Lilly Tech Systems
Learn Vector Databases
Master the storage and retrieval of high-dimensional embeddings — from similarity search and ANN indexing to production deployments with Pinecone, ChromaDB, Weaviate, and pgvector.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What are vector databases? Why AI needs similarity search and how vectors change everything.
2. How They Work
Indexing algorithms (HNSW, IVF, LSH), distance metrics, and the trade-offs behind fast vector search.
3. Pinecone
Fully managed vector database. Create indexes, upsert vectors, query with filters, and scale effortlessly.
4. ChromaDB
Open-source, embeddable vector database. Perfect for prototyping and local development.
5. Weaviate
AI-native vector database with built-in vectorizers, hybrid search, and GraphQL API.
6. pgvector
Add vector search to PostgreSQL. Ideal when you already have a PostgreSQL infrastructure.
7. Comparison
Side-by-side comparison of Pinecone, ChromaDB, Weaviate, Qdrant, Milvus, and pgvector.
8. Best Practices
Production tips: index tuning, cost optimization, scaling, security, and common pitfalls.
What You'll Learn
By the end of this course, you'll be able to:
Understand Vector Search
Know how ANN algorithms index and retrieve high-dimensional vectors at scale.
Use Popular Vector DBs
Build with Pinecone, ChromaDB, Weaviate, and pgvector using Python SDKs.
Build AI Applications
Power RAG pipelines, semantic search, and recommendation systems with vector databases.
Choose the Right Tool
Compare vector databases and pick the best one for your use case, budget, and scale.