@langchain/community
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Third-party integrations for LangChain.js
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{"version":3,"file":"common.cjs","names":["SaveableVectorStore","embeddings: EmbeddingsInterface","args: {\n space: \"l2\" | \"ip\" | \"cosine\";\n numDimensions?: number;\n maxElements?: number;\n }","credentials?: CloseVectorCredentials","instance: CloseVectorHNSWImplementation","documents: Document[]","directory: string","vectors: number[][]","query: number[]","k: number","filter?: this[\"FilterType\"]","x: { pageContent: string; metadata: Record<string, unknown> }","mapped: [Document<Record<string, unknown>>, number][]","Document","params: { directory: string }","texts: string[]","metadatas: object[] | object","docs: Document[]"],"sources":["../../../src/vectorstores/closevector/common.ts"],"sourcesContent":["import type { CloseVectorSaveableVectorStore } from \"closevector-common\";\n\nimport type { EmbeddingsInterface } from \"@langchain/core/embeddings\";\nimport { Document } from \"@langchain/core/documents\";\nimport { SaveableVectorStore } from \"@langchain/core/vectorstores\";\n\ntype CloseVectorCredentials = {\n key?: string;\n secret?: string;\n};\n\n/**\n * package closevector is largely based on hnswlib.ts in the current folder with the following exceptions:\n * 1. It uses a modified version of hnswlib-node to ensure the generated index can be loaded by closevector_web.ts.\n * 2. It adds features to upload and download the index to/from the CDN provided by CloseVector.\n *\n * For more information, check out https://closevector-docs.getmegaportal.com/\n */\n\n/**\n * Class that implements a vector store using Hierarchical Navigable Small\n * World (HNSW) graphs. It extends the SaveableVectorStore class and\n * provides methods for adding documents and vectors, performing\n * similarity searches, and saving and loading the vector store.\n */\nexport abstract class CloseVector<\n CloseVectorHNSWImplementation extends CloseVectorSaveableVectorStore,\n> extends SaveableVectorStore {\n declare FilterType: (doc: Document) => boolean;\n\n _instance?: CloseVectorHNSWImplementation;\n\n // credentials will not be saved to disk\n credentials?: CloseVectorCredentials;\n\n _vectorstoreType(): string {\n return \"closevector\";\n }\n\n constructor(\n embeddings: EmbeddingsInterface,\n args: {\n space: \"l2\" | \"ip\" | \"cosine\";\n numDimensions?: number;\n maxElements?: number;\n },\n credentials?: CloseVectorCredentials\n ) {\n super(embeddings, args);\n this.credentials = credentials;\n }\n\n public get instance(): CloseVectorHNSWImplementation {\n if (!this._instance) {\n throw new Error(\n \"Vector store not initialised yet. Try calling `addTexts` first.\"\n );\n }\n return this._instance;\n }\n\n protected set instance(instance: CloseVectorHNSWImplementation) {\n this._instance = instance;\n }\n\n /**\n * Method to add documents to the vector store. It first converts the\n * documents to vectors using the embeddings, then adds the vectors to the\n * vector store.\n * @param documents The documents to be added to the vector store.\n * @returns A Promise that resolves when the documents have been added.\n */\n async addDocuments(documents: Document[]): Promise<void> {\n await this.instance.addDocuments(documents);\n }\n\n abstract saveToCloud(_options: Record<string, unknown>): Promise<void>;\n\n /**\n * Method to save the vector store to a directory. It saves the HNSW\n * index, the arguments, and the document store to the directory.\n * @param directory The directory to which to save the vector store. In CloseVector, we use IndexedDB to mock the file system. Therefore, this parameter is can be treated as a key to the contents stored.\n * @returns A Promise that resolves when the vector store has been saved.\n */\n async save(directory: string): Promise<void> {\n await this.instance.save(directory);\n }\n\n /**\n * Method to add vectors to the vector store. It first initializes the\n * index if it hasn't been initialized yet, then adds the vectors to the\n * index and the documents to the document store.\n * @param vectors The vectors to be added to the vector store.\n * @param documents The documents corresponding to the vectors.\n * @returns A Promise that resolves when the vectors and documents have been added.\n */\n async addVectors(vectors: number[][], documents: Document[]) {\n await this.instance.addVectors(vectors, documents);\n }\n\n /**\n * Method to perform a similarity search in the vector store using a query\n * vector. It returns the k most similar documents along with their\n * similarity scores. An optional filter function can be provided to\n * filter the documents.\n * @param query The query vector.\n * @param k The number of most similar documents to return.\n * @param filter An optional filter function to filter the documents.\n * @returns A Promise that resolves to an array of tuples, where each tuple contains a document and its similarity score.\n */\n async similaritySearchVectorWithScore(\n query: number[],\n k: number,\n filter?: this[\"FilterType\"]\n ) {\n const resp = await this.instance.similaritySearchVectorWithScore(\n query,\n k,\n filter\n ? (x: { pageContent: string; metadata: Record<string, unknown> }) =>\n filter?.({\n pageContent: x.pageContent,\n metadata: x.metadata || {},\n }) || false\n : undefined\n );\n const mapped: [Document<Record<string, unknown>>, number][] = resp.map(\n (x) => [\n new Document({\n pageContent: x[0].pageContent,\n metadata: x[0].metadata || {},\n }),\n 1 - x[1],\n ]\n );\n return mapped;\n }\n\n /**\n * Method to delete the vector store from a directory. It deletes the\n * hnswlib.index file, the docstore.json file, and the args.json file from\n * the directory.\n * @param params An object with a directory property that specifies the directory from which to delete the vector store.\n * @returns A Promise that resolves when the vector store has been deleted.\n */\n async delete(params: { directory: string }) {\n return await this.instance.delete(params);\n }\n\n static textsToDocuments(texts: string[], metadatas: object[] | object) {\n const docs: Document[] = [];\n for (let i = 0; i < texts.length; i += 1) {\n const metadata = Array.isArray(metadatas) ? metadatas[i] : metadatas;\n const newDoc = new Document({\n pageContent: texts[i],\n metadata,\n });\n docs.push(newDoc);\n }\n return docs;\n }\n}\n"],"mappings":";;;;;;;;;;;;;;;;;;AAyBA,IAAsB,cAAtB,cAEUA,kDAAoB;CAG5B;CAGA;CAEA,mBAA2B;AACzB,SAAO;CACR;CAED,YACEC,YACAC,MAKAC,aACA;EACA,MAAM,YAAY,KAAK;EACvB,KAAK,cAAc;CACpB;CAED,IAAW,WAA0C;AACnD,MAAI,CAAC,KAAK,UACR,OAAM,IAAI,MACR;AAGJ,SAAO,KAAK;CACb;CAED,IAAc,SAASC,UAAyC;EAC9D,KAAK,YAAY;CAClB;;;;;;;;CASD,MAAM,aAAaC,WAAsC;EACvD,MAAM,KAAK,SAAS,aAAa,UAAU;CAC5C;;;;;;;CAUD,MAAM,KAAKC,WAAkC;EAC3C,MAAM,KAAK,SAAS,KAAK,UAAU;CACpC;;;;;;;;;CAUD,MAAM,WAAWC,SAAqBF,WAAuB;EAC3D,MAAM,KAAK,SAAS,WAAW,SAAS,UAAU;CACnD;;;;;;;;;;;CAYD,MAAM,gCACJG,OACAC,GACAC,QACA;EACA,MAAM,OAAO,MAAM,KAAK,SAAS,gCAC/B,OACA,GACA,SACI,CAACC,MACC,SAAS;GACP,aAAa,EAAE;GACf,UAAU,EAAE,YAAY,CAAE;EAC3B,EAAC,IAAI,QACR,OACL;EACD,MAAMC,SAAwD,KAAK,IACjE,CAAC,MAAM,CACL,IAAIC,oCAAS;GACX,aAAa,EAAE,GAAG;GAClB,UAAU,EAAE,GAAG,YAAY,CAAE;EAC9B,IACD,IAAI,EAAE,EACP,EACF;AACD,SAAO;CACR;;;;;;;;CASD,MAAM,OAAOC,QAA+B;AAC1C,SAAO,MAAM,KAAK,SAAS,OAAO,OAAO;CAC1C;CAED,OAAO,iBAAiBC,OAAiBC,WAA8B;EACrE,MAAMC,OAAmB,CAAE;AAC3B,OAAK,IAAI,IAAI,GAAG,IAAI,MAAM,QAAQ,KAAK,GAAG;GACxC,MAAM,WAAW,MAAM,QAAQ,UAAU,GAAG,UAAU,KAAK;GAC3D,MAAM,SAAS,IAAIJ,oCAAS;IAC1B,aAAa,MAAM;IACnB;GACD;GACD,KAAK,KAAK,OAAO;EAClB;AACD,SAAO;CACR;AACF"}