embedding_search

LangChain is a framework for developing applications powered by language models with a plugable architecture.

langchain-embedding_search uses a Supabase Postgres database as its vector store.

Installation

Copyselect dbdev.install('langchain-embedding_search');
create extension if not exists vector;
create extension "langchain-embedding_search"
    schema public
    version '1.1.0';

Note:

vector is a dependency of langchain-embedding_search. Dependency resolution is currently under development. In the near future it will not be necessary to manually create dependencies.

Standard Usage

The below example shows how to perform a basic similarity search with Supabase:

Once created, you can access the vector store for search using langchain as shown below:

Copyimport { SupabaseVectorStore } from "langchain/vectorstores/supabase";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { createClient } from "@supabase/supabase-js";

const privateKey = process.env.SUPABASE_PRIVATE_KEY;
if (!privateKey) throw new Error(`Expected env var SUPABASE_PRIVATE_KEY`);

const url = process.env.SUPABASE_URL;
if (!url) throw new Error(`Expected env var SUPABASE_URL`);

export const run = async () => {
  const client = createClient(url, privateKey);

  const vectorStore = await SupabaseVectorStore.fromTexts(
    ["Hello world", "Bye bye", "What's this?"],
    [{ id: 2 }, { id: 1 }, { id: 3 }],
    new OpenAIEmbeddings(),
    {
      client,
      tableName: "documents",
      queryName: "match_documents",
    }
  );

  const resultOne = await vectorStore.similaritySearch("Hello world", 1);

  console.log(resultOne);
};

Metadata Filtering

Given the above match_documents Postgres function, you can also pass a filter parameter to only documents with a specific metadata field value.

Copyimport { SupabaseVectorStore } from "langchain/vectorstores/supabase";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { createClient } from "@supabase/supabase-js";

// First, follow set-up instructions at
// https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabase

const privateKey = process.env.SUPABASE_PRIVATE_KEY;
if (!privateKey) throw new Error(`Expected env var SUPABASE_PRIVATE_KEY`);

const url = process.env.SUPABASE_URL;
if (!url) throw new Error(`Expected env var SUPABASE_URL`);

export const run = async () => {
  const client = createClient(url, privateKey);

  const vectorStore = await SupabaseVectorStore.fromTexts(
    ["Hello world", "Hello world", "Hello world"],
    [{ user_id: 2 }, { user_id: 1 }, { user_id: 3 }],
    new OpenAIEmbeddings(),
    {
      client,
      tableName: "documents",
      queryName: "match_documents",
    }
  );

  const result = await vectorStore.similaritySearch("Hello world", 1, {
    user_id: 3,
  });

  console.log(result);
};

For more details, checkout the LangChain Supabase integration docs: https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabase