Welcome to the Python AI Agent Frameworks Demos repository! This collection showcases various examples using Python AI agent frameworks that integrate seamlessly with GitHub Models and Azure OpenAI. Whether you're a developer looking to enhance your AI skills or a researcher exploring new technologies, this repository offers valuable insights and practical demonstrations.
In the era of artificial intelligence, the ability to create intelligent agents is more important than ever. This repository serves as a resource for developers and researchers to explore the capabilities of Python AI agent frameworks. By leveraging GitHub Models and Azure OpenAI, you can build powerful applications that can perform complex tasks with minimal input.
To get started, you can download the latest releases from our Releases section. Each release includes the necessary files and instructions to run the examples. Simply download the desired version, extract the files, and follow the provided guidelines.
Before diving into the examples, ensure you have the following installed:
- Python 3.x
- Pip (Python package installer)
- Access to Azure OpenAI services
- Clone the repository:
git clone https://github.com/tomasaap/python-ai-agent-frameworks-demos.git
- Navigate to the project directory:
cd python-ai-agent-frameworks-demos - Install the required packages:
pip install -r requirements.txt
This repository focuses on several key frameworks that facilitate the development of AI agents. Below are some of the frameworks you will encounter:
Rasa is an open-source machine learning framework for building contextual AI assistants. It enables developers to create chatbots and voice assistants that can understand user intent and manage conversations effectively.
Langchain is a framework designed for developing applications powered by language models. It simplifies the integration of various language processing capabilities, allowing developers to create robust AI solutions.
Haystack is an open-source framework for building search systems that utilize natural language processing. It enables developers to create applications that can retrieve and process information from various sources.
This repository includes a variety of examples that demonstrate the capabilities of the frameworks mentioned above. Below are some highlighted examples:
This example demonstrates how to build a simple chatbot using Rasa. The chatbot can handle user queries and provide relevant responses based on predefined intents.
- Files Included:
data/,actions.py,config.yml - Instructions: Follow the README in the
rasa-chatbotdirectory for setup and execution.
In this example, you will learn how to create an application that utilizes a language model to generate text based on user input.
- Files Included:
app.py,requirements.txt - Instructions: Refer to the README in the
langchain-appdirectory for setup and execution.
This example showcases how to build a search system that leverages Haystack for natural language queries. Users can input questions, and the system will retrieve relevant documents.
- Files Included:
search.py,data/ - Instructions: Check the README in the
haystack-searchdirectory for setup and execution.
We welcome contributions to this repository! If you would like to add new examples, improve documentation, or fix bugs, please follow these steps:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Make your changes and commit them.
- Push your branch to your fork.
- Create a pull request.
Your contributions help improve this repository and benefit the community.
This project is licensed under the MIT License. See the LICENSE file for details.
For questions or suggestions, feel free to reach out:
- Email: your-email@example.com
- GitHub: tomasaap
Thank you for visiting the Python AI Agent Frameworks Demos repository! We hope you find the examples helpful and inspiring. Don’t forget to check the Releases section for the latest updates and downloads. Happy coding!