Course · Intermediate
Vector Databases and Search
As embedding-based applications scale, brute-force similarity search becomes impractical. Vector databases solve this problem using approximate nearest neighbor algorithms that deliver results in milliseconds instead of seconds. But understanding when and how to use vector databases involves more than just speed—you need to know chunking strategies, metadata filtering, hybrid search approaches, and production deployment patterns. This course takes you from ChromaDB basics through production considerations, culminating in a portfolio-ready knowledge base search system.
- Intermediate friendly
- 12 hrs
- 6 lessons
- Premium
Course overview
Learn how vector databases enable fast semantic search at scale. Build production-ready systems with ChromaDB, implement hybrid search strategies, and explore caching patterns for LLM applications.
What's inside
6 lessons
- 01 120 minIntroduction to Vector Databases using ChromaDB
Learn when brute-force breaks, how vector databases speed up semantic search, and how to build fast queries with ChromaDB and ANN indexing.
- 02 120 minDocument Chunking Strategies for Vector Databases
Learn why chunking matters for long documents and compare fixed-token and sentence-based strategies to improve semantic search with ChromaDB.
- 03 120 minMetadata Filtering and Hybrid Search for Vector Databases
Learn metadata filtering and hybrid search for vector databases, compare BM25 with semantic search, and see when hybrid scoring adds value.
- 04 120 minVector Database Practice Project: Building a Knowledge Base Search System
Build a complete knowledge base search system from scratch: collect 1,000+ documents, implement chunking strategies, set up a vector database, create hybrid search combining semantic and keyword matching, and evaluate performance with real queries. Portfolio-ready project.
- 05 120 minProduction Vector Databases
Learn how production vector databases handle real filtering workloads. Compare pgvector, Qdrant, and Pinecone with benchmarks and tradeoffs.
- 06 120 minSemantic Caching and Memory Patterns for Vector Databases
Learn how semantic caching and conversation memory cut LLM costs, reduce latency, and support multi turn queries using vector databases.
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