LANGCHAIN — What Is Langchains Relationship with Supabase?

Measuring programming progress by lines of code is like measuring aircraft building progress by weight. — Bill Gates

LangChain and Supabase have a tight relationship, and together they offer a powerful set of tools for building and deploying AI applications. In this article, we will explore the various ways in which LangChain and Supabase can be used together and provide code examples to demonstrate their integration.

Supabase VectorStore

Supabase provides support for storing embeddings of documents in a vector store, which is crucial for building AI applications involving semantic search. You can integrate Supabase’s VectorStore with LangChain by following the code snippet below:

// Example code for integrating Supabase VectorStore with LangChain
const { createVectorStore } = require('@langchain/vectorstore');

const supabase = createSupabaseClient('YOUR_SUPABASE_URL', 'YOUR_SUPABASE_KEY');
const vectorStore = createVectorStore(supabase);

For a detailed walkthrough, you can refer to the documentation here.

Supabase Hybrid Search

In addition to vector stores, Supabase also supports hybrid search techniques, which can significantly improve document retrieval performance. You can leverage Supabase’s hybrid search functionality with LangChain using the following code snippet:

// Example code for using Supabase hybrid search with LangChain
const { createHybridSearch } = require('@langchain/hybridsearch');

const supabase = createSupabaseClient('YOUR_SUPABASE_URL', 'YOUR_SUPABASE_KEY');
const hybridSearch = createHybridSearch(supabase);

For a step-by-step guide, you can explore the documentation here.

Supabase + LangChain Starter Template

To streamline the development process of a full-stack AI application using Supabase and LangChain, a starter template has been created. This template includes various components and configurations essential for building a robust AI application. Here are some snippets from the template:

// Example code from the Supabase + LangChain Starter Template
// Setting up Supabase project
const { createClient } = require('@supabase/supabase-js');

const supabase = createClient('YOUR_SUPABASE_URL', 'YOUR_SUPABASE_KEY');

// Supabase Edge Function using LangChain
const { callGPT } = require('@langchain/gpt');

exports.handler = async (event, context) => {
  // Your code to call GPT-3.5 API using LangChain
};

// Postgres migration for setting up the Supabase Vector Store
exports.up = async (supabase) => {
  // Your migration code for setting up the Vector Store
};

// Example of calling the Chat function in the frontend
// Your React component code using Supabase SDK

The starter template GitHub repository provides detailed instructions for setting up and deploying the application.

By integrating LangChain with Supabase, developers can harness the power of both platforms to build and deploy sophisticated AI applications. Whether it’s leveraging vector stores for semantic search or utilizing hybrid search techniques, the combination of LangChain and Supabase offers a comprehensive solution for AI application development.