for adding documents.
 */
export type AzureAISearchAddDocumentsOptions = {
    ids?: string[];
};
/**
 * Azure AI Search filter type.
 */
export type AzureAISearchFilterType = {
    /** OData filter. */
    filterExpression?: string;
    /** Determines whether or not filters are applied before or after the vector search is performed. */
    vectorFilterMode?: VectorFilterMode;
    /** Determines whether or not to include the embeddings in the search results. */
    includeEmbeddings?: boolean;
};
/**
 * Azure AI Search vector store.
 * To use this, you should have:
 * - the `@azure/search-documents` NPM package installed
 * - an endpoint and key to the Azure AI Search instance
 *
 * If you directly provide a `SearchClient` instance, you need to ensure that
 * an index has been created. When using and endpoint and key, the index will
 * be created automatically if it does not exist.
 */
export declare class AzureAISearchVectorStore extends VectorStore {
    FilterType: AzureAISearchFilterType;
    get lc_secrets(): {
        [key: string]: string;
    };
    _vectorstoreType(): string;
    private readonly initPromise;
    private readonly client;
    private readonly indexName;
    private readonly options;
    constructor(embeddings: EmbeddingsInterface, config: AzureAISearchConfig);
    /**
     * Removes specified documents from the AzureAISearchVectorStore using IDs or a filter.
     * @param params Object that includes either an array of IDs or a filter for the data to be deleted.
     * @returns A promise that resolves when the documents have been removed.
     */
    delete(params: {
        ids?: string | string[];
        filter?: AzureAISearchFilterType;
    }): Promise<void>;
    /**
     * Removes specified documents from the AzureAISearchVectorStore using a filter.
     * @param filter Filter options to find documents to delete.
     * @returns A promise that resolves when the documents have been removed.
     */
    private deleteMany;
    /**
     * Removes specified documents from the AzureAISearchVectorStore.
     * @param ids IDs of the documents to be removed.
     * @returns A promise that resolves when the documents have been removed.
     */
    private deleteById;
    /**
     * Adds documents to the AzureAISearchVectorStore.
     * @param documents The documents to add.
     * @param options Options for adding documents.
     * @returns A promise that resolves to the ids of the added documents.
     */
    addDocuments(documents: Document[], options?: AzureAISearchAddDocumentsOptions): Promise<string[]>;
    /**
     * Adds vectors to the AzureAISearchVectorStore.
     * @param vectors Vectors to be added.
     * @param documents Corresponding documents to be added.
     * @param options Options for adding documents.
     * @returns A promise that resolves to the ids of the added documents.
     */
    addVectors(vectors: number[][], documents: Document[], options?: AzureAISearchAddDocumentsOptions): Promise<string[]>;
    /**
     * Performs a similarity search using query type specified in configuration.
     * If the query type is not specified, it defaults to similarity search.
     * @param query Query text for the similarity search.
     * @param k=4 Number of nearest neighbors to return.
     * @param filter Optional filter options for the documents.
     * @returns Promise that resolves to a list of documents and their corresponding similarity scores.
     */
    similaritySearch(query: string, k?: number, filter?: this["FilterType"] | undefined): Promise<Document[]>;
    /**
     * Performs a similarity search using query type specified in configuration.
     * If the query type is not specified, it defaults to similarity hybrid search.
     * @param query Query text for the similarity search.
     * @param k=4 Number of nearest neighbors to return.
     * @param filter Optional filter options for the documents.
     * @returns Promise that resolves to a list of documents and their corresponding similarity scores.
     */
    similaritySearchWithScore(query: string, k?: number, filter?: this["FilterType"] | undefined): Promise<[Document, number][]>;
    /**
     * Performs a hybrid search using query text.
     * @param query Query text for the similarity search.
     * @param queryVector Query vector for the similarity search.
     *    If not provided, the query text will be embedded.
     * @param k=4 Number of nearest neighbors to return.
     * @param filter Optional filter options for the documents.
     * @returns Promise that resolves to a list of documents and their corresponding similarity scores.
     */
    hybridSearchVectorWithScore(query: string, queryVector?: number[], k?: number, filter?: this["FilterType"] | undefined): Promise<[Document, number][]>;
    /**
     * Performs a hybrid search with semantic reranker using query text.
     * @param query Query text for the similarity search.
     * @param queryVector Query vector for the similarity search.
     *    If not provided, the query text will be embedded.
     * @param k=4 Number of nearest neighbors to return.
     * @param filter Optional filter options for the documents.
     * @returns Promise that resolves to a list of documents and their corresponding similarity scores.
     */
    semanticHybridSearchVectorWithScore(query: string, queryVector?: number[], k?: number, filter?: this["FilterType"] | undefined): Promise<[Document, number][]>;
    /**
     * Performs a similarity search on the vectors stored in the collection.
     * @param queryVector Query vector for the similarity search.
     * @param k=4 Number of nearest neighbors to return.
     * @param filter Optional filter options for the documents.
     * @returns Promise that resolves to a list of documents and their corresponding similarity scores.
     */
    similaritySearchVectorWithScore(query: number[], k: number, filter?: this["FilterType"]): Promise<[Document, number][]>;
    /**
     * Return documents selected using the maximal marginal relevance.
     * Maximal marginal relevance optimizes for similarity to the query AND
     * diversity among selected documents.
     * @param query Text to look up documents similar to.
     * @param options.k Number of documents to return.
     * @param options.fetchK=20 Number of documents to fetch before passing to
     *     the MMR algorithm.
     * @param options.lambda=0.5 Number between 0 and 1 that determines the
     *     degree of diversity among the results, where 0 corresponds to maximum
     *     diversity and 1 to minimum diversity.
     * @returns List of documents selected by maximal marginal relevance.
     */
    maxMarginalRelevanceSearch(query: string, options: MaxMarginalRelevanceSearchOptions<this["FilterType"]>): Promise<Document[]>;
    /**
     * Ensures that an index exists on the AzureAISearchVectorStore.
     * @param indexClient The Azure AI Search index client.
     * @returns A promise that resolves when the AzureAISearchVectorStore index has been initialized.
     * @protected
     */
    protected ensureIndexExists(indexClient: SearchIndexClient): Promise<void>;
    /**
     * Prepares the search index definition for Azure AI Search.
     * @param indexName The name of the index.
     * @returns The SearchIndex object.
     * @protected
     */
    protected createSearchIndexDefinition(indexName: string): Promise<SearchIndex>;
    /**
     * Static method to create an instance of AzureAISearchVectorStore from a
     * list of texts. It first converts the texts to vectors and then adds
     * them to the collection.
     * @param texts List of texts to be converted to vectors.
     * @param metadatas Metadata for the texts.
     * @param embeddings Embeddings to be used for conversion.
     * @param config Database configuration for Azure AI Search.
     * @returns Promise that resolves to a new instance of AzureAISearchVectorStore.
     */
    static fromTexts(texts: string[], metadatas: object[] | object, embeddings: EmbeddingsInterface, config: AzureAISearchConfig): Promise<AzureAISearchVectorStore>;
    /**
     * Static method to create an instance of AzureAISearchVectorStore from a
     * list of documents. It first converts the documents to vectors and then
     * adds them to the database.
     * @param docs List of documents to be converted to vectors.
     * @param embeddings Embeddings to be used for conversion.
     * @param config Database configuration for Azure AI Search.
     * @returns Promise that resolves to a new instance of AzureAISearchVectorStore.
     */
    static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, config: AzureAISearchConfig, options?: AzureAISearchAddDocumentsOptions): Promise<AzureAISearchVectorStore>;
}