- New Step 04.5 bm25_prefilter: cheap lexical BM25 pre-cut right after
hard_filter, before potion_broad's expensive embedding pass. Shrinks
potion's input from ~74K to 10K rows using JD-keyword overlap alone.
composite_narrow still runs its full potion+soft+bm25 blend later, once
those signals exist, so ranking quality is unchanged. Falls back to a
full passthrough on an empty/failed JD spec rather than an arbitrary cut.
- broad_retrieval/composite_narrow/precision all use fixed top_n counts
now (500/200/125) instead of a mix of top_pct and top_n.
- Moved the candidates Parquet cache down into load_candidates() itself
(was one layer up in run_preprocess, which Streamlit's per-phase
explorer bypasses). Now both the CLI and Streamlit share one cache —
verified Streamlit's eager warm-up dropped from 14.6s to 0.27s.
- Streamlit's _load_phase_input switched to st.cache_resource (avoids
copying the 100K-row frame on every cache hit; safe since every
downstream Polars st…
Add JD parsing, central config, checkpoints & LLM
Hemprasad Badgujar
700e896
Introduce a central Settings system (config/settings.yaml + src/config.py) and enable live JD extraction (problem/job_description.md) via a new JD parsing pipeline (src/jd/*) that emits a unified jd_extraction.json. Add a local GGUF LLM client and helper (src/models/llm.py, src/models/__init__.py) to drive the JD calls and model prefetching. Implement safe atomic file IO and per-phase Parquet checkpoints (src/common/io.py, src/common/checkpoint.py) and wire them into common exports. Expand preprocess functionality (schema overrides, date helper, pipeline exports) and update the CLI to run preprocess and accept a --checkpoints flag. Update Dockerfile to include central config and data, and refresh pyproject dependencies for the new modules. Add a suite of tests for the new components.
