https://github.com/huangy22/deep_research_tutorial
https://github.com/huangy22/deep_research_tutorial
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/huangy22/deep_research_tutorial
- Owner: huangy22
- Created: 2025-06-26T06:33:45.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-27T06:05:45.000Z (3 months ago)
- Last Synced: 2025-06-27T06:28:49.861Z (3 months ago)
- Language: Python
- Size: 809 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Deep Research Tutorial
A comprehensive AI-powered chemistry research system that combines database querying, literature analysis, and automated report generation. This project demonstrates how to build a multi-agent system for scientific research using Google's Agent Development Kit (ADK).
## 🚀 Features
### Core Capabilities
- **Multi-Agent Architecture**: Hierarchical agent system with specialized sub-agents
- **Database Integration**: SQLite-based local database with CSV data loading
- **Literature Analysis**: Automated paper reading and content extraction
- **Report Generation**: AI-powered scientific report creation
- **RESTful API**: Flask-based API for database operations
### Agent System
- **Chemistry Research Agent**: Main orchestrator agent that coordinates research tasks
- **Database Agent**: Handles structured data queries and fact retrieval
- **Deep Research Agent**: Performs literature reviews and generates reports
- **Paper Agent**: Processes individual research papers
- **Report Agent**: Synthesizes findings into comprehensive reports
## 📁 Project Structure
```
deep_research_tutorial/
├── agent/ # Multi-agent system
│ ├── agent.py # Main chemistry research agent
│ ├── callbacks.py # Agent callback functions
│ ├── llm_config.py # LLM configuration
│ ├── database_agent/ # Database query agent
│ ├── deep_research_agent/ # Literature research agent
│ │ ├── agent.py # Deep research orchestrator
│ │ ├── paper_agent/ # Individual paper processor
│ │ └── report_agent/ # Report generation agent
│ └── tools/ # Agent tools and utilities
├── database_server/ # Database and API services
│ ├── service.py # Flask API and database service
│ ├── polymer.csv # Polymer data
│ ├── paper_metadata.csv # Paper metadata
│ └── paper_text.csv # Paper content data
├── my_data.db # SQLite database file
├── pyproject.toml # Project dependencies
└── README.md # This file
```
## 🛠️ Installation
### Prerequisites
- Python 3.12 or higher
- UV package manager (recommended)
### Setup
1. **Clone the repository**
```bash
git clone
cd deep_research_tutorial
```
2. **Install dependencies**
```bash
uv sync
```
3. **Set up environment variables**
```bash
# Create a .env file with your API keys
echo "OPENAI_API_KEY=your_openai_key_here" > .env
echo "GOOGLE_API_KEY=your_google_key_here" >> .env
```
## 🚀 Quick Start
### 1. Start the Database Server
```bash
python database_server/service.py
```
This will:
- Load CSV data into SQLite database
- Start Flask API server on `http://localhost:5000`
- Make database accessible via REST API
### 2. Use the Agent System in Google ADK Web UI
```bash
uv run adk web
```
## 🤖 Agent Workflow
### 1. User Query Processing
The main chemistry research agent:
- Analyzes user intent
- Creates a step-by-step research plan
- Proposes actions one at a time
### 2. Database Querying
The database agent:
- Searches structured data for facts
- Retrieves relevant paper metadata
- Returns formatted results
### 3. Literature Research
The deep research agent:
- Processes relevant papers in parallel
- Extracts key information
- Synthesizes findings
### 4. Report Generation
The report agent:
- Compiles all findings
- Generates comprehensive reports
- Formats output for user consumption
## 🔍 Example Use Cases
### Chemical Property Queries
```
"What is the melting point of paracetamol?"
"Find solvents for recrystallizing benzoic acid"
"What are the properties of polyimide polymers?"
```
### Literature Research
```
"Recent advancements in asymmetric catalysis for ibuprofen synthesis"
"Biocompatibility studies of PLA polymers"
"Novel methods for polymer characterization"
```
## 🛠️ Configuration
### LLM Configuration
Edit `agent/llm_config.py` to customize:
- Model selection (GPT-4, Claude, etc.)
- API endpoints
- Temperature and other parameters
### Database Configuration
Modify `database_server/service.py` to:
- Change database file path
- Add new data sources
- Customize API endpoints
## 📝 Dependencies
Key dependencies include:
- `google-adk>=1.5.0` - Google Agent Development Kit
- `litellm>=1.73.1` - LLM abstraction layer
- `openai>=1.91.0` - OpenAI API client
- `pandas>=2.3.0` - Data manipulation
- `flask>=3.1.1` - Web framework
- `pydantic>=2.11.7` - Data validation
- `requests>=2.32.4` - HTTP client
## 🤝 Contributing
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request
## 📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
## 🙏 Acknowledgments
- Google Agent Development Kit (ADK) team
- OpenAI for LLM APIs
- The chemistry research community at DP Technology
## 📞 Support
For questions and support:
- Open an issue on GitHub
- Check the documentation
- Review the example usage
---
**Note**: This is a tutorial project demonstrating AI-powered research workflows. For production use, ensure proper security, validation, and error handling.