majiayu000 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 URLSUPABASE_PRIVATE_KEY- Supabase service role keyOPENAI_API_KEY- For embeddings and LLMSUPABASE_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