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Contributing to AgenticRAG

Thank you for your interest in contributing to the AgenticRAG! This project aims to provide a state-of-the-art, production-grade agentic RAG framework implementing the latest research from 2024-2025.

Table of Contents

  1. Development Setup
  2. Project Structure
  3. Coding Standards
  4. Documentation Standards
  5. Testing & Benchmarking
  6. Pull Request Process

Development Setup

We use uv for dependency management and virtual environments.

# Clone the repository
git clone https://github.com/heshamfs/agentic-rag.git
cd agentic-rag

# Create virtual environment and install dependencies
uv venv --python 3.12
source .venv/bin/activate  # or .venv\Scripts\activate on Windows

# Install in editable mode with dev dependencies
uv pip install -e ".[dev]"

Environment Configuration

Copy the .env.example to .env and fill in your API keys:

cp .env.example .env

Project Structure

src/agentic_rag/
├── agents/          # Multi-agent orchestration (Router, Retriever, Evaluator, Generator)
├── chunking/        # Chunking strategies (Semantic, Late, RAPTOR, Contextual)
├── embeddings/      # Embedding model integrations (Qwen3-Embedding)
├── generation/      # LLM provider clients (Claude, OpenAI, Gemini, Local)
├── graph/           # GraphRAG implementation (Entities, Relationships, Communities)
├── ingestion/       # Document loading and processing
├── pipeline/        # Pipeline orchestration and builder
├── retrieval/       # Retrieval strategies (Hybrid, HyDE, Multi-Query)
├── reranking/       # Reranking models (ColBERT, Cross-Encoders)
├── vectordb/        # Vector database clients (Qdrant)
├── evaluation/      # RAGAS metrics and Self-RAG reflection
└── observability/   # OpenTelemetry and tracing

Coding Standards

  • Type Hints: All new code must use Python type hints.
  • Async/Await: The pipeline is async-first. Use async/await for all I/O operations.
  • Pydantic: Use Pydantic models for data structures and settings.
  • Linting: We use ruff for linting and formatting. 
    ruff check src/
ruff format src/
  • Type Checking: We use mypy for static type checking. 
    mypy src/

Documentation Standards

  • Docstrings: All public classes and methods must have Google-style docstrings.
  • Type Information: Include type information in docstrings if it helps clarity, although type hints are preferred.
  • README Updates: If you add a new feature, update the README.md and relevant files in docs/.

Testing & Benchmarking

Running Tests

pytest

Running Benchmarks

The benchmark system is critical for verifying performance gains.

# Run the core benchmark
python scripts/run_benchmark.py

# Run industry comparison (requires benchmark collection to be created first)
python scripts/run_comparison.py

Pull Request Process

  1. Create a new branch for your feature or bugfix.
  2. Ensure all tests pass and linting is clean.
  3. Update documentation if necessary.
  4. Submit a PR with a clear description of the changes and their impact on performance (if applicable).
  5. Link any related issues.

License

By contributing, you agree that your contributions will be licensed under the project's MIT License.