Langchain Retrieval
- 44 installs
- 15 repo stars
- Updated June 16, 2026
- eng0ai/eng0-template-skills
Build a retrieval-augmented generation system with LangChain.
About
LangChain template for RAG pipelines. Use for building knowledge-aware AI systems.
- Retrieval-augmented generation
- LangChain integration
Langchain Retrieval by the numbers
- 44 all-time installs (skills.sh)
- Ranked #7,851 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Jul 30, 2026 (Skillselion catalog sync)
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| Installs | 44 |
|---|---|
| repo stars | ★ 15 |
| Last updated | June 16, 2026 |
| Repository | eng0ai/eng0-template-skills ↗ |
What it does
Build a retrieval-augmented generation system with LangChain.
Files
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_template2. Remove Git History (Optional)
rm -rf .git
git init3. Install Dependencies
pnpm install4. 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 buildDevelopment
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