https://github.com/rahulsamant37/agentic_rag_crewai
A powerful Retrieval-Augmented Generation (RAG) system built with CrewAI that intelligently searches through documents and falls back to web search when needed. Features local LLM support with deep-seek-r1 or llama 3.2!
https://github.com/rahulsamant37/agentic_rag_crewai
agentic-rag crewai deepseek firecrawl llama3 retrieval-augmented-generation serperapi uv
Last synced: about 1 month ago
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A powerful Retrieval-Augmented Generation (RAG) system built with CrewAI that intelligently searches through documents and falls back to web search when needed. Features local LLM support with deep-seek-r1 or llama 3.2!
- Host: GitHub
- URL: https://github.com/rahulsamant37/agentic_rag_crewai
- Owner: rahulsamant37
- License: mit
- Created: 2025-05-23T09:07:26.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-05-23T17:46:11.000Z (5 months ago)
- Last Synced: 2025-06-24T13:43:41.612Z (4 months ago)
- Topics: agentic-rag, crewai, deepseek, firecrawl, llama3, retrieval-augmented-generation, serperapi, uv
- Language: Python
- Homepage:
- Size: 564 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🤖 Agentic RAG using CrewAI
A powerful Retrieval-Augmented Generation (RAG) system built with CrewAI that intelligently searches through documents and falls back to web search when needed. Features local LLM support with deep-seek-r1 or llama 3.2!
## 🌟 Features
- 📚 Document-based search with RAG capabilities
- 🌐 Automatic fallback to web search
- 🤖 Local LLM support (deep-seek-r1 or llama 3.2)
- 🔄 Seamless integration with CrewAI
- 💨 Fast and efficient document processing
- 🎯 Precise answer synthesis
## 🔄 System Flow
Below is the detailed flow diagram of how the system processes queries and generates responses:
```mermaid
graph TD
A[Start] --> B[Initialize Streamlit App]
B --> C[Load LLM Model]
C --> D[Initialize Session State]
D --> E{PDF Uploaded?}
E -->|Yes| F[Create DocumentSearchTool]
E -->|No| G[Wait for PDF Upload]
F --> H[Index PDF Document]
H --> I[Create Crew]
I --> J[Create Retriever Agent]
I --> K[Create Response Synthesizer Agent]
J --> L[Add Tools to Retriever Agent]
L --> L1[PDF Search Tool]
L --> L2[Web Search Tool]
K --> M[Configure Response Agent]
J & K --> N[Create Tasks]
N --> N1[Retrieval Task]
N --> N2[Response Task]
N --> O[User Enters Query]
O --> P[Process Query]
P --> Q[Show User Message]
Q --> R[Crew Kickoff]
R --> S[Sequential Processing]
S --> T1[Retriever Agent Searches]
T1 --> T2[Response Agent Synthesizes]
T2 --> U[Stream Response]
U --> V[Update Chat History]
V --> W[Wait for Next Query]
W --> O
```
## 🚀 Prerequisites
Before running the application, ensure you have:
1. **API Keys**:
- FireCrawl API or SEPER API key for web search capabilities
- LLM API key (if required for your chosen model)
2. **Python Environment**:
- Python 3.11 or later
- Conda (recommended for environment management)
## 💻 Installation
1. **Create and Activate Environment**:
```bash
conda create -n env_crewai python==3.12 -y
conda activate env_crewai
```
2. **Install Dependencies**:
```bash
# Install package management tools
uv lock
uv sync
# Install required packages
pip install crewai crewai-tools markitdown qdrant-client fastembed
```
## 🎮 Running the Application
Choose your preferred LLM model:
- **For deep-seek-r1**:
```bash
streamlit run app_deep_seek.py
```
- **For llama 3.2**:
```bash
streamlit run app_llama3.2.py
```
## 🛠️ System Architecture
The system consists of two main agents:
1. **Retriever Agent**:
- Handles document searching
- Manages web search fallback
- Uses both PDF and web search tools
2. **Response Synthesizer Agent**:
- Processes retrieved information
- Generates coherent responses
- Ensures context relevance
## 📚 Usage Examples
1. **Document Search**:
- Upload your PDF document
- Enter your query
- Receive contextual answers from the document
2. **Web Search Fallback**:
- System automatically detects when document search isn't sufficient
- Seamlessly switches to web search
- Combines information from multiple sources
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- [CrewAI](https://github.com/joaomdmoura/crewai) for the amazing framework
- The open-source community for various tools and libraries used in this project
---
Made with ❤️ by Rahul Samant