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https://github.com/hmshb/langchain-google-gemini-integration
This repo contains multiple examples of LangChain with Google Gemini LLM
https://github.com/hmshb/langchain-google-gemini-integration
ai google-gemini google-gemini-ai langchain large-language-models machine-learning machine-learning-models multimodel-large-language-model programming python
Last synced: 2 days ago
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This repo contains multiple examples of LangChain with Google Gemini LLM
- Host: GitHub
- URL: https://github.com/hmshb/langchain-google-gemini-integration
- Owner: hmshb
- Created: 2024-12-06T07:30:37.000Z (18 days ago)
- Default Branch: main
- Last Pushed: 2024-12-06T10:36:19.000Z (18 days ago)
- Last Synced: 2024-12-06T11:26:11.091Z (18 days ago)
- Topics: ai, google-gemini, google-gemini-ai, langchain, large-language-models, machine-learning, machine-learning-models, multimodel-large-language-model, programming, python
- Language: Python
- Homepage: https://github.com/hmshb/langchain-google-gemini-integration
- Size: 249 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# LangChain + Google Gemini AI Model Integration Example
This repository demonstrates how to integrate [LangChain](https://github.com/hwchase17/langchain) with [Google Gemini](https://ai.google/) 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.
---
## 🛠️ Setup Instructions
Follow these steps to set up and run the project on your local machine:
### 1. Clone the Repository
```bash
git clone https://github.com/hmshb/langchain-google-gemini-integration.git
cd langchain-google-gemini-integration
```
### 2. Create a Virtual Environment
```bash
python -m venv venv
source venv/bin/activate # For Linux/Mac
.\venv\Scripts\activate # For Windows
```
### 3. Install Dependencies
```bash
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
```bash
python index.py
```
---
## 📂 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
```
---
## ⭐ Acknowledgments
Special thanks to:
- **[LangChain](https://github.com/hwchase17/langchain)** for providing a robust framework for building LLM applications.
- **[Google Gemini](https://ai.google/)** for their powerful multimodal AI capabilities.
---
## 📜 License
This project is open-source and licensed under the [MIT License](LICENSE).
---
## 📢 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!