@langchain/core/dist/vectorstores.d.cts.map

Core LangChain.js abstractions and schemas

{"version":3,"file":"vectorstores.d.cts","names":["EmbeddingsInterface","DocumentInterface","BaseRetriever","BaseRetrieverInterface","BaseRetrieverInput","Serializable","CallbackManagerForRetrieverRun","Callbacks","AddDocumentOptions","Record","MaxMarginalRelevanceSearchOptions","FilterType","VectorStoreRetrieverMMRSearchKwargs","VectorStoreRetrieverInput","VectorStoreInterface","V","VectorStoreRetrieverInterface","Promise","VectorStoreRetriever","Partial","VectorStore","SaveableVectorStore"],"sources":["../src/vectorstores.d.ts"],"sourcesContent":["import type { EmbeddingsInterface } from \"./embeddings.js\";\nimport type { DocumentInterface } from \"./documents/document.js\";\nimport { BaseRetriever, BaseRetrieverInterface, type BaseRetrieverInput } from \"./retrievers/index.js\";\nimport { Serializable } from \"./load/serializable.js\";\nimport { CallbackManagerForRetrieverRun, Callbacks } from \"./callbacks/manager.js\";\n/**\n * Type for options when adding a document to the VectorStore.\n */\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\ntype AddDocumentOptions = Record<string, any>;\n/**\n * Options for configuring a maximal marginal relevance (MMR) search.\n *\n * MMR search optimizes for both similarity to the query and diversity\n * among the results, balancing the retrieval of relevant documents\n * with variation in the content returned.\n *\n * Fields:\n *\n * - `fetchK` (optional): The initial number of documents to retrieve from the\n *   vector store before applying the MMR algorithm. This larger set provides a\n *   pool of documents from which the algorithm can select the most diverse\n *   results based on relevance to the query.\n *\n * - `filter` (optional): A filter of type `FilterType` to refine the search\n *   results, allowing additional conditions to target specific subsets\n *   of documents.\n *\n * - `k`: The number of documents to return in the final results. This is the\n *   primary count of documents that are most relevant to the query.\n *\n * - `lambda` (optional): A value between 0 and 1 that determines the balance\n *   between relevance and diversity:\n *   - A `lambda` of 0 emphasizes diversity, maximizing content variation.\n *   - A `lambda` of 1 emphasizes similarity to the query, focusing on relevance.\n *    Values between 0 and 1 provide a mix of relevance and diversity.\n *\n * @template FilterType - The type used for filtering results, as defined\n *                        by the vector store.\n */\nexport type MaxMarginalRelevanceSearchOptions<FilterType> = {\n    k: number;\n    fetchK?: number;\n    lambda?: number;\n    filter?: FilterType;\n};\n/**\n * Options for configuring a maximal marginal relevance (MMR) search\n * when using the `VectorStoreRetriever`.\n *\n * These parameters control how the MMR algorithm balances relevance to the\n * query and diversity among the retrieved documents.\n *\n * Fields:\n * - `fetchK` (optional): Specifies the initial number of documents to fetch\n *   before applying the MMR algorithm. This larger set provides a pool of\n *   documents from which the algorithm can select the most diverse results\n *   based on relevance to the query.\n *\n * - `lambda` (optional): A value between 0 and 1 that determines the balance\n *   between relevance and diversity:\n *   - A `lambda` of 0 maximizes diversity among the results, prioritizing varied content.\n *   - A `lambda` of 1 maximizes similarity to the query, prioritizing relevance.\n *   Values between 0 and 1 provide a mix of relevance and diversity.\n */\nexport type VectorStoreRetrieverMMRSearchKwargs = {\n    fetchK?: number;\n    lambda?: number;\n};\n/**\n * Input configuration options for creating a `VectorStoreRetriever` instance.\n *\n * This type combines properties from `BaseRetrieverInput` with specific settings\n * for the `VectorStoreRetriever`, including options for similarity or maximal\n * marginal relevance (MMR) search types.\n *\n * Fields:\n *\n * - `callbacks` (optional): An array of callback functions that handle various\n *   events during retrieval, such as logging, error handling, or progress updates.\n *\n * - `tags` (optional): An array of strings used to add contextual tags to\n *   retrieval operations, allowing for easier categorization and tracking.\n *\n * - `metadata` (optional): A record of key-value pairs to store additional\n *   contextual information for retrieval operations, which can be useful\n *   for logging or auditing purposes.\n *\n * - `verbose` (optional): A boolean flag that, if set to `true`, enables\n *   detailed logging and output during the retrieval process. Defaults to `false`.\n *\n * - `vectorStore`: The `VectorStore` instance implementing `VectorStoreInterface`\n *   that will be used for document storage and retrieval.\n *\n * - `k` (optional): Specifies the number of documents to retrieve per search\n *   query. Defaults to 4 if not specified.\n *\n * - `filter` (optional): A filter of type `FilterType` (defined by the vector store)\n *   to refine the set of documents returned, allowing for targeted search results.\n *\n * - `searchType`: Determines the type of search to perform:\n *   - `\"similarity\"`: Executes a similarity search, retrieving documents based purely\n *     on vector similarity to the query.\n *   - `\"mmr\"`: Executes a maximal marginal relevance (MMR) search, balancing similarity\n *     and diversity in the search results.\n *\n * - `searchKwargs` (optional): Used only if `searchType` is `\"mmr\"`, this object\n *   provides additional options for MMR search, including:\n *   - `fetchK`: Specifies the number of documents to initially fetch before applying\n *     the MMR algorithm, providing a pool from which the most diverse results are selected.\n *   - `lambda`: A diversity parameter, where 0 emphasizes diversity and 1 emphasizes\n *     relevance to the query. Values between 0 and 1 provide a balance of relevance and diversity.\n *\n * @template V - The type of vector store implementing `VectorStoreInterface`.\n */\nexport type VectorStoreRetrieverInput<V extends VectorStoreInterface> = BaseRetrieverInput & ({\n    vectorStore: V;\n    k?: number;\n    filter?: V[\"FilterType\"];\n    searchType?: \"similarity\";\n} | {\n    vectorStore: V;\n    k?: number;\n    filter?: V[\"FilterType\"];\n    searchType: \"mmr\";\n    searchKwargs?: VectorStoreRetrieverMMRSearchKwargs;\n});\n/**\n * Interface for a retriever that uses a vector store to store and retrieve\n * document embeddings. This retriever interface allows for adding documents\n * to the underlying vector store and conducting retrieval operations.\n *\n * `VectorStoreRetrieverInterface` extends `BaseRetrieverInterface` to provide\n * document retrieval capabilities based on vector similarity.\n *\n * @interface VectorStoreRetrieverInterface\n * @extends BaseRetrieverInterface\n */\nexport interface VectorStoreRetrieverInterface<V extends VectorStoreInterface = VectorStoreInterface> extends BaseRetrieverInterface {\n    vectorStore: V;\n    /**\n     * Adds an array of documents to the vector store.\n     *\n     * This method embeds the provided documents and stores them within the\n     * vector store. Additional options can be specified for custom behavior\n     * during the addition process.\n     *\n     * @param documents - An array of documents to embed and add to the vector store.\n     * @param options - Optional settings to customize document addition.\n     * @returns A promise that resolves to an array of document IDs or `void`,\n     *          depending on the implementation.\n     */\n    addDocuments(documents: DocumentInterface[], options?: AddDocumentOptions): Promise<string[] | void>;\n}\n/**\n * Class for retrieving documents from a `VectorStore` based on vector similarity\n * or maximal marginal relevance (MMR).\n *\n * `VectorStoreRetriever` extends `BaseRetriever`, implementing methods for\n * adding documents to the underlying vector store and performing document\n * retrieval with optional configurations.\n *\n * @class VectorStoreRetriever\n * @extends BaseRetriever\n * @implements VectorStoreRetrieverInterface\n * @template V - Type of vector store implementing `VectorStoreInterface`.\n */\nexport declare class VectorStoreRetriever<V extends VectorStoreInterface = VectorStoreInterface> extends BaseRetriever implements VectorStoreRetrieverInterface {\n    static lc_name(): string;\n    get lc_namespace(): string[];\n    /**\n     * The instance of `VectorStore` used for storing and retrieving document embeddings.\n     * This vector store must implement the `VectorStoreInterface` to be compatible\n     * with the retriever’s operations.\n     */\n    vectorStore: V;\n    /**\n     * Specifies the number of documents to retrieve for each search query.\n     * Defaults to 4 if not specified, providing a basic result count for similarity or MMR searches.\n     */\n    k: number;\n    /**\n     * Determines the type of search operation to perform on the vector store.\n     *\n     * - `\"similarity\"` (default): Conducts a similarity search based purely on vector similarity\n     *   to the query.\n     * - `\"mmr\"`: Executes a maximal marginal relevance (MMR) search, balancing relevance and\n     *   diversity in the retrieved results.\n     */\n    searchType: string;\n    /**\n     * Additional options specific to maximal marginal relevance (MMR) search, applicable\n     * only if `searchType` is set to `\"mmr\"`.\n     *\n     * Includes:\n     * - `fetchK`: The initial number of documents fetched before applying the MMR algorithm,\n     *   allowing for a larger selection from which to choose the most diverse results.\n     * - `lambda`: A parameter between 0 and 1 to adjust the relevance-diversity balance,\n     *   where 0 prioritizes diversity and 1 prioritizes relevance.\n     */\n    searchKwargs?: VectorStoreRetrieverMMRSearchKwargs;\n    /**\n     * Optional filter applied to search results, defined by the `FilterType` of the vector store.\n     * Allows for refined, targeted results by restricting the returned documents based\n     * on specified filter criteria.\n     */\n    filter?: V[\"FilterType\"];\n    /**\n     * Returns the type of vector store, as defined by the `vectorStore` instance.\n     *\n     * @returns {string} The vector store type.\n     */\n    _vectorstoreType(): string;\n    /**\n     * Initializes a new instance of `VectorStoreRetriever` with the specified configuration.\n     *\n     * This constructor configures the retriever to interact with a given `VectorStore`\n     * and supports different retrieval strategies, including similarity search and maximal\n     * marginal relevance (MMR) search. Various options allow customization of the number\n     * of documents retrieved per query, filtering based on conditions, and fine-tuning\n     * MMR-specific parameters.\n     *\n     * @param fields - Configuration options for setting up the retriever:\n     *\n     *   - `vectorStore` (required): The `VectorStore` instance implementing `VectorStoreInterface`\n     *     that will be used to store and retrieve document embeddings. This is the core component\n     *     of the retriever, enabling vector-based similarity and MMR searches.\n     *\n     *   - `k` (optional): Specifies the number of documents to retrieve per search query. If not\n     *     provided, defaults to 4. This count determines the number of most relevant documents returned\n     *     for each search operation, balancing performance with comprehensiveness.\n     *\n     *   - `searchType` (optional): Defines the search approach used by the retriever, allowing for\n     *     flexibility between two methods:\n     *       - `\"similarity\"` (default): A similarity-based search, retrieving documents with high vector\n     *         similarity to the query. This type prioritizes relevance and is often used when diversity\n     *         among results is less critical.\n     *       - `\"mmr\"`: Maximal Marginal Relevance search, which combines relevance with diversity. MMR\n     *         is useful for scenarios where varied content is essential, as it selects results that\n     *         both match the query and introduce content diversity.\n     *\n     *   - `filter` (optional): A filter of type `FilterType`, defined by the vector store, that allows\n     *     for refined and targeted search results. This filter applies specified conditions to limit\n     *     which documents are eligible for retrieval, offering control over the scope of results.\n     *\n     *   - `searchKwargs` (optional, applicable only if `searchType` is `\"mmr\"`): Additional settings\n     *     for configuring MMR-specific behavior. These parameters allow further tuning of the MMR\n     *     search process:\n     *       - `fetchK`: The initial number of documents fetched from the vector store before the MMR\n     *         algorithm is applied. Fetching a larger set enables the algorithm to select a more\n     *         diverse subset of documents.\n     *       - `lambda`: A parameter controlling the relevance-diversity balance, where 0 emphasizes\n     *         diversity and 1 prioritizes relevance. Intermediate values provide a blend of the two,\n     *         allowing customization based on the importance of content variety relative to query relevance.\n     */\n    constructor(fields: VectorStoreRetrieverInput<V>);\n    /**\n     * Retrieves relevant documents based on the specified query, using either\n     * similarity or maximal marginal relevance (MMR) search.\n     *\n     * If `searchType` is set to `\"mmr\"`, performs an MMR search to balance\n     * similarity and diversity among results. If `searchType` is `\"similarity\"`,\n     * retrieves results purely based on similarity to the query.\n     *\n     * @param query - The query string used to find relevant documents.\n     * @param runManager - Optional callback manager for tracking retrieval progress.\n     * @returns A promise that resolves to an array of `DocumentInterface` instances\n     *          representing the most relevant documents to the query.\n     * @throws {Error} Throws an error if MMR search is requested but not supported\n     *                 by the vector store.\n     * @protected\n     */\n    _getRelevantDocuments(query: string, runManager?: CallbackManagerForRetrieverRun): Promise<DocumentInterface[]>;\n    /**\n     * Adds an array of documents to the vector store, embedding them as part of\n     * the storage process.\n     *\n     * This method delegates document embedding and storage to the `addDocuments`\n     * method of the underlying vector store.\n     *\n     * @param documents - An array of documents to embed and add to the vector store.\n     * @param options - Optional settings to customize document addition.\n     * @returns A promise that resolves to an array of document IDs or `void`,\n     *          depending on the vector store's implementation.\n     */\n    addDocuments(documents: DocumentInterface[], options?: AddDocumentOptions): Promise<string[] | void>;\n}\n/**\n * Interface defining the structure and operations of a vector store, which\n * facilitates the storage, retrieval, and similarity search of document vectors.\n *\n * `VectorStoreInterface` provides methods for adding, deleting, and searching\n * documents based on vector embeddings, including support for similarity\n * search with optional filtering and relevance-based retrieval.\n *\n * @extends Serializable\n */\nexport interface VectorStoreInterface extends Serializable {\n    /**\n     * Defines the filter type used in search and delete operations. Can be an\n     * object for structured conditions or a string for simpler filtering.\n     */\n    FilterType: object | string;\n    /**\n     * Instance of `EmbeddingsInterface` used to generate vector embeddings for\n     * documents, enabling vector-based search operations.\n     */\n    embeddings: EmbeddingsInterface;\n    /**\n     * Returns a string identifying the type of vector store implementation,\n     * useful for distinguishing between different vector storage backends.\n     *\n     * @returns {string} A string indicating the vector store type.\n     */\n    _vectorstoreType(): string;\n    /**\n     * Adds precomputed vectors and their corresponding documents to the vector store.\n     *\n     * @param vectors - An array of vectors, with each vector representing a document.\n     * @param documents - An array of `DocumentInterface` instances corresponding to each vector.\n     * @param options - Optional configurations for adding documents, potentially covering indexing or metadata handling.\n     * @returns A promise that resolves to an array of document IDs or void, depending on implementation.\n     */\n    addVectors(vectors: number[][], documents: DocumentInterface[], options?: AddDocumentOptions): Promise<string[] | void>;\n    /**\n     * Adds an array of documents to the vector store.\n     *\n     * @param documents - An array of documents to be embedded and stored in the vector store.\n     * @param options - Optional configurations for embedding and storage operations.\n     * @returns A promise that resolves to an array of document IDs or void, depending on implementation.\n     */\n    addDocuments(documents: DocumentInterface[], options?: AddDocumentOptions): Promise<string[] | void>;\n    /**\n     * Deletes documents from the vector store based on the specified parameters.\n     *\n     * @param _params - A flexible object containing key-value pairs that define\n     *                  the conditions for selecting documents to delete.\n     * @returns A promise that resolves once the deletion operation is complete.\n     */\n    // eslint-disable-next-line @typescript-eslint/no-explicit-any\n    delete(_params?: Record<string, any>): Promise<void>;\n    /**\n     * Searches for documents similar to a given vector query and returns them\n     * with similarity scores.\n     *\n     * @param query - A vector representing the query for similarity search.\n     * @param k - The number of similar documents to return.\n     * @param filter - Optional filter based on `FilterType` to restrict results.\n     * @returns A promise that resolves to an array of tuples, each containing a\n     *          `DocumentInterface` and its corresponding similarity score.\n     */\n    similaritySearchVectorWithScore(query: number[], k: number, filter?: this[\"FilterType\"]): Promise<[DocumentInterface, number][]>;\n    /**\n     * Searches for documents similar to a text query, embedding the query\n     * and retrieving documents based on vector similarity.\n     *\n     * @param query - The text query to search for.\n     * @param k - Optional number of similar documents to return.\n     * @param filter - Optional filter based on `FilterType` to restrict results.\n     * @param callbacks - Optional callbacks for tracking progress or events\n     *                    during the search process.\n     * @returns A promise that resolves to an array of `DocumentInterface`\n     *          instances representing similar documents.\n     */\n    similaritySearch(query: string, k?: number, filter?: this[\"FilterType\"], callbacks?: Callbacks): Promise<DocumentInterface[]>;\n    /**\n     * Searches for documents similar to a text query and includes similarity\n     * scores in the result.\n     *\n     * @param query - The text query to search for.\n     * @param k - Optional number of similar documents to return.\n     * @param filter - Optional filter based on `FilterType` to restrict results.\n     * @param callbacks - Optional callbacks for tracking progress or events\n     *                    during the search process.\n     * @returns A promise that resolves to an array of tuples, each containing\n     *          a `DocumentInterface` and its similarity score.\n     */\n    similaritySearchWithScore(query: string, k?: number, filter?: this[\"FilterType\"], callbacks?: Callbacks): Promise<[DocumentInterface, number][]>;\n    /**\n     * Return documents selected using the maximal marginal relevance.\n     * Maximal marginal relevance optimizes for similarity to the query AND diversity\n     * among selected documents.\n     *\n     * @param {string} query - Text to look up documents similar to.\n     * @param {number} options.k - Number of documents to return.\n     * @param {number} options.fetchK - Number of documents to fetch before passing to the MMR algorithm.\n     * @param {number} options.lambda - Number between 0 and 1 that determines the degree of diversity among the results,\n     *                 where 0 corresponds to maximum diversity and 1 to minimum diversity.\n     * @param {this[\"FilterType\"]} options.filter - Optional filter\n     * @param _callbacks\n     *\n     * @returns {Promise<DocumentInterface[]>} - List of documents selected by maximal marginal relevance.\n     */\n    maxMarginalRelevanceSearch?(query: string, options: MaxMarginalRelevanceSearchOptions<this[\"FilterType\"]>, callbacks: Callbacks | undefined): Promise<DocumentInterface[]>;\n    /**\n     * Converts the vector store into a retriever, making it suitable for use in\n     * retrieval-based workflows and allowing additional configuration.\n     *\n     * @param kOrFields - Optional parameter for specifying either the number of\n     *                    documents to retrieve or partial retriever configurations.\n     * @param filter - Optional filter based on `FilterType` for retrieval restriction.\n     * @param callbacks - Optional callbacks for tracking retrieval events or progress.\n     * @param tags - General-purpose tags to add contextual information to the retriever.\n     * @param metadata - General-purpose metadata providing additional context\n     *                   for retrieval.\n     * @param verbose - If `true`, enables detailed logging during retrieval.\n     * @returns An instance of `VectorStoreRetriever` configured with the specified options.\n     */\n    asRetriever(kOrFields?: number | Partial<VectorStoreRetrieverInput<this>>, filter?: this[\"FilterType\"], callbacks?: Callbacks, tags?: string[], metadata?: Record<string, unknown>, verbose?: boolean): VectorStoreRetriever<this>;\n}\n/**\n * Abstract class representing a vector storage system for performing\n * similarity searches on embedded documents.\n *\n * `VectorStore` provides methods for adding precomputed vectors or documents,\n * removing documents based on criteria, and performing similarity searches\n * with optional scoring. Subclasses are responsible for implementing specific\n * storage mechanisms and the exact behavior of certain abstract methods.\n *\n * @abstract\n * @extends Serializable\n * @implements VectorStoreInterface\n */\nexport declare abstract class VectorStore extends Serializable implements VectorStoreInterface {\n    FilterType: object | string;\n    /**\n     * Namespace within LangChain to uniquely identify this vector store's\n     * location, based on the vector store type.\n     *\n     * @internal\n     */\n    // Only ever instantiated in main LangChain\n    lc_namespace: string[];\n    /**\n     * Embeddings interface for generating vector embeddings from text queries,\n     * enabling vector-based similarity searches.\n     */\n    embeddings: EmbeddingsInterface;\n    /**\n     * Initializes a new vector store with embeddings and database configuration.\n     *\n     * @param embeddings - Instance of `EmbeddingsInterface` used to embed queries.\n     * @param dbConfig - Configuration settings for the database or storage system.\n     */\n    // eslint-disable-next-line @typescript-eslint/no-explicit-any\n    constructor(embeddings: EmbeddingsInterface, dbConfig: Record<string, any>);\n    /**\n     * Returns a string representing the type of vector store, which subclasses\n     * must implement to identify their specific vector storage type.\n     *\n     * @returns {string} A string indicating the vector store type.\n     * @abstract\n     */\n    abstract _vectorstoreType(): string;\n    /**\n     * Adds precomputed vectors and corresponding documents to the vector store.\n     *\n     * @param vectors - An array of vectors representing each document.\n     * @param documents - Array of documents associated with each vector.\n     * @param options - Optional configuration for adding vectors, such as indexing.\n     * @returns A promise resolving to an array of document IDs or void, based on implementation.\n     * @abstract\n     */\n    abstract addVectors(vectors: number[][], documents: DocumentInterface[], options?: AddDocumentOptions): Promise<string[] | void>;\n    /**\n     * Adds documents to the vector store, embedding them first through the\n     * `embeddings` instance.\n     *\n     * @param documents - Array of documents to embed and add.\n     * @param options - Optional configuration for embedding and storing documents.\n     * @returns A promise resolving to an array of document IDs or void, based on implementation.\n     * @abstract\n     */\n    abstract addDocuments(documents: DocumentInterface[], options?: AddDocumentOptions): Promise<string[] | void>;\n    /**\n     * Deletes documents from the vector store based on the specified parameters.\n     *\n     * @param _params - Flexible key-value pairs defining conditions for document deletion.\n     * @returns A promise that resolves once the deletion is complete.\n     */\n    // eslint-disable-next-line @typescript-eslint/no-explicit-any\n    delete(_params?: Record<string, any>): Promise<void>;\n    /**\n     * Performs a similarity search using a vector query and returns results\n     * along with their similarity scores.\n     *\n     * @param query - Vector representing the search query.\n     * @param k - Number of similar results to return.\n     * @param filter - Optional filter based on `FilterType` to restrict results.\n     * @returns A promise resolving to an array of tuples containing documents and their similarity scores.\n     * @abstract\n     */\n    abstract similaritySearchVectorWithScore(query: number[], k: number, filter?: this[\"FilterType\"]): Promise<[DocumentInterface, number][]>;\n    /**\n     * Searches for documents similar to a text query by embedding the query and\n     * performing a similarity search on the resulting vector.\n     *\n     * @param query - Text query for finding similar documents.\n     * @param k - Number of similar results to return. Defaults to 4.\n     * @param filter - Optional filter based on `FilterType`.\n     * @param _callbacks - Optional callbacks for monitoring search progress\n     * @returns A promise resolving to an array of `DocumentInterface` instances representing similar documents.\n     */\n    similaritySearch(query: string, k?: number, filter?: this[\"FilterType\"] | undefined, _callbacks?: Callbacks | undefined // implement passing to embedQuery later\n    ): Promise<DocumentInterface[]>;\n    /**\n     * Searches for documents similar to a text query by embedding the query,\n     * and returns results with similarity scores.\n     *\n     * @param query - Text query for finding similar documents.\n     * @param k - Number of similar results to return. Defaults to 4.\n     * @param filter - Optional filter based on `FilterType`.\n     * @param _callbacks - Optional callbacks for monitoring search progress\n     * @returns A promise resolving to an array of tuples, each containing a\n     *          document and its similarity score.\n     */\n    similaritySearchWithScore(query: string, k?: number, filter?: this[\"FilterType\"] | undefined, _callbacks?: Callbacks | undefined // implement passing to embedQuery later\n    ): Promise<[DocumentInterface, number][]>;\n    /**\n     * Return documents selected using the maximal marginal relevance.\n     * Maximal marginal relevance optimizes for similarity to the query AND diversity\n     * among selected documents.\n     *\n     * @param {string} query - Text to look up documents similar to.\n     * @param {number} options.k - Number of documents to return.\n     * @param {number} options.fetchK - Number of documents to fetch before passing to the MMR algorithm.\n     * @param {number} options.lambda - Number between 0 and 1 that determines the degree of diversity among the results,\n     *                 where 0 corresponds to maximum diversity and 1 to minimum diversity.\n     * @param {this[\"FilterType\"]} options.filter - Optional filter\n     * @param _callbacks\n     *\n     * @returns {Promise<DocumentInterface[]>} - List of documents selected by maximal marginal relevance.\n     */\n    maxMarginalRelevanceSearch?(query: string, options: MaxMarginalRelevanceSearchOptions<this[\"FilterType\"]>, _callbacks: Callbacks | undefined // implement passing to embedQuery later\n    ): Promise<DocumentInterface[]>;\n    /**\n     * Creates a `VectorStore` instance from an array of text strings and optional\n     * metadata, using the specified embeddings and database configuration.\n     *\n     * Subclasses must implement this method to define how text and metadata\n     * are embedded and stored in the vector store. Throws an error if not overridden.\n     *\n     * @param _texts - Array of strings representing the text documents to be stored.\n     * @param _metadatas - Metadata for the texts, either as an array (one for each text)\n     *                     or a single object (applied to all texts).\n     * @param _embeddings - Instance of `EmbeddingsInterface` to embed the texts.\n     * @param _dbConfig - Database configuration settings.\n     * @returns A promise that resolves to a new `VectorStore` instance.\n     * @throws {Error} Throws an error if this method is not overridden by a subclass.\n     */\n    static fromTexts(_texts: string[], _metadatas: object[] | object, _embeddings: EmbeddingsInterface, \n    // eslint-disable-next-line @typescript-eslint/no-explicit-any\n    _dbConfig: Record<string, any>): Promise<VectorStore>;\n    /**\n     * Creates a `VectorStore` instance from an array of documents, using the specified\n     * embeddings and database configuration.\n     *\n     * Subclasses must implement this method to define how documents are embedded\n     * and stored. Throws an error if not overridden.\n     *\n     * @param _docs - Array of `DocumentInterface` instances representing the documents to be stored.\n     * @param _embeddings - Instance of `EmbeddingsInterface` to embed the documents.\n     * @param _dbConfig - Database configuration settings.\n     * @returns A promise that resolves to a new `VectorStore` instance.\n     * @throws {Error} Throws an error if this method is not overridden by a subclass.\n     */\n    static fromDocuments(_docs: DocumentInterface[], _embeddings: EmbeddingsInterface, \n    // eslint-disable-next-line @typescript-eslint/no-explicit-any\n    _dbConfig: Record<string, any>): Promise<VectorStore>;\n    /**\n     * Creates a `VectorStoreRetriever` instance with flexible configuration options.\n     *\n     * @param kOrFields\n     *    - If a number is provided, it sets the `k` parameter (number of items to retrieve).\n     *    - If an object is provided, it should contain various configuration options.\n     * @param filter\n     *    - Optional filter criteria to limit the items retrieved based on the specified filter type.\n     * @param callbacks\n     *    - Optional callbacks that may be triggered at specific stages of the retrieval process.\n     * @param tags\n     *    - Tags to categorize or label the `VectorStoreRetriever`. Defaults to an empty array if not provided.\n     * @param metadata\n     *    - Additional metadata as key-value pairs to add contextual information for the retrieval process.\n     * @param verbose\n     *    - If `true`, enables detailed logging for the retrieval process. Defaults to `false`.\n     *\n     * @returns\n     *    - A configured `VectorStoreRetriever` instance based on the provided parameters.\n     *\n     * @example\n     * Basic usage with a `k` value:\n     * ```typescript\n     * const retriever = myVectorStore.asRetriever(5);\n     * ```\n     *\n     * Usage with a configuration object:\n     * ```typescript\n     * const retriever = myVectorStore.asRetriever({\n     *   k: 10,\n     *   filter: myFilter,\n     *   tags: ['example', 'test'],\n     *   verbose: true,\n     *   searchType: 'mmr',\n     *   searchKwargs: { alpha: 0.5 },\n     * });\n     * ```\n     */\n    asRetriever(kOrFields?: number | Partial<VectorStoreRetrieverInput<this>>, filter?: this[\"FilterType\"], callbacks?: Callbacks, tags?: string[], metadata?: Record<string, unknown>, verbose?: boolean): VectorStoreRetriever<this>;\n}\n/**\n * Abstract class extending `VectorStore` that defines a contract for saving\n * and loading vector store instances.\n *\n * The `SaveableVectorStore` class allows vector store implementations to\n * persist their data and retrieve it when needed.The format for saving and\n * loading data is left to the implementing subclass.\n *\n * Subclasses must implement the `save` method to handle their custom\n * serialization logic, while the `load` method enables reconstruction of a\n * vector store from saved data, requiring compatible embeddings through the\n * `EmbeddingsInterface`.\n *\n * @abstract\n * @extends VectorStore\n */\nexport declare abstract class SaveableVectorStore extends VectorStore {\n    /**\n     * Saves the current state of the vector store to the specified directory.\n     *\n     * This method must be implemented by subclasses to define their own\n     * serialization process for persisting vector data. The implementation\n     * determines the structure and format of the saved data.\n     *\n     * @param directory - The directory path where the vector store data\n     * will be saved.\n     * @abstract\n     */\n    abstract save(directory: string): Promise<void>;\n    /**\n     * Loads a vector store instance from the specified directory, using the\n     * provided embeddings to ensure compatibility.\n     *\n     * This static method reconstructs a `SaveableVectorStore` from previously\n     * saved data. Implementations should interpret the saved data format to\n     * recreate the vector store instance.\n     *\n     * @param _directory - The directory path from which the vector store\n     * data will be loaded.\n     * @param _embeddings - An instance of `EmbeddingsInterface` to align\n     * the embeddings with the loaded vector data.\n     * @returns A promise that resolves to a `SaveableVectorStore` instance\n     * constructed from the saved data.\n     */\n    static load(_directory: string, _embeddings: EmbeddingsInterface): Promise<SaveableVectorStore>;\n}\nexport {};\n"],"mappings":";;;;;;;;;;AAImF;AAoCnF;AAyBA,KAxDKQ,kBAAAA,GAAqBC,MAwDdG,CAAAA,MAAAA,EAAAA,GAAmC,CAAA;AAkD/C;;;;;;;;;AAUsD;AAatD;;;;;;;;;AAAoI;AA6BpI;;;;;;;;;;AAyG+FX,KAxOnFS,iCAwOmFT,CAAAA,UAAAA,CAAAA,GAAAA;EAAiB,CAAA,EAAzBgB,MAAAA;EAAO,MAalEhB,CAAAA,EAAAA,MAAAA;EAAiB,MAAcO,CAAAA,EAAAA,MAAAA;EAAkB,MAAGS,CAAAA,EAjPnEN,UAiPmEM;CAAO;;AAtHwE;AAkI/J;;;;;;;;;;;;;;;;;AAgFuHhB,KAxT3GW,mCAAAA,GAwT2GX;EAAiB,MAA1BgB,CAAAA,EAAAA,MAAAA;EAAO,MAgB7DP,CAAAA,EAAAA,MAAAA;CAAiC;;;;;;;;;AAhG/B;AA8H1D;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;AAwL+JD,KA5enJI,yBA4emJJ,CAAAA,UA5e/GK,oBA4e+GL,CAAAA,GA5evFL,kBA4euFK,GAAAA,CAAAA;EAAM,WAAuCS,EA3e3LH,CA2e2LG;EAAoB,CAAA,CAAA,EAxL9Kb,MAAAA;EAAY,MAAYS,CAAAA,EAjT7DC,CAiT6DD,CAAAA,YAAAA,CAAAA;EAAoB,UAAA,CAAA,EAAA,YAAA;AA0M9F,CAAA,GAA8BO;EAAmB,WAAA,EAxfhCN,CAwfgC;EAAA,CAAA,CAAA,EAYXE,MAAAA;EAAO,MAgBIjB,CAAAA,EAlhBpCe,CAkhBoCf,CAAAA,YAAAA,CAAAA;EAAmB,UAAWqB,EAAAA,KAAAA;EAAmB,YAA3BJ,CAAAA,EAhhBpDL,mCAghBoDK;CAAO,CAAA;AA5BT;;;;;;;;;;;UAvepDD,wCAAwCF,uBAAuBA,8BAA8BX;eAC7FY;;;;;;;;;;;;;0BAaWd,+BAA+BO,qBAAqBS;;;;;;;;;;;;;;;cAe3DC,+BAA+BJ,uBAAuBA,8BAA8BZ,aAAAA,YAAyBc;;;;;;;;eAQjHD;;;;;;;;;;;;;;;;;;;;;;;;;iBAyBEH;;;;;;WAMNG;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;sBAiDWF,0BAA0BE;;;;;;;;;;;;;;;;;oDAiBIT,iCAAiCW,QAAQhB;;;;;;;;;;;;;0BAanEA,+BAA+BO,qBAAqBS;;;;;;;;;;;;UAY/DH,oBAAAA,SAA6BT;;;;;;;;;;cAU9BL;;;;;;;;;;;;;;;;6CAgB+BC,+BAA+BO,qBAAqBS;;;;;;;;0BAQvEhB,+BAA+BO,qBAAqBS;;;;;;;;;mBAS3DR,sBAAsBQ;;;;;;;;;;;4FAWmDA,SAAShB;;;;;;;;;;;;;uFAadM,YAAYU,QAAQhB;;;;;;;;;;;;;gGAaXM,YAAYU,SAAShB;;;;;;;;;;;;;;;;sDAgB/DS,kEAAkEH,wBAAwBU,QAAQhB;;;;;;;;;;;;;;;mCAerHkB,QAAQN,2EAA2EN,uCAAuCE,6CAA6CS;;;;;;;;;;;;;;;uBAe9KE,WAAAA,SAAoBf,YAAAA,YAAwBS;;;;;;;;;;;;;;cAc1Dd;;;;;;;;0BAQYA,+BAA+BS;;;;;;;;;;;;;;;;;;sDAkBHR,+BAA+BO,qBAAqBS;;;;;;;;;;mCAUvEhB,+BAA+BO,qBAAqBS;;;;;;;;mBAQpER,sBAAsBQ;;;;;;;;;;;qGAW4DA,SAAShB;;;;;;;;;;;oGAWVM;KAC/FU,QAAQhB;;;;;;;;;;;;6GAYgGM;KACxGU,SAAShB;;;;;;;;;;;;;;;;sDAgBwCS,mEAAmEH;KACpHU,QAAQhB;;;;;;;;;;;;;;;;iFAgBoED;;aAEpES,sBAAsBQ,QAAQG;;;;;;;;;;;;;;8BAcbnB,kCAAkCD;;aAEnDS,sBAAsBQ,QAAQG;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;mCAuCRD,QAAQN,2EAA2EN,uCAAuCE,6CAA6CS;;;;;;;;;;;;;;;;;;uBAkB9KG,mBAAAA,SAA4BD,WAAAA;;;;;;;;;;;;oCAYpBH;;;;;;;;;;;;;;;;+CAgBWjB,sBAAsBiB,QAAQI"}