mirror of https://github.com/codeflash-ai/codeflash-internal.git synced 2026-05-04 18:25:18 +00:00

Reorganize top-level feature modules under core/ (#2416 )

## Summary

- Move `log_features/` → `core/log_features/` (Django app with
`managed=False` models, no DB impact)
- Move `ranker/`, `workflow_gen/`, `adaptive_optimizer/` →
`core/languages/python/` (Python-focused API modules)
- Update all imports across the codebase (19 files)

## Test plan

- [x] All 548 tests pass
- [x] No stale top-level imports (`from log_features.`, `from ranker.`,
etc.)
- [x] `log_features` AppConfig preserves `label = "log_features"` for
Django app registry compatibility

2026-02-14 17:07:40 -05:00

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AGENTS.md

This file provides guidance to AI agents working with code in this repository.

Project Overview

Django-Ninja backend powering CodeFlash's AI-driven code optimization. Handles LLM interactions, test generation, and optimization workflows.

Commands

# Install dependencies (NEVER use pip)
uv sync
uv sync --group dev

# Run development server
uv run uvicorn aiservice.asgi:application --reload

# Run all tests
uv run pytest

# Run a single test file
uv run pytest tests/optimizer/test_optimizer.py

# Run a specific test
uv run pytest tests/optimizer/test_optimizer.py::test_function_name -v

# Type checking
uv run mypy .
uv run ty check

# Linting (auto-fixes enabled)
uv run ruff check .
uv run ruff format .

Architecture

API Structure

All REST endpoints use Django-Ninja with type-safe Pydantic schemas. APIs are defined in aiservice/urls.py:

/ai/optimize - Code optimization (core/languages/python/optimizer/optimizer.py)
/ai/testgen - Test generation (core/languages/python/testgen/testgen.py)
/ai/refinement - Iterative refinement (core/languages/python/optimizer/refinement.py)
/ai/rank - Optimization ranking (core/shared/ranker/ranker.py)
/ai/explain - Code explanations (core/languages/python/explanations/explanations.py)
/ai/code_repair - Code repair (core/languages/python/code_repair/code_repair.py)
/ai/adaptive_optimize - Adaptive optimization (core/languages/python/adaptive_optimizer/adaptive_optimizer.py)
/ai/workflow-gen - GitHub Actions workflow generation (core/shared/workflow_gen/workflow_gen.py)
/ai/rewrite_jit - JIT rewrite (core/languages/python/jit_rewrite/jit_rewrite.py)
/ai/optimization_review - Optimization review (core/languages/python/optimization_review/optimization_review.py)
/ai/log_features - Feature logging (core/log_features/log_features.py)
/ai/optimize-line-profiler - Line profiler optimization (core/languages/python/optimizer/optimizer_line_profiler.py)

LLM Module (`aiservice/llm.py`)

Unified interface for all LLM calls. Always use this instead of direct API calls:

from aiservice.llm import call_llm, OPTIMIZE_MODEL, LLMResponse

response: LLMResponse = await call_llm(
    llm=OPTIMIZE_MODEL,
    messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}],
    call_type="optimization",
    trace_id=trace_id,
)

Model assignments are configured at the bottom of llm.py. Supports both OpenAI and Anthropic via Azure.

Prompt Files

LLM prompts are stored as .md files alongside their modules:

optimizer/system_prompt.md, optimizer/user_prompt.md
testgen/execute_system_prompt.md, testgen/execute_user_prompt.md
code_repair/CODE_REPAIR_SYSTEM_PROMPT.md

Middleware Stack (`aiservice/middleware/`)

healthcheck.py - Health check endpoint
auth_middleware.py - API authentication
rate_limit.py - Rate limiting
track_usage_middleware.py - Usage tracking

Observability (`aiservice/observability/`)

logger.py - Logging utilities
database.py - Records LLM calls for tracing
Sentry integration for error tracking (production only)

Key Patterns

Use libcst for code manipulation

Always use libcst (not ast) to preserve formatting when modifying code.

Use Pydantic for schemas

All request/response models use Pydantic BaseModel or ninja.Schema.

Async endpoints

Most API endpoints are async. Use asyncio.TaskGroup for concurrent operations.

Context classes

Optimizer and testgen use context classes (BaseOptimizerContext, BaseTestGenContext) to manage state and prompts.

Code Style

Line length: 120 characters
Python: 3.12+ syntax
Tooling: Ruff (linting/formatting), mypy (strict mode), ty (type checker)
Comments: Minimal - explain "why", not "what"
Docstrings: Don't add unless explicitly requested

Git Commits

Use conventional commit format: fix:, feat:, refactor:, docs:, test:, chore:

Agent Rules

@../../.tessl/RULES.md follow the instructions

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