📁 Project Structure

📦 Soojh-AI/
├── 🤖 chatbot.py          # Interactive chat interface
├── 📊 graph.py            # LangGraph workflow definition  
├── 🎯 node.py             # Graph nodes and state management
├── 🛠️ tools.py            # External research tools
├── 🧠 llm.py              # LLM configurations and models
├── ⚙️ agent.py            # Google ADK agent setup
├── 📄 README.md           # This file
├── 🔐 .env.example        # Environment variable template
├── 📋 requirements.txt    # Python dependencies
└── 🚫 .gitignore          # Git ignore rules

🚀 Quick Start

Prerequisites

• Python 3.12+
• OpenAI API key
• Git

Installation

1. Clone the repository

   git clone https://github.com/AMRENDRASINGH-COM/Soojh-ai-git.git
   cd Soojh-ai-git
   

2. Create virtual environment

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # Linux/macOS
   source venv/bin/activate
   

3. Install dependencies

   pip install -r requirements.txt
   

4. Configure environment

   cp .env.example .env
   # Edit .env file and add your OpenAI API key:
   # OPENAI_API_KEY=your_openai_api_key_here
   

5. Run the application

   # Interactive chatbot
   python chatbot.py
   
   # Or import as module
   from graph import build_graph
   graph = build_graph()
   

💻 Usage Examples

Basic Chat Interaction

from chatbot import chat_with_bot

# Simple conversation
response = chat_with_bot("What is machine learning?")
print(response)

# Research query
response = chat_with_bot("Find recent papers on transformer architecture")
print(response)

Advanced Graph Usage

from graph import build_graph

# Build the LangGraph workflow
graph = build_graph()

# Stream responses for complex queries
events = graph.stream({
    "messages": [("user", "Explain quantum computing with recent research")]
}, stream_mode="values")

for event in events:
    print(event["messages"][-1].content)

Multi-Agent System

from agent import root_agent
from google.adk.apps import AdkApp

# Create ADK application
app = AdkApp(agent=root_agent, enable_tracing=True)

# Start interactive session
session = app.create_session(user_id="user123")

# Query the multi-agent system
for event in app.stream_query(
    user_id="user123",
    session_id=session.id,
    message="Research the latest developments in AI safety"
):
    print(event)

🛠️ Core Components

🎯 Graph Nodes (node.py)

• State Management: TypedDict-based state handling
• LLM Integration: GPT-4o with tool binding
• Message Processing: Conversation flow management

📊 Workflow Graph (graph.py)

• StateGraph: LangGraph-based workflow orchestration
• Tool Integration: Conditional tool usage based on queries
• Flow Control: START → Chatbot → Tools → END

🛠️ Research Tools (tools.py)

• Wikipedia Integration: Real-time knowledge retrieval
• Arxiv Access: Academic paper search and analysis
• Configurable Limits: Response length and result count control

🤖 Multi-Agent System (agent.py)

• Google ADK Integration: Enterprise agent framework
• Sub-Agent Coordination: Specialized agent delegation
• LangGraph Bridge: Seamless integration between systems

🎯 Use Cases

🔬 Research Assistant

• Academic paper analysis and summarization
• Cross-reference verification with Wikipedia
• Literature review automation
• Citation and reference management

💬 Intelligent Chatbot

• Context-aware conversations
• Multi-turn dialogue handling
• Domain-specific knowledge queries
• Real-time information retrieval

🏢 Enterprise Applications

• Customer support automation
• Internal knowledge base queries
• Research and development assistance
• Decision support systems

🔧 Configuration

Environment Variables

# Required
OPENAI_API_KEY=your_openai_api_key_here

# Optional
LANGCHAIN_TRACING_V2=true
LANGCHAIN_API_KEY=your_langchain_api_key
LANGCHAIN_PROJECT=soojh-ai

Model Configuration

# In llm.py
llm = ChatOpenAI(
    openai_api_key=OPENAI_API_KEY,
    model="gpt-4o",           # Or gpt-4, gpt-3.5-turbo
    temperature=0.1,          # Adjust creativity
    max_tokens=2000          # Response length limit
)

📚 Dependencies

Core Libraries

• langchain-openai: OpenAI integration
• langchain-community: Community tools and utilities
• langgraph: Graph-based workflow orchestration
• google-adk: Google Agent Development Kit
• python-dotenv: Environment variable management

Research Tools

• wikipedia: Wikipedia API wrapper
• arxiv: Academic paper access
• pydantic: Data validation and settings

🚀 Advanced Features

Custom Agent Creation

from google.adk.agents import Agent
from google.adk.models.lite_llm import LiteLlm

# Create specialized agent
research_agent = Agent(
    model=LiteLlm(model="openai/gpt-4o"),
    name="research_specialist",
    description="Expert in academic research and analysis",
    instruction="Provide detailed, well-sourced research responses",
    tools=[wikipedia_tool, arxiv_tool]
)

Custom Tool Integration

from langchain.tools import BaseTool

class CustomTool(BaseTool):
    name = "custom_research_tool"
    description = "Specialized tool for domain-specific research"
    
    def _run(self, query: str) -> str:
        # Your custom tool implementation
        return f"Custom research result for: {query}"

# Add to tools list
tools.append(CustomTool())

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit your changes: git commit -m 'Add amazing feature'
4. Push to the branch: git push origin feature/amazing-feature
5. Open a Pull Request

Development Setup

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
pytest tests/

# Code formatting
black .
isort .

# Type checking
mypy .

📄 License

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

🙏 Acknowledgments

• LangChain Team: For the excellent LangGraph framework
• Google Cloud: For the Agent Development Kit
• OpenAI: For the powerful GPT models
• Arxiv & Wikipedia: For open access to knowledge

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Project Wiki

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Made with ❤️ by Amrendra Singh

Soojh AI - Where Intelligence Meets Innovation 🚀 "@ > README.md

This comprehensive README includes:
- Professional branding with badges
- Clear architecture diagram  
- Complete installation guide
- Usage examples for all components
- Detailed project structure
- Advanced configuration options
- Contributing guidelines
- Professional formatting

Copy and paste this command to create your README.md file![1][2][3][4][5]