119 lines
6 KiB
TypeScript
119 lines
6 KiB
TypeScript
import { EmbeddingsInterface } from "@langchain/core/embeddings";
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import { VectorStore } from "@langchain/core/vectorstores";
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import { Index as UpstashIndex, type QueryResult } from "@upstash/vector";
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import { DocumentInterface } from "@langchain/core/documents";
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import { AsyncCaller, AsyncCallerParams } from "@langchain/core/utils/async_caller";
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/**
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* This interface defines the arguments for the UpstashVectorStore class.
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*/
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export interface UpstashVectorLibArgs extends AsyncCallerParams {
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index: UpstashIndex;
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filter?: string;
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namespace?: string;
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}
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export type UpstashMetadata = Record<string, any>;
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export type UpstashQueryMetadata = UpstashMetadata & {
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_pageContentLC: any;
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};
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/**
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* Type that defines the parameters for the delete method.
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* It can either contain the target id(s) or the deleteAll config to reset all the vectors.
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*/
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export type UpstashDeleteParams = {
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ids: string | string[];
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deleteAll?: never;
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} | {
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deleteAll: boolean;
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ids?: never;
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};
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/**
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* The main class that extends the 'VectorStore' class. It provides
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* methods for interacting with Upstash index, such as adding documents,
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* deleting documents, performing similarity search and more.
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*/
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export declare class UpstashVectorStore extends VectorStore {
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FilterType: string;
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index: UpstashIndex;
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caller: AsyncCaller;
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useUpstashEmbeddings?: boolean;
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filter?: this["FilterType"];
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namespace?: string;
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_vectorstoreType(): string;
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constructor(embeddings: EmbeddingsInterface, args: UpstashVectorLibArgs);
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/**
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* This method adds documents to Upstash database. Documents are first converted to vectors
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* using the provided embeddings instance, and then upserted to the database.
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* @param documents Array of Document objects to be added to the database.
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* @param options Optional object containing array of ids for the documents.
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* @returns Promise that resolves with the ids of the provided documents when the upsert operation is done.
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*/
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addDocuments(documents: DocumentInterface[], options?: {
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ids?: string[];
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useUpstashEmbeddings?: boolean;
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}): Promise<string[]>;
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/**
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* This method adds the provided vectors to Upstash database.
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* @param vectors Array of vectors to be added to the Upstash database.
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* @param documents Array of Document objects, each associated with a vector.
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* @param options Optional object containing the array of ids foor the vectors.
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* @returns Promise that resolves with the ids of the provided documents when the upsert operation is done.
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*/
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addVectors(vectors: number[][], documents: DocumentInterface[], options?: {
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ids?: string[];
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}): Promise<string[]>;
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/**
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* This method adds the provided documents to Upstash database. The pageContent of the documents will be embedded by Upstash Embeddings.
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* @param documents Array of Document objects to be added to the Upstash database.
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* @param options Optional object containing the array of ids for the documents.
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* @returns Promise that resolves with the ids of the provided documents when the upsert operation is done.
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*/
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protected _addData(documents: DocumentInterface[], options?: {
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ids?: string[];
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}): Promise<string[]>;
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/**
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* This method deletes documents from the Upstash database. You can either
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* provide the target ids, or delete all vectors in the database.
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* @param params Object containing either array of ids of the documents or boolean deleteAll.
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* @returns Promise that resolves when the specified documents have been deleted from the database.
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*/
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delete(params: UpstashDeleteParams): Promise<void>;
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protected _runUpstashQuery(query: number[] | string, k: number, filter?: this["FilterType"], options?: {
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includeVectors: boolean;
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}): Promise<QueryResult<UpstashQueryMetadata>[]>;
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/**
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* This method performs a similarity search in the Upstash database
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* over the existing vectors.
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* @param query Query vector for the similarity search.
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* @param k The number of similar vectors to return as result.
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* @returns Promise that resolves with an array of tuples, each containing
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* Document object and similarity score. The length of the result will be
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* maximum of 'k' and vectors in the index.
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*/
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similaritySearchVectorWithScore(query: number[] | string, k: number, filter?: this["FilterType"]): Promise<[DocumentInterface, number][]>;
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/**
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* This method creates a new UpstashVector instance from an array of texts.
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* The texts are initially converted to Document instances and added to Upstash
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* database.
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* @param texts The texts to create the documents from.
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* @param metadatas The metadata values associated with the texts.
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* @param embeddings Embedding interface of choice, to create the text embeddings.
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* @param dbConfig Object containing the Upstash database configs.
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* @returns Promise that resolves with a new UpstashVector instance.
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*/
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static fromTexts(texts: string[], metadatas: UpstashMetadata | UpstashMetadata[], embeddings: EmbeddingsInterface, dbConfig: UpstashVectorLibArgs): Promise<UpstashVectorStore>;
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/**
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* This method creates a new UpstashVector instance from an array of Document instances.
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* @param docs The docs to be added to Upstash database.
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* @param embeddings Embedding interface of choice, to create the embeddings.
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* @param dbConfig Object containing the Upstash database configs.
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* @returns Promise that resolves with a new UpstashVector instance
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*/
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static fromDocuments(docs: DocumentInterface[], embeddings: EmbeddingsInterface, dbConfig: UpstashVectorLibArgs): Promise<UpstashVectorStore>;
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/**
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* This method creates a new UpstashVector instance from an existing index.
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* @param embeddings Embedding interface of the choice, to create the embeddings.
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* @param dbConfig Object containing the Upstash database configs.
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* @returns
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*/
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static fromExistingIndex(embeddings: EmbeddingsInterface, dbConfig: UpstashVectorLibArgs): Promise<UpstashVectorStore>;
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}
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