from __future__ import annotations
import datetime as dt
from pathlib import Path
import pytest
from chunkhound.autodoc.generator import generate_docsite
from chunkhound.autodoc.models import CleanupConfig
from chunkhound.interfaces.llm_provider import LLMProvider, LLMResponse
from tests.autodoc.site_tree_manifest import build_tree_manifest
class _FrozenDatetime:
@classmethod
def now(cls, tz=None): # noqa: ANN001
return dt.datetime(2025, 1, 1, 0, 0, 0, tzinfo=dt.timezone.utc)
class _Provider(LLMProvider):
def __init__(self) -> None:
self._model = "fake"
@property
def name(self) -> str:
return "fake"
@property
def model(self) -> str:
return self._model
async def complete(
self,
prompt: str,
system: str | None = None,
max_completion_tokens: int = 4096,
) -> LLMResponse:
return LLMResponse(
content="## Overview\nEnd-user overview.\n\n- Use case 1\n- Use case 2",
tokens_used=0,
model=self._model,
finish_reason="stop",
)
async def complete_structured(
self,
prompt: str,
json_schema: dict[str, object],
system: str | None = None,
max_completion_tokens: int = 4096,
) -> dict[str, object]:
return {
"nav": {"groups": [{"title": "Group", "slugs": ["01-topic-one"]}]},
"glossary": [
{"term": "Term", "definition": "Definition.", "pages": ["01-topic-one"]}
],
}
async def batch_complete(
self,
prompts: list[str],
system: str | None = None,
max_completion_tokens: int = 4096,
) -> list[LLMResponse]:
return [
LLMResponse(
content="## Overview\nCleaned.\n\n## Details\nMore.",
tokens_used=0,
model=self._model,
finish_reason="stop",
)
for _ in prompts
]
def estimate_tokens(self, text: str) -> int:
return 0
async def health_check(self) -> dict[str, object]:
return {"ok": True}
def get_usage_stats(self) -> dict[str, object]:
return {}
class _LLMManager:
def __init__(self, provider: LLMProvider) -> None:
self._provider = provider
def get_synthesis_provider(self) -> LLMProvider:
return self._provider
def _write_minimal_input_dir(input_dir: Path) -> None:
(input_dir / "scope_code_mapper_index.md").write_text(
"\n".join(
[
"# AutoDoc Topics (/repo)",
"",
"1. [Topic One](topic_one.md)",
]
),
encoding="utf-8",
)
(input_dir / "topic_one.md").write_text(
"\n".join(
[
"# Topic One",
"",
"Overview body.",
"",
"## Sources",
"",
"└── repo/",
"\t└── [1] x.py (1 chunks: L1-2)",
]
),
encoding="utf-8",
)
_LLM_EXPECTED_MANIFEST: dict[str, str] = {
"README.md": "cbf6809284131ae9896d8c424e845ceb27fa8fe019fd3e7d38e01938737c07d8",
"astro.config.mjs": (
"7fcafc68489f3a8262965976a470c0d86da51979cbc64edb7efde601c5de4c32"
),
"package.json": "02c248cf0220edef9e20ab988cb3e39db533cc00a0940cdfaac121ddae22f71d",
"public/favicon.ico": (
"d014edc031656dd8a5cb7740ed900d658ba3108ff6fcb977fc3ff4f758c10f0b"
),
"public/fonts/DMSerifDisplay-Italic.ttf": (
"df74c0ac387baeaeb0fe4f2324e1668e6a3ed8c09cd9796fe162c71753e19e45"
),
"public/fonts/DMSerifDisplay-Regular.ttf": (
"8cc3643535edf039aa5d95440a8542735e9197e4f4b8d9303e980fefbf5ab616"
),
"public/fonts/SourceSans3VF-Italic.ttf.woff2": (
"b4959abc0569392f87c6c6ac612f90e3fe0104d283724189b7d8b6f61af347d3"
),
"public/fonts/SourceSans3VF-Upright.ttf.woff2": (
"5f16566f7a40d39b339ad26be151fa5a1ab1f0c2574c7a2e619765584a1acbd8"
),
"public/fonts/licenses/DMSerif-LICENSE.txt": (
"aa49c876a28d8c0b0a3027d6c25b9c607eee8c0fb96165011d42a6ace1408fe1"
),
"public/fonts/licenses/DMSerif-OFL.txt": (
"403e1c31512c5670150365359f133dcf7e5e7a172e906d2a56493922bdf70796"
),
"public/fonts/licenses/SourceSans3-LICENSE.md": (
"56af9b9c6715597e458284a474dc118a50a4150e9d547c70f7b4a33c3e6a9328"
),
"src/data/nav.json": (
"e6b5c6c9ebfd335199f501c1c88c11afde89671f89a7bd672c543b318c323af1"
),
"src/data/search.json": (
"934d302ac5555b6e87a1c9c2c79c8dfce3d7d39a81d2766c982d4359d8072deb"
),
"src/data/site.json": (
"33e943d1f4e8512ccd202ad44dc60e917c0e6a6642fee3cd399f9b30a936a182"
),
"src/layouts/DocLayout.astro": (
"55ab6e3eff2a4c4fa20dec670c77899acd237499098805bf786bf115e36e0994"
),
"src/pages/glossary.md": (
"b060fc17e9ce790346481ab7e967a38659a39e850f9f9cb4a1d908eee06235bb"
),
"src/pages/index.md": (
"c711195af8be20151b8a0e9b64c35efbd5d3dc5b6da01afdbe700a7b37ed9087"
),
"src/pages/topics/01-topic-one.md": (
"84639b72b3b8d55111d100aebbe06323a8493771e77cf0d1b46c0ad6f206fa4f"
),
"src/styles/global.css": (
"0b1e07098d5f256c40b73551cb703c05787e0afdc9092d84a4412a0bad891764"
),
"tsconfig.json": "ec0d7fbe45c5b2efb1c617eec3a7ee249d72974163c5309843420904703ee0a4",
}
@pytest.mark.asyncio
async def test_generate_docsite_requires_llm_manager(
tmp_path: Path,
monkeypatch: pytest.MonkeyPatch,
) -> None:
monkeypatch.chdir(tmp_path)
monkeypatch.setattr("chunkhound.autodoc.generator.datetime", _FrozenDatetime)
input_dir = Path("input")
input_dir.mkdir(parents=True)
_write_minimal_input_dir(input_dir)
with pytest.raises(RuntimeError, match="LLM"):
await generate_docsite(
input_dir=input_dir,
output_dir=Path("out"),
llm_manager=None,
cleanup_config=CleanupConfig(
mode="llm",
batch_size=1,
max_completion_tokens=512,
audience="end-user",
),
site_title=None,
site_tagline=None,
)
assert not Path("out").exists()
@pytest.mark.asyncio
async def test_generate_docsite_llm_mode_emits_byte_stable_tree(
tmp_path: Path,
monkeypatch: pytest.MonkeyPatch,
) -> None:
monkeypatch.chdir(tmp_path)
monkeypatch.setattr("chunkhound.autodoc.generator.datetime", _FrozenDatetime)
input_dir = Path("input")
input_dir.mkdir(parents=True)
_write_minimal_input_dir(input_dir)
await generate_docsite(
input_dir=input_dir,
output_dir=Path("out"),
llm_manager=_LLMManager(_Provider()), # type: ignore[arg-type]
cleanup_config=CleanupConfig(
mode="llm",
batch_size=1,
max_completion_tokens=512,
audience="end-user",
),
site_title=None,
site_tagline=None,
)
assert build_tree_manifest(Path("out")) == _LLM_EXPECTED_MANIFEST
