https://github.com/ricard1406/little_agent_chatbot
Simple little agent RAG chatbot based on Ollama, qwen3, Langchain, Gradio.
https://github.com/ricard1406/little_agent_chatbot
agent chatbot gradio granite langchain llama3 llm ollama python qwen rag simple
Last synced: 2 months ago
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Simple little agent RAG chatbot based on Ollama, qwen3, Langchain, Gradio.
- Host: GitHub
- URL: https://github.com/ricard1406/little_agent_chatbot
- Owner: ricard1406
- License: mit
- Created: 2025-07-10T19:01:57.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-29T19:55:27.000Z (2 months ago)
- Last Synced: 2025-07-29T22:07:16.534Z (2 months ago)
- Topics: agent, chatbot, gradio, granite, langchain, llama3, llm, ollama, python, qwen, rag, simple
- Language: Python
- Homepage: https://ricard1406.github.io/Little_Agent_Chatbot/
- Size: 6.25 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🤖 Little Agent Chatbot 🤖
- A lightweight local AI agent chatbot powered by Ollama and Langchain with RAG capabilities.
- LLM: qwen3:1.7b; qwen3:4b; granite3.3:2b; llama3.2:3b
- Tested on low-budget hardware 8GB RAM.
## 🌟 Overview
Little Agent Chatbot is a simple yet powerful local AI assistant that runs entirely on your machine. Built for learning and experimentation, it combines the power of open-source LLMs with advanced retrieval-augmented generation (RAG) to create an intelligent chatbot that can work with your personal documents and provide real-time information.
## ✨ Key Features
- **🏠 Fully Local**: Runs completely on your machine - no data leaves your device
- **💰 Budget-Friendly**: Works on low-resource hardware using efficient models.
- **📚 RAG Integration**: Upload and chat with your PDF documents using ChromaDB and Nomic embeddings
- **🌐 Real-Time Data**: Get live weather information and perform calculations
- **🔧 Agent Framework**: Extensible agent system built with Langchain
- **💻 Easy Interface**: Clean web interface powered by Gradio
- **💻 Interface Options**: Graphic web interface or classic text interface.
- **🔓 Open Source**: MIT licensed and fully customizable
## 🚀 Tech Stack
- **LLM Backend**: [Ollama](https://ollama.ai/) with Qwen3 model (1.7B or 4B variants)
- **Agent Framework**: [Langchain](https://python.langchain.com/) ecosystem
- **Vector Database**: [ChromaDB](https://www.trychroma.com/) for document embeddings
- **Embeddings**: Nomic-embed-text for semantic search
- **Document Processing**: PyPDF, Unstructured, TikToken
- **Web Interface**: [Gradio](https://gradio.app/)
- **Language**: Python
- **License**: MIT
## 🎯 What Makes This Special?
- **Privacy First**: Your conversations and documents stay on your device
- **Cost Effective**: No API costs - runs on your hardware
- **Educational**: Perfect for learning about AI agents and RAG systems
- **Extensible**: Easy to modify and add new capabilities
- **Lightweight**: Designed to work on modest hardware setups
## 🛠️ Installation
### Prerequisites
- Python 3.8+
- [Ollama](https://ollama.ai/) installed on your system
### Setup Steps
1. **Clone the repository**
```bash
git clone ricard1406/Little_Agent_Chatbot
cd Little_Agent_Chatbot
(note: 'data' folder is required for RAG testing)
```
2. **Create and activate virtual environment**
```bash
python3 -m venv .venv
source .venv/bin/activate
```
3. **Install Python dependencies**
```bash
pip install langchain langchain-community langchain-core langchain-ollama chromadb sentence-transformers pypdf python-dotenv unstructured[pdf] tiktoken gradio
```
4. **Install Ollama models**
```bash
# Choose one of these Qwen3 models based on your hardware:
ollama pull qwen3:4b # or LLM your choice
# Install embedding model for RAG functionality
ollama pull nomic-embed-text
```
5. **Run the application**
```bash
python3 Little_Agent_Chatbot [graph|text]
```
6. **Open your browser** and navigate to the provided local URL
## 📖 Usage
### Basic Chat
Simply type your questions and the AI will respond using the local Qwen3 model.
### Document Upload
Upload PDF documents to enable RAG functionality. The chatbot will be able to answer questions based on your documents.
### Agent Capabilities
- **Weather Information**: Get real-time weather data
- **Calculations**: Perform mathematical operations
- **Document Q&A**: Query your uploaded PDFs
## 🏗️ Architecture
```
User Input → Gradio Interface → Langchain Agent → Ollama/Qwen3 → Response
↓
RAG System (PDF Documents)
↓
External Tools (Weather, Calculator)
```
## 🔧 Configuration
Customize the chatbot by modifying:
- Model parameters in `config.py`
- Agent tools and capabilities
- UI appearance and behavior
- RAG document processing settings
## 🤝 Contributing
Contributions are welcome! This project is designed for learning and experimentation. Feel free to:
- Add new agent capabilities
- Improve the UI/UX
- Enhance document processing
- Add new LLM models
- Fix bugs and improve performance
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🎓 Educational Purpose
This project is perfect for:
- Learning about AI agents and RAG systems
- Understanding local LLM deployment
- Experimenting with Langchain framework
- Building privacy-focused AI applications
- Exploring document-based AI interactions
## 🔮 Future Enhancements
- Support for more document formats
- Additional agent tools and capabilities
- Multi-language support
- Voice interface integration
- Mobile-friendly interface
## 📞 Support
If you encounter issues or have questions:
- Open an issue on GitHub
- Check the documentation
- Review the example configurations
## 🌟 Star the Project
If you find this project helpful, please give it a star! It helps others discover the project and motivates continued development.
---
**Made with ❤️ for the AI community**
*Happy chatting with your local AI agent!* 🚀