149 lines
7 KiB
TypeScript
149 lines
7 KiB
TypeScript
import type { WeaviateClient, WhereFilter } from "weaviate-ts-client";
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import { MaxMarginalRelevanceSearchOptions, VectorStore } from "@langchain/core/vectorstores";
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import type { EmbeddingsInterface } from "@langchain/core/embeddings";
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import { Document } from "@langchain/core/documents";
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/**
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* @deprecated Prefer the `@langchain/weaviate` package.
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*/
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export declare const flattenObjectForWeaviate: (obj: Record<string, any>) => Record<string, any>;
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/**
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* @deprecated Prefer the `@langchain/weaviate` package.
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*
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* Interface that defines the arguments required to create a new instance
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* of the `WeaviateStore` class. It includes the Weaviate client, the name
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* of the class in Weaviate, and optional keys for text and metadata.
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*/
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export interface WeaviateLibArgs {
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client: WeaviateClient;
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/**
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* The name of the class in Weaviate. Must start with a capital letter.
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*/
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indexName: string;
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textKey?: string;
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metadataKeys?: string[];
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tenant?: string;
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}
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/**
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* @deprecated Prefer the `@langchain/weaviate` package.
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*
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* Interface that defines a filter for querying data from Weaviate. It
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* includes a distance and a `WhereFilter`.
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*/
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export interface WeaviateFilter {
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distance?: number;
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where: WhereFilter;
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}
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/**
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* @deprecated Prefer the `@langchain/weaviate` package.
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*
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* Class that extends the `VectorStore` base class. It provides methods to
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* interact with a Weaviate index, including adding vectors and documents,
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* deleting data, and performing similarity searches.
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*/
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export declare class WeaviateStore extends VectorStore {
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embeddings: EmbeddingsInterface;
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FilterType: WeaviateFilter;
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private client;
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private indexName;
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private textKey;
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private queryAttrs;
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private tenant?;
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_vectorstoreType(): string;
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constructor(embeddings: EmbeddingsInterface, args: WeaviateLibArgs);
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/**
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* Method to add vectors and corresponding documents to the Weaviate
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* index.
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* @param vectors Array of vectors to be added.
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* @param documents Array of documents corresponding to the vectors.
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* @param options Optional parameter that can include specific IDs for the documents.
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* @returns An array of document IDs.
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*/
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addVectors(vectors: number[][], documents: Document[], options?: {
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ids?: string[];
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}): Promise<string[]>;
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/**
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* Method to add documents to the Weaviate index. It first generates
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* vectors for the documents using the embeddings, then adds the vectors
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* and documents to the index.
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* @param documents Array of documents to be added.
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* @param options Optional parameter that can include specific IDs for the documents.
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* @returns An array of document IDs.
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*/
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addDocuments(documents: Document[], options?: {
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ids?: string[];
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}): Promise<string[]>;
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/**
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* Method to delete data from the Weaviate index. It can delete data based
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* on specific IDs or a filter.
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* @param params Object that includes either an array of IDs or a filter for the data to be deleted.
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* @returns Promise that resolves when the deletion is complete.
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*/
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delete(params: {
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ids?: string[];
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filter?: WeaviateFilter;
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}): Promise<void>;
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/**
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* Method to perform a similarity search on the stored vectors in the
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* Weaviate index. It returns the top k most similar documents and their
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* similarity scores.
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* @param query The query vector.
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* @param k The number of most similar documents to return.
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* @param filter Optional filter to apply to the search.
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* @returns An array of tuples, where each tuple contains a document and its similarity score.
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*/
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similaritySearchVectorWithScore(query: number[], k: number, filter?: WeaviateFilter): Promise<[Document, number][]>;
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/**
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* Method to perform a similarity search on the stored vectors in the
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* Weaviate index. It returns the top k most similar documents, their
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* similarity scores and embedding vectors.
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* @param query The query vector.
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* @param k The number of most similar documents to return.
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* @param filter Optional filter to apply to the search.
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* @returns An array of tuples, where each tuple contains a document, its similarity score and its embedding vector.
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*/
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similaritySearchVectorWithScoreAndEmbedding(query: number[], k: number, filter?: WeaviateFilter): Promise<[Document, number, number[]][]>;
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/**
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* Return documents selected using the maximal marginal relevance.
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* Maximal marginal relevance optimizes for similarity to the query AND diversity
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* among selected documents.
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*
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* @param {string} query - Text to look up documents similar to.
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* @param {number} options.k - Number of documents to return.
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* @param {number} options.fetchK - Number of documents to fetch before passing to the MMR algorithm.
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* @param {number} options.lambda - Number between 0 and 1 that determines the degree of diversity among the results,
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* where 0 corresponds to maximum diversity and 1 to minimum diversity.
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* @param {this["FilterType"]} options.filter - Optional filter
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* @param _callbacks
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*
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* @returns {Promise<Document[]>} - List of documents selected by maximal marginal relevance.
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*/
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maxMarginalRelevanceSearch(query: string, options: MaxMarginalRelevanceSearchOptions<this["FilterType"]>, _callbacks?: undefined): Promise<Document[]>;
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/**
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* Static method to create a new `WeaviateStore` instance from a list of
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* texts. It first creates documents from the texts and metadata, then
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* adds the documents to the Weaviate index.
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* @param texts Array of texts.
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* @param metadatas Metadata for the texts. Can be a single object or an array of objects.
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* @param embeddings Embeddings to be used for the texts.
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* @param args Arguments required to create a new `WeaviateStore` instance.
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* @returns A new `WeaviateStore` instance.
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*/
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static fromTexts(texts: string[], metadatas: object | object[], embeddings: EmbeddingsInterface, args: WeaviateLibArgs): Promise<WeaviateStore>;
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/**
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* Static method to create a new `WeaviateStore` instance from a list of
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* documents. It adds the documents to the Weaviate index.
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* @param docs Array of documents.
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* @param embeddings Embeddings to be used for the documents.
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* @param args Arguments required to create a new `WeaviateStore` instance.
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* @returns A new `WeaviateStore` instance.
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*/
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static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, args: WeaviateLibArgs): Promise<WeaviateStore>;
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/**
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* Static method to create a new `WeaviateStore` instance from an existing
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* Weaviate index.
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* @param embeddings Embeddings to be used for the Weaviate index.
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* @param args Arguments required to create a new `WeaviateStore` instance.
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* @returns A new `WeaviateStore` instance.
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*/
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static fromExistingIndex(embeddings: EmbeddingsInterface, args: WeaviateLibArgs): Promise<WeaviateStore>;
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}
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