{"product_id":"ai-agent-crash-course-with-supabase-vector-store-deploy-real-world-rag-systems-using-pgvector-and-postgres-9798269240602","title":"AI Agent Crash Course with Supabase Vector Store: Deploy Real-World RAG Systems Using pgvector and Postgres","description":"\u003cp\u003e • Author(s): Newman Chandler\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Languages - SQL\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003eAI Agent Crash Course with Supabase Vector Store: Deploy Real-World RAG Systems Using pgvector and Postgres\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eCan your AI agent truly trust its memory?\u003cbr\u003eMost 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 \u003cb\u003ePostgres and Supabase\u003c\/b\u003e, powered by \u003cb\u003epgvector\u003c\/b\u003e and real-world engineering discipline.\u003c\/p\u003e\u003cp\u003eAt 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eBy working through the book, you'll learn how to: \u003c\/b\u003e\u003cbr\u003e- Store and query embeddings directly in Postgres using \u003cb\u003epgvector\u003c\/b\u003e, with optimized index strategies like IVFFlat and HNSW.\u003cbr\u003e- Combine keyword and semantic search through hybrid CTE-based scoring.\u003cbr\u003e- Secure every query using \u003cb\u003eRow-Level Security (RLS)\u003c\/b\u003e and JWT-based access policies.\u003cbr\u003e- Ingest, chunk, and embed documents efficiently with \u003cb\u003eJavaScript and Python pipelines\u003c\/b\u003e.\u003cbr\u003e- Integrate retrieval, reranking, caching, and verification into your AI agent's toolset.\u003cbr\u003e- Evaluate your system with \u003cb\u003eRecall@k\u003c\/b\u003e, \u003cb\u003eMRR\u003c\/b\u003e, and \u003cb\u003eNDCG\u003c\/b\u003e, and tune performance with A\/B runners and load controls.\u003cbr\u003e- Scale gracefully, from single-node prototypes to read-replica architectures with caching, backpressure, and cold-start mitigation.\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eWhether 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eBuild faster. Query smarter. Deploy an agent that remembers accurately.\u003c\/b\u003e\u003cbr\u003eGet your copy and start constructing the most dependable memory layer your AI will ever have.\u003c\/p\u003e","brand":"Atlantic Books","offers":[{"title":"Paperback","offer_id":46332129247383,"sku":"9798269240602","price":2456.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798269240602.webp?v=1768725255","url":"https:\/\/atlanticbooks.com\/products\/ai-agent-crash-course-with-supabase-vector-store-deploy-real-world-rag-systems-using-pgvector-and-postgres-9798269240602","provider":"Atlantic Books","version":"1.0","type":"link"}