In this module, we extend the RAG pipeline from module 1 with vector search. Vector search matches documents by semantic meaning instead of exact keyword overlap. We start from embeddings and end with persistent vector indexes (sqlitesearch, PGVector) and ONNX-based embedders for lightweight deployments.

• Code: code/
• Embeddings runtime: embed/

The lessons cover vector search end to end, from embeddings to persistent indexes.

1. What is Vector Search - Keyword search vs vector search, why it matters
2. Embeddings - Turning text into vectors with sentence-transformers
3. Embedding Our Dataset - Generating embeddings for the FAQ dataset
4. Vector Search - Vector search with numpy
5. Vector Search with minsearch - In-memory vector search
6. RAG with Vector Search - Replacing keyword search with vector search in our RAG pipeline
7. Vector Search with sqlitesearch - Persistent vector search backed by SQLite
8. Vector Search with PGVector - Production vector search with PostgreSQL and pgvector
9. ONNX Embedder (Optional) - Using ONNX Runtime instead of PyTorch for embeddings
10. Next Steps - When to use vector search and what's next

This module was taught as a live workshop, which we chopped into the per-lesson videos above. To watch the full uncut recording:

• Vector Databases: Embeddings, Semantic Search, and Hybrid Retrieval

Earlier cohorts taught vector search differently. See the archived materials for the 2024 and 2025 cohorts.

• Cohort 2026| Notes by Nitin Gupta
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