@langchain/community/dist/vectorstores/typesense.d.ts.map

Third-party integrations for LangChain.js

{"version":3,"file":"typesense.d.ts","names":["Client","MultiSearchRequestSchema","EmbeddingsInterface","VectorStore","Document","AsyncCallerParams","TypesenseConfig","T","Record","Promise","Typesense","Partial"],"sources":["../../src/vectorstores/typesense.d.ts"],"sourcesContent":["import type { Client } from \"typesense\";\nimport type { MultiSearchRequestSchema } from \"typesense/lib/Typesense/MultiSearch.js\";\nimport type { EmbeddingsInterface } from \"@langchain/core/embeddings\";\nimport { VectorStore } from \"@langchain/core/vectorstores\";\nimport { Document } from \"@langchain/core/documents\";\nimport { AsyncCallerParams } from \"@langchain/core/utils/async_caller\";\n/**\n * Typesense vector store configuration.\n */\nexport interface TypesenseConfig extends AsyncCallerParams {\n    /**\n     * Typesense client.\n     */\n    typesenseClient: Client;\n    /**\n     * Typesense schema name in which documents will be stored and searched.\n     */\n    schemaName: string;\n    /**\n     * Typesense search parameters.\n     * @default { q: '*', per_page: 5, query_by: '' }\n     */\n    searchParams?: MultiSearchRequestSchema;\n    /**\n     * Column names.\n     */\n    columnNames?: {\n        /**\n         * Vector column name.\n         * @default 'vec'\n         */\n        vector?: string;\n        /**\n         * Page content column name.\n         * @default 'text'\n         */\n        pageContent?: string;\n        /**\n         * Metadata column names.\n         * @default []\n         */\n        metadataColumnNames?: string[];\n    };\n    /**\n     * Replace default import function.\n     * Default import function will update documents if there is a document with the same id.\n     * @param data\n     * @param collectionName\n     */\n    import?<T extends Record<string, unknown> = Record<string, unknown>>(data: T[], collectionName: string): Promise<void>;\n}\n/**\n * Typesense vector store.\n */\nexport declare class Typesense extends VectorStore {\n    FilterType: Partial<MultiSearchRequestSchema>;\n    private client;\n    private schemaName;\n    private searchParams;\n    private vectorColumnName;\n    private pageContentColumnName;\n    private metadataColumnNames;\n    private caller;\n    private import;\n    _vectorstoreType(): string;\n    constructor(embeddings: EmbeddingsInterface, config: TypesenseConfig);\n    private importToTypesense;\n    /**\n     * Transform documents to Typesense records.\n     * @param documents\n     * @returns Typesense records.\n     */\n    _documentsToTypesenseRecords(documents: Document[], vectors: number[][]): Record<string, unknown>[];\n    /**\n     * Transform the Typesense records to documents.\n     * @param typesenseRecords\n     * @returns documents\n     */\n    _typesenseRecordsToDocuments(typesenseRecords: {\n        document?: Record<string, unknown>;\n        vector_distance: number;\n    }[] | undefined): [Document, number][];\n    /**\n     * Add documents to the vector store.\n     * Will be updated if in the metadata there is a document with the same id if is using the default import function.\n     * Metadata will be added in the columns of the schema based on metadataColumnNames.\n     * @param documents Documents to add.\n     */\n    addDocuments(documents: Document[]): Promise<void>;\n    /**\n     * Adds vectors to the vector store.\n     * @param vectors Vectors to add.\n     * @param documents Documents associated with the vectors.\n     */\n    addVectors(vectors: number[][], documents: Document[]): Promise<void>;\n    /**\n     * Search for similar documents with their similarity score.\n     * @param vectorPrompt vector to search for\n     * @param k amount of results to return\n     * @returns similar documents with their similarity score\n     */\n    similaritySearchVectorWithScore(vectorPrompt: number[], k?: number, filter?: this[\"FilterType\"]): Promise<[Document<Record<string, any>>, number][]>;\n    /**\n     * Delete documents from the vector store.\n     * @param documentIds ids of the documents to delete\n     */\n    deleteDocuments(documentIds: string[]): Promise<void>;\n    /**\n     * Create a vector store from documents.\n     * @param docs documents\n     * @param embeddings embeddings\n     * @param config Typesense configuration\n     * @returns Typesense vector store\n     * @warning You can omit this method, and only use the constructor and addDocuments.\n     */\n    static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, config: TypesenseConfig): Promise<Typesense>;\n    /**\n     * Create a vector store from texts.\n     * @param texts\n     * @param metadatas\n     * @param embeddings\n     * @param config\n     * @returns Typesense vector store\n     */\n    static fromTexts(texts: string[], metadatas: object[], embeddings: EmbeddingsInterface, config: TypesenseConfig): Promise<Typesense>;\n}\n//# 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