Overview

AI Agent Crash Course with Supabase Vector Store: Deploy Real-World RAG Systems Using pgvector and Postgres Can your AI agent truly trust its memory? Most agents answer fast but think shallow, they fetch fragments of information scattered across external APIs, often unreliable or unsecured. This book shows you how to bring that memory home, building retrieval-augmented systems grounded directly in Postgres and Supabase, powered by pgvector and real-world engineering discipline. At its core, this is a hands-on guide to deploying retrieval-augmented generation (RAG) pipelines that are fast, secure, and production-ready. You'll turn a managed Postgres database into a full vector store, with embeddings, hybrid search, caching, access control, and metrics all orchestrated through clean, auditable SQL and RPCs. Each chapter delivers practical implementation steps, from schema design to scalable deployment, with no external dependencies or theoretical filler. By working through the book, you'll learn how to: - Store and query embeddings directly in Postgres using pgvector, with optimized index strategies like IVFFlat and HNSW. - Combine keyword and semantic search through hybrid CTE-based scoring. - Secure every query using Row-Level Security (RLS) and JWT-based access policies. - Ingest, chunk, and embed documents efficiently with JavaScript and Python pipelines. - Integrate retrieval, reranking, caching, and verification into your AI agent's toolset. - Evaluate your system with Recall@k, MRR, and NDCG, and tune performance with A/B runners and load controls. - Scale gracefully, from single-node prototypes to read-replica architectures with caching, backpressure, and cold-start mitigation. This book isn't about frameworks; it's about fundamentals that last. You'll build a Supabase-powered memory system your AI can trust, one that's transparent, secure, and measurable. Whether you're an AI engineer, backend developer, or team lead deploying your first RAG stack, this crash course equips you with the clarity and code to move from prototype to production confidently. Build faster. Query smarter. Deploy an agent that remembers accurately. Get your copy and start constructing the most dependable memory layer your AI will ever have.

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