import { type Pool, type PoolClient, type PoolConfig } from "pg";
import { MaxMarginalRelevanceSearchOptions, VectorStore } from "@langchain/core/vectorstores";
import type { EmbeddingsInterface } from "@langchain/core/embeddings";
import { Document } from "@langchain/core/documents";
type Metadata = Record<string, unknown>;
export type DistanceStrategy = "cosine" | "innerProduct" | "euclidean";
/**
 * Interface that defines the arguments required to create a
 * `PGVectorStore` instance. It includes Postgres connection options,
 * table name, filter, and verbosity level.
 */
export interface PGVectorStoreArgs {
    postgresConnectionOptions?: PoolConfig;
    pool?: Pool;
    tableName: string;
    collectionTableName?: string;
    collectionName?: string;
    collectionMetadata?: Metadata | null;
    schemaName?: string | null;
    extensionSchemaName?: string | null;
    columns?: {
        idColumnName?: string;
        vectorColumnName?: string;
        contentColumnName?: string;
        metadataColumnName?: string;
    };
    filter?: Metadata;
    verbose?: boolean;
    /**
     * The amount of documents to chunk by when
     * adding vectors.
     * @default 500
     */
    chunkSize?: number;
    ids?: string[];
    distanceStrategy?: DistanceStrategy;
}
/**
 * PGVector vector store integration.
 *
 * Setup:
 * Install `@langchain/community` and `pg`.
 *
 * If you wish to generate ids, you should also install the `uuid` package.
 *
 * ```bash
 * npm install @langchain/community pg uuid
 * ```
 *
 * ## [Constructor args](https://api.js.langchain.com/classes/_langchain_community.vectorstores_pgvector.PGVectorStore.html#constructor)
 *
 * <details open>
 * <summary><strong>Instantiate</strong></summary>
 *
 * ```typescript
 * import {
 *   PGVectorStore,
 *   DistanceStrategy,
 * } from "@langchain/community/vectorstores/pgvector";
 *
 * // Or other embeddings
 * import { OpenAIEmbeddings } from "@langchain/openai";
 * import { PoolConfig } from "pg";
 *
 * const embeddings = new OpenAIEmbeddings({
 *   model: "text-embedding-3-small",
 * });
 *
 * // Sample config
 * const config = {
 *   postgresConnectionOptions: {
 *     type: "postgres",
 *     host: "127.0.0.1",
 *     port: 5433,
 *     user: "myuser",
 *     password: "ChangeMe",
 *     database: "api",
 *   } as PoolConfig,
 *   tableName: "testlangchainjs",
 *   columns: {
 *     idColumnName: "id",
 *     vectorColumnName: "vector",
 *     contentColumnName: "content",
 *     metadataColumnName: "metadata",
 *   },
 *   // supported distance strategies: cosine (default), innerProduct, or euclidean
 *   distanceStrategy: "cosine" as DistanceStrategy,
 * };
 *
 * const vectorStore = await PGVectorStore.initialize(embeddings, config);
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Add documents</strong></summary>
 *
 * ```typescript
 * import type { Document } from '@langchain/core/documents';
 *
 * const document1 = { pageContent: "foo", metadata: { baz: "bar" } };
 * const document2 = { pageContent: "thud", metadata: { bar: "baz" } };
 * const document3 = { pageContent: "i will be deleted :(", metadata: {} };
 *
 * const documents: Document[] = [document1, document2, document3];
 * const ids = ["1", "2", "3"];
 * await vectorStore.addDocuments(documents, { ids });
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Delete documents</strong></summary>
 *
 * ```typescript
 * await vectorStore.delete({ ids: ["3"] });
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Similarity search</strong></summary>
 *
 * ```typescript
 * const results = await vectorStore.similaritySearch("thud", 1);
 * for (const doc of results) {
 *   console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
 * }
 * // Output: * thud [{"baz":"bar"}]
 * ```
 * </details>
 *
 * <br />
 *
 *
 * <details>
 * <summary><strong>Similarity search with filter</strong></summary>
 *
 * ```typescript
 * const resultsWithFilter = await vectorStore.similaritySearch("thud", 1, { baz: "bar" });
 *
 * for (const doc of resultsWithFilter) {
 *   console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
 * }
 * // Output: * foo [{"baz":"bar"}]
 * ```
 * </details>
 *
 * <br />
 *
 *
 * <details>
 * <summary><strong>Similarity search with score</strong></summary>
 *
 * ```typescript
 * const resultsWithScore = await vectorStore.similaritySearchWithScore("qux", 1);
 * for (const [doc, score] of resultsWithScore) {
 *   console.log(`* [SIM=${score.toFixed(6)}] ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
 * }
 * // Output: * [SIM=0.000000] qux [{"bar":"baz","baz":"bar"}]
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>As a retriever</strong></summary>
 *
 * ```typescript
 * const retriever = vectorStore.asRetriever({
 *   searchType: "mmr", // Leave blank for standard similarity search
 *   k: 1,
 * });
 * const resultAsRetriever = await retriever.invoke("thud");
 * console.log(resultAsRetriever);
 *
 * // Output: [Document({ metadata: { "baz":"bar" }, pageContent: "thud" })]
 * ```
 * </details>
 *
 * <br />
 */
export declare class PGVectorStore extends VectorStore {
    FilterType: Metadata;
    tableName: string;
    collectionTableName?: string;
    collectionName: string;
    collectionMetadata: Metadata | null;
    schemaName: string | null;
    idColumnName: string;
    vectorColumnName: string;
    contentColumnName: string;
    extensionSchemaName: string | null;
    metadataColumnName: string;
    filter?: Metadata;
    _verbose?: boolean;
    pool: Pool;
    client?: PoolClient;
    chunkSize: number;
    distanceStrategy?: DistanceStrategy;
    _vectorstoreType(): string;
    constructor(embeddings: EmbeddingsInterface, config: PGVectorStoreArgs);
    get computedTableName(): string;
    get computedCollectionTableName(): string;
    get computedOperatorString(): string;
    /**
     * Static method to create a new `PGVectorStore` instance from a
     * connection. It creates a table if one does not exist, and calls
     * `connect` to return a new instance of `PGVectorStore`.
     *
     * @param embeddings - Embeddings instance.
     * @param fields - `PGVectorStoreArgs` instance
     * @param fields.dimensions Number of dimensions in your vector data type. For example, use 1536 for OpenAI's `text-embedding-3-small`. If not set, indexes like HNSW might not be used during query time.
     * @returns A new instance of `PGVectorStore`.
     */
    static initialize(embeddings: EmbeddingsInterface, config: PGVectorStoreArgs & {
        dimensions?: number;
    }): Promise<PGVectorStore>;
    protected _initializeClient(): Promise<void>;
    /**
     * Method to add documents to the vector store. It converts the documents into
     * vectors, and adds them to the store.
     *
     * @param documents - Array of `Document` instances.
     * @param options - Optional arguments for adding documents
     * @returns Promise that resolves when the documents have been added.
     */
    addDocuments(documents: Document[], options?: {
        ids?: string[];
    }): Promise<void>;
    /**
     * Inserts a row for the collectionName provided at initialization if it does not
     * exist and returns the collectionId.
     *
     * @returns The collectionId for the given collectionName.
     */
    getOrCreateCollection(): Promise<string>;
    /**
     * Generates the SQL placeholders for a specific row at the provided index.
     *
     * @param index - The index of the row for which placeholders need to be generated.
     * @param numOfColumns - The number of columns we are inserting data into.
     * @returns The SQL placeholders for the row values.
     */
    private generatePlaceholderForRowAt;
    /**
     * Constructs the SQL query for inserting rows into the specified table.
     *
     * @param rows - The rows of data to be inserted, consisting of values and records.
     * @returns The complete SQL INSERT INTO query string.
     */
    private buildInsertQuery;
    /**
     * Method to add vectors to the vector store. It converts the vectors into
     * rows and inserts them into the database.
     *
     * @param vectors - Array of vectors.
     * @param documents - Array of `Document` instances.
     * @param options - Optional arguments for adding documents
     * @returns Promise that resolves when the vectors have been added.
     */
    addVectors(vectors: number[][], documents: Document[], options?: {
        ids?: string[];
    }): Promise<void>;
    /**
     * Method to delete documents from the vector store. It deletes the
     * documents that match the provided ids.
     *
     * @param ids - Array of document ids.
     * @returns Promise that resolves when the documents have been deleted.
     */
    private deleteById;
    /**
     * Method to delete documents from the vector store. It deletes the
     * documents whose metadata contains the filter.
     *
     * @param filter - An object representing the Metadata filter.
     * @returns Promise that resolves when the documents have been deleted.
     */
    private deleteByFilter;
    /**
     * Method to delete documents from the vector store. It deletes the
     * documents that match the provided ids or metadata filter. Matches ids
     * exactly and metadata filter according to postgres jsonb containment. Ids and filter
     * are mutually exclusive.
     *
     * @param params - Object containing either an array of ids or a metadata filter object.
     * @returns Promise that resolves when the documents have been deleted.
     * @throws Error if neither ids nor filter are provided, or if both are provided.
     * @example <caption>Delete by ids</caption>
     * await vectorStore.delete({ ids: ["id1", "id2"] });
     * @example <caption>Delete by filter</caption>
     * await vectorStore.delete({ filter: { a: 1, b: 2 } });
     */
    delete(params: {
        ids?: string[];
        filter?: Metadata;
    }): Promise<void>;
    /**
     * Method to perform a similarity search in the vector store. It returns the `k` most similar documents to the query text.
     * @param query - Query vector.
     * @param k - Number of most similar documents to return.
     * @param filter - Optional filter to apply to the search.
     * @param includeEmbedding Whether to include the embedding vectors in the results.
     * @returns Promise that resolves with an array of tuples, each containing a `Document` and its similarity score.
     */
    private searchPostgres;
    /**
     * Method to perform a similarity search in the vector store. It returns
     * the `k` most similar documents to the query vector, along with their
     * similarity scores.
     * @param query - Query vector.
     * @param k - Number of most similar documents to return.
     * @param filter - Optional filter to apply to the search.
     * @returns Promise that resolves with an array of tuples, each containing a `Document` and its similarity score.
     */
    similaritySearchVectorWithScore(query: number[], k: number, filter?: this["FilterType"]): Promise<[Document, number][]>;
    /**
     * Method to ensure the existence of the table in the database. It creates
     * the table if it does not already exist.
     * @param dimensions Number of dimensions in your vector data type. For example, use 1536 for OpenAI's `text-embedding-3-small`. If not set, indexes like HNSW might not be used during query time.
     * @returns Promise that resolves when the table has been ensured.
     */
    ensureTableInDatabase(dimensions?: number): Promise<void>;
    /**
     * Method to ensure the existence of the collection table in the database.
     * It creates the table if it does not already exist.
     *
     * @returns Promise that resolves when the collection table has been ensured.
     */
    ensureCollectionTableInDatabase(): Promise<void>;
    /**
     * Static method to create a new `PGVectorStore` instance from an
     * array of texts and their metadata. It converts the texts into
     * `Document` instances and adds them to the store.
     *
     * @param texts - Array of texts.
     * @param metadatas - Array of metadata objects or a single metadata object.
     * @param embeddings - Embeddings instance.
     * @param dbConfig - `PGVectorStoreArgs` instance.
     * @returns Promise that resolves with a new instance of `PGVectorStore`.
     */
    static fromTexts(texts: string[], metadatas: object[] | object, embeddings: EmbeddingsInterface, dbConfig: PGVectorStoreArgs & {
        dimensions?: number;
    }): Promise<PGVectorStore>;
    /**
     * Static method to create a new `PGVectorStore` instance from an
     * array of `Document` instances. It adds the documents to the store.
     *
     * @param docs - Array of `Document` instances.
     * @param embeddings - Embeddings instance.
     * @param dbConfig - `PGVectorStoreArgs` instance.
     * @returns Promise that resolves with a new instance of `PGVectorStore`.
     */
    static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, dbConfig: PGVectorStoreArgs & {
        dimensions?: number;
    }): Promise<PGVectorStore>;
    /**
     * Closes all the clients in the pool and terminates the pool.
     *
     * @returns Promise that resolves when all clients are closed and the pool is terminated.
     */
    end(): Promise<void>;
    /**
     * Method to create the HNSW index on the vector column.
     *
     * @param dimensions - Defines the number of dimensions in your vector data type, up to 2000. For example, use 1536 for OpenAI's text-embedding-ada-002 and Amazon's amazon.titan-embed-text-v1 models.
     * @param m - The max number of connections per layer (16 by default). Index build time improves with smaller values, while higher values can speed up search queries.
     * @param efConstruction -  The size of the dynamic candidate list for constructing the graph (64 by default). A higher value can potentially improve the index quality at the cost of index build time.
     * @param distanceFunction -  The distance function name you want to use, is automatically selected based on the distanceStrategy.
     * @param namespace -  The namespace is used to create the index with a specific name. This is useful when you want to create multiple indexes on the same database schema (within the same schema in PostgreSQL, the index name must be unique across all tables).
     * @returns Promise that resolves with the query response of creating the index.
     */
    createHnswIndex(config: {
        dimensions: number;
        m?: number;
        efConstruction?: number;
        distanceFunction?: string;
        namespace?: string;
    }): Promise<void>;
    /**
     * 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=4 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[]>;
}
export {};