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refactor: move all root-level files to repo root
- inference.py now at root (validator requirement) - Dockerfile, README.md, openenv.yaml, pyproject.toml at root - tests/ moved to root level - server/app.py shim for openenv validate - data_cleaning_env/ is now purely the Python package - All from data_cleaning_env.xxx imports unchanged - Updated CI workflow for new structure Fixes: validator could not find inference.py, no LLM proxy calls observed7492bfe -
fix: packaging, grading baseline, action registry, observations, tests, deploy config
P0: Fix packaging - package-qualified imports, proper wheel build via hatchling force-include, remove sys.path hacks from server/environment. P0: Fix grading baseline - store per-episode dirty_df and initial_quality, pass dirty_baseline to grade_episode so custom difficulty/synthetic episodes are graded against their actual starting state. P1: Fix observation contract - columns reflect current df state, grader accuracy denominator uses n_clean to penalize row drops. P1: Add action_registry.py as single source of truth for all 16 actions, costs, and parameter schemas. /tasks, inference.py, openenv.yaml all derive from it. P1: Add pytest suite (52 tests) covering grader, models, registry, datasets, noise, environment, and packaging. Replace inline CI checks. P2: Skip quality rescoring for non-mutating actions (profile_column). P2: Add configurable SERVER_PORT, LAZY_PRELOAD, EPISODE_TTL env vars. Episodes auto-evict after TTL on each reset.7a14011
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fix(ci): use openenv CLI directly, not python -m
026a1e3 -
ci: add Docker build, unit tests, and inference smoke test workflow
Three parallel jobs: 1. docker-build: builds image, starts container, tests all endpoints (health, tasks, reset x4, step, grader with breakdown) 2. unit-tests: grader determinism, dataset loading, noise injection, models validation, openenv validate, YAML check 3. inference-smoke: starts server, runs inference.py heuristic mode, validates [START]/[STEP]/[END] log format, runs baselinecddc1c6