chimera measure rag
Recall@k of each retriever over a real folder — lexical, and vector when an embedder is set. This is the measurement `chimera/rag/__init__.py` names when it says the retriever's existence is not a claim that it helps. That sentence pointed at a module you could not run: `rag_bench` had no caller outside its own test and was not exported from `chimera.eval`. Without `--semantic` no embedder is passed, so the vector and hybrid figures come back as None rather than zero — an embedder that was never called did not fail, and printing 0.0 invites the wrong conclusion. With it, the run that `bench/rag/RESULTS.md` reports is reproducible from the CLI rather than from a script somebody has to write. It costs an embedding pass over the corpus: about two cents for this repository's 3,459 chunks and 400 probes, and the figure it produces belongs to the embedder that produced it — vector spaces do not convert between models.
Arguments
ROOTpathrequiredFolder to index and probe.
Options
--kintdefault:10Retrieve this many chunks per probe.
--max-probesintdefault:200Cap the probe count; each one is a query.
--semanticbooleanMeasure the vector and hybrid arms too. Costs money.