Deep Research AI System

comprehensive research, data analysis, and report generation. The system leverages modern AI technologies, including LangChain and Groq LLM, Agentic AI, to provide users with in-depth analysis on a wide range of topics, with particular strength in financial and market research. An advanced AI-powered research system that leverages multiple specialized agents to perform comprehensive research, data analysis, and report generation.


Features

Research Capabilities

• Multi-Agent Architecture: Specialized agents for general queries, deep research, and report generation
• Web Research: Comprehensive web crawling using Tavily API with configurable depth (5-20 sources)
• Financial Analysis:
  • Real-time stock and cryptocurrency data analysis
  • Price trend visualization
  • Comparative performance analysis
  • Technical indicators and market insights

User Interface

• Dark/Light Mode:
  • Customizable theme preference
  • Persistent theme settings
  • Modern, responsive design
• Search History:
  • Track and manage previous queries
  • One-click reuse of past searches
  • Search through history
  • Clear history option

Data Visualization

• Dynamic Charts:
  • Stock price trends
  • Cryptocurrency performance
  • Multi-asset comparisons
  • Technical analysis indicators
• Interactive Graphs:
  • Zoom and pan capabilities
  • Tooltips with detailed information
  • Responsive design for all screen sizes

Export Options

• PDF Reports:
  • Professional formatting
  • Embedded charts and visualizations
  • Source citations
  • Unique filenames for easy tracking
• Word Documents:
  • Editable format
  • Complete research findings
  • Charts and tables included

Tech Stack

Frontend

• React 18+
• Bootstrap 5
• Context API for state management
• Responsive design components

Backend

• FastAPI
• LangChain with Groq LLM
• Matplotlib for visualizations
• ReportLab and python-docx for document generation

Installation

Prerequisites

1. Python Environment:

   • Python 3.10 or higher
   • pip package manager

2. Node.js Environment:

   • Node.js 14 or higher
   • npm package manager

3. API Keys:

   • Groq API key (for LLM)
   • Tavily API key (for web search)

Backend Setup

1. Clone the repository:

git clone https://github.com/Gh-Novel/Deep_research-.git
cd Deep-research

2. Create and activate a virtual environment (Windows):

python -m venv venv
.\venv\Scripts\activate

3. Install backend dependencies:

cd backend
pip install -r requirements.txt

4. Create .env file in the backend directory:

GROQ_API_KEY=your_groq_api_key
TAVILY_API_KEY=your_tavily_api_key

Frontend Setup

1. Install frontend dependencies:

cd ../frontend
npm install

2. Create .env file in the frontend directory:

REACT_APP_API_URL=http://localhost:8000

Running the Application

Start Backend Server (Windows)

1. Navigate to backend directory:

cd backend

2. Run the FastAPI server:

python app.py

The backend will be available at http://localhost:8000

Start Frontend Development Server

1. Navigate to frontend directory:

cd frontend

2. Start the development server:

npm start

The application will open automatically at http://localhost:3000

Usage Guide

Basic Research

1. Enter your query in the search box
2. Select research type:
   • Normal: Quick analysis with 5 sources
   • Deep Research: Comprehensive analysis with up to 20 sources
3. Click "Start Research"

Financial Analysis

1. Enter stock symbols or cryptocurrency names
2. System will automatically:
   • Generate price charts
   • Compare performance
   • Analyze trends
   • Provide market insights

Managing Results

1. View generated charts and analysis
2. Access source citations
3. Export options:
   • Generate PDF report
   • Export to Word document
4. Save to search history

Using Search History

1. Access previous searches from history panel
2. Click on any history item to rerun the query
3. Search through history for specific queries
4. Clear individual items or entire history

Development

Project Structure

deep-research/
├── backend/
│   ├── agents/           # AI agents implementation
│   │   └── app.py           # Main FastAPI application
│   ├── routers/          # API endpoints
│   ├── utils/            # Helper functions
│   └── README.md
├── frontend/
│   ├── src/
│   │   ├── components/  # React components
│   │   ├── contexts/    # React contexts
│   │   ├── services/    # API services
│   │   └── assets/      # Styles and images
│   └── public/          # Static files
└── README.md

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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

Acknowledgments

• LangChain - AI framework
• Groq - LLM API provider
• Tavily - Web search API
• FastAPI - Backend framework
• React - Frontend framework