b9e37952e6
Replaces the previous tree entirely. The pipeline is four separable pieces, each
testable on its own:
1. leanbench/llm.py provider-neutral OpenAI-compatible connector, stdlib only,
with retries, JSON coercion and a scripted client so the
rest of the code is testable without a network.
2. leanbench/detect.py per-page detection of formalizable mathematics. Case 1 is
mathematics asserted as fact (definition/theorem/lemma/...,
deliberately not sub-classified); case 2 is a problem left
open to the reader. Hits come back as spans -- start page
and index, end page and index -- so several per page is
normal. A hit cut off by the page end makes the detector
re-read that page together with the next, repeatedly, so an
item spread over three pages is found whole.
Offsets come from verbatim quotes resolved by
leanbench/anchors.py (exact -> whitespace-insensitive ->
fuzzy), because models quote well and count badly.
3. leanbench/formalize.py + leanbench/verify.py
each span becomes one Lean 4 declaration, judged by the real
compiler: full elaboration for case 1, statement legality
only for case 2, which has no proof to check. Lean's own
diagnostics feed back for a repair attempt. "verified" (Lean
accepted it) and "proved" (accepted with no sorry) are kept
strictly apart.
4. leanbench/db.py indexed SQLite plus JSONL export. Every item is indexed by
the types of its inputs, or by its own type when it is not a
proof, and records the source book (sha256) and the page it
starts on -- so `drop --source H --pages A-B` followed by a
re-ingest redoes exactly those pages and nothing else.
Infrastructure:
- lean/ Lake project pinned to Mathlib.
- docker/ image carrying Lean 4 and a prebuilt Mathlib. The Mathlib
revision and the toolchain are resolved against each other at
build time rather than both hardcoded, so the build does not rot.
docker/smoke.py runs the whole pipeline inside the image against
the real compiler, with the LLM scripted.
- .gitea/workflows ci (lint + tests in a venv, since the runner image's Python is
Debian-managed and refuses installs into itself), docker (build,
verify, optionally push), ingest (submit a book by hand).
The docker workflow is not a throwaway build: it leaves the image, the layer cache,
an idle `lean-bench` container and the `lean-bench-data` / `lean-bench-books`
volumes in place on the runner, and never prunes, so the environment is there to be
used afterwards. Pushing to the registry is optional and cannot fail the build --
Gitea's container registry does not accept the built-in Actions token, so that needs
a REGISTRY_TOKEN secret with write:package.
58 tests; they need neither network nor Lean.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ci5T3gWDhhmqDec3e1XtYZ
135 lines
4.4 KiB
Python
135 lines
4.4 KiB
Python
"""End-to-end smoke test for the image, with the real Lean compiler attached.
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The LLM is scripted (the image has no API key and CI should not need one), but
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everything else is real: pages are extracted, spans are resolved to page/index,
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Lean elaborates the generated code, and the items land in an indexed database.
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Run it against a built image without mounting anything:
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docker run --rm -i --entrypoint python3 lean-bench:latest - < docker/smoke.py
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"""
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from __future__ import annotations
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import json
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import os
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import sys
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import tempfile
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from leanbench.config import Settings
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from leanbench.db import Database
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from leanbench.llm import ScriptedClient
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from leanbench.pipeline import build_checker, ingest
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BOOK_PAGE_ONE = "Theorem 1. For every natural number n, n + 0 = n.\n"
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BOOK_PAGE_TWO = "Exercise 2. Show that 2 + 2 = 4.\n"
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def detection(items: list[dict]) -> str:
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return json.dumps({"items": items})
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def main() -> int:
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workdir = tempfile.mkdtemp(prefix="leanbench-smoke-")
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book = os.path.join(workdir, "book.txt")
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with open(book, "w", encoding="utf-8") as stream:
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stream.write(BOOK_PAGE_ONE + "\f" + BOOK_PAGE_TWO)
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client = ScriptedClient(
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[
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detection(
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[
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{
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"kind": "statement",
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"label": "Theorem 1",
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"summary": "adding zero changes nothing",
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"start_quote": "Theorem 1. For every natural number n",
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"end_quote": "n + 0 = n.",
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"continues_past_end": False,
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}
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]
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),
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detection(
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[
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{
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"kind": "problem",
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"label": "Exercise 2",
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"summary": "two plus two",
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"start_quote": "Exercise 2. Show that",
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"end_quote": "2 + 2 = 4.",
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"continues_past_end": False,
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}
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]
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),
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json.dumps(
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{
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"skip": False,
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"imports": ["Mathlib"],
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"name": "add_zero_nat",
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"lean_code": "theorem add_zero_nat (n : Nat) : n + 0 = n := by simp",
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"has_proof": True,
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"informal": "n + 0 = n",
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"index_types": ["Nat"],
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"confidence": 1.0,
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}
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),
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json.dumps(
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{
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"skip": False,
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"imports": ["Mathlib"],
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"name": "two_add_two",
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"lean_code": "theorem two_add_two : (2 : Nat) + 2 = 4 := by sorry",
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"has_proof": False,
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"informal": "2 + 2 = 4",
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"index_types": ["Nat"],
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"confidence": 1.0,
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}
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),
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]
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)
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settings = Settings.from_env(dotenv=None)
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settings.database = os.path.join(workdir, "smoke.sqlite")
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settings.repair_attempts = 0
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checker = build_checker(settings)
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if not getattr(checker, "available", lambda: False)():
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print("FAIL: this image has no working Lean; verification would be skipped")
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return 1
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database = Database(settings.database)
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result = ingest(
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book,
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settings=settings,
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client=client,
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database=database,
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checker=checker,
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title="smoke",
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)
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summary = result.summary()
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print(json.dumps(summary, indent=2))
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failures = []
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if summary["detected"] != 2:
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failures.append(f"expected 2 detections, got {summary['detected']}")
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if summary["verified"] != 2:
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failures.append(f"Lean should have accepted both, got {summary['verified']}")
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if summary["proved"] != 1:
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failures.append(f"exactly one item carries a real proof, got {summary['proved']}")
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if not database.search(types=["Nat"]):
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failures.append("the type index is empty")
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problems = database.search(kind="problem")
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if len(problems) != 1 or problems[0]["verify_mode"] != "statement":
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failures.append("the open problem should be checked as a statement only")
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database.close()
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for line in failures:
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print("FAIL:", line)
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if failures:
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return 1
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print("pipeline + lean end to end: ok")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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