8cf0cc7e01
- codex/synthesis.py: Lead/Provenance dataclasses, find_connections/gaps/improvements, propose_conjectures (status=unverified, quarantined in leads/conjectures/), write_leads (grounded→leads/grounded/, conjectures→leads/conjectures/ HARD invariant) - codex/cli.py: synthesis leads/conjectures/report command group - codex/config.py: F-13 settings (leads_dir, synthesis_llm_*, synthesis_top_k, synthesis_min_grounded_ratio, synthesis_gap_min_coverage) - tests/synthesis/: 42 tests covering model, grounding, conjecture invariant, quarantine, CLI - spike/: F-13 spike script + output (live run attempted) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
125 lines
3.7 KiB
Python
125 lines
3.7 KiB
Python
"""Shared synthesis fixtures — keep DB/LLM/embed fully mocked."""
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from __future__ import annotations
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from pathlib import Path
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from typing import Any
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from unittest.mock import MagicMock
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import pytest
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# Shared corpus fixture used across the test suite.
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FIXTURE_CHUNKS: list[dict[str, Any]] = [
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{
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"id": 1,
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"paper_id": "springborn-2008",
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"ord": 16,
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"content": (
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"For an ideal tetrahedron with dihedral angles the hyperbolic volume is "
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"V = L(gamma1) + L(gamma2) + L(gamma3). "
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"The volume function V0 is strictly concave on the angle domain."
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),
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"bibkey": "Springborn2008",
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},
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{
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"id": 2,
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"paper_id": "bps-2015",
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"ord": 3,
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"content": (
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"A discrete conformal map is defined by logarithmic scale factors u_i "
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"at each vertex such that the edge lengths satisfy a compatibility condition."
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),
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"bibkey": "BobenkoPinkallSpringborn2015",
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},
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{
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"id": 3,
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"paper_id": "luo-2004",
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"ord": 0,
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"content": (
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"The combinatorial Yamabe flow drives the edge lengths towards a target "
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"curvature on a triangulated surface."
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),
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"bibkey": "Luo2004",
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},
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]
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class StubLLM:
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"""Deterministic mock LLM that returns a preset response."""
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def __init__(self, response: str | list[str]) -> None:
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if isinstance(response, str):
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self._responses = [response]
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else:
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self._responses = list(response)
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self._calls: list[tuple[str, str]] = []
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def generate(self, prompt: str, model: str) -> str:
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self._calls.append((prompt, model))
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if not self._responses:
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return ""
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if len(self._responses) == 1:
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return self._responses[0]
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return self._responses.pop(0)
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@property
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def calls(self) -> list[tuple[str, str]]:
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return list(self._calls)
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@pytest.fixture
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def stub_llm() -> StubLLM:
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"""Default stub returning empty string (override per test as needed)."""
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return StubLLM("")
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@pytest.fixture
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def fixture_chunks() -> list[dict[str, Any]]:
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return [dict(c) for c in FIXTURE_CHUNKS]
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@pytest.fixture
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def mock_retrieve(monkeypatch: pytest.MonkeyPatch) -> None:
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"""Patch _retrieve_chunks to return deterministic fixture chunks (no DB)."""
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monkeypatch.setattr(
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"codex.synthesis._retrieve_chunks",
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lambda queries, top_k: [dict(c) for c in FIXTURE_CHUNKS],
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)
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@pytest.fixture
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def mock_paper_titles(monkeypatch: pytest.MonkeyPatch) -> None:
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"""Patch _list_paper_titles to return a deterministic three-paper corpus."""
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monkeypatch.setattr(
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"codex.synthesis._list_paper_titles",
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lambda db_conn: [
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("Springborn2008", "A variational principle for weighted Delaunay triangulations"),
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(
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"BobenkoPinkallSpringborn2015",
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"Discrete conformal maps and ideal hyperbolic polyhedra",
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),
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("Luo2004", "Combinatorial Yamabe Flow on Surfaces"),
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],
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)
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@pytest.fixture
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def mock_settings(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> Path:
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"""Settings stub pointing leads_dir at tmp_path/leads."""
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from codex.config import Settings
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leads_path = tmp_path / "leads"
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mock = MagicMock(spec=Settings)
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mock.leads_dir = str(leads_path)
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mock.synthesis_llm_model = "test-model"
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mock.synthesis_llm_url = None
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mock.ollama_base_url = "http://localhost:11434"
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mock.synthesis_top_k = 6
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mock.synthesis_min_grounded_ratio = 0.5
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mock.synthesis_gap_min_coverage = 2
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mock.wiki_dir = str(tmp_path / "wiki")
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monkeypatch.setattr("codex.synthesis.get_settings", lambda: mock)
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return leads_path
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