Pytest's default collection pattern matches any class starting with
"Test". These domain models (TestType, TestResults, TestFiles, etc.)
are not test classes — mark them explicitly so pytest skips them.
Both packages had tests/__init__.py, creating competing `tests`
packages under --import-mode=importlib. Remove both __init__.py files
and change github-app imports from `from tests.helpers` to
`from helpers` via sys.path insertion in conftest.py.
- Remove _line_profiler.py, observability/models.py, _optimizer.py,
_rate_limit.py, _usage.py from tree (never created)
- Add _background.py, _markdown.py, _xml.py that actually exist
- Mark java/ and js_ts/ as stubs
- Update endpoint count from 15 to 14, note log_features stub
- Fix Depends() example to use Annotated[] pattern
- Add deferred items: optimize-line-profiler, observability DB writes
Port test generation from Django reference: prompt templates (Jinja2
with model-type-aware formatting), LLM call orchestration with
even/odd model selection, AST-based code validation with regex
fallback, preamble repair, and ellipsis detection. Instrumentation
and postprocessing are deferred — all four response fields return
the same validated code for now.
Add fire-and-forget background task manager (background.py) and
LLM call recording (recording.py). Every LLMClient.call now records
trace_id, model, latency, tokens, cost, and errors via fire-and-forget.
drain() awaits pending tasks on shutdown. Currently logs only —
database persistence deferred until llm_calls table is wired.
12 tests covering all Queries methods against a real PostgreSQL
instance via testcontainers. Automatically skipped when Docker is
unavailable. Tests: api key lookup, last_used update, organization
fetch, subscription CRUD, usage increment, cumulative increments.
- Narrow search_start guard in search/replace parser
- Type optimizations_limit as int|str instead of object
- Wrap cost calculation return in float()
- Cast binary op result to int in CST evaluator
- Suppress import-untyped for asyncpg (no stubs available)
- Suppress arg-type for OpenAI messages (dict→union mismatch)
- Type isort kwargs as Any, add Coroutine import for refinement
- Narrow feature_version to tuple[int, int] for ast.parse
- Rename shadowed loop variable in annotation walker
- Add mypy strict=true config to pyproject.toml
FastAPI with `from __future__ import annotations` cannot resolve
Annotated[AuthenticatedUser, Depends()] when AuthenticatedUser is
only imported under TYPE_CHECKING — it becomes a query parameter
instead of a dependency. Move to runtime import in all 11 routers
with noqa: TC001 suppression.
30 integration tests cover all endpoints (success, invalid trace_id,
LLM failure, edge cases) using httpx ASGITransport with mocked LLM.
Faithful port of the Python optimization pipeline from Django aiservice:
- schemas.py: Pydantic request/response models (OptimizeRequest, OptimizeResponse)
- _markdown.py: markdown code block extraction, splitting, grouping
- _context.py: BaseOptimizerContext with Single/Multi variants for
prompt assembly, LLM response extraction, and postprocessing
- _pipeline.py: parallel LLM orchestration with model distribution
(GPT-5-mini + Claude Sonnet 4.5), diversity via line profiler toggling
- _router.py: POST /ai/optimize with auth, rate limiting, usage tracking
- 11 prompt templates copied verbatim from Django reference
- LLM client wired into app lifespan
Dual-provider client (Azure OpenAI + Anthropic Bedrock) behind
a common async interface with cache-aware cost calculation and
event-loop-safe client lifecycle.
SHA-384 + base64url key hashing matching the JS client. FastAPI
dependencies for require_auth, check_rate_limit, and track_usage
with Annotated[Depends()] pattern. Per-user per-endpoint rate
limiting with employee bypass. Atomic subscription usage tracking
with enterprise org and employee exemptions. DB queries module
with asyncpg raw SQL for auth tables. 27 new tests covering auth
flow, rate limits, usage enforcement, and edge cases.
FastAPI app factory with lifespan, CORS, optional Sentry. Pydantic-settings
config for all env vars. Full directory structure for all 15 endpoints per
the architecture doc. Workspace integration: ruff src paths, isort, pytest
testpaths, per-file ignores. aiohttp for production, httpx for test client.
CLAUDE.md with full package structure, layer boundaries, endpoint map,
implementation order, business logic audit, and design decisions.
Rules: architecture (layer boundaries, model conventions), testing
(coverage requirements, mocking strategy), porting (reference files,
what to port vs skip).
