Open Deep Research LlamaIndex

This project is inspired by Open Deep Research, which uses LangGraph for implementation. Our version leverages LlamaIndex to create a powerful, modular workflow for research and analysis.

Open Deep Research LlamaIndex provides a structured approach to generating comprehensive research reports on any topic using large language models, with a focus on modularity, extensibility, and real-time results.

Features

• Modular Workflow Architecture: Easily extensible with nested workflows
• Automated Research: Generate queries and perform web searches
• Structured Reports: Create well-organized reports with customizable sections
• Performance Monitoring: Track execution time of workflow steps
• Streaming Results: Get real-time updates as the report is generated

Installation

git clone https://github.com/Davy-hou/open_deep_research_llamaIndex.git
cd open_deep_research_llamaindex
pip install -r requirements.txt

Quick Start

go to examples folder and run basic_research.py

Configuration

Create a .env file in your project root (see .env.example for a template):

# API Keys
OPEN_ROUTER_API_KEY=your_openrouter_api_key_here
TAVILY_API_KEY=your_tavily_api_key_here

Project Structure

├── src/
│   └── research/
│       ├── config/       # Configuration management
│       ├── models/       # Data models using Pydantic
│       ├── utils/        # Utility functions and prompts
│       └── workflows/    # Core workflow implementations
├── examples/             # Example usage scripts
├── README.md             # Project documentation
└── LICENSE               # MIT License

Workflow Architecture

The project follows a modular architecture with nested workflows:

1. ResearchWorkflow: Orchestrates the overall report generation process

   • generate_report_plan: Creates the structure of the report
   • generate_sections: Generates content for each section using search results
   • format_final_report: Compiles the final report

2. SearchWorkflow: Handles search operations as a nested workflow

   • generate_queries: Creates search queries based on section topics
   • perform_searches: Executes parallel searches and processes results

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.