Vector Databases Fundamentals to Production [2026 Edition] (Udemy.com)
Master Pinecone, Chroma & pgvector for RAG Applications | LangChain Integration, Hybrid Search, Production Deployment
Created by: Paulo Dichone
Last updated August 2026
What you will 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
How it compares
Against the other 388 rated Udemy courses in SQL on CourseDuck:
- Rating4.5 out of 5higher than 48% of them
- Reviews3,627#54 of 389 by number of reviews
- Length8.3 hourslonger than 61% of them
- Last updatedAugust 2026more recently than 92% of them
Course 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.
Instructor Details
- 4.5 Rating
3,627 Reviews
Paulo Dichone
Hi, I’m Paulo – Your Guide to Mastering Development, Cloud, and AI Engineering
With a passion for empowering learners, I’ve had the privilege of teaching over 350,000 students across 175 countries. Whether you’re diving into Android, Java, Flutter, AWS Cloud, or venturing into the world of AI engineering, I’m here to help you unlock your full potential.
My Expertise
I bring extensive hands-on experience in:
AI Engineering
Mobile App Development (Android & iOS)
Cross-Platform Development (Flutter, Dart, and JavaFX)
AWS Cloud Solutions
And now, I’m also focused on the AI engineering landscape, helping developers leverage the power of machine learning and automation in their projects.
My Mission
No matter where you are in your journey—whether you're just starting or looking to sharpen advanced skills—my courses are designed to make you an exceptional developer and AWS Cloud Practitioner, equipped to tackle real-world challenges. Beyond coding, I enjoy spending time with my growing family, playing the guitar and mandolin, and traveling whenever I get the chance.
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Reviews
By Anastasiya Barska on 8/17/2026
Amazing job, Paolo, thanks a lot and especially for decision trees, they are priceless and they definitely stand out when I compare your courses to other courses available on Udemy
By Vijay SZ on 8/12/2026
The instructors explanation is great. His simplification of hard concepts, shows his experience in teaching. I would definitely recommend all his courses, after experiencing 10% of this course, I confidently say that, he is the best at what is he is teaching.
By B. on 6/25/2026
Very good course and clear explanation of the topic of vector databases. It helped me to understand what actually a RAG system is and how to build it.
By Anonymized User on 6/15/2026
I recently completed this course, and I must say it was extremely insightful and well‑structured. The content does a great job of covering both the fundamentals and practical aspects of vector databases, making it highly relevant for anyone working with modern AI applications. What I found particularly valuable was the hands‑on approach and the clear explanation of concepts like RAG pipelines, hybrid search, embeddings, and vector indexing, which helped me build a strong conceptual understanding along with practical implementation knowledge. I would highly recommend this course to anyone looking to ease the learning curve in this space. If you’re curious about vector databases or want to develop a solid understanding of how they power modern AI systems, this course is definitely worth exploring.
By Anderson Contreira on 3/26/2026
I can say that the course is good at what it sets out to do, which is to demonstrate the fundamentals of vector databases and tools. I had to learn the concepts in practice, meaning I couldn't finish the course before having to solve a problem, but after dealing with two projects and returning to the course, I can say that it covers the basic concepts that will be used in everyday work.
By Ian Wallace on 3/25/2026
The instructor is explaining everything clearly and the way this is present is very engaging so far. Looking forward to the rest of the course.
By Tom on 3/12/2026
There are no codes provided in python. It’s important for me to get codes in order to follow the lectures and not to waste my time on technical issues. I am very disappointed
By Sita Karki on 1/15/2026
I am really enjoying Paulo’s course. His delivery is engaging, and he clearly has a strong understanding of the material. Before this, I took a vector database course from another instructor, but it felt very dry. The content was mostly read directly from slides, which made it hard to stay focused. Paulo’s teaching style is a refreshing change and keeps me genuinely engaged. I am a project manager, so I loved the level of detail he covered in the course. At last, his voice was very soothing, so it was a bonus!
By Mbodempudi on 1/6/2026
This is the first time I looked at Vector databases and this course gave me lot of confidence that I can use vector database and lang chain very easily. Instructor is excellent in communication and showed everything required to understand vector databases from basics and gave very good overview. I definitely recommend this course to anyone who are new to vector databases , LLM, Langchain.
By Adriane Del Villar on 12/10/2025
Python codes shown are working, but after a while different codes are shown compared to earlier. Some challenges on the extensions specially when migrated. After repeated install/uninstall using ChromaDB it worked.
Quality Score
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Overall Score : 90 / 100

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