@langchain/community/dist/vectorstores/azion_edgesql.d.cts.map

Third-party integrations for LangChain.js

{"version":3,"file":"azion_edgesql.d.cts","names":["VectorStore","EmbeddingsInterface","Document","AzionFilter","Operator","Column","AzionSetupOptions","HybridSearchOptions","FullTextSearchOptions","SimilaritySearchOptions","AzionVectorStoreArgs","AzionVectorStore","Promise","Record"],"sources":["../../src/vectorstores/azion_edgesql.d.ts"],"sourcesContent":["import { VectorStore } from \"@langchain/core/vectorstores\";\nimport type { EmbeddingsInterface } from \"@langchain/core/embeddings\";\nimport { Document } from \"@langchain/core/documents\";\n/**\n * Represents a filter condition for querying the Azion database\n * @property operator - The comparison operator to use (e.g. =, !=, >, <, etc)\n * @property column - The database column to filter on\n * @property value - The value to compare against\n */\nexport type AzionFilter = {\n    operator: Operator;\n    column: Column;\n    value: string;\n};\n/**\n * Represents a database column name\n */\nexport type Column = string;\n/**\n * Valid SQL operators that can be used in filter conditions\n */\nexport type Operator = \"=\" | \"!=\" | \">\" | \"<>\" | \"<\" | \">=\" | \"<=\" | \"LIKE\" | \"NOT LIKE\" | \"IN\" | \"NOT IN\" | \"IS NULL\" | \"IS NOT NULL\";\n/**\n * Interface for configuring the Azion vector store setup\n * @property {string[]} columns - Additional columns to create in the database table. If expandedMetadata is true, this is required.\n * @property {\"vector\" | \"hybrid\"} mode - The search mode to enable:\n *                                       \"vector\" - Only vector similarity search\n *                                       \"hybrid\" - Both vector and full-text search capabilities\n */\ninterface AzionSetupOptions {\n    columns?: string[];\n    mode: \"vector\" | \"hybrid\";\n}\n/**\n * Interface for configuring hybrid search options that combines vector and full-text search\n * @property {number} kfts - Number of results to return from full-text search\n * @property {number} kvector - Number of results to return from vector similarity search\n * @property {AzionFilter[]} [filter] - Optional array of filters to apply to search results\n * @property {string[]} [metadataItems] - Optional array of metadata fields to include in results\n */\ninterface HybridSearchOptions {\n    kfts: number;\n    kvector: number;\n    filter?: AzionFilter[];\n    metadataItems?: string[];\n}\n/**\n * Interface for configuring full-text search options\n * @property {number} kfts - Number of results to return from full-text search\n * @property {AzionFilter[]} [filter] - Optional array of filters to apply to search results\n * @property {string[]} [metadataItems] - Optional array of metadata fields to include in results\n */\ninterface FullTextSearchOptions {\n    kfts: number;\n    filter?: AzionFilter[];\n    metadataItems?: string[];\n}\n/**\n * Interface for configuring vector similarity search options\n * @property {number} kvector - Number of results to return from vector similarity search\n * @property {AzionFilter[]} [filter] - Optional array of filters to apply to search results\n * @property {string[]} [metadataItems] - Optional array of metadata fields to include in results\n */\ninterface SimilaritySearchOptions {\n    kvector: number;\n    filter?: AzionFilter[];\n    metadataItems?: string[];\n}\n/**\n * Interface for the arguments required to initialize an Azion library.\n */\nexport interface AzionVectorStoreArgs {\n    tableName: string;\n    filter?: AzionFilter[];\n    dbName: string;\n    expandedMetadata?: boolean;\n}\n/**\n * Example usage:\n * ```ts\n * // Initialize the vector store\n * const vectorStore = new AzionVectorStore(embeddings, {\n *   dbName: \"mydb\",\n *   tableName: \"documents\"\n * });\n *\n * // Setup database with hybrid search and metadata columns\n * await vectorStore.setupDatabase({\n *   columns: [\"topic\", \"language\"],\n *   mode: \"hybrid\"\n * });\n *\n *\n * // OR: Initialize using the static create method\n * const vectorStore = await AzionVectorStore.initialize(embeddings, {\n *   dbName: \"mydb\",\n *   tableName: \"documents\"\n * }, {\n *   columns: [\"topic\", \"language\"],\n *   mode: \"hybrid\"\n * });\n *\n * By default, the columns are not expanded, meaning that the metadata is stored in a single column:\n *\n * // Setup database with hybrid search and metadata columns\n * await vectorStore.setupDatabase({\n *   columns: [\"*\"],\n *   mode: \"hybrid\"\n * });\n *\n * // Add documents to the vector store\n * await vectorStore.addDocuments([\n *   new Document({\n *     pageContent: \"Australia is known for its unique wildlife\",\n *     metadata: { topic: \"nature\", language: \"en\" }\n *   })\n * ]);\n *\n * // Perform similarity search\n * const results = await vectorStore.similaritySearch(\n *   \"coral reefs in Australia\",\n *   2, // Return top 2 results\n *   { filter: [{ operator: \"=\", column: \"topic\", string: \"biology\" }] } // Optional AzionFilter\n * );\n *\n * // Perform full text search\n * const ftResults = await vectorStore.fullTextSearch(\n *   \"Sydney Opera House\",\n *   1, // Return top result\n *   { filter: [{ operator: \"=\", column: \"language\", string: \"en\" }] } // Optional AzionFilter\n * );\n * ```\n */\nexport declare class AzionVectorStore extends VectorStore {\n    /** Type declaration for filter type */\n    FilterType: AzionFilter[];\n    /** Name of the main table to store vectors and documents */\n    tableName: string;\n    /** Name of the database to use */\n    dbName: string;\n    /** Whether the metadata is contained in a single column or multiple columns */\n    expandedMetadata: boolean;\n    _vectorstoreType(): string;\n    constructor(embeddings: EmbeddingsInterface, args: AzionVectorStoreArgs);\n    /**\n     * Creates a new vector store instance and sets up the database.\n     * @param {EmbeddingsInterface} embeddings - The embeddings interface to use for vectorizing documents\n     * @param {AzionVectorStoreArgs} args - Configuration options:\n     *   @param {string} args.dbName - Name of the database to create/use\n     *   @param {string} args.tableName - Name of the table to create/use\n     * @param {AzionSetupOptions} setupOptions - Database setup options:\n     *   @param {string[]} setupOptions.columns - Additional columns to create in the table beyond the required ones. If expandedMetadata is true, this is required.\n     *   @param {\"vector\"|\"hybrid\"} setupOptions.mode - The search mode to enable:\n     *     - \"vector\": Only vector similarity search capabilities\n     *     - \"hybrid\": Both vector and full-text search capabilities\n     * @returns {Promise<AzionVectorStore>} A promise that resolves with the configured vector store instance\n     */\n    static initialize(embeddings: EmbeddingsInterface, args: AzionVectorStoreArgs, setupOptions: AzionSetupOptions): Promise<AzionVectorStore>;\n    /**\n     * Adds documents to the vector store.\n     * @param {Document[]} documents The documents to add.\n     * @param {Object} options Optional parameters for adding the documents.\n     * @returns A promise that resolves when the documents have been added.\n     */\n    addDocuments(documents: Document[]): Promise<void>;\n    /**\n     * Adds vectors to the vector store.\n     * @param {number[][]} vectors The vectors to add.\n     * @param {Document[]} documents The documents associated with the vectors.\n     * @param {Object} options Optional parameters for adding the vectors.\n     * @returns A promise that resolves with the IDs of the added vectors when the vectors have been added.\n     */\n    addVectors(vectors: number[][], documents: Document[]): Promise<void>;\n    private getEmbeddingsDimensions;\n    private mapRowsFromDocuments;\n    /**\n     * Sets up the database and tables.\n     * @param {AzionSetupOptions} setupOptions The setup options:\n     *   - columns: string[] - The metadata columns to add to the table\n     *   - mode: \"vector\" | \"hybrid\" - The mode to use for the table. \"vector\" for vector search only, \"hybrid\" for vector and full-text search\n     * @returns {Promise<void>} A promise that resolves when the database and tables have been set up.\n     */\n    setupDatabase(setupOptions: AzionSetupOptions): Promise<void>;\n    private handleTables;\n    /**\n     * Handles the error.\n     * @param {Object} error The error object.\n     * @param {string} message The message to display.\n     * @returns {void} A void value.\n     */\n    private errorHandler;\n    /**\n     * Checks if the tables are setup.\n     * @param {string | number | string[] | number[]} tables The tables.\n     * @param {string} mode The mode.\n     * @returns {boolean} Whether the tables are setup.\n     */\n    private areTablesSetup;\n    private handleDatabase;\n    private waitDatabaseCreation;\n    private setupTables;\n    private insertChunks;\n    /**\n     * Extracts the metadata columns from the rows.\n     * @param {RowsInterface[]} rows The rows to extract the metadata columns from.\n     * @returns {string[]} The metadata columns.\n     */\n    private extractMetadataColumns;\n    /**\n     * Creates the insert statement for a row.\n     * @param {RowsInterface} row The row to create the insert statement for.\n     * @param {string[]} metadataColumns The metadata columns.\n     * @returns {string} The insert statement.\n     */\n    private createInsertStatement;\n    /**\n     * Creates the insert statements for the rows.\n     * @param {RowsInterface[]} rows The rows to create the insert statements for.\n     * @returns {string[]} The insert statements.\n     */\n    private createStatements;\n    /**\n     * Creates the insert chunks for the statements.\n     * @param {string[]} statements The statements to create the insert chunks for.\n     * @returns {string[][]} The insert chunks.\n     */\n    private createInsertChunks;\n    /**\n     * Gets the number of bytes in a string.\n     * @param {string} str The string to get the number of bytes for.\n     * @returns {number} The number of bytes in the string.\n     */\n    private getStringBytes;\n    /**\n     * Performs a similarity search on the vector store and returns the top 'similarityK' similar documents.\n     * @param {number[]} vector The vector to search for.\n     * @param {number} k The number of documents to return.\n     * @param {AzionFilter[]} filter Optional filters to apply to the search.\n     * @param {string[]} metadataItems Optional metadata items to include in the search.\n     * @returns {Promise<[Document, number][]>} A promise that resolves with the similarity search results when the search is complete.\n     */\n    similaritySearchVectorWithScore(vector: number[], k: number, filter?: AzionFilter[], metadataItems?: string[]): Promise<[Document, number][]>;\n    /**\n     * Performs a full-text search on the vector store and returns the top 'k' similar documents.\n     * @param query The query string to search for\n     * @param options The options for the full-text search, including:\n     *                - kfts: The number of full-text search results to return\n     *                - filter: Optional filters to apply to narrow down the search results\n     *                - metadataItems: Optional metadata fields to include in the results\n     * @returns A promise that resolves with the full-text search results when the search is complete.\n     */\n    azionFullTextSearch(query: string, options: FullTextSearchOptions): Promise<[Document<Record<string, any>>, number][]>;\n    /**\n     * Performs a hybrid search on the vector store and returns the top 'k' similar documents.\n     * @param query The query string to search for\n     * @param options The options for the hybrid search, including:\n     *                - kfts: The number of full-text search results to return\n     *                - kvector: The number of vector search results to return\n     *                - filter: Optional filters to apply to narrow down the search results\n     *                - metadataItems: Optional metadata fields to include in the results\n     * @returns A promise that resolves with the hybrid search results when the search is complete.\n     */\n    azionHybridSearch(query: string, hybridSearchOptions: HybridSearchOptions): Promise<[Document, number][]>;\n    /**\n     * Performs a similarity search on the vector store and returns the top 'k' similar documents.\n     * @param query The query string.\n     * @param options The options for the similarity search, including:\n     *                - kvector: The number of vector search results to return\n     *                - filter: Optional filters to apply to the search\n     *                - metadataItems: Optional metadata fields to include in results\n     * @returns A promise that resolves with the similarity search results when the search is complete.\n     */\n    azionSimilaritySearch(query: string, options: SimilaritySearchOptions): Promise<[Document, number][]>;\n    /**\n     * Generates an error document based on the provided error information\n     * @param {Object} error The error object containing details about the issue\n     * @returns {Promise<[Document, number][]>} A promise that resolves to an array containing a single Document representing the error\n     */\n    private searchError;\n    /**\n     * Deletes documents from the vector store.\n     * @param {string[]} ids The IDs of the documents to delete.\n     * @returns {Promise<void>} A promise that resolves when the documents have been deleted.\n     */\n    delete(ids: string[]): Promise<void>;\n    /**\n     * Removes duplicate results from the search results, prioritizing a mix of similarity and FTS results.\n     * @param {[Document, number][]} results - The array of search results to process, containing document and score pairs\n     * @param {number} kfts - Maximum number of full-text search results to include\n     * @param {number} kvector - Maximum number of vector similarity search results to include\n     * @returns {[Document, number][]} An array of unique search results, limited by kfts and kvector parameters\n     */\n    private removeDuplicates;\n    /**\n     * Converts query results to SearchEmbeddingsResponse objects.\n     * @param {QueryResult[]} results - The raw query results from the database.\n     * @returns {SearchEmbeddingsResponse[]} An array of SearchEmbeddingsResponse objects.\n     */\n    private mapRows;\n    /**\n     * Maps search results to Document objects.\n     * @param {SearchEmbeddingsResponse[]} searches An array of SearchEmbeddingsResponse objects.\n     * @returns An array of tuples, each containing a single Document object.\n     */\n    private mapSearches;\n    /**\n     * Generates the metadata string for the SQL query.\n     * @param {string[]} metadataItems - The metadata items to include in the query.\n     * @param {string} searchType - The type of search.\n     * @returns {string} The metadata string.\n     */\n    private generateMetadata;\n    /**\n     * Generates the filters string for the SQL query.\n     * @param {AzionFilter[]} filters The filters to apply to the query.\n     * @returns {string} The filters string.\n     */\n    private generateFilters;\n    /**\n     * Creates the insert sql query for a row.\n     * @param {string[]} columnNames The column names.\n     * @param {string[]} values The values.\n     * @returns {string} The insert sql query.\n     */\n    private createInsertString;\n    /**\n     * Escapes the quotes in the value.\n     * @param {string} value The value to escape the quotes in.\n     * @returns {string} The value with the quotes escaped.\n     */\n    private escapeQuotes;\n    /**\n     * Sanitizes an item by removing non-alphanumeric characters.\n     * @param {string} item The item to sanitize.\n     * @returns {string} The sanitized item.\n     */\n    private sanitizeItem;\n    /**\n     * Converts a query to a FTS query.\n     * @param query The user query\n     * @returns The converted FTS query\n     */\n    protected convert2FTSQuery(query: string): string;\n}\nexport {};\n//# sourceMappingURL=azion_edgesql.d.ts.map"],"mappings":";;;;;;;;AASA;AAQA;AAIA;AAAuI;AAmB7HO,KA/BEJ,WAAAA,GA+BiB;EAYnBK,QAAAA,EA1CIJ,QA0CJI;EAWAC,MAAAA,EApDEJ,MAoDFI;EAQOC,KAAAA,EAAAA,MAAAA;AA8DjB,CAAA;;;;AAwBkCT,KA5ItBI,MAAAA,GA4IsBJ,MAAAA;;;;AAAmFW,KAxIzGR,QAAAA,GAwIyGQ,GAAAA,GAAAA,IAAAA,GAAAA,GAAAA,GAAAA,IAAAA,GAAAA,GAAAA,GAAAA,IAAAA,GAAAA,IAAAA,GAAAA,MAAAA,GAAAA,UAAAA,GAAAA,IAAAA,GAAAA,QAAAA,GAAAA,SAAAA,GAAAA,aAAAA;;;;;;;;UAhI3GN,iBAAAA,CAoNmHJ;EAATU,OAAAA,CAAAA,EAAAA,MAAAA,EAAAA;EAUpEJ,IAAAA,EAAAA,QAAAA,GAAAA,QAAAA;;;;;;;;;UAnNtCD,mBAAAA,CAwOkEK;EAYjDA,IAAAA,EAAAA,MAAAA;EAvJmBZ,OAAAA,EAAAA,MAAAA;EAAW,MAAA,CAAA,EA1F5CG,WA0F4C,EAAA;;;;;;;;;UAjF/CK,qBAAAA;;WAEGL;;;;;;;;;UASHM,uBAAAA;;WAEGN;;;;;;UAMIO,oBAAAA;;WAEJP;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;cA4DQQ,gBAAAA,SAAyBX,WAAAA;;cAE9BG;;;;;;;;0BAQYF,2BAA2BS;;;;;;;;;;;;;;gCAcrBT,2BAA2BS,oCAAoCJ,oBAAoBM,QAAQD;;;;;;;0BAOjGT,aAAaU;;;;;;;;6CAQMV,aAAaU;;;;;;;;;;8BAU5BN,oBAAoBM;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;wEA2DsBT,0CAA0CS,SAASV;;;;;;;;;;8CAU7EM,wBAAwBI,SAASV,SAASW;;;;;;;;;;;wDAWhCN,sBAAsBK,SAASV;;;;;;;;;;gDAUvCO,0BAA0BG,SAASV;;;;;;;;;;;;yBAY1DU"}