Answer Questions from Documents with RAG Using Supabase, OpenAI, & Cohere Reranker

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This comprehensive RAG workflow enables your AI agents to answer user questions with contextual knowledge pulled from your own documents, using metadata-rich embeddings stored in Supabase. **Key Features:** - RAG Agents powered by GPT-4.5 or GPT-3.5 via OpenRouter or OpenAI. - Supabase Vector Store to store and retrieve document embeddings. - Cohere Reranker to improve response relevance and quality. - Metadata Agent to enrich vectorized data before ingestion. - PDF Extraction Flow to automatically parse and upload documents with metadata. **Setup Steps:** 1. Connect your Supabase Vector Store. 2. Use OpenAI Embeddings (e.g., text-embedding-3-small). 3. Add API keys for OpenAI and/or OpenRouter. 4. Connect a reranker like Cohere. 5. Process documents with metadata before embedding. 6. Start chatting - your AI agent now returns context-rich answers from your own knowledge base! Perfect for building AI assistants that can reason, search, and answer based on internal company data, academic papers, support docs, or personal notes.