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lean-bench/leanbench/typeindex.py
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Rebuild lean-bench: pages in, verified indexed Lean out
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.
                   One Docker layer per Lean step, each long one under `timeout`:
                   the gigabyte cache fetch earns a layer that a later failure never
                   invalidates, and a stalled step fails with a message instead of
                   sitting silent for an hour. There is deliberately no `lake build`
                   of our own library -- verification runs `lake env lean` against
                   Mathlib's oleans and needs nothing else, and building the library
                   was pure cost. 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 build runs with --progress=plain, because BuildKit's
                   default renderer collapses a long step into one rewritten line
                   and a stall then looks exactly like progress.

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
2026-09-19 16:22:09 +00:00

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"""What an item is *about*, in terms the database can index and search on.
An item is indexed by the types it takes in -- or, when it is not a proof, by the
type it is itself. Those types are what make one item relevant to another, so the
index is the thing retrieval later hangs off.
Types are read straight out of the Lean signature (so they reflect the code that
actually elaborated), and merged with whatever the model volunteered.
"""
from __future__ import annotations
import re
from dataclasses import dataclass
PROOF_KEYWORDS = {"theorem", "lemma", "example"}
DECL_KEYWORDS = PROOF_KEYWORDS | {
"def", "abbrev", "instance", "structure", "class", "inductive", "opaque", "axiom",
}
ROLE_INPUT = "input" # a type the item consumes (binders, hypotheses)
ROLE_GOAL = "goal" # the proposition a proof concludes
ROLE_OWN = "own" # the type of a definition, i.e. what it *is*
_DECL = re.compile(
r"^[ \t]*(?:@\[[^\]]*\][ \t\n]*)*"
r"(?:(?:private|protected|noncomputable|partial|unsafe|scoped|local)[ \t]+)*"
r"(" + "|".join(sorted(DECL_KEYWORDS, key=len, reverse=True)) + r")[ \t]+"
r"([^\s:(\[{⦃]+)",
re.MULTILINE,
)
_IDENT = re.compile(r"[A-Za-z_][A-Za-z0-9_'!?₀-₉]*(?:\.[A-Za-z_][A-Za-z0-9_'!?₀-₉]*)*")
UNICODE_TYPES = {
"ℕ": "Nat", "ℤ": "Int", "ℚ": "Rat", "ℝ": "Real", "ℂ": "Complex",
"𝔽": "Field", "𝕜": "Field", "Ω": "Omega", "ℍ": "Quaternion",
}
NOISE = {
"fun", "by", "at", "with", "then", "else", "if", "do", "let", "have", "show",
"from", "this", "sorry", "in", "forall", "exists", "and", "or", "not", "iff",
"true", "false", "Type", "Sort", "Prop", "_root_", "match", "deriving", "where",
}
def _strip_comments(code: str) -> str:
code = re.sub(r"/-.*?-/", " ", code, flags=re.DOTALL)
return re.sub(r"--[^\n]*", " ", code)
def _split_top_level(text: str) -> list[tuple[str, str]]:
"""Split a signature into binder groups: [(opener, inner text), ...]."""
groups: list[tuple[str, str]] = []
pairs = {"(": ")", "{": "}", "[": "]", "⦃": "⦄", "⟨": "⟩"}
depth = 0
opener = ""
start = 0
for index, char in enumerate(text):
if char in pairs:
if depth == 0:
opener, start = char, index + 1
depth += 1
elif char in pairs.values():
depth -= 1
if depth == 0:
groups.append((opener, text[start:index]))
depth = max(depth, 0)
return groups
def _top_level_result(signature: str) -> str:
"""Whatever follows the final colon that is not inside a binder group."""
depth = 0
last_colon = -1
pairs = {"(": ")", "{": "}", "[": "]", "⦃": "⦄", "⟨": "⟩"}
for index, char in enumerate(signature):
if char in pairs:
depth += 1
elif char in pairs.values():
depth = max(0, depth - 1)
elif char == ":" and depth == 0:
if signature[index : index + 2] == ":=":
continue
last_colon = index
return signature[last_colon + 1 :] if last_colon != -1 else ""
SORTS = {"Prop", "Type", "Sort"}
def type_names(fragment: str, *, allow_sorts: bool = False) -> list[str]:
"""Candidate type names inside a type expression.
``allow_sorts`` keeps ``Prop``/``Type``: noise in a hypothesis, but the whole
answer for ``def divides (a b : Int) : Prop``, whose own type *is* Prop.
"""
found: list[str] = []
for symbol, name in UNICODE_TYPES.items():
if symbol in fragment:
found.append(name)
for match in _IDENT.finditer(fragment):
token = match.group(0)
if token in NOISE and not (allow_sorts and token in SORTS):
continue
head = token.split(".")[0]
if not head[:1].isupper():
continue
found.append(token)
ordered: list[str] = []
for name in found:
if name not in ordered:
ordered.append(name)
return ordered
@dataclass
class Signature:
keyword: str
name: str
is_proof: bool
inputs: list[str]
goal: list[str]
own: list[str]
def as_pairs(self) -> list[tuple[str, str]]:
pairs = [(name, ROLE_INPUT) for name in self.inputs]
pairs += [(name, ROLE_GOAL) for name in self.goal]
pairs += [(name, ROLE_OWN) for name in self.own]
seen: set[tuple[str, str]] = set()
out: list[tuple[str, str]] = []
for pair in pairs:
if pair not in seen:
seen.add(pair)
out.append(pair)
return out
def parse_signature(code: str) -> Signature | None:
"""Parse the first declaration in ``code`` into its indexable types."""
clean = _strip_comments(code)
match = _DECL.search(clean)
if not match:
return None
keyword, name = match.group(1), match.group(2)
rest = clean[match.end() :]
depth = 0
cut = len(rest)
pairs = {"(": ")", "{": "}", "[": "]", "⦃": "⦄", "⟨": "⟩"}
for index, char in enumerate(rest):
if char in pairs:
depth += 1
elif char in pairs.values():
depth = max(0, depth - 1)
elif depth == 0:
if rest[index : index + 2] == ":=":
cut = index
break
if rest[index : index + 6] == "\nwhere":
cut = index
break
signature = rest[:cut]
inputs: list[str] = []
for opener, inner in _split_top_level(signature):
body = inner.split(":", 1)[1] if ":" in inner and opener != "[" else inner
inputs.extend(type_names(body))
is_proof = keyword in PROOF_KEYWORDS
result_text = _top_level_result(signature)
result = type_names(result_text, allow_sorts=not is_proof)
return Signature(
keyword=keyword,
name=name,
is_proof=is_proof,
inputs=_unique(inputs),
goal=_unique(result) if is_proof else [],
own=[] if is_proof else _unique(result),
)
def _unique(names: list[str]) -> list[str]:
out: list[str] = []
for name in names:
if name not in out:
out.append(name)
return out
def index_types(code: str, extra: list[str] | None = None) -> list[tuple[str, str]]:
"""The (type name, role) pairs an item should be indexed under."""
signature = parse_signature(code)
pairs = signature.as_pairs() if signature else []
known = {name for name, _ in pairs}
role = ROLE_INPUT if (signature is None or signature.is_proof) else ROLE_OWN
for name in extra or []:
cleaned = str(name).strip()
if not cleaned:
continue
for candidate in type_names(cleaned) or [cleaned]:
if candidate not in known and candidate not in NOISE:
known.add(candidate)
pairs.append((candidate, role))
return pairs