Course overview
Vector databases enable semantic search at scale by using approximate nearest neighbor algorithms instead of brute-force comparison. In this course, you’ll learn to build production-ready vector search systems using ChromaDB, implement document chunking and metadata filtering strategies, compare production databases, apply semantic caching patterns, and create a complete knowledge base search system combining hybrid search and performance optimization.
Key skills
- Building fast semantic search systems using vector databases and ANN algorithms
- Selecting appropriate chunking strategies based on systematic performance evaluation
- Implementing filtered searches and hybrid scoring for complex query requirements
- Choosing production vector databases based on performance characteristics and tradeoffs
- Reducing LLM costs and latency using semantic caching with vector similarity
- Deploying end-to-end knowledge base systems combining multiple vector database techniques
Course outline
Vector Databases and Search [6 lessons]
Introduction to Vector Databases using ChromaDB 2h
Lesson Objectives- Set up ChromaDB and create vector database collections
- Insert embeddings efficiently using proper batch insertion patterns
- Run vector similarity queries returning ranked semantic results
- Understand HNSW indexing mechanics and accuracy-speed trade-offs
- Compare performance characteristics: ChromaDB versus NumPy brute-force search
Document Chunking Strategies for Vector Databases 2h
Lesson Objectives- Understand why chunking strategies affect retrieval quality
- Implement fixed token windows and sentence-based chunking approaches
- Generate embeddings for chunks and store in ChromaDB
- Build systematic evaluation frameworks to compare strategies
- Make informed chunking decisions using real performance measurements
Metadata Filtering and Hybrid Search for Vector Databases 2h
Lesson Objectives- Design metadata schemas enabling powerful filtering without pitfalls
- Implement filtered vector searches using metadata constraints in ChromaDB
- Measure and understand performance overhead of different filter types
- Build BM25 keyword search alongside vector search implementation
- Combine vector similarity and keyword matching using weighted scoring
- Evaluate different search strategies systematically using category precision
- Make informed decisions about when filtering and hybrid search add value
Production Vector Databases 2h
Lesson Objectives- Set up and configure three production vector databases
- Measure and compare performance across different filtering scenarios
- Understand tradeoffs between speed, filtering efficiency, and operations
- Match database characteristics to specific application requirements
- Implement queries using SQL, HTTP APIs, and managed services
Semantic Caching and Memory Patterns for Vector Databases 2h
Lesson Objectives- Implement two-tier semantic caching for LLM applications
- Tune similarity thresholds to balance accuracy and cost
- Build conversation memory systems using vector databases
- Measure cache performance with realistic query workloads
- Apply guardrails for time-sensitive and user-specific queries
Vector Database Practice Project: Building a Knowledge Base Search System 2h
Lesson Objectives- Collect 1,000-3,000 documents with rich metadata
- Implement justified chunking strategy and generate embeddings
- Set up production vector database with metadata filtering
- Build hybrid search combining semantic and keyword matching
- Evaluate system performance with test queries and metrics
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