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Third-party integrations for LangChain.js
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{"version":3,"file":"couchbase_search.cjs","names":["VectorStore","embedding: EmbeddingsInterface","config: CouchbaseSearchVectorStoreArgs","embeddings: EmbeddingsInterface","scopeCollectionMap: Record<string, any>","fields: any","metadataFields: { [key: string]: any }","queryEmbeddings: number[]","filter: CouchbaseSearchVectorStoreFilter","SearchRequest","VectorSearch","VectorQuery","docsWithScore: [Document, number][]","Document","query: string","documentsToInsert: {\n [x: string]: any;\n }[]","successfulDocIds: string[]","vectors: number[][]","documents: Document[]","options: AddVectorOptions","ids: string[] | undefined","metadata: any","id: string","index: number","docIds: string[]","texts: string[]","metadatas: any","ids: string[]"],"sources":["../../src/vectorstores/couchbase_search.ts"],"sourcesContent":["/* eslint-disable @typescript-eslint/no-explicit-any */\n/* eslint-disable import/no-extraneous-dependencies */\nimport { EmbeddingsInterface } from \"@langchain/core/embeddings\";\nimport { VectorStore } from \"@langchain/core/vectorstores\";\nimport {\n Bucket,\n Cluster,\n Collection,\n Scope,\n SearchRequest,\n VectorQuery,\n VectorSearch,\n} from \"couchbase\";\nimport { Document } from \"@langchain/core/documents\";\nimport { v4 as uuid } from \"uuid\";\n\n/**\n * This interface define the optional fields for adding vector\n * - `ids` - vector of ids for each document. If undefined, then uuid will be used\n * - `metadata` - vector of metadata object for each document\n */\nexport interface AddVectorOptions {\n ids?: string[];\n metadata?: Record<string, any>[];\n}\n\n/**\n * This interface defines the fields required to initialize a vector store\n * These are the fields part of config:\n * @property {Cluster} cluster - The Couchbase cluster that the store will interact with.\n * @property {string} bucketName - The name of the bucket in the Couchbase cluster.\n * @property {string} scopeName - The name of the scope within the bucket.\n * @property {string} collectionName - The name of the collection within the scope.\n * @property {string} indexName - The name of the index to be used for vector search.\n * @property {string} textKey - The key to be used for text in the documents. Defaults to \"text\".\n * @property {string} embeddingKey - The key to be used for embeddings in the documents. Defaults to \"embedding\".\n * @property {boolean} scopedIndex - Whether to use a scoped index for vector search. Defaults to true.\n * @property {AddVectorOptions} addVectorOptions - Options for adding vectors with specific id/metadata\n */\nexport interface CouchbaseSearchVectorStoreArgs {\n cluster: Cluster;\n bucketName: string;\n scopeName: string;\n collectionName: string;\n indexName: string;\n textKey?: string;\n embeddingKey?: string;\n scopedIndex?: boolean;\n addVectorOptions?: AddVectorOptions;\n}\n\n/**\n * This type defines the search filters used in couchbase vector search\n * - `fields`: Optional list of fields to include in the\n * metadata of results. Note that these need to be stored in the index.\n * If nothing is specified, defaults to all the fields stored in the index.\n * - `searchOptions`: Optional search options that are passed to Couchbase search. Defaults to empty object.\n */\ntype CouchbaseSearchVectorStoreFilter = {\n fields?: any;\n searchOptions?: any;\n};\n\n/**\n * Class for interacting with the Couchbase database. It extends the\n * VectorStore class and provides methods for adding vectors and\n * documents, and searching for similar vectors.\n * Initiate the class using initialize() method.\n */\nexport class CouchbaseSearchVectorStore extends VectorStore {\n declare FilterType: CouchbaseSearchVectorStoreFilter;\n\n private metadataKey = \"metadata\";\n\n private readonly defaultTextKey = \"text\";\n\n private readonly defaultScopedIndex = true;\n\n private readonly defaultEmbeddingKey = \"embedding\";\n\n private cluster: Cluster;\n\n private _bucket: Bucket;\n\n private _scope: Scope;\n\n private _collection: Collection;\n\n private bucketName: string;\n\n private scopeName: string;\n\n private collectionName: string;\n\n private indexName: string;\n\n private textKey = this.defaultTextKey;\n\n private embeddingKey = this.defaultEmbeddingKey;\n\n private scopedIndex: boolean;\n\n /**\n * The private constructor used to provide embedding to parent class.\n * Initialize the class using static initialize() method\n * @param embedding - object to generate embedding\n * @param config - the fields required to initialize a vector store\n */\n private constructor(\n embedding: EmbeddingsInterface,\n config: CouchbaseSearchVectorStoreArgs\n ) {\n super(embedding, config);\n }\n\n /**\n * initialize class for interacting with the Couchbase database.\n * It extends the VectorStore class and provides methods\n * for adding vectors and documents, and searching for similar vectors.\n * This also verifies the params\n *\n * @param embeddings - object to generate embedding\n * @param config - the fields required to initialize a vector store\n */\n static async initialize(\n embeddings: EmbeddingsInterface,\n config: CouchbaseSearchVectorStoreArgs\n ) {\n const store = new CouchbaseSearchVectorStore(embeddings, config);\n\n const {\n cluster,\n bucketName,\n scopeName,\n collectionName,\n indexName,\n textKey,\n embeddingKey,\n scopedIndex,\n } = config;\n\n store.cluster = cluster;\n store.bucketName = bucketName;\n store.scopeName = scopeName;\n store.collectionName = collectionName;\n store.indexName = indexName;\n if (textKey) {\n store.textKey = textKey;\n } else {\n store.textKey = store.defaultTextKey;\n }\n\n if (embeddingKey) {\n store.embeddingKey = embeddingKey;\n } else {\n store.embeddingKey = store.defaultEmbeddingKey;\n }\n\n if (scopedIndex !== undefined) {\n store.scopedIndex = scopedIndex;\n } else {\n store.scopedIndex = store.defaultScopedIndex;\n }\n\n try {\n store._bucket = store.cluster.bucket(store.bucketName);\n store._scope = store._bucket.scope(store.scopeName);\n store._collection = store._scope.collection(store.collectionName);\n } catch {\n throw new Error(\n \"Error connecting to couchbase, Please check connection and credentials\"\n );\n }\n\n try {\n if (\n !(await store.checkBucketExists()) ||\n !(await store.checkIndexExists()) ||\n !(await store.checkScopeAndCollectionExists())\n ) {\n throw new Error(\"Error while initializing vector store\");\n }\n } catch (err) {\n throw new Error(`Error while initializing vector store: ${err}`);\n }\n return store;\n }\n\n /**\n * An asynchrononous method to verify the search indexes.\n * It retrieves all indexes and checks if specified index is present.\n *\n * @throws - If the specified index does not exist in the database.\n *\n * @returns - returns promise true if no error is found\n */\n private async checkIndexExists(): Promise<boolean> {\n if (this.scopedIndex) {\n const allIndexes = await this._scope.searchIndexes().getAllIndexes();\n const indexNames = allIndexes.map((index) => index.name);\n if (!indexNames.includes(this.indexName)) {\n throw new Error(\n `Index ${this.indexName} does not exist. Please create the index before searching.`\n );\n }\n } else {\n const allIndexes = await this.cluster.searchIndexes().getAllIndexes();\n const indexNames = allIndexes.map((index) => index.name);\n if (!indexNames.includes(this.indexName)) {\n throw new Error(\n `Index ${this.indexName} does not exist. Please create the index before searching.`\n );\n }\n }\n return true;\n }\n\n /**\n * An asynchronous method to verify the existence of a bucket.\n * It retrieves the bucket using the bucket manager and checks if the specified bucket is present.\n *\n * @throws - If the specified bucket does not exist in the database.\n *\n * @returns - Returns a promise that resolves to true if no error is found, indicating the bucket exists.\n */\n private async checkBucketExists(): Promise<boolean> {\n const bucketManager = this.cluster.buckets();\n try {\n await bucketManager.getBucket(this.bucketName);\n return true;\n } catch {\n throw new Error(\n `Bucket ${this.bucketName} does not exist. Please create the bucket before searching.`\n );\n }\n }\n\n /**\n * An asynchronous method to verify the existence of a scope and a collection within that scope.\n * It retrieves all scopes and collections in the bucket, and checks if the specified scope and collection are present.\n *\n * @throws - If the specified scope does not exist in the bucket, or if the specified collection does not exist in the scope.\n *\n * @returns - Returns a promise that resolves to true if no error is found, indicating the scope and collection exist.\n */\n private async checkScopeAndCollectionExists(): Promise<boolean> {\n const scopeCollectionMap: Record<string, any> = {};\n\n // Get a list of all scopes in the bucket\n const scopes = await this._bucket.collections().getAllScopes();\n for (const scope of scopes) {\n scopeCollectionMap[scope.name] = [];\n\n // Get a list of all the collections in the scope\n for (const collection of scope.collections) {\n scopeCollectionMap[scope.name].push(collection.name);\n }\n }\n\n // Check if the scope exists\n if (!Object.keys(scopeCollectionMap).includes(this.scopeName)) {\n throw new Error(\n `Scope ${this.scopeName} not found in Couchbase bucket ${this.bucketName}`\n );\n }\n\n // Check if the collection exists in the scope\n if (!scopeCollectionMap[this.scopeName].includes(this.collectionName)) {\n throw new Error(\n `Collection ${this.collectionName} not found in scope ${this.scopeName} in Couchbase bucket ${this.bucketName}`\n );\n }\n return true;\n }\n\n _vectorstoreType(): string {\n return \"couchbase-search\";\n }\n\n /**\n * Formats couchbase metadata by removing `metadata.` from initials\n * @param fields - all the fields of row\n * @returns - formatted metadata fields\n */\n private formatMetadata = (fields: any) => {\n const fieldsCopy = { ...fields };\n delete fieldsCopy[this.textKey];\n const metadataFields: { [key: string]: any } = {};\n for (const key in fieldsCopy) {\n if (Object.prototype.hasOwnProperty.call(fieldsCopy, key)) {\n const newKey = key.replace(`${this.metadataKey}.`, \"\");\n metadataFields[newKey] = fieldsCopy[key];\n }\n }\n return metadataFields;\n };\n\n /**\n * Performs a similarity search on the vectors in the Couchbase database and returns the documents and their corresponding scores.\n *\n * @param queryEmbeddings - Embedding vector to look up documents similar to.\n * @param k - Number of documents to return. Defaults to 4.\n * @param filter - Optional search filter that are passed to Couchbase search. Defaults to empty object.\n * - `fields`: Optional list of fields to include in the\n * metadata of results. Note that these need to be stored in the index.\n * If nothing is specified, defaults to all the fields stored in the index.\n * - `searchOptions`: Optional search options that are passed to Couchbase search. Defaults to empty object.\n *\n * @returns - Promise of list of [document, score] that are the most similar to the query vector.\n *\n * @throws If the search operation fails.\n */\n async similaritySearchVectorWithScore(\n queryEmbeddings: number[],\n k = 4,\n filter: CouchbaseSearchVectorStoreFilter = {}\n ): Promise<[Document, number][]> {\n let { fields } = filter;\n const { searchOptions } = filter;\n\n if (!fields) {\n fields = [\"*\"];\n }\n if (\n !(fields.length === 1 && fields[0] === \"*\") &&\n !fields.includes(this.textKey)\n ) {\n fields.push(this.textKey);\n }\n\n const searchRequest = new SearchRequest(\n VectorSearch.fromVectorQuery(\n new VectorQuery(this.embeddingKey, queryEmbeddings).numCandidates(k)\n )\n );\n\n let searchIterator;\n const docsWithScore: [Document, number][] = [];\n try {\n if (this.scopedIndex) {\n searchIterator = this._scope.search(this.indexName, searchRequest, {\n limit: k,\n fields,\n raw: searchOptions,\n });\n } else {\n searchIterator = this.cluster.search(this.indexName, searchRequest, {\n limit: k,\n fields,\n raw: searchOptions,\n });\n }\n\n const searchRows = (await searchIterator).rows;\n for (const row of searchRows) {\n const text = row.fields[this.textKey];\n const metadataFields = this.formatMetadata(row.fields);\n const searchScore = row.score;\n const doc = new Document({\n pageContent: text,\n metadata: metadataFields,\n });\n docsWithScore.push([doc, searchScore]);\n }\n } catch (err) {\n console.log(\"error received\");\n throw new Error(`Search failed with error: ${err}`);\n }\n return docsWithScore;\n }\n\n /**\n * Return documents that are most similar to the vector embedding.\n *\n * @param queryEmbeddings - Embedding to look up documents similar to.\n * @param k - The number of similar documents to return. Defaults to 4.\n * @param filter - Optional search filter that are passed to Couchbase search. Defaults to empty object.\n * - `fields`: Optional list of fields to include in the\n * metadata of results. Note that these need to be stored in the index.\n * If nothing is specified, defaults to all the fields stored in the index.\n * - `searchOptions`: Optional search options that are passed to Couchbase search. Defaults to empty object.\n *\n * @returns - A promise that resolves to an array of documents that match the similarity search.\n */\n async similaritySearchByVector(\n queryEmbeddings: number[],\n k = 4,\n filter: CouchbaseSearchVectorStoreFilter = {}\n ): Promise<Document[]> {\n const docsWithScore = await this.similaritySearchVectorWithScore(\n queryEmbeddings,\n k,\n filter\n );\n const docs = [];\n for (const doc of docsWithScore) {\n docs.push(doc[0]);\n }\n return docs;\n }\n\n /**\n * Return documents that are most similar to the query.\n *\n * @param query - Query to look up for similar documents\n * @param k - The number of similar documents to return. Defaults to 4.\n * @param filter - Optional search filter that are passed to Couchbase search. Defaults to empty object.\n * - `fields`: Optional list of fields to include in the\n * metadata of results. Note that these need to be stored in the index.\n * If nothing is specified, defaults to all the fields stored in the index.\n * - `searchOptions`: Optional search options that are passed to Couchbase search. Defaults to empty object.\n *\n * @returns - Promise of list of documents that are most similar to the query.\n */\n async similaritySearch(\n query: string,\n k = 4,\n filter: CouchbaseSearchVectorStoreFilter = {}\n ): Promise<Document[]> {\n const queryEmbeddings = await this.embeddings.embedQuery(query);\n const docsWithScore = await this.similaritySearchVectorWithScore(\n queryEmbeddings,\n k,\n filter\n );\n const docs = [];\n for (const doc of docsWithScore) {\n docs.push(doc[0]);\n }\n return docs;\n }\n\n /**\n * Return documents that are most similar to the query with their scores.\n *\n * @param query - Query to look up for similar documents\n * @param k - The number of similar documents to return. Defaults to 4.\n * @param filter - Optional search filter that are passed to Couchbase search. Defaults to empty object.\n * - `fields`: Optional list of fields to include in the\n * metadata of results. Note that these need to be stored in the index.\n * If nothing is specified, defaults to all the fields stored in the index.\n * - `searchOptions`: Optional search options that are passed to Couchbase search. Defaults to empty object.\n *\n * @returns - Promise of list of documents that are most similar to the query.\n */\n async similaritySearchWithScore(\n query: string,\n k = 4,\n filter: CouchbaseSearchVectorStoreFilter = {}\n ): Promise<[Document, number][]> {\n const queryEmbeddings = await this.embeddings.embedQuery(query);\n const docsWithScore = await this.similaritySearchVectorWithScore(\n queryEmbeddings,\n k,\n filter\n );\n return docsWithScore;\n }\n\n /**\n * upsert documents asynchronously into a couchbase collection\n * @param documentsToInsert Documents to be inserted into couchbase collection with embeddings, original text and metadata\n * @returns DocIds of the inserted documents\n */\n private async upsertDocuments(\n documentsToInsert: {\n [x: string]: any;\n }[]\n ) {\n // Create promises for each document to be upserted\n const upsertDocumentsPromises = documentsToInsert.map((document) => {\n const currentDocumentKey = Object.keys(document)[0];\n return this._collection\n .upsert(currentDocumentKey, document[currentDocumentKey])\n .then(() => currentDocumentKey)\n .catch((e) => {\n console.error(\"error received while upserting document\", e);\n throw new Error(`Upsert failed with error: ${e}`);\n });\n });\n\n try {\n // Upsert all documents asynchronously\n const docIds = await Promise.all(upsertDocumentsPromises);\n const successfulDocIds: string[] = [];\n for (const id of docIds) {\n if (id) {\n successfulDocIds.push(id);\n }\n }\n return successfulDocIds;\n } catch (e) {\n console.error(\n \"An error occurred with Promise.all at upserting all documents\",\n e\n );\n throw e;\n }\n }\n\n /**\n * Add vectors and corresponding documents to a couchbase collection\n * If the document IDs are passed, the existing documents (if any) will be\n * overwritten with the new ones.\n * @param vectors - The vectors to be added to the collection.\n * @param documents - The corresponding documents to be added to the collection.\n * @param options - Optional parameters for adding vectors.\n * This may include the IDs and metadata of the documents to be added. Defaults to an empty object.\n *\n * @returns - A promise that resolves to an array of document IDs that were added to the collection.\n */\n public async addVectors(\n vectors: number[][],\n documents: Document[],\n options: AddVectorOptions = {}\n ): Promise<string[]> {\n // Get document ids. if ids are not available then use UUIDs for each document\n let ids: string[] | undefined = options ? options.ids : undefined;\n if (ids === undefined) {\n ids = Array.from({ length: documents.length }, () => uuid());\n }\n\n // Get metadata for each document. if metadata is not available, use empty object for each document\n let metadata: any = options ? options.metadata : undefined;\n if (metadata === undefined) {\n metadata = Array.from({ length: documents.length }, () => ({}));\n }\n\n const documentsToInsert = ids.map((id: string, index: number) => ({\n [id]: {\n [this.textKey]: documents[index].pageContent,\n [this.embeddingKey]: vectors[index],\n [this.metadataKey]: metadata[index],\n },\n }));\n\n let docIds: string[] = [];\n try {\n docIds = await this.upsertDocuments(documentsToInsert);\n } catch (err) {\n console.error(\"Error while adding vectors\", err);\n throw err;\n }\n\n return docIds;\n }\n\n /**\n * Run texts through the embeddings and persist in vectorstore.\n * If the document IDs are passed, the existing documents (if any) will be\n * overwritten with the new ones.\n * @param documents - The corresponding documents to be added to the collection.\n * @param options - Optional parameters for adding documents.\n * This may include the IDs and metadata of the documents to be added. Defaults to an empty object.\n *\n * @returns - A promise that resolves to an array of document IDs that were added to the collection.\n */\n public async addDocuments(\n documents: Document[],\n options: AddVectorOptions = {}\n ) {\n const texts = documents.map(({ pageContent }) => pageContent);\n const metadatas = documents.map((doc) => doc.metadata);\n const optionsWithMetadata = {\n ...options,\n metadata: options.metadata ?? metadatas,\n };\n return this.addVectors(\n await this.embeddings.embedDocuments(texts),\n documents,\n optionsWithMetadata\n );\n }\n\n /**\n * Create a new CouchbaseVectorStore from a set of documents.\n * This function will initialize a new store, add the documents to it, and then return the store.\n * @param documents - The documents to be added to the new store.\n * @param embeddings - The embeddings to be used for the documents.\n * @param config - The configuration for the new CouchbaseVectorStore. This includes the options for adding vectors.\n *\n * @returns - A promise that resolves to the new CouchbaseVectorStore that contains the added documents.\n */\n static async fromDocuments(\n documents: Document[],\n embeddings: EmbeddingsInterface,\n config: CouchbaseSearchVectorStoreArgs\n ): Promise<CouchbaseSearchVectorStore> {\n const store = await this.initialize(embeddings, config);\n await store.addDocuments(documents, config.addVectorOptions);\n return store;\n }\n\n /**\n * Create a new CouchbaseVectorStore from a set of texts.\n * This function will convert each text and its corresponding metadata into a Document,\n * initialize a new store, add the documents to it, and then return the store.\n * @param texts - The texts to be converted into Documents and added to the new store.\n * @param metadatas - The metadata for each text. If an array is passed, each text will have its corresponding metadata.\n * If not, all texts will have the same metadata.\n * @param embeddings - The embeddings to be used for the documents.\n * @param config - The configuration for the new CouchbaseVectorStore. This includes the options for adding vectors.\n *\n * @returns - A promise that resolves to the new CouchbaseVectorStore that contains the added documents.\n */\n static async fromTexts(\n texts: string[],\n metadatas: any,\n embeddings: EmbeddingsInterface,\n config: CouchbaseSearchVectorStoreArgs\n ): Promise<CouchbaseSearchVectorStore> {\n const docs = [];\n\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 await this.fromDocuments(docs, embeddings, config);\n }\n\n /**\n * Delete documents asynchronously from the collection.\n * This function will attempt to remove each document in the provided list of IDs from the collection.\n * If an error occurs during the deletion of a document, an error will be thrown with the ID of the document and the error message.\n * @param ids - An array of document IDs to be deleted from the collection.\n *\n * @returns - A promise that resolves when all documents have been attempted to be deleted. If a document could not be deleted, an error is thrown.\n */\n public async delete(ids: string[]): Promise<void> {\n const deleteDocumentsPromises = ids.map((id) =>\n this._collection.remove(id).catch((err) => {\n throw new Error(\n `Error while deleting document - Document Id: ${id}, Error: ${err}`\n );\n })\n );\n try {\n await Promise.all(deleteDocumentsPromises);\n } catch (err) {\n throw new Error(`Error while deleting documents, Error: ${err}`);\n }\n 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