@langchain/openai/dist/tools/fileSearch.d.cts.map

OpenAI integrations for LangChain.js

{"version":3,"file":"fileSearch.d.cts","names":["OpenAI","OpenAIClient","ServerTool","FileSearchComparisonType","FileSearchComparisonFilter","Array","FileSearchCompoundFilter","FileSearchFilter","FileSearchHybridSearchWeights","FileSearchRankingOptions","FileSearchOptions","FileSearchTool","Responses","fileSearch"],"sources":["../../src/tools/fileSearch.d.ts"],"sourcesContent":["import { OpenAI as OpenAIClient } from \"openai\";\nimport type { ServerTool } from \"@langchain/core/tools\";\n/**\n * Comparison operators for file attribute filtering.\n */\nexport type FileSearchComparisonType = \"eq\" | \"ne\" | \"gt\" | \"gte\" | \"lt\" | \"lte\";\n/**\n * A filter used to compare a specified attribute key to a given value.\n */\nexport interface FileSearchComparisonFilter {\n    /**\n     * The comparison operator to use.\n     * - `eq`: equals\n     * - `ne`: not equal\n     * - `gt`: greater than\n     * - `gte`: greater than or equal\n     * - `lt`: less than\n     * - `lte`: less than or equal\n     */\n    type: FileSearchComparisonType;\n    /**\n     * The attribute key to compare against.\n     */\n    key: string;\n    /**\n     * The value to compare against the attribute key.\n     * Supports string, number, boolean, or array of strings/numbers.\n     */\n    value: string | number | boolean | Array<string | number>;\n}\n/**\n * Combine multiple filters using `and` or `or`.\n */\nexport interface FileSearchCompoundFilter {\n    /**\n     * Type of operation: `and` or `or`.\n     */\n    type: \"and\" | \"or\";\n    /**\n     * Array of filters to combine.\n     */\n    filters: Array<FileSearchComparisonFilter | FileSearchCompoundFilter>;\n}\n/**\n * Filter type for file search - can be a comparison or compound filter.\n */\nexport type FileSearchFilter = FileSearchComparisonFilter | FileSearchCompoundFilter;\n/**\n * Weights for hybrid search balancing semantic and keyword matches.\n */\nexport interface FileSearchHybridSearchWeights {\n    /**\n     * The weight of semantic embedding matches (0-1).\n     */\n    embeddingWeight: number;\n    /**\n     * The weight of keyword/text matches (0-1).\n     */\n    textWeight: number;\n}\n/**\n * Ranking options for file search results.\n */\nexport interface FileSearchRankingOptions {\n    /**\n     * The ranker to use for the file search.\n     * - `auto`: Automatically select the best ranker\n     * - `default-2024-11-15`: Default ranker as of November 2024\n     */\n    ranker?: \"auto\" | \"default-2024-11-15\";\n    /**\n     * The score threshold for results (0-1).\n     * Numbers closer to 1 return only the most relevant results but may return fewer.\n     */\n    scoreThreshold?: number;\n    /**\n     * Weights that control how reciprocal rank fusion balances semantic\n     * embedding matches versus sparse keyword matches when hybrid search is enabled.\n     */\n    hybridSearch?: FileSearchHybridSearchWeights;\n}\n/**\n * Options for the File Search tool.\n */\nexport interface FileSearchOptions {\n    /**\n     * The IDs of the vector stores to search.\n     * You must have previously created vector stores and uploaded files to them.\n     */\n    vectorStoreIds: string[];\n    /**\n     * The maximum number of results to return.\n     * Must be between 1 and 50 inclusive.\n     */\n    maxNumResults?: number;\n    /**\n     * A filter to apply based on file attributes/metadata.\n     * Use this to narrow down search results to specific categories or file types.\n     */\n    filters?: FileSearchFilter;\n    /**\n     * Ranking options to customize how results are scored and ordered.\n     */\n    rankingOptions?: FileSearchRankingOptions;\n}\n/**\n * OpenAI File Search tool type for the Responses API.\n */\nexport type FileSearchTool = OpenAIClient.Responses.FileSearchTool;\n/**\n * Creates a File Search tool that allows models to search your files\n * for relevant information using semantic and keyword search.\n *\n * File Search enables models to retrieve information from a knowledge base\n * of previously uploaded files stored in vector stores. This is a hosted tool\n * managed by OpenAI - you don't need to implement the search execution yourself.\n *\n * **Prerequisites**: Before using File Search, you must:\n * 1. Upload files to the File API with `purpose: \"assistants\"`\n * 2. Create a vector store\n * 3. Add files to the vector store\n *\n * @see {@link https://platform.openai.com/docs/guides/tools-file-search | OpenAI File Search Documentation}\n *\n * @param options - Configuration options for the File Search tool\n * @returns A File Search tool definition to be passed to the OpenAI Responses API\n *\n * @example\n * ```typescript\n * import { ChatOpenAI, tools } from \"@langchain/openai\";\n *\n * const model = new ChatOpenAI({ model: \"gpt-4.1\" });\n *\n * // Basic usage with a vector store\n * const response = await model.invoke(\n *   \"What is deep research by OpenAI?\",\n *   {\n *     tools: [tools.fileSearch({\n *       vectorStoreIds: [\"vs_abc123\"]\n *     })]\n *   }\n * );\n *\n * // Limit the number of results for lower latency\n * const response = await model.invoke(\n *   \"Find information about pricing\",\n *   {\n *     tools: [tools.fileSearch({\n *       vectorStoreIds: [\"vs_abc123\"],\n *       maxNumResults: 5\n *     })]\n *   }\n * );\n *\n * // With metadata filtering\n * const response = await model.invoke(\n *   \"Find recent blog posts about AI\",\n *   {\n *     tools: [tools.fileSearch({\n *       vectorStoreIds: [\"vs_abc123\"],\n *       filters: {\n *         type: \"eq\",\n *         key: \"category\",\n *         value: \"blog\"\n *       }\n *     })]\n *   }\n * );\n *\n * // With compound filters (AND/OR)\n * const response = await model.invoke(\n *   \"Find technical docs from 2024\",\n *   {\n *     tools: [tools.fileSearch({\n *       vectorStoreIds: [\"vs_abc123\"],\n *       filters: {\n *         type: \"and\",\n *         filters: [\n *           { type: \"eq\", key: \"category\", value: \"technical\" },\n *           { type: \"gte\", key: \"year\", value: 2024 }\n *         ]\n *       }\n *     })]\n *   }\n * );\n *\n * // With ranking options for more relevant results\n * const response = await model.invoke(\n *   \"Find the most relevant information\",\n *   {\n *     tools: [tools.fileSearch({\n *       vectorStoreIds: [\"vs_abc123\"],\n *       rankingOptions: {\n *         scoreThreshold: 0.8,\n *         ranker: \"auto\"\n *       }\n *     })]\n *   }\n * );\n *\n * // Search multiple vector stores\n * const response = await model.invoke(\n *   \"Search across all knowledge bases\",\n *   {\n *     tools: [tools.fileSearch({\n *       vectorStoreIds: [\"vs_abc123\", \"vs_def456\"]\n *     })]\n *   }\n * );\n * ```\n *\n * @remarks\n * - Vector stores must be created and populated before using this tool\n * - The tool returns file citations in the response annotations\n * - Use `include: [\"file_search_call.results\"]` in the API call to get search results\n * - Supported file types include PDF, DOCX, TXT, MD, and many code file formats\n */\nexport declare function fileSearch(options: FileSearchOptions): ServerTool;\n//# sourceMappingURL=fileSearch.d.ts.map"],"mappings":";;;;;;;AAKA;AAIiBI,KAJLD,wBAAAA,GAI+B,IAAA,GAUjCA,IAAAA,GAAAA,IAAAA,GAAAA,KAAAA,GAAAA,IAS6BE,GAAAA,KAAK;AAK5C;;;AAQaA,UAhCID,0BAAAA,CAgCJC;EAAK;AAKlB;AAIA;AAaA;AAqBA;AAwBA;AA6GA;;;QAtMUF;;;;;;;;;qCAS6BE;;;;;UAKtBC,wBAAAA;;;;;;;;WAQJD,MAAMD,6BAA6BE;;;;;KAKpCC,gBAAAA,GAAmBH,6BAA6BE;;;;UAI3CE,6BAAAA;;;;;;;;;;;;;UAaAC,wBAAAA;;;;;;;;;;;;;;;;iBAgBED;;;;;UAKFE,iBAAAA;;;;;;;;;;;;;;;YAeHH;;;;mBAIOE;;;;;KAKTE,cAAAA,GAAiBV,QAAAA,CAAaW,SAAAA,CAAUD;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;iBA6G5BE,UAAAA,UAAoBH,oBAAoBR"}