AI Agent with LangChain, Google Gemini, and DuckDuckGo

This repository demonstrates how to create an AI Agent using LangChain with Google Gemini and DuckDuckGo to build AI-powered applications using multimodal capabilities. It's designed as a starting point for developers looking to explore and experiment with LangChain AI Agents.

🚀 Features

• Beginner-friendly: Simple setup to get started quickly.
• Modular Architecture: Uses LangChain to chain together prompts, tools, and reasoning steps.
• LLM Integration: Powered by Google Gemini for advanced reasoning and natural language capabilities.
• Search Integration: Employs DuckDuckGo as a tool, allowing the agent to query the web, retrieve information, and enhance its knowledge of current events or data outside the training corpus.
• Extensible: Easily adapt the agent to incorporate new tools, models, or additional data sources.

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🛠️ Setup Instructions

Follow these steps to set up and run the project on your local machine:

1. Clone the Repository

git clone https://github.com/hmshb/langchain-ai-agent-google-gemini.git
cd langchain-ai-agent-google-gemini

2. Create a Virtual Environment

python -m venv venv
source venv/bin/activate  # For Linux/Mac

.\venv\Scripts\activate # For Windows

3. Install Dependencies

pip install -r requirements.txt

4. Generate a Google API Key

1. Visit Google AI Studio.
2. Create an API key for accessing Google Gemini.
3. Copy the generated API key.

5. Update .env File

• Add the following line to the .env file, replacing your_google_api_key with your actual API key:

  GEMINI_API_KEY=your_google_api_key
  

6. Run the project

python index.py

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📂 Project Structure

langchain-ai-agent-google-gemini/
├── .env                 # API key configuration file
├── gemini.py            # Gemini AI LLM Model configurations
├── index.py             # Actual script with whole feature
├── requirements.txt     # Optional: Use for dependencies
├── README.md            # Documentation file
├── venv/                # Virtual environment

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🛠️ Troubleshooting & FAQ

• Gemini Authentication Errors: Ensure GEMINI_API_KEY is correctly set. If you face issues, verify your key’s validity and permissions.
• No Search Results: DuckDuckGo might return minimal results if the query is very niche. Consider adjusting the query or checking network connectivity.
• Rate Limits: If you frequently hit rate limits on the LLM side, consider strategies like caching results, batching requests, or upgrading your plan.

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⭐ Acknowledgments

Special thanks to:

• LangChain for providing a robust framework for building LLM applications.
• Google Gemini for their powerful multimodal AI capabilities.
• DuckDuckGo for allowing the agent to query the web.

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📜 License

This project is open-source and licensed under the MIT License.

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📢 Get Involved!

If you find this repository helpful, please consider:

• ⭐ Starring the Repository to show your support.
• 📤 Forking the Repository to explore further and make your own customizations.
• 💬 Sharing Your Feedback by opening issues or discussions.

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📝 Notes

Google Gemini, Langchain and DuckDuckGo is currently in limited or preview release (depending on your region and timing), and integration details may change as the service evolves. Always refer to official documentation for the most up-to-date guidance.

Let's build amazing AI-powered applications together!