arunpshankar/react-from-scratch

This repo compiles a collection of examples that demonstrate the effective use of the ReAct pattern in LLM prompting. It includes variations and implementations of agents that leverage the ReAct pattern, optimized for Gemini.

🤖 Building ReAct Agents from Scratch: A Hands-On Guide with Gemini

This repository provides a comprehensive guide and implementation for creating ReAct (Reasoning and Acting) agents from scratch using Python and leveraging Google's Gemini as the Large Language Model (LLM) of choice.

📚 Contents

• Step-by-step implementation of the ReAct pattern
• Multiple examples showcasing ReAct agents in action
• Optimizations specific to the Gemini model
• Tools integration (Google Search and Wikipedia)

🚀 Getting Started

Prerequisites

• Python 3.8+
• Git
• Poetry (for dependency management)

Installation

1. Clone the repository:

   git clone https://github.com/username/react-agents-from-scratch.git
   cd react-agents-from-scratch
   

2. Set up a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows, use `.venv\Scripts\activate`
   

3. Install Poetry (if not already installed):

   pip install poetry
   

4. Install project dependencies:

   poetry install
   

5. Set up environment variables:

   export PYTHONDONTWRITEBYTECODE=1
   export PYTHONPATH=$PYTHONPATH:.
   

Setting up Credentials

1. Create a credentials folder in the project root:

   mkdir credentials
   

2. Set up GCP service account credentials:

   • Go to the Google Cloud Console (https://console.cloud.google.com/).
   • Create a new project or select an existing one.
   • Navigate to "APIs & Services" > "Credentials".
   • Click "Create Credentials" > "Service Account Key".
   • Select your service account, choose JSON as the key type, and click "Create".
   • Save the downloaded JSON file as key.json in the credentials folder.

3. Set up SERP API credentials:

   • Sign up for a SERP API account at https://serpapi.com/.
   • Obtain your API key from the dashboard.
   • Create a file named key.yml in the credentials folder.
   • Add your SERP API token in the following format:

     serp:
       key: your_serp_api_key_here

Note: The credentials folder is included in .gitignore to prevent sensitive information from being committed.

🛠️ Project Structure

• src/tools/: Contains implementations for Google Search (via SERP API) and Wikipedia search.
• src/react/: Houses the core ReAct agent implementation.
• data/input/: Stores input prompts for the ReAct agent.
• data/output/: Contains output traces from example runs.

🖥️ Usage

1. Ensure you're in the project root directory with your virtual environment activated.

2. Run the ReAct agent:

   python src/react/agent.py
   

3. The agent uses the prompt from ./data/input/react.txt and generates output traces in ./data/output/.

4. To run individual tools:

   • Google Search: python src/tools/serp.py
   • Wikipedia Search: python src/tools/wiki.py

5. For a non-agentic approach with programmatic routing:

   python src/tools/manager.py
   

🤝 Contributing

We welcome contributions! Please see our CONTRIBUTING.md for details on how to submit pull requests, report issues, or request features.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

📚 Further Reading

For a detailed explanation of the ReAct pattern and this implementation, check out our accompanying Medium article: [Building ReAct Agents from Scratch: A Hands-On Guide using Gemini]