@langchain/community/dist/vectorstores/milvus.d.cts.map

Third-party integrations for LangChain.js

{"version":3,"file":"milvus.d.cts","names":["MilvusClient","ClientConfig","keyValueObj","EmbeddingsInterface","VectorStore","Document","MilvusLibArgs","IndexCreateOptions","IndexType","MetricType","Milvus","Promise"],"sources":["../../src/vectorstores/milvus.d.ts"],"sourcesContent":["import { MilvusClient, ClientConfig, keyValueObj } from \"@zilliz/milvus2-sdk-node\";\nimport type { EmbeddingsInterface } from \"@langchain/core/embeddings\";\nimport { VectorStore } from \"@langchain/core/vectorstores\";\nimport { Document } from \"@langchain/core/documents\";\n/**\n * Interface for the arguments required by the Milvus class constructor.\n */\nexport interface MilvusLibArgs {\n    collectionName?: string;\n    partitionName?: string;\n    primaryField?: string;\n    vectorField?: string;\n    textField?: string;\n    url?: string;\n    ssl?: boolean;\n    username?: string;\n    password?: string;\n    textFieldMaxLength?: number;\n    clientConfig?: ClientConfig;\n    autoId?: boolean;\n    indexCreateOptions?: IndexCreateOptions;\n    partitionKey?: string;\n    partitionKeyMaxLength?: number;\n}\nexport interface IndexCreateOptions {\n    index_type: IndexType;\n    metric_type: MetricType;\n    params?: keyValueObj;\n    search_params?: keyValueObj;\n}\nexport type MetricType = \"L2\" | \"IP\" | \"COSINE\";\n/**\n * Type representing the type of index used in the Milvus database.\n */\ntype IndexType = \"FLAT\" | \"IVF_FLAT\" | \"IVF_SQ8\" | \"IVF_PQ\" | \"HNSW\" | \"RHNSW_FLAT\" | \"RHNSW_SQ\" | \"RHNSW_PQ\" | \"IVF_HNSW\" | \"ANNOY\";\n/**\n * Class for interacting with a Milvus database. Extends the VectorStore\n * class.\n */\nexport declare class Milvus extends VectorStore {\n    embeddings: EmbeddingsInterface;\n    get lc_secrets(): {\n        [key: string]: string;\n    };\n    _vectorstoreType(): string;\n    FilterType: string;\n    collectionName: string;\n    partitionName?: string;\n    numDimensions?: number;\n    autoId?: boolean;\n    primaryField: string;\n    vectorField: string;\n    textField: string;\n    textFieldMaxLength: number;\n    partitionKey?: string;\n    partitionKeyMaxLength?: number;\n    fields: string[];\n    client: MilvusClient;\n    indexCreateParams: IndexCreateOptions;\n    indexSearchParams: keyValueObj;\n    constructor(embeddings: EmbeddingsInterface, args: MilvusLibArgs);\n    /**\n     * Adds documents to the Milvus database.\n     * @param documents Array of Document instances to be added to the database.\n     * @param options Optional parameter that can include specific IDs for the documents.\n     * @returns Promise resolving to void.\n     */\n    addDocuments(documents: Document[], options?: {\n        ids?: string[];\n    }): Promise<void>;\n    /**\n     * Adds vectors to the Milvus database.\n     * @param vectors Array of vectors to be added to the database.\n     * @param documents Array of Document instances associated with the vectors.\n     * @param options Optional parameter that can include specific IDs for the documents.\n     * @returns Promise resolving to void.\n     */\n    addVectors(vectors: number[][], documents: Document[], options?: {\n        ids?: string[];\n    }): Promise<void>;\n    /**\n     * Searches for vectors in the Milvus database that are similar to a given\n     * vector.\n     * @param query Vector to compare with the vectors in the database.\n     * @param k Number of similar vectors to return.\n     * @param filter Optional filter to apply to the search.\n     * @returns Promise resolving to an array of tuples, each containing a Document instance and a similarity score.\n     */\n    similaritySearchVectorWithScore(query: number[], k: number, filter?: string): Promise<[Document, number][]>;\n    /**\n     * Ensures that a collection exists in the Milvus database.\n     * @param vectors Optional array of vectors to be used if a new collection needs to be created.\n     * @param documents Optional array of Document instances to be used if a new collection needs to be created.\n     * @returns Promise resolving to void.\n     */\n    ensureCollection(vectors?: number[][], documents?: Document[]): Promise<void>;\n    /**\n     * Ensures that a partition exists in the Milvus collection.\n     * @returns Promise resolving to void.\n     */\n    ensurePartition(): Promise<void>;\n    /**\n     * Creates a collection in the Milvus database.\n     * @param vectors Array of vectors to be added to the new collection.\n     * @param documents Array of Document instances to be added to the new collection.\n     * @returns Promise resolving to void.\n     */\n    createCollection(vectors: number[][], documents: Document[]): Promise<void>;\n    /**\n     * Retrieves the fields of a collection in the Milvus database.\n     * @returns Promise resolving to void.\n     */\n    grabCollectionFields(): Promise<void>;\n    /**\n     * Creates a Milvus instance from a set of texts and their associated\n     * metadata.\n     * @param texts Array of texts to be added to the database.\n     * @param metadatas Array of metadata objects associated with the texts.\n     * @param embeddings Embeddings instance used to generate vector embeddings for the texts.\n     * @param dbConfig Optional configuration for the Milvus database.\n     * @returns Promise resolving to a new Milvus instance.\n     */\n    static fromTexts(texts: string[], metadatas: object[] | object, embeddings: EmbeddingsInterface, dbConfig?: MilvusLibArgs): Promise<Milvus>;\n    /**\n     * Creates a Milvus instance from a set of Document instances.\n     * @param docs Array of Document instances to be added to the database.\n     * @param embeddings Embeddings instance used to generate vector embeddings for the documents.\n     * @param dbConfig Optional configuration for the Milvus database.\n     * @returns Promise resolving to a new Milvus instance.\n     */\n    static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, dbConfig?: MilvusLibArgs): Promise<Milvus>;\n    /**\n     * Creates a Milvus instance from an existing collection in the Milvus\n     * database.\n     * @param embeddings Embeddings instance used to generate vector embeddings for the documents in the collection.\n     * @param dbConfig Configuration for the Milvus database.\n     * @returns Promise resolving to a new Milvus instance.\n     */\n    static fromExistingCollection(embeddings: EmbeddingsInterface, dbConfig: MilvusLibArgs): Promise<Milvus>;\n    /**\n     * Deletes data from the Milvus database.\n     * @param params Object containing a filter to apply to the deletion.\n     * @returns Promise resolving to void.\n     */\n    delete(params: {\n        filter?: string;\n        ids?: string[];\n    }): Promise<void>;\n}\nexport {};\n//# 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