agsamantha/node_modules/@langchain/community/dist/vectorstores/supabase.d.ts
2024-10-02 15:15:21 -05:00

247 lines
9 KiB
TypeScript

import type { SupabaseClient } from "@supabase/supabase-js";
import type { PostgrestFilterBuilder } from "@supabase/postgrest-js";
import { MaxMarginalRelevanceSearchOptions, VectorStore } from "@langchain/core/vectorstores";
import type { EmbeddingsInterface } from "@langchain/core/embeddings";
import { Document } from "@langchain/core/documents";
export type SupabaseMetadata = Record<string, any>;
export type SupabaseFilter = PostgrestFilterBuilder<any, any, any>;
export type SupabaseFilterRPCCall = (rpcCall: SupabaseFilter) => SupabaseFilter;
/**
* Interface for the response returned when searching embeddings.
*/
interface SearchEmbeddingsResponse {
id: number;
content: string;
metadata: object;
embedding: number[];
similarity: number;
}
/**
* Interface for the arguments required to initialize a Supabase library.
*/
export interface SupabaseLibArgs {
client: SupabaseClient;
tableName?: string;
queryName?: string;
filter?: SupabaseMetadata | SupabaseFilterRPCCall;
upsertBatchSize?: number;
}
/**
* Supabase vector store integration.
*
* Setup:
* Install `@langchain/community` and `@supabase/supabase-js`.
*
* ```bash
* npm install @langchain/community @supabase/supabase-js
* ```
*
* See https://js.langchain.com/docs/integrations/vectorstores/supabase for
* instructions on how to set up your Supabase instance.
*
* ## [Constructor args](https://api.js.langchain.com/classes/_langchain_community.vectorstores_supabase.SupabaseVectorStore.html#constructor)
*
* <details open>
* <summary><strong>Instantiate</strong></summary>
*
* ```typescript
* import { SupabaseVectorStore } from "@langchain/community/vectorstores/supabase";
* import { OpenAIEmbeddings } from "@langchain/openai";
*
* import { createClient } from "@supabase/supabase-js";
*
* const embeddings = new OpenAIEmbeddings({
* model: "text-embedding-3-small",
* });
*
* const supabaseClient = createClient(
* process.env.SUPABASE_URL,
* process.env.SUPABASE_PRIVATE_KEY
* );
*
* const vectorStore = new SupabaseVectorStore(embeddings, {
* client: supabaseClient,
* tableName: "documents",
* queryName: "match_documents",
* });
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Add documents</strong></summary>
*
* ```typescript
* import type { Document } from '@langchain/core/documents';
*
* const document1 = { pageContent: "foo", metadata: { baz: "bar" } };
* const document2 = { pageContent: "thud", metadata: { bar: "baz" } };
* const document3 = { pageContent: "i will be deleted :(", metadata: {} };
*
* const documents: Document[] = [document1, document2, document3];
* const ids = ["1", "2", "3"];
* await vectorStore.addDocuments(documents, { ids });
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Delete documents</strong></summary>
*
* ```typescript
* await vectorStore.delete({ ids: ["3"] });
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Similarity search</strong></summary>
*
* ```typescript
* const results = await vectorStore.similaritySearch("thud", 1);
* for (const doc of results) {
* console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
* }
* // Output: * thud [{"baz":"bar"}]
* ```
* </details>
*
* <br />
*
*
* <details>
* <summary><strong>Similarity search with filter</strong></summary>
*
* ```typescript
* const resultsWithFilter = await vectorStore.similaritySearch("thud", 1, { baz: "bar" });
*
* for (const doc of resultsWithFilter) {
* console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
* }
* // Output: * foo [{"baz":"bar"}]
* ```
* </details>
*
* <br />
*
*
* <details>
* <summary><strong>Similarity search with score</strong></summary>
*
* ```typescript
* const resultsWithScore = await vectorStore.similaritySearchWithScore("qux", 1);
* for (const [doc, score] of resultsWithScore) {
* console.log(`* [SIM=${score.toFixed(6)}] ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
* }
* // Output: * [SIM=0.000000] qux [{"bar":"baz","baz":"bar"}]
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>As a retriever</strong></summary>
*
* ```typescript
* const retriever = vectorStore.asRetriever({
* searchType: "mmr", // Leave blank for standard similarity search
* k: 1,
* });
* const resultAsRetriever = await retriever.invoke("thud");
* console.log(resultAsRetriever);
*
* // Output: [Document({ metadata: { "baz":"bar" }, pageContent: "thud" })]
* ```
* </details>
*
* <br />
*/
export declare class SupabaseVectorStore extends VectorStore {
FilterType: SupabaseMetadata | SupabaseFilterRPCCall;
client: SupabaseClient;
tableName: string;
queryName: string;
filter?: SupabaseMetadata | SupabaseFilterRPCCall;
upsertBatchSize: number;
_vectorstoreType(): string;
constructor(embeddings: EmbeddingsInterface, args: SupabaseLibArgs);
/**
* Adds documents to the vector store.
* @param documents The documents to add.
* @param options Optional parameters for adding the documents.
* @returns A promise that resolves when the documents have been added.
*/
addDocuments(documents: Document[], options?: {
ids?: string[] | number[];
}): Promise<string[]>;
/**
* Adds vectors to the vector store.
* @param vectors The vectors to add.
* @param documents The documents associated with the vectors.
* @param options Optional parameters for adding the vectors.
* @returns A promise that resolves with the IDs of the added vectors when the vectors have been added.
*/
addVectors(vectors: number[][], documents: Document[], options?: {
ids?: string[] | number[];
}): Promise<string[]>;
/**
* Deletes vectors from the vector store.
* @param params The parameters for deleting vectors.
* @returns A promise that resolves when the vectors have been deleted.
*/
delete(params: {
ids: string[] | number[];
}): Promise<void>;
protected _searchSupabase(query: number[], k: number, filter?: this["FilterType"]): Promise<SearchEmbeddingsResponse[]>;
/**
* Performs a similarity search on the vector store.
* @param query The query vector.
* @param k The number of results to return.
* @param filter Optional filter to apply to the search.
* @returns A promise that resolves with the search results when the search is complete.
*/
similaritySearchVectorWithScore(query: number[], k: number, filter?: this["FilterType"]): 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 {string} query - Text to look up documents similar to.
* @param {number} options.k - Number of documents to return.
* @param {number} options.fetchK=20- Number of documents to fetch before passing to the MMR algorithm.
* @param {number} 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.
* @param {SupabaseLibArgs} options.filter - Optional filter to apply to the search.
*
* @returns {Promise<Document[]>} - List of documents selected by maximal marginal relevance.
*/
maxMarginalRelevanceSearch(query: string, options: MaxMarginalRelevanceSearchOptions<this["FilterType"]>): Promise<Document[]>;
/**
* Creates a new SupabaseVectorStore instance from an array of texts.
* @param texts The texts to create documents from.
* @param metadatas The metadata for the documents.
* @param embeddings The embeddings to use.
* @param dbConfig The configuration for the Supabase database.
* @returns A promise that resolves with a new SupabaseVectorStore instance when the instance has been created.
*/
static fromTexts(texts: string[], metadatas: object[] | object, embeddings: EmbeddingsInterface, dbConfig: SupabaseLibArgs): Promise<SupabaseVectorStore>;
/**
* Creates a new SupabaseVectorStore instance from an array of documents.
* @param docs The documents to create the instance from.
* @param embeddings The embeddings to use.
* @param dbConfig The configuration for the Supabase database.
* @returns A promise that resolves with a new SupabaseVectorStore instance when the instance has been created.
*/
static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, dbConfig: SupabaseLibArgs): Promise<SupabaseVectorStore>;
/**
* Creates a new SupabaseVectorStore instance from an existing index.
* @param embeddings The embeddings to use.
* @param dbConfig The configuration for the Supabase database.
* @returns A promise that resolves with a new SupabaseVectorStore instance when the instance has been created.
*/
static fromExistingIndex(embeddings: EmbeddingsInterface, dbConfig: SupabaseLibArgs): Promise<SupabaseVectorStore>;
}
export {};