🤖 Its ReAct Multifunctionality Agent 🚀

This project demonstrates a ReAct (Reason + Act) agent implemented using the Groq API and LLama 2 70b Chat model within a Jupyter Notebook environment. The agent is designed to perform various tasks by reasoning through a problem and taking appropriate actions.

✨ Features

• ReAct Architecture: Implements the ReAct pattern for agent decision-making.
• Groq Integration: Utilizes the Groq API for seamless interaction with LLama 2.
• Tool Usage: The agent can use predefined tools (e.g., calculator, planet mass lookup) to gather information.
• Multi-Step Reasoning: Can break down complex problems into smaller steps and solve them sequentially.
• Jupyter Notebook Interface: Provides an interactive environment for experimenting with the agent.

⚙️ Installation

1. Clone the repository:

   git clone https://github.com/AnonymousCoderArtist/MultifunctionCalling-ReAct-Agent.git 

2. Set up Groq API Key:
   • Create a .env file in the project root directory.
   • Add your Groq API key to the .env file:

     GROQ_API_KEY=your_groq_api_key
     

🚀 Usage

1. Open the Jupyter Notebook: Navigate to the cloned repository and open the noice.ipynb file in Jupyter Notebook.
2. Run the notebook cells: Execute the notebook cells sequentially to install dependencies, initialize the agent, and define the tools.
3. Interact with the agent: Use the loop() function to provide queries to the agent. The loop() function takes an optional query parameter as input.

📝 Example

The following example demonstrates how to ask the agent a question that requires multiple steps and tool usage:

loop(query="What is the mass of Earth plus the mass of Saturn and all of that times 2?") 

The agent will then reason through the problem, use the get_planet_mass and calculate tools, and provide the final answer within the notebook output.

🤝 Contributing

Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request on the GitHub repository.