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Third-party integrations for LangChain.js
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{"version":3,"file":"faiss.cjs","names":["SaveableVectorStore","embeddings: EmbeddingsInterface","args: FaissLibArgs","SynchronousInMemoryDocstore","documents: Document[]","options?: { ids?: string[] }","index: IndexFlatL2","vectors: number[][]","query: number[]","k: number","uuid","directory: string","params: { ids: string[] }","targetIndex: FaissStore","directory","Document","store","texts: string[]","metadatas: object[] | object","dbConfig?: {\n docstore?: SynchronousInMemoryDocstore;\n }","docs: Document[]","err: any"],"sources":["../../src/vectorstores/faiss.ts"],"sourcesContent":["import type { IndexFlatL2 } from \"faiss-node\";\nimport type { NameRegistry, Parser } from \"pickleparser\";\nimport * as uuid from \"uuid\";\nimport type { EmbeddingsInterface } from \"@langchain/core/embeddings\";\nimport { SaveableVectorStore } from \"@langchain/core/vectorstores\";\nimport { Document } from \"@langchain/core/documents\";\nimport { SynchronousInMemoryDocstore } from \"@langchain/classic/stores/doc/in_memory\";\n\n/**\n * Interface for the arguments required to initialize a FaissStore\n * instance.\n */\nexport interface FaissLibArgs {\n docstore?: SynchronousInMemoryDocstore;\n index?: IndexFlatL2;\n mapping?: Record<number, string>;\n}\n\n/**\n * A class that wraps the FAISS (Facebook AI Similarity Search) vector\n * database for efficient similarity search and clustering of dense\n * vectors.\n */\nexport class FaissStore extends SaveableVectorStore {\n _index?: IndexFlatL2;\n\n _mapping: Record<number, string>;\n\n docstore: SynchronousInMemoryDocstore;\n\n args: FaissLibArgs;\n\n _vectorstoreType(): string {\n return \"faiss\";\n }\n\n getMapping(): Record<number, string> {\n return this._mapping;\n }\n\n getDocstore(): SynchronousInMemoryDocstore {\n return this.docstore;\n }\n\n constructor(embeddings: EmbeddingsInterface, args: FaissLibArgs) {\n super(embeddings, args);\n this.args = args;\n this._index = args.index;\n this._mapping = args.mapping ?? {};\n this.embeddings = embeddings;\n this.docstore = args?.docstore ?? new SynchronousInMemoryDocstore();\n }\n\n /**\n * Adds an array of Document objects to the store.\n * @param documents An array of Document objects.\n * @returns A Promise that resolves when the documents have been added.\n */\n async addDocuments(documents: Document[], options?: { ids?: string[] }) {\n const texts = documents.map(({ pageContent }) => pageContent);\n return this.addVectors(\n await this.embeddings.embedDocuments(texts),\n documents,\n options\n );\n }\n\n public get index(): IndexFlatL2 {\n if (!this._index) {\n throw new Error(\n \"Vector store not initialised yet. Try calling `fromTexts`, `fromDocuments` or `fromIndex` first.\"\n );\n }\n return this._index;\n }\n\n private set index(index: IndexFlatL2) {\n this._index = index;\n }\n\n /**\n * Adds an array of vectors and their corresponding Document objects to\n * the store.\n * @param vectors An array of vectors.\n * @param documents An array of Document objects corresponding to the vectors.\n * @returns A Promise that resolves with an array of document IDs when the vectors and documents have been added.\n */\n async addVectors(\n vectors: number[][],\n documents: Document[],\n options?: { ids?: string[] }\n ) {\n if (vectors.length === 0) {\n return [];\n }\n if (vectors.length !== documents.length) {\n throw new Error(`Vectors and documents must have the same length`);\n }\n const dv = vectors[0].length;\n if (!this._index) {\n const { IndexFlatL2 } = await FaissStore.importFaiss();\n this._index = new IndexFlatL2(dv);\n }\n const d = this.index.getDimension();\n if (dv !== d) {\n throw new Error(\n `Vectors must have the same length as the number of dimensions (${d})`\n );\n }\n\n const docstoreSize = this.index.ntotal();\n const documentIds = options?.ids ?? documents.map(() => uuid.v4());\n for (let i = 0; i < vectors.length; i += 1) {\n const documentId = documentIds[i];\n const id = docstoreSize + i;\n this.index.add(vectors[i]);\n this._mapping[id] = documentId;\n this.docstore.add({ [documentId]: documents[i] });\n }\n return documentIds;\n }\n\n /**\n * Performs a similarity search in the vector store using a query vector\n * and returns the top k results along with their scores.\n * @param query A query vector.\n * @param k The number of top results to return.\n * @returns A Promise that resolves with an array of tuples, each containing a Document and its corresponding score.\n */\n async similaritySearchVectorWithScore(query: number[], k: number) {\n const d = this.index.getDimension();\n if (query.length !== d) {\n throw new Error(\n `Query vector must have the same length as the number of dimensions (${d})`\n );\n }\n if (k > this.index.ntotal()) {\n const total = this.index.ntotal();\n console.warn(\n `k (${k}) is greater than the number of elements in the index (${total}), setting k to ${total}`\n );\n // eslint-disable-next-line no-param-reassign\n k = total;\n }\n const result = this.index.search(query, k);\n return result.labels.map((id, index) => {\n const uuid = this._mapping[id];\n return [this.docstore.search(uuid), result.distances[index]] as [\n Document,\n number,\n ];\n });\n }\n\n /**\n * Saves the current state of the FaissStore to a specified directory.\n * @param directory The directory to save the state to.\n * @returns A Promise that resolves when the state has been saved.\n */\n async save(directory: string) {\n const fs = await import(\"node:fs/promises\");\n const path = await import(\"node:path\");\n await fs.mkdir(directory, { recursive: true });\n await Promise.all([\n this.index.write(path.join(directory, \"faiss.index\")),\n await fs.writeFile(\n path.join(directory, \"docstore.json\"),\n JSON.stringify([\n Array.from(this.docstore._docs.entries()),\n this._mapping,\n ])\n ),\n ]);\n }\n\n /**\n * Method to delete documents.\n * @param params Object containing the IDs of the documents to delete.\n * @returns A promise that resolves when the deletion is complete.\n */\n async delete(params: { ids: string[] }) {\n const documentIds = params.ids;\n if (documentIds == null) {\n throw new Error(\"No documentIds provided to delete.\");\n }\n\n const mappings = new Map(\n Object.entries(this._mapping).map(([key, value]) => [\n parseInt(key, 10),\n value,\n ])\n );\n const reversedMappings = new Map(\n Array.from(mappings, (entry) => [entry[1], entry[0]])\n );\n\n const missingIds = new Set(\n documentIds.filter((id) => !reversedMappings.has(id))\n );\n if (missingIds.size > 0) {\n throw new Error(\n `Some specified documentIds do not exist in the current store. DocumentIds not found: ${Array.from(\n missingIds\n ).join(\", \")}`\n );\n }\n\n const indexIdToDelete = documentIds.map((id) => reversedMappings.get(id)!);\n\n // remove from index\n this.index.removeIds(indexIdToDelete);\n // remove from docstore\n documentIds.forEach((id) => {\n this.docstore._docs.delete(id);\n });\n // remove from mappings\n indexIdToDelete.forEach((id) => {\n mappings.delete(id);\n });\n\n this._mapping = { ...Array.from(mappings.values()) };\n }\n\n /**\n * Merges the current FaissStore with another FaissStore.\n * @param targetIndex The FaissStore to merge with.\n * @returns A Promise that resolves with an array of document IDs when the merge is complete.\n */\n async mergeFrom(targetIndex: FaissStore) {\n const targetIndexDimensions = targetIndex.index.getDimension();\n if (!this._index) {\n const { IndexFlatL2 } = await FaissStore.importFaiss();\n this._index = new IndexFlatL2(targetIndexDimensions);\n }\n const d = this.index.getDimension();\n if (targetIndexDimensions !== d) {\n throw new Error(\"Cannot merge indexes with different dimensions.\");\n }\n const targetMapping = targetIndex.getMapping();\n const targetDocstore = targetIndex.getDocstore();\n const targetSize = targetIndex.index.ntotal();\n const documentIds = [];\n const currentDocstoreSize = this.index.ntotal();\n for (let i = 0; i < targetSize; i += 1) {\n const targetId = targetMapping[i];\n documentIds.push(targetId);\n const targetDocument = targetDocstore.search(targetId);\n const id = currentDocstoreSize + i;\n this._mapping[id] = targetId;\n this.docstore.add({ [targetId]: targetDocument });\n }\n this.index.mergeFrom(targetIndex.index);\n return documentIds;\n }\n\n /**\n * Loads a FaissStore from a specified directory.\n * @param directory The directory to load the FaissStore from.\n * @param embeddings An Embeddings object.\n * @returns A Promise that resolves with a new FaissStore instance.\n */\n static async load(directory: string, embeddings: EmbeddingsInterface) {\n const fs = await import(\"node:fs/promises\");\n const path = await import(\"node:path\");\n const readStore = (directory: string) =>\n fs\n .readFile(path.join(directory, \"docstore.json\"), \"utf8\")\n .then(JSON.parse) as Promise<\n [Map<string, Document>, Record<number, string>]\n >;\n const readIndex = async (directory: string) => {\n const { IndexFlatL2 } = await this.importFaiss();\n return IndexFlatL2.read(path.join(directory, \"faiss.index\"));\n };\n const [[docstoreFiles, mapping], index] = await Promise.all([\n readStore(directory),\n readIndex(directory),\n ]);\n const docstore = new SynchronousInMemoryDocstore(new Map(docstoreFiles));\n return new this(embeddings, { docstore, index, mapping });\n }\n\n static async loadFromPython(\n directory: string,\n embeddings: EmbeddingsInterface\n ) {\n const fs = await import(\"node:fs/promises\");\n const path = await import(\"node:path\");\n const { Parser, NameRegistry } = await this.importPickleparser();\n\n class PyDocument extends Map {\n toDocument(): Document {\n return new Document({\n pageContent: this.get(\"page_content\"),\n metadata: this.get(\"metadata\"),\n });\n }\n }\n\n class PyInMemoryDocstore {\n _dict: Map<string, PyDocument>;\n\n toInMemoryDocstore(): SynchronousInMemoryDocstore {\n const s = new SynchronousInMemoryDocstore();\n for (const [key, value] of Object.entries(this._dict)) {\n s._docs.set(key, value.toDocument());\n }\n return s;\n }\n }\n\n const readStore = async (directory: string) => {\n const pkl = await fs.readFile(\n path.join(directory, \"index.pkl\"),\n \"binary\"\n );\n const buffer = Buffer.from(pkl, \"binary\");\n\n const registry = new NameRegistry()\n .register(\n \"langchain.docstore.in_memory\",\n \"InMemoryDocstore\",\n PyInMemoryDocstore\n )\n .register(\n \"langchain_community.docstore.in_memory\",\n \"InMemoryDocstore\",\n PyInMemoryDocstore\n )\n .register(\"langchain.schema\", \"Document\", PyDocument)\n .register(\"langchain.docstore.document\", \"Document\", PyDocument)\n .register(\"langchain.schema.document\", \"Document\", PyDocument)\n .register(\"langchain_core.documents.base\", \"Document\", PyDocument)\n .register(\"pathlib\", \"WindowsPath\", (...args) => args.join(\"\\\\\"))\n .register(\"pathlib\", \"PosixPath\", (...args) => args.join(\"/\"));\n\n const pickleparser = new Parser({\n nameResolver: registry,\n });\n const [rawStore, mapping] =\n pickleparser.parse<[PyInMemoryDocstore, Record<number, string>]>(\n buffer\n );\n const store = rawStore.toInMemoryDocstore();\n return { store, mapping };\n };\n const readIndex = async (directory: string) => {\n const { IndexFlatL2 } = await this.importFaiss();\n return IndexFlatL2.read(path.join(directory, \"index.faiss\"));\n };\n const [store, index] = await Promise.all([\n readStore(directory),\n readIndex(directory),\n ]);\n return new this(embeddings, {\n docstore: store.store,\n index,\n mapping: store.mapping,\n });\n }\n\n /**\n * Creates a new FaissStore from an array of texts, their corresponding\n * metadata, and an Embeddings object.\n * @param texts An array of texts.\n * @param metadatas An array of metadata corresponding to the texts, or a single metadata object to be used for all texts.\n * @param embeddings An Embeddings object.\n * @param dbConfig An optional configuration object for the document store.\n * @returns A Promise that resolves with a new FaissStore instance.\n */\n static async fromTexts(\n texts: string[],\n metadatas: object[] | object,\n embeddings: EmbeddingsInterface,\n dbConfig?: {\n docstore?: SynchronousInMemoryDocstore;\n }\n ): Promise<FaissStore> {\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 this.fromDocuments(docs, embeddings, dbConfig);\n }\n\n /**\n * Creates a new FaissStore from an array of Document objects and an\n * Embeddings object.\n * @param docs An array of Document objects.\n * @param embeddings An Embeddings object.\n * @param dbConfig An optional configuration object for the document store.\n * @returns A Promise that resolves with a new FaissStore instance.\n */\n static async fromDocuments(\n docs: Document[],\n embeddings: EmbeddingsInterface,\n dbConfig?: {\n docstore?: SynchronousInMemoryDocstore;\n }\n ): Promise<FaissStore> {\n const args: FaissLibArgs = {\n docstore: dbConfig?.docstore,\n };\n const instance = new this(embeddings, args);\n await instance.addDocuments(docs);\n return instance;\n }\n\n /**\n * Creates a new FaissStore from an existing FaissStore and an Embeddings\n * object.\n * @param targetIndex An existing FaissStore.\n * @param embeddings An Embeddings object.\n * @param dbConfig An optional configuration object for the document store.\n * @returns A Promise that resolves with a new FaissStore instance.\n */\n static async fromIndex(\n targetIndex: FaissStore,\n embeddings: EmbeddingsInterface,\n dbConfig?: {\n docstore?: SynchronousInMemoryDocstore;\n }\n ): Promise<FaissStore> {\n const args: FaissLibArgs = {\n docstore: dbConfig?.docstore,\n };\n const instance = new this(embeddings, args);\n await instance.mergeFrom(targetIndex);\n return instance;\n }\n\n static async importFaiss(): Promise<{ IndexFlatL2: typeof IndexFlatL2 }> {\n try {\n const {\n default: { IndexFlatL2 },\n } = await import(\"faiss-node\");\n\n return { IndexFlatL2 };\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n } catch (err: any) {\n throw new Error(\n `Could not import faiss-node. Please install faiss-node as a dependency with, e.g. \\`npm install -S faiss-node\\`.\\n\\nError: ${err?.message}`\n );\n }\n }\n\n static async importPickleparser(): Promise<{\n Parser: typeof Parser;\n NameRegistry: typeof NameRegistry;\n }> {\n try {\n const {\n default: { Parser, NameRegistry },\n } = await import(\"pickleparser\");\n\n return { Parser, NameRegistry };\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n } catch (err: any) {\n throw new Error(\n `Could not import pickleparser. Please install pickleparser as a dependency with, e.g. \\`npm install -S pickleparser\\`.\\n\\nError: ${err?.message}`\n );\n }\n 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