LangChain + Google Gemini AI Model Integration Example

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

🚀 Features

• Seamless integration: Connect LangChain with Google Gemini for text and multimodal tasks.
• Beginner-friendly: Simple setup to get started quickly.
• Extensible: Encourage contributions and customizations for advanced use cases.

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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-google-gemini-integration.git
cd langchain-google-gemini-integration

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-google-gemini-integration/
├── .env                                     # API key configuration file
├── gemini.py                                # Gemini AI LLM Model configurations file
├── index.py                                 # Simple script that translate word from English to Italian
├── prompt_template_single_variable.py       # Example of single variable usage in Prompt Template
├── prompt_template_multiple_variable.py     # Example of multiple variable usage in Prompt Template
├── prompt_template_conditional_prompts.py   # Example of conditional variable usage in Prompt Template
├── requirements.txt                         # Optional: Use for dependencies
├── README.md                                # Documentation file
├── venv/                                    # Virtual environment

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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.

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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.

Let's build amazing AI-powered applications together!