https://github.com/awrsha/llm-rag-research
An integrated AI suite combining intelligent PDF analysis, automated research capabilities, and multi-agent academic paper generation, powered by both cloud and local language models to streamline research and document processing workflows.
https://github.com/awrsha/llm-rag-research
crewai docker flask groq langchain llm multi-agent open-webui rag tinyllama
Last synced: about 1 month ago
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An integrated AI suite combining intelligent PDF analysis, automated research capabilities, and multi-agent academic paper generation, powered by both cloud and local language models to streamline research and document processing workflows.
- Host: GitHub
- URL: https://github.com/awrsha/llm-rag-research
- Owner: Awrsha
- License: mit
- Created: 2025-01-04T19:41:10.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-04T19:55:32.000Z (over 1 year ago)
- Last Synced: 2025-01-04T20:38:24.960Z (over 1 year ago)
- Topics: crewai, docker, flask, groq, langchain, llm, multi-agent, open-webui, rag, tinyllama
- Language: HTML
- Homepage:
- Size: 0 Bytes
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🤖 AI Research & Analysis Suite
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
*A comprehensive suite of AI-powered research and analysis tools*
[📚 Documentation](#documentation) •
[🚀 Quick Start](#quick-start) •
[🛠️ Components](#components) •
[💻 Installation](#installation) •
[📘 Usage](#usage)
## 🎯 Suite Components
### 1. [📑 AI PDF Assistant](./pdf-assistant)
- Intelligent PDF document analysis
- Question-answering system
- Secure document handling
### 2. [🔬 AI Research Assistant](./research-assistant)
- Automated framework research
- Web-based information gathering
- Comprehensive report generation
### 3. [🤖 Autonomous Research Framework](./autonomous-framework)
- Multi-agent research system
- Academic paper generation
- Dual model support (Groq/TinyLlama)
## 🚀 Quick Start
```bash
# Create virtual environment
python -m venv venv
# Activate virtual environment
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows
# Install core dependencies
pip install -r requirements.txt
```
## 📦 Core Requirements
```text
flask>=2.0.0
groq>=0.9.0
langchain>=0.1.0
langchain-groq>=0.1.0
python-dotenv>=1.0.0
markdown2>=2.4.0
torch>=2.0.0
transformers>=4.35.0
gradio>=3.50.0
```
## ⚙️ Global Configuration
Create a `.env` file in the root directory:
```env
GROQ_API_KEY=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
```
## 🔧 System Requirements
- Python 3.8+
- 8GB+ RAM
- 10GB+ Disk Space
- NVIDIA GPU (optional)
- Docker (for TinyLlama)
- Internet Connection
## 🔐 Security Features
- API key management
- Secure file handling
- Rate limiting
- Input validation
- Temporary storage management
## 🤝 Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## 📘 Documentation
Detailed documentation for each component:
- [PDF Assistant Documentation](./pdf-assistant/README.md)
- [Research Assistant Documentation](./research-assistant/README.md)
- [Autonomous Framework Documentation](./autonomous-framework/README.md)
## 📊 Feature Comparison
| Feature | PDF Assistant | Research Assistant | Autonomous Framework |
|---------|--------------|-------------------|---------------------|
| Input | PDF Documents | Research Topics | Multiple Sources |
| Output | Q&A Responses | Research Reports | Academic Papers |
| Model | Groq | Groq | Groq/TinyLlama |
| Interface | Web UI | Web UI | Open WebUI |
| Agents | Single | Single | Multi-Agent |
## 🌟 Use Cases
- 📚 Academic Research
- 📊 Market Analysis
- 📝 Document Processing
- 🔍 Literature Review
- 📈 Trend Analysis
- 🎓 Educational Support
## 📜 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- [Groq](https://groq.com) for LLM services
- [LangChain](https://langchain.com) for the framework
- [CrewAI](https://github.com/crewai) for multi-agent capabilities
- [TinyLlama](https://github.com/tinyllama) for local model support
- [Flask](https://flask.palletsprojects.com/) for web framework
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