Remove the LFM2.5/T2 polish and related SmolLM2 fallback; T1 and T2 now reuse the Qwen3.5-2B JD-parse model. Update configs, comments and README to reflect the smaller prefetch set and adjusted baked size (~2.3 GB). Factor ONNX session loading into a new src/retrieve/onnx.py and update granite/reranker to use it. Remove orjson-backed JSON/text atomic writers and their exports, simplify models/llm API (drop polish-related helpers), and clean up imports/logging. Add presentation (docs/Redrob_Submission_SoloLeveling.pptx) and various doc/UI text updates to match the new model policy.
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.
