langchain-retrieval

Original：🇺🇸 English
Translated

Document Q&A with RAG using Supabase pgvector store.

6installs
Sourceeng0ai/eng0-template-skills
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NPX Install

npx skill4agent add eng0ai/eng0-template-skills langchain-retrieval

Tags

Translated version includes tags in frontmatter
rag-implementationsupabase-pgvectorlangchain-jsdocument-qanextjs-ai-application

SKILL.md Content

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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

bash
git clone --depth 1 https://github.com/Eng0AI/langchain-retrieval.git .
If the directory is not empty:
bash
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)

bash
rm -rf .git
git init

3. Install Dependencies

bash
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

bash
pnpm build

Development

bash
pnpm dev