Why
The README roadmap flags hybrid sparse + dense retrieval "if any single library's recall plateaus." A contributor could add a sparse (BM25) signal alongside the current dense vector search and measure the delta with the eval harness. Meaty, self-contained research task with a clear success metric (recall on the corpus).
What to do
- Add an optional sparse retrieval path, fuse scores with the dense hits (e.g. Reciprocal Rank Fusion), gate it behind a setting, and report before/after eval numbers in the PR.
Acceptance criteria
- Eval harness shows the accuracy/recall delta; feature is opt-in and documented.
Pointers
sidecar/src/docchat_sidecar/tools.py (SearchDocsTool); evals/ for measurement.
Larger scope - comment before starting so we can align on the approach.
Why
The README roadmap flags hybrid sparse + dense retrieval "if any single library's recall plateaus." A contributor could add a sparse (BM25) signal alongside the current dense vector search and measure the delta with the eval harness. Meaty, self-contained research task with a clear success metric (recall on the corpus).
What to do
Acceptance criteria
Pointers
sidecar/src/docchat_sidecar/tools.py(SearchDocsTool);evals/for measurement.