Experiments (#68)

* feat: finance bench

* docs: results

* chore: ignroe data README

* feat: fix financebench

* feat: laion, also required idmaps support

* style: format

* style: format

* fix: resolve ruff linting errors

- Remove unused variables in benchmark scripts
- Rename unused loop variables to follow convention

* feat: enron email bench

* experiments for running DiskANN & BM25 on Arch 4090

* style: format

* chore(ci): remove paru-bin submodule and config to fix checkout --recurse-submodules

* docs: data

* docs: data updated

* fix: as package

* fix(ci): only run pre-commit

* chore: use http url of astchunk; use group for some dev deps

* fix(ci): should checkout modules as well since `uv sync` checks

* fix(ci): run with lint only

* fix: find links to install wheels available

* CI: force local wheels in uv install step

* CI: install local wheels via file paths

* CI: pick wheels matching current Python tag

* CI: handle python tag mismatches for local wheels

* CI: use matrix python venv and set macOS deployment target

* CI: revert install step to match main

* CI: use uv group install with local wheel selection

* CI: rely on setup-uv for Python and tighten group install

* CI: install build deps with uv python interpreter

* CI: use temporary uv venv for build deps

* CI: add build venv scripts path for wheel repair

benchmarks/bm25_diskann_baselines/README.md

@@ -0,0 +1,23 @@
+BM25 vs DiskANN Baselines
+
+```bash
+aws s3 sync s3://powerrag-diskann-rpj-wiki-20250824-224037-194d640c/bm25_rpj_wiki/index_en_only/ benchmarks/data/indices/bm25_index/
+aws s3 sync s3://powerrag-diskann-rpj-wiki-20250824-224037-194d640c/diskann_rpj_wiki/ benchmarks/data/indices/diskann_rpj_wiki/
+```
+
+- Dataset: `benchmarks/data/queries/nq_open.jsonl` (Natural Questions)
+- Machine-specific; results measured locally with the current repo.
+
+DiskANN (NQ queries, search-only)
+- Command: `uv run --script benchmarks/bm25_diskann_baselines/run_diskann.py`
+- Settings: `recompute_embeddings=False`, embeddings precomputed (excluded from timing), batching off, caching off (`cache_mechanism=2`, `num_nodes_to_cache=0`)
+- Result: avg 0.011093 s/query, QPS 90.15 (p50 0.010731 s, p95 0.015000 s)
+
+BM25
+- Command: `uv run --script benchmarks/bm25_diskann_baselines/run_bm25.py`
+- Settings: `k=10`, `k1=0.9`, `b=0.4`, queries=100
+- Result: avg 0.028589 s/query, QPS 34.97 (p50 0.026060 s, p90 0.043695 s, p95 0.053260 s, p99 0.055257 s)
+
+Notes
+- DiskANN measures search-only latency on real NQ queries (embeddings computed beforehand and excluded from timing).
+- Use `benchmarks/bm25_diskann_baselines/run_diskann.py` for DiskANN; `benchmarks/bm25_diskann_baselines/run_bm25.py` for BM25.