Vector Databases, Embeddings & Semantic Search

Dense embeddings, HNSW graph indexing, PgVector, Qdrant, and hybrid search.

2 weeks · wk 10–11Core / deep pace4 topics12 Interview Questions

Why this pace:Vector databases are the persistent external memory layer for modern AI systems. Designing production retrieval requires deep understanding of index topologies, cosine math, and latency/recall tradeoffs.

SEC FOCUS

Ensure vector index dimensions match embedding models strictly; failing to isolate tenant vectors can cause cross-tenant enterprise data leaks.

Topics & Action Items (4)

Dense vs Sparse Embeddings & Distance Metrics

4 hrs

Approximate Nearest Neighbor (ANN): HNSW & IVFFlat

6 hrs

Production Vector Stores: Qdrant, Pinecone & PgVector

5 hrs

Hybrid Search & Reciprocal Rank Fusion (RRF)

4 hrs

Previous Stage

Prompt Engineering, Structured Outputs & Tool Use

Next Stage

Advanced Retrieval-Augmented Generation (RAG)