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PatchPro Development Guide

This guide helps collaborators set up and run PatchPro locally for development and testing.

Prerequisites

  • Python 3.12+ (required)
  • uv package manager (recommended) or pip
  • OpenAI API Key (for LLM features)

Quick Setup

1. Clone and Setup Environment

git clone <repository-url>
cd patchpro-bot-agent-dev

2. Install Dependencies

Using uv (recommended):

uv sync

Using pip:

python -m venv .venv
source .venv/bin/activate  # or `.venv/bin/activate.fish` for fish shell
pip install -e .

3. Configure OpenAI API Key

Create a .env file in the project root:

# .env
OPENAI_API_KEY=sk-proj-your-openai-api-key-here

Get your API key from: https://platform.openai.com/api-keys

Running PatchPro Locally

Option 1: Test with Demo Repository

# Clone the demo repository
git clone <demo-repo-url>
cd patchpro-demo-repo

# Generate analysis artifacts
mkdir -p artifact/analysis
ruff check --output-format json . > artifact/analysis/ruff_output.json || true
semgrep --config .semgrep.yml --json . > artifact/analysis/semgrep_output.json || true

# Run PatchPro (from demo repo)
uv run --with /path/to/patchpro-bot-agent-dev python -m patchpro_bot.run_ci

Option 2: Test with Your Own Repository

  1. Setup your repository:

    cd your-repository

# Add proper pyproject.toml
cat > pyproject.toml << EOF
[project]
name = "your-project"
version = "0.1.0"
requires-python = ">=3.8"
dependencies = []

[build-system]
requires = ["setuptools>=68", "wheel"]
build-backend = "setuptools.build_meta"
EOF

  2. Generate analysis:

    mkdir -p artifact/analysis
ruff check --output-format json . > artifact/analysis/ruff_output.json || true
semgrep --json . > artifact/analysis/semgrep_output.json || true

  3. Run PatchPro:

    uv run --with /path/to/patchpro-bot-agent-dev python -m patchpro_bot.run_ci

Option 3: Run from Agent Repository

cd patchpro-bot-agent-dev

# Point to external artifacts
PP_ARTIFACTS=/path/to/repo/artifact uv run python -m patchpro_bot.run_ci

Understanding the Output

PatchPro generates several artifacts in the artifact/ directory:

  • report.md - Comprehensive analysis report
  • patch_combined_*.diff - AI-generated code fixes
  • patch_summary_*.md - Summary of changes
  • analysis/ - Raw analysis data from tools

Configuration Options

Environment Variables

  • OPENAI_API_KEY - OpenAI API key for LLM features
  • PP_ARTIFACTS - Path to artifacts directory (default: artifact)

Model Settings (optional)

Add to your .env file:

OPENAI_MODEL=gpt-4o-mini        # Default model
OPENAI_MAX_TOKENS=8192          # Max response tokens
OPENAI_TEMPERATURE=0.1          # Generation temperature

Troubleshooting

Python Version Issues

# Check Python version
python --version  # Should be 3.12+

# Use specific Python version with uv
uv python install 3.12
uv venv --python 3.12

API Key Issues

# Test API key
export OPENAI_API_KEY="your-key"
curl -H "Authorization: Bearer $OPENAI_API_KEY" https://api.openai.com/v1/models

File Path Issues

  • Ensure you're running from the correct directory
  • Check that source files exist in the expected locations
  • Verify PP_ARTIFACTS path is correct

Missing Dependencies

# Reinstall dependencies
uv sync --force
# or
pip install -e . --force-reinstall

Development Workflow

  1. Make Changes to PatchPro code
  2. Test Locally using one of the methods above
  3. Check Logs in artifact/patchpro_enhanced.log
  4. Review Output patches and reports
  5. Commit Changes when satisfied

Performance Notes

  • First run may be slower (model initialization)
  • Subsequent runs benefit from caching
  • Large repositories are processed in intelligent batches
  • API costs vary by model and code complexity

Getting Help

  • Check logs in artifact/patchpro_enhanced.log
  • Review error messages for specific issues
  • Ensure all prerequisites are met
  • Verify file paths and permissions