claude-bot
69c5b788b9
The database was the missing half: extraction and assembly existed, but nothing turned the book's main-content statements into something an agent could actually cite mid-proof, and nothing searched it. Two new scripts. textbook/build_database.py takes the main-content statements and emits both halves of a usable premise library: - an importable Lean file where every entry is a named declaration, so a proof can cite goodman_1_6_20 by name; - a JSONL search index carrying each entry's name, statement, mentioned identifiers and status. An entry is emitted as an `axiom` (asserted from the textbook) until a real lean-bench run proves it; passing --results upgrades those entries to real theorems carrying the exact verified proof. This mirrors how the book itself works -- its exercises are posed assuming its body results, the same way using Mathlib does not mean re-proving Mathlib. The cost is explicit: anything proven through an axiom inherits it, so a garbled extraction asserted as an axiom makes downstream proofs worthless. Guards: --require-checked refuses entries that have not passed check_statements.py (all 21 section 1.6 entries have, and are now marked accordingly), and the verifier's existing #print axioms check surfaces what a finished proof rests on. textbook/search_database.py is the retrieval step the proof loop calls at each line: given the current goal, it ranks premises by identifier and symbol overlap, weighting rare identifiers far above common ones (IsGCDOfList matching means much more than Int matching), preferring a proven premise over an asserted one, and formats the top few for the prompt. Verified against three real goals: the exactly-applicable premise tops each ranking by a wide margin (11.4 vs 1.6 for the gcd goal, 5.1 vs 3.7 for the prime-divides-product goal). It is a ranking heuristic, not a decision procedure -- only Lean can say whether a premise applies. Replacing score_premise() is the whole job of swapping in embeddings. Also: renamed library_candidates/ to database/ to match what it is; generated lib/Goodman/Chapter01Section06.lean (21 premises, 0 proven, 21 asserted) and database/goodman-1.6.index.jsonl; noted a latent bug in the older build_library.py, which attaches each entry's non-import prelude per-theorem and so would emit a shared `def` twice in one file -- build_database.py hoists and dedupes, and has a test pinning that. 13 new tests, all green (33 total, the one failure remains the pre-existing unrelated lean_agent import error in test_dataset.py). Still not done, and worth being clear about: nothing has been proven yet. The bench targets have not been elaboration-checked, no lean-bench run has happened over either file, and the goal-directed loop that would consume this retrieval does not exist -- lean-bench's solve() is still single-shot. Separately, lean-bench cannot currently run at all in this checkout, because lean_bench/cli.py imports lean_agent at module load and lean-agent is not installed. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_017vbLFFJiHSZqGZHEkjvG5x
14 lines
298 B
Plaintext
14 lines
298 B
Plaintext
__pycache__/
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*.egg-info/
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.venv/
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.env
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results/
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# Source textbook and raw/derived full-text dumps: copyrighted material, never commit.
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# Only code and the final short Lean statement datasets under datasets/ are tracked.
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*.pdf
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textbook/*.txt
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textbook/*_raw.txt
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.DS_Store
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textbook/*.elab-report.jsonl
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