Beyond Vector Search - Advanced Retrieval
Vector search fails on exact matches. Build Hybrid Search combining semantic vectors with full-text search, then filter noise with FlashRank reranking.
Vector search can find "revenue growth" when you query "quarterly earnings." Full-text search can find exact identifiers instantly. We built both in the previous tutorial.
But neither works alone. Search "NVDA H20 export requirements" with vector search and you get generic export documents - the embedding model treats "H20" as just another word. Switch to full-text search and query "quarterly earnings", you'll get nothing, because the document says "revenue growth" instead.
Financial queries need both approaches at once:
- Ticker symbols: "NVDA", "AAPL"
- Specific dates: "October 26, 2025"
- Exact phrases: "material weakness"
Hybrid Search combines vector similarity with keyword search. Add reranking to filter noise, and you're well on your way to a production RAG system.
What You'll Build
- Hybrid Search combining vector similarity and full-text search
- Document metadata filtering for ticker, fiscal year, and report type
- Reranking with FlashRank to filter noise
- HyDE (Hypothetical Document Embeddings) for question-style queries
- Complete retrieval pipeline
The Keyword Blindness Problem
Members onlyJoin 900+ members
Become a member for full access
“Best educational investment in my ML/AI journey.”
Ana Clara MedeirosAI Developer
Renews yearly at the price you joined at. Cancel anytime.
30-day money-back guarantee. One email, full refund. Yearly includes live cohort access.
What you get
6 modules and 42 hands-on lessons with runnable code, failure diagnosis, and measured comparisons
2 capstone repositories you build, test, evaluate, and keep as portfolio proof
Videos, source code, complete projects and architecture diagrams
Recordings of live sessions
Discord community access
Not ready? Try these free lessons first
Instant access after paymentSecure checkout via Stripe
