Vector Databases Fundamentals to Production [2026 Edition]

<div>In the era of AI-powered applications, vector databases are the foundation of every RAG pipeline, semantic search system, and intelligent application.</div><div><br></div><div>This comprehensive course takes you from fundamentals to production deployment with the three databases that matter in 2026: Pinecone, Chroma and pgvector.</div><div><br></div><div>Fully Updated April 2026</div><div><ul><li><span style="font-size: 1rem;">All code works with current APIs.&nbsp; LangChain LCEL patterns. No deprecated imports.&nbsp; &nbsp;</span></li></ul></div><div><span style="font-size: 1rem;">What You Will Learn:</span></div><div><ul><li><span style="font-size: 1rem;">Foundations of Vector Databases: Understand how vector databases work, why they outperform traditional databases for AI applications, and the mathematics behind embeddings and similarity search.</span></li></ul></div><div><span style="font-size: 1rem;">Master Three Leading Databases:</span></div><div><ul><li><span style="font-size: 1rem;">Chroma - Perfect for prototyping and local development</span></li><li><span style="font-size: 1rem;">Pinecone - Managed cloud solution that scales automatically</span></li><li><span style="font-size: 1rem;">pgvector - PostgreSQL extension for production deployments (NEW - 7 lectures)</span></li></ul></div><div><br></div><div>Advanced Chunking Strategies (NEW):&nbsp; Learn why chunking makes or breaks your RAG pipeline. Master fixed, recursive, and semantic chunking with hands-on implementation.</div><div><br></div><div>Hybrid Search (NEW): Combine BM25 keyword search with vector similarity for dramatically better retrieval accuracy.</div><div><br></div><div>LangChain Integration: Build complete RAG pipelines using modern LCEL patterns - no deprecated chains.</div><div><br></div><div>Production Deployment (NEW): Index tuning (HNSW parameters), scaling strategies, and real cost analysis - actual infrastructure bills, not marketing prices.</div><div><br></div><div>Decision Framework (NEW): 9 concrete scenarios with clear recommendations. Know exactly which database to choose for YOUR use case</div><div><br></div><div>&nbsp;Why This Course?</div><div><ul><li><span style="font-size: 1rem;">8+ Hours of Content - Nearly doubled from the original course with substantive new material.</span></li><li><span style="font-size: 1rem;">Zero Broken Code - Every example tested with April 2026 APIs (LangChain, Pinecone v3, pgvector).</span></li><li><span style="font-size: 1rem;">Real-World Focus - Production costs, scaling decisions, and infrastructure trade-offs that tutorials skip.</span></li><li><span style="font-size: 1rem;">Hands-On Projects - Build working RAG pipelines, semantic search systems, and hybrid retrieval solutions.</span></li></ul></div><div><span style="font-size: 1rem;">Who Should Enroll?</span></div><div><ul><li><span style="font-size: 1rem;">Developers building RAG applications and AI-powered search</span></li><li><span style="font-size: 1rem;">Data Scientists adding semantic search to existing systems</span></li><li><span style="font-size: 1rem;">Engineers evaluating Pinecone vs Chroma vs pgvector for production</span></li><li><span style="font-size: 1rem;">Anyone building with LangChain who needs reliable vector storage&nbsp;</span></li></ul></div><div><span style="font-size: 1rem;">Prerequisites</span></div><div><ul><li><span style="font-size: 1rem;">Basic Python programming</span></li><li><span style="font-size: 1rem;">Familiarity with APIs</span></li><li><span style="font-size: 1rem;">No ML background required - math explained intuitively&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;</span></li></ul></div><div>Transform your understanding of vector databases from tutorial-level to production-ready.</div><div><br></div><div>Enroll now.</div>

What you'll learn:

• Build production-ready RAG applications with Chroma, Pinecone, and pgvector using April 2026 APIs
• Master pgvector - the PostgreSQL extension enterprises are adopting for vector search
• Implement hybrid search combining BM25 keywords with vector similarity for better accuracy
• Apply advanced chunking strategies that separate amateur RAG from production-quality retrieval
• Tune HNSW index parameters to optimize speed, accuracy, and memory for your use case
• Build complete LangChain pipelines using modern LCEL patterns - no deprecated code
• Make informed database decisions using real cost data and a practical decision framework
• Understand the mathematics behind embeddings and why similarity metrics capture meaning