RETRIEVAL PRELUDE
Watch the winner change before the answer exists.
This page is the missing scoring surface between vector search and grounded answers. Adjust semantic weight, lexical overlap, metadata bonuses, and reranking logic. The result is immediate: different chunks rise or fall before any prose is generated.
INTERACTIVE LAB
Blend semantic, lexical, and rerank signals.
Use the query presets to change the scenario. Then adjust the weights and see how the top result, top-3 precision, and full ranking shift.
Semantic
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precision@3
Hybrid
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precision@3
Reranked
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precision@3
WHAT WE HAVE ESTABLISHED
The repo now has a full path from vectors to grounded answers.
These are no longer disconnected topics. They now form a teaching sequence with visible handoffs.
Embeddings
Vectors encode position in a learned space, but they are not retrieval by themselves.
Vector stores
Vectors need IDs, metadata, and a search contract so ranking stays attached to something usable.
Retrieval evaluation
Semantic, lexical, hybrid, and rerank stages should be inspectable before the answer layer hides mistakes.
Grounded answers
Only after ranking is credible does citation-backed answer synthesis become worth trusting.
NEXT PATH
Use this with the bridge notebooks and Chapter 10.
The recommended path is `7.4`, `7.5`, `10.0`, then the rest of Chapter 10.