@langchain/community/dist/retrievers/bm25.d.ts.map

Third-party integrations for LangChain.js

{"version":3,"file":"bm25.d.ts","names":["BaseRetriever","BaseRetrieverInput","Document","BM25RetrieverOptions","BM25Retriever","Omit","Record","Promise"],"sources":["../../src/retrievers/bm25.d.ts"],"sourcesContent":["import { BaseRetriever, BaseRetrieverInput } from \"@langchain/core/retrievers\";\nimport { Document } from \"@langchain/core/documents\";\nexport type BM25RetrieverOptions = {\n    docs: Document[];\n    k: number;\n    includeScore?: boolean;\n} & BaseRetrieverInput;\n/**\n * A retriever that uses the BM25 algorithm to rank documents based on their\n * similarity to a query. It uses the \"okapibm25\" package for BM25 scoring.\n * The k parameter determines the number of documents to return for each query.\n */\nexport declare class BM25Retriever extends BaseRetriever {\n    includeScore: boolean;\n    static lc_name(): string;\n    lc_namespace: string[];\n    static fromDocuments(documents: Document[], options: Omit<BM25RetrieverOptions, \"docs\">): BM25Retriever;\n    docs: Document[];\n    k: number;\n    constructor(options: BM25RetrieverOptions);\n    private preprocessFunc;\n    _getRelevantDocuments(query: string): Promise<(Document<Record<string, any>> | Document<{\n        bm25Score: number;\n    }>)[]>;\n}\n//# sourceMappingURL=bm25.d.ts.map"],"mappings":";;;;KAEYG,oBAAAA;QACFD;EADEC,CAAAA,EAAAA,MAAAA;EAUSC,YAAAA,CAAAA,EAAAA,OAAa;CAIEF,GAVhCD,kBAUgCC;;;;;;AAKwBI,cATvCF,aAAAA,SAAsBJ,aAAAA,CASiBM;EAATJ,YAAAA,EAAAA,OAAAA;EAAgCA,OAAAA,OAAAA,CAAAA,CAAAA,EAAAA,MAAAA;EAAzCK,YAAAA,EAAAA,MAAAA,EAAAA;EATCP,OAAAA,aAAAA,CAAAA,SAAAA,EAIPE,QAJOF,EAAAA,EAAAA,OAAAA,EAIcK,IAJdL,CAImBG,oBAJnBH,EAAAA,MAAAA,CAAAA,CAAAA,EAImDI,aAJnDJ;EAAa,IAAA,EAK9CE,QAL8C,EAAA;;uBAO/BC;;wCAEiBI,SAASL,SAASI,uBAAuBJ"}