@langchain/community

import { Document as MongoDBDocument, MongoClient, Filter } from "mongodb"; import type { EmbeddingsInterface } from "@langchain/core/embeddings"; import { MaxMarginalRelevanceSearchOptions, VectorStore } from "@langchain/core/vectorstores"; import { Document, DocumentInterface } from "@langchain/core/documents"; /** Cosmos DB Similarity type. */ export declare const AzureCosmosDBSimilarityType: { /** CosineSimilarity */ readonly COS: "COS"; /** Inner - product */ readonly IP: "IP"; /** Euclidian distance */ readonly L2: "L2"; }; /** Cosmos DB Similarity type. */ export type AzureCosmosDBSimilarityType = (typeof AzureCosmosDBSimilarityType)[keyof typeof AzureCosmosDBSimilarityType]; /** Azure Cosmos DB Index Options. */ export type AzureCosmosDBIndexOptions = { /** Skips automatic index creation. */ readonly skipCreate?: boolean; /** Number of clusters that the inverted file (IVF) index uses to group the vector data. */ readonly numLists?: number; /** Number of dimensions for vector similarity. */ readonly dimensions?: number; /** Similarity metric to use with the IVF index. */ readonly similarity?: AzureCosmosDBSimilarityType; }; /** Azure Cosmos DB Delete Parameters. */ export type AzureCosmosDBDeleteParams = { /** List of IDs for the documents to be removed. */ readonly ids?: string | string[]; /** MongoDB filter object or list of IDs for the documents to be removed. */ readonly filter?: Filter<MongoDBDocument>; }; /** * Configuration options for the `AzureCosmosDBVectorStore` constructor. */ export interface AzureCosmosDBConfig { readonly client?: MongoClient; readonly connectionString?: string; readonly databaseName?: string; readonly collectionName?: string; readonly indexName?: string; readonly textKey?: string; readonly embeddingKey?: string; readonly indexOptions?: AzureCosmosDBIndexOptions; } /** * Azure Cosmos DB for MongoDB vCore vector store. * To use this, you should have both: * - the `mongodb` NPM package installed * - a connection string associated with a MongoDB VCore Cluster * * You do not need to create a database or collection, it will be created * automatically. * * Though you do need to create an index on the collection, which can be done * using the `createIndex` method. */ export declare class AzureCosmosDBVectorStore extends VectorStore { get lc_secrets(): { [key: string]: string; }; private connectPromise; private readonly initPromise; private readonly client; private database; private collection; readonly indexName: string; readonly textKey: string; readonly embeddingKey: string; private readonly indexOptions; _vectorstoreType(): string; constructor(embeddings: EmbeddingsInterface, dbConfig: AzureCosmosDBConfig); /** * Checks if the specified index name during instance construction exists * on the collection. * @returns A promise that resolves to a boolean indicating if the index exists. */ checkIndexExists(): Promise<boolean>; /** * Deletes the index specified during instance construction if it exists. * @returns A promise that resolves when the index has been deleted. */ deleteIndex(): Promise<void>; /** * Creates an index on the collection with the specified index name during * instance construction. * * Setting the numLists parameter correctly is important for achieving good * accuracy and performance. * Since the vector store uses IVF as the indexing strategy, you should * create the index only after you have loaded a large enough sample * documents to ensure that the centroids for the respective buckets are * faily distributed. * * We recommend that numLists is set to documentCount/1000 for up to * 1 million documents and to sqrt(documentCount) for more than 1 million * documents. * As the number of items in your database grows, you should tune numLists * to be larger in order to achieve good latency performance for vector * search. * * If you're experimenting with a new scenario or creating a small demo, * you can start with numLists set to 1 to perform a brute-force search * across all vectors. * This should provide you with the most accurate results from the vector * search, however be aware that the search speed and latency will be slow. * After your initial setup, you should go ahead and tune the numLists * parameter using the above guidance. * @param numLists This integer is the number of clusters that the inverted * file (IVF) index uses to group the vector data. * We recommend that numLists is set to documentCount/1000 for up to * 1 million documents and to sqrt(documentCount) for more than 1 million * documents. * Using a numLists value of 1 is akin to performing brute-force search, * which has limited performance * @param dimensions Number of dimensions for vector similarity. * The maximum number of supported dimensions is 2000. * If no number is provided, it will be determined automatically by * embedding a short text. * @param similarity Similarity metric to use with the IVF index. * Possible options are: * - CosmosDBSimilarityType.COS (cosine distance) * - CosmosDBSimilarityType.L2 (Euclidean distance) * - CosmosDBSimilarityType.IP (inner product) * @returns A promise that resolves when the index has been created. */ createIndex(numLists?: number, dimensions?: number | undefined, similarity?: AzureCosmosDBSimilarityType): Promise<void>; /** * Removes specified documents from the AzureCosmosDBVectorStore. * If no IDs or filter are specified, all documents will be removed. * @param params Parameters for the delete operation. * @returns A promise that resolves when the documents have been removed. */ delete(params?: AzureCosmosDBDeleteParams | string[]): Promise<void>; /** * Closes any newly instanciated Azure Cosmos DB client. * If the client was passed in the constructor, it will not be closed. * @returns A promise that resolves when any newly instanciated Azure * Cosmos DB client been closed. */ close(): Promise<void>; /** * Method for adding vectors to the AzureCosmosDBVectorStore. * @param vectors Vectors to be added. * @param documents Corresponding documents to be added. * @returns A promise that resolves to the added documents IDs. */ addVectors(vectors: number[][], documents: DocumentInterface[]): Promise<string[]>; /** * Method for adding documents to the AzureCosmosDBVectorStore. It first converts * the documents to texts and then adds them as vectors. * @param documents The documents to add. * @returns A promise that resolves to the added documents IDs. */ addDocuments(documents: DocumentInterface[]): Promise<string[]>; /** * Method that performs a similarity search on the vectors stored in the * collection. It returns a list of documents and their corresponding * similarity scores. * @param queryVector Query vector for the similarity search. * @param k=4 Number of nearest neighbors to return. * @returns Promise that resolves to a list of documents and their corresponding similarity scores. */ similaritySearchVectorWithScore(queryVector: number[], k?: number): Promise<[Document, number][]>; /** * Return documents selected using the maximal marginal relevance. * Maximal marginal relevance optimizes for similarity to the query AND * diversity among selected documents. * @param query Text to look up documents similar to. * @param options.k Number of documents to return. * @param options.fetchK=20 Number of documents to fetch before passing to * the MMR algorithm. * @param options.lambda=0.5 Number between 0 and 1 that determines the * degree of diversity among the results, where 0 corresponds to maximum * diversity and 1 to minimum diversity. * @returns List of documents selected by maximal marginal relevance. */ maxMarginalRelevanceSearch(query: string, options: MaxMarginalRelevanceSearchOptions<this["FilterType"]>): Promise<Document[]>; /** * Initializes the AzureCosmosDBVectorStore by connecting to the database. * @param client The MongoClient to use for connecting to the database. * @param databaseName The name of the database to use. * @param collectionName The name of the collection to use. * @returns A promise that resolves when the AzureCosmosDBVectorStore has been initialized. */ private init; /** * Static method to create an instance of AzureCosmosDBVectorStore from a * list of texts. It first converts the texts to vectors and then adds * them to the collection. * @param texts List of texts to be converted to vectors. * @param metadatas Metadata for the texts. * @param embeddings Embeddings to be used for conversion. * @param dbConfig Database configuration for Azure Cosmos DB for MongoDB vCore. * @returns Promise that resolves to a new instance of AzureCosmosDBVectorStore. */ static fromTexts(texts: string[], metadatas: object[] | object, embeddings: EmbeddingsInterface, dbConfig: AzureCosmosDBConfig): Promise<AzureCosmosDBVectorStore>; /** * Static method to create an instance of AzureCosmosDBVectorStore from a * list of documents. It first converts the documents to vectors and then * adds them to the collection. * @param docs List of documents to be converted to vectors. * @param embeddings Embeddings to be used for conversion. * @param dbConfig Database configuration for Azure Cosmos DB for MongoDB vCore. * @returns Promise that resolves to a new instance of AzureCosmosDBVectorStore. */ static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, dbConfig: AzureCosmosDBConfig): Promise<AzureCosmosDBVectorStore>; }