Vector Databases: The Foundation of AI Apps
Course Introduction
Welcome to Vector Databases
If you're building AI applications in 2026, you need to understand vector databases. They're not optional anymore—they're the foundation that makes modern AI applications possible.
Every time you use a chatbot that remembers your previous conversations, a search engine that understands what you mean (not just what you type), or an AI assistant that can access company knowledge—there's a vector database working behind the scenes.
This course will take you from understanding what vectors are to deploying production-ready vector search systems using the most popular databases in the industry.
Why Vector Databases Matter
Traditional databases are great at exact matches:
SELECT * FROM products WHERE name = 'iPhone 15'
But what if you want to find products similar to "a phone with a great camera for photography"? Traditional databases can't do that. They match keywords, not meaning.
Vector databases solve this problem by:
- Converting text, images, and other data into numerical representations (embeddings)
- Storing these embeddings efficiently
- Finding similar items based on meaning, not just keywords
This enables:
- Semantic search: Find documents by meaning, not keywords
- Recommendation systems: "Users who liked this also liked..."
- RAG (Retrieval-Augmented Generation): Give LLMs access to your data
- Image/audio search: Find similar media content
- Anomaly detection: Find unusual patterns in data
What You'll Learn
This course is structured to take you from fundamentals to production:
Foundations (Modules 1-4)
- What vector databases are and why they exist
- How embeddings work—turning text into numbers
- Similarity search algorithms and their tradeoffs
- The vector database landscape—comparing your options
Hands-On Setup (Modules 5-7)
- Setting up Pinecone (managed, cloud-native)
- Setting up pgvector (PostgreSQL extension)
- Setting up Chroma (local, open-source)
Advanced Concepts (Modules 8-11)
- Indexing strategies for performance
- Querying and filtering techniques
- Metadata and hybrid search
- Performance optimization
Production Considerations (Modules 12-15)
- Scaling strategies
- Cost analysis and comparison
- Choosing the right database for your use case
- Integration with LangChain and Vercel AI SDK
Prerequisites
To get the most from this course, you should have:
Required:
- Basic programming knowledge (JavaScript/TypeScript or Python examples provided)
- Familiarity with APIs and HTTP requests
- Understanding of basic database concepts (tables, queries)
Helpful but not required:
- Experience with AI/ML concepts
- Familiarity with PostgreSQL
- Understanding of RAG (Retrieval-Augmented Generation)
You don't need to be a machine learning expert. We'll explain the concepts you need as we go.
The Tech Stack
Throughout this course, you'll work with:
Vector Databases:
- Pinecone: Managed, cloud-native, purpose-built
- pgvector: PostgreSQL extension, self-hosted or managed
- Chroma: Open-source, local-first, great for development
- Qdrant: High-performance, feature-rich (covered in comparisons)
- Weaviate: GraphQL-based, modular (covered in comparisons)
Embedding Models:
- OpenAI text-embedding-3-small/large: High quality, easy to use
- Cohere Embed: Multilingual support
- Open-source alternatives: Sentence Transformers, Hugging Face models
Integration Tools:
- LangChain: Framework for LLM applications
- Vercel AI SDK: Modern AI development toolkit
- LlamaIndex: Data framework for LLM apps
Who This Course Is For
Perfect for:
- Developers building AI applications (chatbots, search, recommendations)
- Backend engineers adding semantic search to existing applications
- Full-stack developers working with RAG systems
- Anyone building applications that need to understand meaning, not just keywords
You'll get the most value if you're:
- Building production AI applications
- Evaluating vector databases for your project
- Looking to add semantic search to an existing application
- Implementing RAG for LLM-powered features
How to Use This Course
Follow Along: Each module includes code examples you can run. Set up your development environment early and code along with the examples.
Compare Options: We cover three databases in depth. Try each one to understand their differences firsthand.
Build Something Real: By the end, you should implement vector search in a real project. The concepts stick when you apply them.
Reference Later: This course is designed to be a reference you come back to. Bookmark the comparison and decision-making modules.
A Note on the Evolving Landscape
The vector database space is evolving rapidly. New features, pricing changes, and entirely new databases appear regularly.
What you'll learn in this course are the fundamental concepts that transcend any specific tool:
- How embeddings capture meaning
- Why approximate search is necessary at scale
- Tradeoffs between different index types
- How to evaluate databases for your needs
Master these fundamentals, and you'll be able to evaluate any new option that emerges.
Let's Get Started
Vector databases are the infrastructure that makes modern AI applications possible. By the end of this course, you'll understand not just how to use them, but why they work the way they do.
Whether you're building a semantic search engine, a RAG-powered chatbot, or a recommendation system, you'll have the knowledge to choose the right database and implement it correctly.
Ready? Let's dive in.
Next up: Module 1 - What Are Vector Databases?