Module 4: Vector Databases Engineering Slides

Slide Outline

1. Vector Databases Engineering - ANN algorithms, indexing, metadata filtering, and production deployment with Qdrant and pgvector
2. Learning Objectives - 4 outcomes for this module
3. Why This Module Matters - The vector database is the engine of your RAG system. A bad choice means poor performance at scale, inability to filter
4. ANN Algorithms: How Fast Search Works - Lesson section from the full module
5. Qdrant: Production Vector Search - Lesson section from the full module
6. pgvector: Vector Search in PostgreSQL - Lesson section from the full module
7. Common Mistakes to Avoid - 4 mistakes covered
8. Hands-On Labs - 2 hands-on labs
9. Key Takeaways - 5 points to remember

Learning Objectives

• Understand ANN algorithms (HNSW, IVF) and their tradeoffs
• Deploy and operate Qdrant for production vector search
• Use pgvector for PostgreSQL-integrated vector search
• Design metadata filtering and multi-tenancy strategies

Why This Module Matters

The vector database is the engine of your RAG system. A bad choice means poor performance at scale, inability to filter by metadata (tenant, date, category), and operational headaches. This module teaches production vector database engineering, not just API calls.

Common Mistakes

• Using ChromaDB in production (designed for prototyping, not operations)
• Not creating HNSW indexes (falls back to brute-force search)
• Storing vectors without metadata (cannot filter by category, tenant, date)
• Not monitoring collection size and search latency as data grows

Key Takeaways

• HNSW is the dominant ANN algorithm — O(log n) with 95-99% recall
• Qdrant is production-grade: fast, filterable, multi-tenant, open source
• pgvector works if you already use PostgreSQL and need moderate scale
• Metadata filtering is critical — vector similarity alone is not enough
• Choose your vector DB based on scale, ops model, and existing infrastructure

Hands-On Labs

1. Setup Qdrant and Build Semantic Search

   Deploy Qdrant and build a search API.

   35 min - Intermediate

   • Deploy Qdrant with Docker
   • Create a collection with HNSW index
   • Ingest 1000 documents with metadata
   • Build a FastAPI search endpoint with filtering

   View lab files on GitHub

2. Build Search APIs with pgvector

   Use PostgreSQL for vector search.

   30 min - Intermediate

   • Setup PostgreSQL with pgvector extension
   • Create table with vector column and HNSW index
   • Insert embeddings and query with SQL
   • Compare performance with Qdrant

   View lab files on GitHub