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Keep shoppingVector Databases Fundamentals to Production [2026 Edition]
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15,036 students
Vector Databases Fundamentals to Production [2026 Edition]
Master Pinecone, Chroma & pgvector for RAG Applications | LangChain Integration, Hybrid Search, Production Deployment
Last updated 4/2026
English
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
Explore related topics
Course content
17 sections • 84 lectures • 8h 17m total length
Requirements
- Basic Programming Knowledge
- A keen interest in data science, AI, or related fields will enhance your learning experience
Description
In the era of AI-powered applications, vector databases are the foundation of every RAG pipeline, semantic search system, and intelligent application.
This comprehensive course takes you from fundamentals to production deployment with the three databases that matter in 2026: Pinecone, Chroma and pgvector.
Fully Updated April 2026
- All code works with current APIs. LangChain LCEL patterns. No deprecated imports.
What You Will Learn:
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.
Master Three Leading Databases:
- Chroma - Perfect for prototyping and local development
- Pinecone - Managed cloud solution that scales automatically
- pgvector - PostgreSQL extension for production deployments (NEW - 7 lectures)
Advanced Chunking Strategies (NEW): Learn why chunking makes or breaks your RAG pipeline. Master fixed, recursive, and semantic chunking with hands-on implementation.
Hybrid Search (NEW): Combine BM25 keyword search with vector similarity for dramatically better retrieval accuracy.
LangChain Integration: Build complete RAG pipelines using modern LCEL patterns - no deprecated chains.
Production Deployment (NEW): Index tuning (HNSW parameters), scaling strategies, and real cost analysis - actual infrastructure bills, not marketing prices.
Decision Framework (NEW): 9 concrete scenarios with clear recommendations. Know exactly which database to choose for YOUR use case.
Why This Course?
8+ Hours of Content - Nearly doubled from the original course with substantive new material.
Zero Broken Code - Every example tested with April 2026 APIs (LangChain, Pinecone v3, pgvector).
Real-World Focus - Production costs, scaling decisions, and infrastructure trade-offs that tutorials skip.
Hands-On Projects - Build working RAG pipelines, semantic search systems, and hybrid retrieval solutions.
Who Should Enroll?
Developers building RAG applications and AI-powered search
Data Scientists adding semantic search to existing systems
Engineers evaluating Pinecone vs Chroma vs pgvector for production
Anyone building with LangChain who needs reliable vector storage
Prerequisites
Basic Python programming
Familiarity with APIs
No ML background required - math explained intuitively
Transform your understanding of vector databases from tutorial-level to production-ready.
Enroll now.
Who this course is for:
- Data Scientists and Analysts
- Developers and Engineers
- AI Enthusiasts and Researchers
- Beginners in Data Management