{"version":3,"file":"vectorstores.cjs","names":["VectorStore","PineconeIndex","AsyncCaller","Document"],"sources":["../src/vectorstores.ts"],"sourcesContent":["import { v4 as uuid } from \"@langchain/core/utils/uuid\";\nimport flatten from \"flat\";\n\nimport {\n RecordMetadata,\n PineconeRecord,\n Index as PineconeIndex,\n ScoredPineconeRecord,\n} from \"@pinecone-database/pinecone\";\n\nimport type { EmbeddingsInterface } from \"@langchain/core/embeddings\";\nimport {\n VectorStore,\n type MaxMarginalRelevanceSearchOptions,\n} from \"@langchain/core/vectorstores\";\nimport { Document, type DocumentInterface } from \"@langchain/core/documents\";\nimport {\n AsyncCaller,\n AsyncCallerParams,\n} from \"@langchain/core/utils/async_caller\";\nimport { chunkArray } from \"@langchain/core/utils/chunk_array\";\nimport { maximalMarginalRelevance } from \"@langchain/core/utils/math\";\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\ntype PineconeMetadata = Record;\n\ntype HTTPHeaders = {\n [key: string]: string;\n};\n\n/**\n * Database config for your vectorstore.\n */\nexport interface PineconeStoreParams extends AsyncCallerParams {\n /**\n * The Pinecone index to use.\n * Either this or pineconeConfig must be provided.\n */\n pineconeIndex?: PineconeIndex;\n textKey?: string;\n namespace?: string;\n filter?: PineconeMetadata;\n /**\n * Configuration for the Pinecone index.\n * Either this or pineconeIndex must be provided.\n */\n pineconeConfig?: {\n indexName: ConstructorParameters[0];\n config: ConstructorParameters[1];\n namespace?: string;\n indexHostUrl?: string;\n additionalHeaders?: HTTPHeaders;\n };\n}\n\n/**\n * Type that defines the parameters for the delete operation in the\n * PineconeStore class. It includes ids, filter, deleteAll flag, and namespace.\n */\nexport type PineconeDeleteParams = {\n ids?: string[];\n deleteAll?: boolean;\n filter?: object;\n namespace?: string;\n};\n\n/**\n * Pinecone vector store integration.\n *\n * Setup:\n * Install `@langchain/pinecone` and `@pinecone-database/pinecone` to pass a client in.\n *\n * ```bash\n * npm install @langchain/pinecone @pinecone-database/pinecone\n * ```\n *\n * ## [Constructor args](https://api.js.langchain.com/classes/_langchain_pinecone.PineconeStore.html#constructor)\n *\n *
\n * Instantiate\n *\n * ```typescript\n * import { PineconeStore } from '@langchain/pinecone';\n * // Or other embeddings\n * import { OpenAIEmbeddings } from '@langchain/openai';\n *\n * import { Pinecone as PineconeClient } from \"@pinecone-database/pinecone\";\n *\n * const pinecone = new PineconeClient();\n *\n * // Will automatically read the PINECONE_API_KEY env var\n * const pineconeIndex = pinecone.Index(process.env.PINECONE_INDEX!);\n *\n * const embeddings = new OpenAIEmbeddings({\n * model: \"text-embedding-3-small\",\n * });\n *\n * const vectorStore = await PineconeStore.fromExistingIndex(embeddings, {\n * pineconeIndex,\n * // Maximum number of batch requests to allow at once. Each batch is 1000 vectors.\n * maxConcurrency: 5,\n * // You can pass a namespace here too\n * // namespace: \"foo\",\n * });\n * ```\n *
\n *\n *
\n *\n *
\n * Add documents\n *\n * ```typescript\n * import type { Document } from '@langchain/core/documents';\n *\n * const document1 = { pageContent: \"foo\", metadata: { baz: \"bar\" } };\n * const document2 = { pageContent: \"thud\", metadata: { bar: \"baz\" } };\n * const document3 = { pageContent: \"i will be deleted :(\", metadata: {} };\n *\n * const documents: Document[] = [document1, document2, document3];\n * const ids = [\"1\", \"2\", \"3\"];\n * await vectorStore.addDocuments(documents, { ids });\n * ```\n *
\n *\n *
\n *\n *
\n * Delete documents\n *\n * ```typescript\n * await vectorStore.delete({ ids: [\"3\"] });\n * ```\n *
\n *\n *
\n *\n *
\n * Similarity search\n *\n * ```typescript\n * const results = await vectorStore.similaritySearch(\"thud\", 1);\n * for (const doc of results) {\n * console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);\n * }\n * // Output: * thud [{\"baz\":\"bar\"}]\n * ```\n *
\n *\n *
\n *\n *\n *
\n * Similarity search with filter\n *\n * ```typescript\n * const resultsWithFilter = await vectorStore.similaritySearch(\"thud\", 1, { baz: \"bar\" });\n *\n * for (const doc of resultsWithFilter) {\n * console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);\n * }\n * // Output: * foo [{\"baz\":\"bar\"}]\n * ```\n *
\n *\n *
\n *\n *\n *
\n * Similarity search with score\n *\n * ```typescript\n * const resultsWithScore = await vectorStore.similaritySearchWithScore(\"qux\", 1);\n * for (const [doc, score] of resultsWithScore) {\n * console.log(`* [SIM=${score.toFixed(6)}] ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);\n * }\n * // Output: * [SIM=0.000000] qux [{\"bar\":\"baz\",\"baz\":\"bar\"}]\n * ```\n *
\n *\n *
\n *\n *
\n * As a retriever\n *\n * ```typescript\n * const retriever = vectorStore.asRetriever({\n * searchType: \"mmr\", // Leave blank for standard similarity search\n * k: 1,\n * });\n * const resultAsRetriever = await retriever.invoke(\"thud\");\n * console.log(resultAsRetriever);\n *\n * // Output: [Document({ metadata: { \"baz\":\"bar\" }, pageContent: \"thud\" })]\n * ```\n *
\n *\n *
\n */\nexport class PineconeStore extends VectorStore {\n declare FilterType: PineconeMetadata;\n\n textKey: string;\n\n namespace?: string;\n\n pineconeIndex: PineconeIndex;\n\n filter?: PineconeMetadata;\n\n caller: AsyncCaller;\n\n _vectorstoreType(): string {\n return \"pinecone\";\n }\n\n constructor(embeddings: EmbeddingsInterface, params: PineconeStoreParams) {\n super(embeddings, params);\n this.embeddings = embeddings;\n\n const {\n namespace,\n pineconeIndex,\n textKey,\n filter,\n pineconeConfig,\n ...asyncCallerArgs\n } = params;\n this.namespace = namespace;\n if (!pineconeIndex && !pineconeConfig) {\n throw new Error(\"pineconeConfig or pineconeIndex must be provided.\");\n }\n if (pineconeIndex && pineconeConfig) {\n throw new Error(\n \"Only one of pineconeConfig or pineconeIndex can be provided.\"\n );\n }\n\n if (pineconeIndex) {\n this.pineconeIndex = pineconeIndex;\n } else if (pineconeConfig) {\n this.pineconeIndex = new PineconeIndex(\n pineconeConfig.indexName,\n {\n ...pineconeConfig.config,\n sourceTag: \"langchainjs\",\n },\n pineconeConfig.namespace,\n pineconeConfig.indexHostUrl,\n pineconeConfig.additionalHeaders\n );\n }\n\n this.textKey = textKey ?? \"text\";\n this.filter = filter;\n this.caller = new AsyncCaller(asyncCallerArgs);\n }\n\n /**\n * Method that adds documents to the Pinecone database.\n *\n * @param documents Array of documents to add to the Pinecone database.\n * @param options Optional ids for the documents.\n * @returns Promise that resolves with the ids of the added documents.\n */\n async addDocuments(\n documents: Document[],\n options?: { ids?: string[]; namespace?: string } | string[]\n ): Promise {\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 /**\n * Method that adds vectors to the Pinecone database.\n *\n * @param vectors Array of vectors to add to the Pinecone database.\n * @param documents Array of documents associated with the vectors.\n * @param options Optional ids for the vectors.\n * @returns Promise that resolves with the ids of the added vectors.\n */\n async addVectors(\n vectors: number[][],\n documents: Document[],\n options?: { ids?: string[]; namespace?: string } | string[]\n ) {\n const ids = Array.isArray(options) ? options : options?.ids;\n const documentIds = ids == null ? documents.map(() => uuid()) : ids;\n const pineconeVectors = vectors.map((values, idx) => {\n // Pinecone doesn't support nested objects, so we flatten them\n const documentMetadata = { ...documents[idx].metadata };\n // preserve string arrays which are allowed\n const stringArrays: Record = {};\n for (const key of Object.keys(documentMetadata)) {\n if (\n Array.isArray(documentMetadata[key]) &&\n documentMetadata[key].every((el: unknown) => typeof el === \"string\")\n ) {\n stringArrays[key] = documentMetadata[key];\n delete documentMetadata[key];\n }\n }\n const metadata: {\n [key: string]: string | number | boolean | string[] | null;\n } = {\n ...flatten(documentMetadata),\n ...stringArrays,\n [this.textKey]: documents[idx].pageContent,\n };\n // Pinecone doesn't support null values, so we remove them\n for (const key of Object.keys(metadata)) {\n if (metadata[key] == null) {\n delete metadata[key];\n } else if (\n typeof metadata[key] === \"object\" &&\n Object.keys(metadata[key] as unknown as object).length === 0\n ) {\n delete metadata[key];\n }\n }\n\n return {\n id: documentIds[idx],\n metadata,\n values,\n } as PineconeRecord;\n });\n\n const optionsNamespace =\n !Array.isArray(options) && options?.namespace\n ? options.namespace\n : this.namespace;\n const namespace = this.pineconeIndex.namespace(optionsNamespace ?? \"\");\n // Pinecone recommends a limit of 100 vectors per upsert request\n const chunkSize = 100;\n const chunkedVectors = chunkArray(pineconeVectors, chunkSize);\n const batchRequests = chunkedVectors.map((chunk) =>\n this.caller.call(async () => {\n try {\n await namespace.upsert(chunk);\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n } catch (e: any) {\n if (e.message.includes(\"404\")) {\n e.statusCode = 404;\n }\n throw e;\n }\n })\n );\n\n await Promise.all(batchRequests);\n\n return documentIds;\n }\n\n /**\n * Method that deletes vectors from the Pinecone database.\n * @param params Parameters for the delete operation.\n * @returns Promise that resolves when the delete operation is complete.\n */\n async delete(params: PineconeDeleteParams): Promise {\n const { deleteAll, ids, filter } = params;\n const optionsNamespace = params.namespace ?? this.namespace;\n const namespace = this.pineconeIndex.namespace(optionsNamespace ?? \"\");\n\n if (deleteAll) {\n await namespace.deleteAll();\n } else if (ids) {\n const batchSize = 1000;\n for (let i = 0; i < ids.length; i += batchSize) {\n const batchIds = ids.slice(i, i + batchSize);\n await namespace.deleteMany(batchIds);\n }\n } else if (filter) {\n await namespace.deleteMany(filter);\n } else {\n throw new Error(\"Either ids or delete_all must be provided.\");\n }\n }\n\n protected async _runPineconeQuery(\n query: number[],\n k: number,\n filter?: PineconeMetadata,\n options?: { includeValues: boolean }\n ) {\n if (filter && this.filter) {\n throw new Error(\"cannot provide both `filter` and `this.filter`\");\n }\n let _filter = filter ?? this.filter;\n\n let optionsNamespace = this.namespace ?? \"\";\n if (_filter && \"namespace\" in _filter) {\n optionsNamespace = _filter.namespace;\n delete _filter.namespace;\n }\n\n if (Object.keys(_filter ?? {}).length === 0) {\n _filter = undefined;\n }\n\n const namespace = this.pineconeIndex.namespace(optionsNamespace ?? \"\");\n\n const results = await namespace.query({\n includeMetadata: true,\n topK: k,\n vector: query,\n filter: _filter,\n ...options,\n });\n return results;\n }\n\n /**\n * Format the matching results from the Pinecone query.\n * @param matches Matching results from the Pinecone query.\n * @returns An array of arrays, where each inner array contains a document and its score.\n */\n private _formatMatches(\n matches: ScoredPineconeRecord[] = []\n ): [Document, number][] {\n const documentsWithScores: [Document, number][] = [];\n\n for (const record of matches) {\n const {\n id,\n score,\n metadata: { [this.textKey]: pageContent, ...metadata } = {\n [this.textKey]: \"\",\n },\n } = record;\n\n if (score) {\n documentsWithScores.push([\n new Document({\n id,\n pageContent: pageContent?.toString() ?? \"\",\n metadata,\n }),\n score,\n ]);\n }\n }\n\n return documentsWithScores;\n }\n\n /**\n * Method that performs a similarity search in the Pinecone database and\n * returns the results along with their scores.\n * @param query Query vector for the similarity search.\n * @param k Number of top results to return.\n * @param filter Optional filter to apply to the search.\n * @returns Promise that resolves with an array of documents and their scores.\n */\n async similaritySearchVectorWithScore(\n query: number[],\n k: number,\n filter?: PineconeMetadata\n ): Promise<[Document, number][]> {\n const { matches = [] } = await this._runPineconeQuery(query, k, filter);\n const records = this._formatMatches(matches);\n\n return records;\n }\n\n /**\n * Return documents selected using the maximal marginal relevance.\n * Maximal marginal relevance optimizes for similarity to the query AND diversity\n * among selected documents.\n *\n * @param {string} query - Text to look up documents similar to.\n * @param {number} options.k - Number of documents to return.\n * @param {number} options.fetchK=20 - Number of documents to fetch before passing to the MMR algorithm.\n * @param {number} options.lambda=0.5 - Number between 0 and 1 that determines the degree of diversity among the results,\n * where 0 corresponds to maximum diversity and 1 to minimum diversity.\n * @param {PineconeMetadata} options.filter - Optional filter to apply to the search.\n *\n * @returns {Promise} - List of documents selected by maximal marginal relevance.\n */\n async maxMarginalRelevanceSearch(\n query: string,\n options: MaxMarginalRelevanceSearchOptions\n ): Promise {\n const queryEmbedding = await this.embeddings.embedQuery(query);\n\n const results = await this._runPineconeQuery(\n queryEmbedding,\n options.fetchK ?? 20,\n options.filter,\n { includeValues: true }\n );\n\n const { matches = [] } = results;\n const embeddingList = matches.map((match) => match.values!);\n\n const mmrIndexes = maximalMarginalRelevance(\n queryEmbedding,\n embeddingList,\n options.lambda,\n options.k\n );\n\n const topMmrMatches = mmrIndexes.map((idx) => matches[idx]);\n const records = this._formatMatches(topMmrMatches);\n return records.map(([doc, _score]) => doc);\n }\n\n /**\n * Static method that creates a new instance of the PineconeStore class\n * from texts.\n * @param texts Array of texts to add to the Pinecone database.\n * @param metadatas Metadata associated with the texts.\n * @param embeddings Embeddings to use for the texts.\n * @param dbConfig Configuration for the Pinecone database.\n * @returns Promise that resolves with a new instance of the PineconeStore class.\n */\n static async fromTexts(\n texts: string[],\n metadatas: object[] | object,\n embeddings: EmbeddingsInterface,\n dbConfig:\n | {\n pineconeIndex: PineconeIndex;\n textKey?: string;\n namespace?: string | undefined;\n }\n | PineconeStoreParams\n ): Promise {\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\n const args: PineconeStoreParams = {\n pineconeIndex: dbConfig.pineconeIndex,\n textKey: dbConfig.textKey,\n namespace: dbConfig.namespace,\n };\n return PineconeStore.fromDocuments(docs, embeddings, args);\n }\n\n /**\n * Static method that creates a new instance of the PineconeStore class\n * from documents.\n * @param docs Array of documents to add to the Pinecone database.\n * @param embeddings Embeddings to use for the documents.\n * @param dbConfig Configuration for the Pinecone database.\n * @returns Promise that resolves with a new instance of the PineconeStore class.\n */\n static async fromDocuments(\n docs: Document[],\n embeddings: EmbeddingsInterface,\n dbConfig: PineconeStoreParams\n ): Promise {\n const args = dbConfig;\n args.textKey = dbConfig.textKey ?? \"text\";\n\n const instance = new this(embeddings, args);\n await instance.addDocuments(docs);\n return instance;\n }\n\n /**\n * Static method that creates a new instance of the PineconeStore class\n * from an existing index.\n * @param embeddings Embeddings to use for the documents.\n * @param dbConfig Configuration for the Pinecone database.\n * @returns Promise that resolves with a new instance of the PineconeStore class.\n */\n static async fromExistingIndex(\n embeddings: EmbeddingsInterface,\n dbConfig: PineconeStoreParams\n ): Promise {\n const instance = new this(embeddings, dbConfig);\n return instance;\n 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