https://github.com/muhammadadilnaeem/on-device-ai-rag-using-objectbox-vector-database-and-langchain
Welcome to the On-Device AI RAG project! This repository demonstrates how to utilize the ObjectBox Vector Database and LangChain to build a robust Retrieval-Augmented Generation (RAG) system directly on your device.
https://github.com/muhammadadilnaeem/on-device-ai-rag-using-objectbox-vector-database-and-langchain
groq-api langchain langchain-groq openai-api rag streamlit
Last synced: 7 months ago
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Welcome to the On-Device AI RAG project! This repository demonstrates how to utilize the ObjectBox Vector Database and LangChain to build a robust Retrieval-Augmented Generation (RAG) system directly on your device.
- Host: GitHub
- URL: https://github.com/muhammadadilnaeem/on-device-ai-rag-using-objectbox-vector-database-and-langchain
- Owner: muhammadadilnaeem
- License: mit
- Created: 2024-07-26T04:12:53.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-26T04:35:30.000Z (about 1 year ago)
- Last Synced: 2025-01-17T15:52:51.271Z (9 months ago)
- Topics: groq-api, langchain, langchain-groq, openai-api, rag, streamlit
- Language: Python
- Homepage:
- Size: 3.04 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
---
# **On-Device AI RAG using ObjectBox Vector Database and LangChain 📱🤖**
Welcome to the **On-Device AI RAG** project! This repository demonstrates how to utilize the **ObjectBox Vector Database** and **LangChain** to build a robust Retrieval-Augmented Generation (RAG) system directly on your device.
## **🚀 Features**
- **On-Device Processing**: No need for constant internet access.
- **Efficient Data Retrieval**: Fast and reliable vector search with ObjectBox.
- **Powerful Generation**: Leverage LangChain for sophisticated text generation.
https://github.com/user-attachments/assets/e231a732-51a8-42cb-b3f1-ca82dec21c44
## **📚 Overview**
This project combines the strengths of ObjectBox and LangChain to provide a seamless on-device AI experience. It is designed to:
1. **Ingest Data**: Easily add and store data in the ObjectBox vector database.
2. **Search and Retrieve**: Quickly find relevant information using vector search.
3. **Generate Responses**: Use LangChain to create meaningful responses based on retrieved data.
## **🛠️ Installation**
Follow these steps to get started:
1. **Clone the repository**:
```bash
git clone https://github.com/muhammadadilnaeem/On-Device-AI-RAG-using-ObjectBox-Vector-Database-and-LangChain.git
cd On-Device-AI-RAG-using-ObjectBox-Vector-Database-and-LangChain
```
2. **Install dependencies**:
```bash
pip install -r requirements.txt
```
3. **Run the application**:
```bash
streamlit run app.py
```
## **📈 How This Can Be Improved**
- **User Interface**: Enhance the UI to make it more intuitive and user-friendly.
- **Customization**: Allow users to upload their own datasets for personalized experiences.
- **Optimization**: Improve the efficiency of data processing and retrieval.
## **💡 Potential Uses**
This project can be a foundation for various applications:
- **Personal Assistants**: Create an on-device AI assistant that works offline.
- **Educational Tools**: Build tools that provide instant information and explanations.
- **Business Solutions**: Develop systems for quick data access and decision support.
## **🌟 How It Helps Common People**
By enabling powerful AI functionalities directly on their devices, users can:
- **Access Information Anywhere**: No need to rely on internet connectivity.
- **Ensure Privacy**: Keep their data and interactions private and secure.
- **Enjoy Faster Responses**: Benefit from the speed of on-device processing.
## **🤝 Contributing**
Contributions are welcome! Feel free to open issues or submit pull requests.
## **📄 License**
This project is licensed under the MIT License. See the [LICENSE](https://github.com/muhammadadilnaeem/On-Device-AI-RAG-using-ObjectBox-Vector-Database-and-LangChain/blob/main/LICENSE) file for details.
---