248 lines
9 KiB
TypeScript
248 lines
9 KiB
TypeScript
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import type { SupabaseClient } from "@supabase/supabase-js";
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import type { PostgrestFilterBuilder } from "@supabase/postgrest-js";
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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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export type SupabaseMetadata = Record<string, any>;
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export type SupabaseFilter = PostgrestFilterBuilder<any, any, any>;
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export type SupabaseFilterRPCCall = (rpcCall: SupabaseFilter) => SupabaseFilter;
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/**
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* Interface for the response returned when searching embeddings.
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*/
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interface SearchEmbeddingsResponse {
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id: number;
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content: string;
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metadata: object;
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embedding: number[];
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similarity: number;
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}
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/**
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* Interface for the arguments required to initialize a Supabase library.
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*/
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export interface SupabaseLibArgs {
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client: SupabaseClient;
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tableName?: string;
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queryName?: string;
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filter?: SupabaseMetadata | SupabaseFilterRPCCall;
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upsertBatchSize?: number;
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}
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/**
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* Supabase vector store integration.
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*
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* Setup:
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* Install `@langchain/community` and `@supabase/supabase-js`.
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*
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* ```bash
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* npm install @langchain/community @supabase/supabase-js
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* ```
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*
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* See https://js.langchain.com/docs/integrations/vectorstores/supabase for
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* instructions on how to set up your Supabase instance.
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*
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* ## [Constructor args](https://api.js.langchain.com/classes/_langchain_community.vectorstores_supabase.SupabaseVectorStore.html#constructor)
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*
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* <details open>
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* <summary><strong>Instantiate</strong></summary>
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*
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* ```typescript
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* import { SupabaseVectorStore } from "@langchain/community/vectorstores/supabase";
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* import { OpenAIEmbeddings } from "@langchain/openai";
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*
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* import { createClient } from "@supabase/supabase-js";
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*
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* const embeddings = new OpenAIEmbeddings({
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* model: "text-embedding-3-small",
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* });
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*
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* const supabaseClient = createClient(
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* process.env.SUPABASE_URL,
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* process.env.SUPABASE_PRIVATE_KEY
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* );
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*
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* const vectorStore = new SupabaseVectorStore(embeddings, {
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* client: supabaseClient,
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* tableName: "documents",
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* queryName: "match_documents",
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* });
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Add documents</strong></summary>
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*
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* ```typescript
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* import type { Document } from '@langchain/core/documents';
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*
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* const document1 = { pageContent: "foo", metadata: { baz: "bar" } };
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* const document2 = { pageContent: "thud", metadata: { bar: "baz" } };
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* const document3 = { pageContent: "i will be deleted :(", metadata: {} };
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*
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* const documents: Document[] = [document1, document2, document3];
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* const ids = ["1", "2", "3"];
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* await vectorStore.addDocuments(documents, { ids });
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Delete documents</strong></summary>
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*
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* ```typescript
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* await vectorStore.delete({ ids: ["3"] });
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Similarity search</strong></summary>
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*
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* ```typescript
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* const results = await vectorStore.similaritySearch("thud", 1);
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* for (const doc of results) {
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* console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
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* }
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* // Output: * thud [{"baz":"bar"}]
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* ```
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* </details>
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*
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* <br />
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*
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*
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* <details>
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* <summary><strong>Similarity search with filter</strong></summary>
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*
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* ```typescript
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* const resultsWithFilter = await vectorStore.similaritySearch("thud", 1, { baz: "bar" });
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*
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* for (const doc of resultsWithFilter) {
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* console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
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* }
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* // Output: * foo [{"baz":"bar"}]
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* ```
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* </details>
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*
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* <br />
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*
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*
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* <details>
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* <summary><strong>Similarity search with score</strong></summary>
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*
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* ```typescript
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* const resultsWithScore = await vectorStore.similaritySearchWithScore("qux", 1);
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* for (const [doc, score] of resultsWithScore) {
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* console.log(`* [SIM=${score.toFixed(6)}] ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
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* }
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* // Output: * [SIM=0.000000] qux [{"bar":"baz","baz":"bar"}]
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>As a retriever</strong></summary>
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*
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* ```typescript
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* const retriever = vectorStore.asRetriever({
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* searchType: "mmr", // Leave blank for standard similarity search
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* k: 1,
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* });
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* const resultAsRetriever = await retriever.invoke("thud");
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* console.log(resultAsRetriever);
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*
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* // Output: [Document({ metadata: { "baz":"bar" }, pageContent: "thud" })]
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* ```
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* </details>
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*
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* <br />
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*/
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export declare class SupabaseVectorStore extends VectorStore {
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FilterType: SupabaseMetadata | SupabaseFilterRPCCall;
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client: SupabaseClient;
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tableName: string;
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queryName: string;
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filter?: SupabaseMetadata | SupabaseFilterRPCCall;
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upsertBatchSize: number;
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_vectorstoreType(): string;
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constructor(embeddings: EmbeddingsInterface, args: SupabaseLibArgs);
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/**
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* Adds documents to the vector store.
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* @param documents The documents to add.
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* @param options Optional parameters for adding the documents.
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* @returns A promise that resolves when the documents have been added.
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*/
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addDocuments(documents: Document[], options?: {
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ids?: string[] | number[];
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}): Promise<string[]>;
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/**
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* Adds vectors to the vector store.
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* @param vectors The vectors to add.
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* @param documents The documents associated with the vectors.
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* @param options Optional parameters for adding the vectors.
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* @returns A promise that resolves with the IDs of the added vectors when the vectors have been added.
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*/
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addVectors(vectors: number[][], documents: Document[], options?: {
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ids?: string[] | number[];
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}): Promise<string[]>;
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/**
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* Deletes vectors from the vector store.
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* @param params The parameters for deleting vectors.
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* @returns A promise that resolves when the vectors have been deleted.
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*/
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delete(params: {
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ids: string[] | number[];
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}): Promise<void>;
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protected _searchSupabase(query: number[], k: number, filter?: this["FilterType"]): Promise<SearchEmbeddingsResponse[]>;
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/**
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* Performs a similarity search on the vector store.
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* @param query The query vector.
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* @param k The number of results to return.
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* @param filter Optional filter to apply to the search.
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* @returns A promise that resolves with the search results when the search is complete.
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*/
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similaritySearchVectorWithScore(query: number[], k: number, filter?: this["FilterType"]): Promise<[Document, 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=20- Number of documents to fetch before passing to the MMR algorithm.
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* @param {number} options.lambda=0.5 - 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 {SupabaseLibArgs} options.filter - Optional filter to apply to the search.
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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"]>): Promise<Document[]>;
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/**
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* Creates a new SupabaseVectorStore instance from an array of texts.
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* @param texts The texts to create documents from.
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* @param metadatas The metadata for the documents.
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* @param embeddings The embeddings to use.
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* @param dbConfig The configuration for the Supabase database.
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* @returns A promise that resolves with a new SupabaseVectorStore instance when the instance has been created.
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*/
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static fromTexts(texts: string[], metadatas: object[] | object, embeddings: EmbeddingsInterface, dbConfig: SupabaseLibArgs): Promise<SupabaseVectorStore>;
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/**
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* Creates a new SupabaseVectorStore instance from an array of documents.
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* @param docs The documents to create the instance from.
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* @param embeddings The embeddings to use.
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* @param dbConfig The configuration for the Supabase database.
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* @returns A promise that resolves with a new SupabaseVectorStore instance when the instance has been created.
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*/
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static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, dbConfig: SupabaseLibArgs): Promise<SupabaseVectorStore>;
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/**
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* Creates a new SupabaseVectorStore instance from an existing index.
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* @param embeddings The embeddings to use.
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* @param dbConfig The configuration for the Supabase database.
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* @returns A promise that resolves with a new SupabaseVectorStore instance when the instance has been created.
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*/
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static fromExistingIndex(embeddings: EmbeddingsInterface, dbConfig: SupabaseLibArgs): Promise<SupabaseVectorStore>;
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}
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export {};
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