RAG Q&A from Documents: Supabase, OpenAI & Cohere Reranker

This advanced RAG (Retrieval Augmented Generation) workflow empowers AI agents to provide accurate answers to user queries by retrieving relevant information from your own documents. It leverages Supabase as a vector database to store metadata-rich embeddings, OpenAI for generating embeddings (e.g., text-embedding-3-small) and responses via GPT models, and Cohere Reranker to boost relevance by re-ranking retrieved chunks. The pipeline includes PDF extraction, metadata enrichment via a dedicated agent, and seamless integration for self-hosted n8n.

Key benefits include superior response quality through reranking, which minimizes hallucinations and ensures contextually grounded answers; scalability for large document sets like company knowledge bases or property listings; and time savings by automating ingestion, embedding, and querying. Ideal for real estate applications, such as querying property documents, listings, or legal PDFs to assist agents with client questions.

Use cases span internal AI assistants for support docs, academic papers, personal notes, or business data. In real estate, it excels at analyzing property listings, contracts, and rules (e.g., golf course docs as demo) to deliver instant insights. Setup is straightforward: connect services, upload docs, and chat—transforming static files into a dynamic knowledge base.