CONTRIBUTING.md

Contributing to phidata

Phidata is an open-source project and we welcome contributions.

👩‍💻 How to contribute

Please follow the fork and pull request workflow:

• Fork the repository.
• Create a new branch for your feature.
• Add your feature or improvement.
• Send a pull request.
• We appreciate your support & input!

Development setup

1. Clone the repository.
2. Create a virtual environment:
   • For Unix, use ./scripts/create_venv.sh.
   • For Windows, use .\scripts\create_venv.bat.
   • This setup will:
     • Create a phienv virtual environment in the current directory.
     • Install the required packages.
     • Install the phidata package in editable mode.
3. Activate the virtual environment:
   • On Unix: source phienv/bin/activate
   • On Windows: phienv\Scripts\activate

Formatting and validation

Ensure your code meets our quality standards by running the appropriate formatting and validation script before submitting a pull request:

• For Unix:
  • ./scripts/format.sh
  • ./scripts/validate.sh
• For Windows:
  • .\scripts\format.bat
  • .\scripts\validate.bat

These scripts will perform code formatting with ruff, static type checks with mypy, and run unit tests with pytest.

Adding a new Vector Database

1. Setup your local environment by following the Development setup.
2. Create a new directory under phi/vectordb for the new vector database.
3. Create a Class for your VectorDb that implements the VectorDb interface
   • Your Class will be in the phi/vectordb/<your_db>/<your_db>.py file.
   • The VectorDb interface is defined in `phi/vectordb/base
   • Import your VectorDb Class in phi/vectordb/<your_db>/__init__.py.
   • Checkout the phi/vectordb/pgvector/pgvector file for an example.
4. Add a recipe for using your VectorDb under cookbook/vectordb/<your_db>.
   • Checkout phidata/cookbook/vectordb/pgvector for an example.
5. Important: Format and validate your code by running ./scripts/format.sh and ./scripts/validate.sh.
6. Submit a pull request.

Adding a new Model Provider

1. Setup your local environment by following the Development setup.
2. Create a new directory under phi/model for the new Model provider.
3. If the Model provider supports the OpenAI API spec:
   • Create a Class for your LLM provider that inherits the OpenAILike Class from phi/model/openai/like.py.
   • Your Class will be in the phi/model/<your_model>/<your_model>.py file.
   • Import your Class in the phi/model/<your_model>/__init__.py file.
   • Checkout the phi/model/xai/xai.py file for an example.
4. If the Model provider does not support the OpenAI API spec:
   • Reach out to us on Discord or open an issue to discuss the best way to integrate your LLM provider.
   • Checkout phi/model/anthropic/claude.py or phi/model/cohere/chat.py for inspiration.
5. Add a recipe for using your Model provider under cookbook/providers/<your_model>.
   • Checkout phidata/cookbook/provider/claude for an example.
6. Important: Format and validate your code by running ./scripts/format.sh and ./scripts/validate.sh.
7. Submit a pull request.

Adding a new Tool.

1. Setup your local environment by following the Development setup.
2. Create a new directory under phi/tools for the new Tool.
3. Create a Class for your Tool that inherits the Toolkit Class from phi/tools/toolkit/.py.
   • Your Class will be in phi/tools/<your_tool>.py.
   • Make sure to register all functions in your class via a flag.
   • Checkout the phi/tools/youtube_tools.py file for an example.
   • If your tool requires an API key, checkout the phi/tools/serpapi_tools.py as well.
4. Add a recipe for using your Tool under cookbook/tools/<your_tool>.
   • Checkout phidata/cookbook/tools/youtube_tools for an example.
5. Important: Format and validate your code by running ./scripts/format.sh and ./scripts/validate.sh.
6. Submit a pull request.

Message us on Discord or post on Discourse if you have any questions or need help with credits.

📚 Resources

• Documentation
• Discord
• Discourse

📝 License

This project is licensed under the terms of the MPL-2.0 license