Vector Databases
The long-term memory layer for AI — storing and retrieving knowledge semantically.
Every RAG system needs a place to store the embeddings it retrieves. Vector databases — specialized data stores optimized for high-dimensional vector similarity search — are that place. Understanding how they work under the hood (specifically the HNSW graph indexing algorithm) is essential for building RAG systems that are both fast and accurate at scale.
Text gets converted to a vector (an array of 1,536 numbers for OpenAI embeddings) that encodes semantic meaning. Similar texts have similar vectors. Vector databases efficiently find the most similar vectors to a query using approximate nearest neighbor search — which is why they can retrieve from millions of documents in milliseconds without scanning every one.
This track covers the theory (how embeddings encode meaning, how HNSW indexing works) and the practice (setting up ChromaDB locally, integrating it with LlamaIndex, and choosing the right vector database for your scale). ChromaDB for local development, Pinecone or Weaviate for production — I explain the tradeoffs.
📚 Learning Path
- How embeddings encode semantic meaning
- HNSW indexing algorithm explained
- ChromaDB: setup and query patterns
- Choosing between ChromaDB, Pinecone, and Weaviate
- Integrating vector DBs with LlamaIndex and LangChain