GreatHayat/supabase-edge-functions-rag
📦 Open Source ProjectGreatHayat
A lightweight RAG pipeline implementation using LangChain and Supabase Edge Functions for serverless AI applications.
The GreatHayat/supabase-edge-functions-rag repository serves as a foundational template for developers looking to integrate AI-driven search and retrieval into their Supabase projects. The project demonstrates how to orchestrate a RAG pipeline entirely within the serverless Edge Functions environment. Key technical components include the use of LangChain for managing LLM chains and document processing, and Supabase's pgvector extension for efficient vector similarity searches. By offloading the RAG logic to the edge, developers can reduce latency and minimize infrastructure overhead. The implementation covers the essential flow: embedding user queries, performing vector similarity searches against stored document chunks, and generating context-aware responses. This repository is particularly useful for those building chat interfaces or knowledge-base assistants that need to query private data dynamically while maintaining strict performance standards.
💡Highlights
- ├─Serverless RAG architecture
- ├─LangChain & pgvector integration
- └─TypeScript-based edge logic
🎯For
- ├─Full-stack developers
- ├─AI engineers
- └─Supabase users