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- Add sefaria_llm_interface/commentary_scoring package with input/output dataclasses - Add commentary_scoring app with OpenAI-powered scoring functionality - Implement CommentaryScorer class for evaluating how well commentaries explain cited texts - Add Celery task integration for async commentary processing - Include text processing utilities for HTML stripping and content flattening - Update Celery autodiscovery to include commentary_scoring tasks
…classes instead of commit_scoring
- added to CommentaryScoringOutput debugging fields: request_status and request_status_message - updated CommentaryScorer, so it will return CommentaryScoringOutput instead of dictionary; this update also influences commentary_scoring.py NOTE: by now importing from sefaria-llm-interface are local and not package-style, since the version with necessary files was not yet released
…ling (0/1)
- Replace 0–4 ExplanationLevel with binary ExplainsFlag {0: NOT_EXPLAINED, 1: EXPLAINED}
- Clamp/validate scores to 0/1 in _validate_level
- Update function-calling JSON schema to minimum: 0, maximum: 1 per cited key
- Rewrite prompt to policy:
-- Return 1 if the commentary provides any substantive interpretation of any part of the citation (incl. methodological/kabbalistic reads)
-- Return 0 if citation is decorative/prooftext/only paraphrased
-- If A is cited only via B and C adds no new interpretation of A beyond B → 0
-- Partial coverage still counts as 1
- Explanations: ask model to begin each rationale with Explained spans: '<phrase1>'; ... then 1–2 sentence justification (no schema change)
- Logging: report explained X/Y (Z%) instead of average 0–4
--
BREAKING BEHAVIOR: numeric scale semantics changed from graded (0–4) to binary (0/1).
…o package importing
- added README with explanation of the code - removed unnecessary imports from commentary_scoring_input.py, commentary_scoring_output.py - in openai_commentary_scorer.py changed the sefaria-llm-inteface importing from local folder importing to package importing; added comments to some functions; removed unnecessary spaces in functions definitions and added spaces after commas. same for text_utils.py - added textwrap.dedent to prompt definition - in tasks.py changed the sefaria-llm-inteface importing from local folder importing to package importing;
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This PR is the first working version of a commentary scoring system that uses an OpenAI model to automatically evaluate how well a Jewish commentary explains the base texts it cites.
🔍 Core Functionality
CommentaryScorer class:
Prompt Engineering:
⚙️ Additional Components
✅ Output Format
Standardized output includes:
Does not yet support chunking for long commentaries.
Empirical testing suggests chunking may not be strictly necessary.