Introduce ONNX-based precision embedding (granite) and cross-encoder reranker with CPU-tuned runtime settings. Adds src/common/runtime.py to set thread-pool env vars and provide onnx session options, and new retrieve modules (granite.py, reranker.py) implementing embedding and pair-scoring via onnxruntime + tokenizers. Wire up configure_compute() in entrypoints (rank.py, src/cli.py, src/streamlit_app.py), expose new retrieve functions in src/retrieve/__init__.py, and extend config (src/config.py and config/settings.yaml) with precision/rerank settings and model defaults. Reduce composite_narrow.top_n to 500 and add precision.top_n=300. Add lightweight tests for passthrough guards (tests/test_granite.py, tests/test_reranker.py) and update pyproject.toml to include onnxruntime and tokenizers dependencies.
Add retrieval & soft/composite ranking stages
Hemprasad Badgujar
08c3203
Introduce broad retrieval (potion Model2Vec) + soft_filter scoring + composite_narrow (BM25 + z-blend) pipeline steps. Adds retrieval implementations (potion static embedder, BM25 tokenizer/scorer), ranking modules (soft_filter, composite_narrow), and tests for bm25, potion, composite, soft_filter and hard_filter behavior. Update config/settings to expose new tuning blocks (broad_retrieval, soft_filter, composite_narrow) and default embedder, refine hard_filter inactive logic and integrity toggles, and export new rank/retrieve APIs. Streamlit explorer updated to include rank stages and show JD spec; pyproject dependencies added model2vec and numpy. These changes narrow the candidate pool deterministically before expensive embedding and blend semantic + lexical + quality signals for the top-N cut.
