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langchain-retrieval

langchain-retrieval

github.com/rebyteai-templateDevelopment1756 installsUpdated 19w ago
Repository

About langchain-retrieval

langchain-retrieval is a Next.js template for building document Q&A with RAG, using LangChain.js and Supabase pgvector. It is aimed at developers who need a starting point for an OpenAI-backed retrieval application.

Key features

  1. 01Document Q&A with RAG
  2. 02Supabase pgvector vector store
  3. 03LangChain.js and AI SDK stack
  4. 04Next.js project template

Use cases

  1. 01Building a document question-answering app
  2. 02Starting a Supabase-backed RAG project

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/rebyteai-template/rebyte-skills --skill langchain-retrieval

After installing, type/langchain-retrievalin Claude Code to confirm it loaded.

Details

From the project README

LangChain Retrieval

Document Q&A with RAG (Retrieval Augmented Generation) using Supabase vector store.

Tech Stack

  • Framework: Next.js
  • AI: LangChain.js, AI SDK
  • Vector Store: Supabase pgvector
  • Package Manager: pnpm

Prerequisites

  • Supabase project with pgvector extension
  • OpenAI API key

Setup

1. Clone the Template
git clone --depth 1 https://github.com/Eng0AI/langchain-retrieval.git .

If the directory is not empty:

git clone --depth 1 https://github.com/Eng0AI/langchain-retrieval.git _temp_template
mv _temp_template/* _temp_template/.* . 2>/dev/null || true
rm -rf _temp_template
2. Remove Git History (Optional)
rm -rf .git
git init
3. Install Dependencies
pnpm install
4. Setup Environment Variables

Create .env with required variables:

  • SUPABASE_URL - Supabase project URL
  • SUPABASE_PRIVATE_KEY - Supabase service role key
  • OPENAI_API_KEY - For embeddings and LLM
  • SUPABASE_DB_URL - Direct PostgreSQL connection URL
5. Setup Vector Store

Initialize pgvector extension and create documents table in Supabase.

Build

pnpm build

Development

pnpm dev

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