📦 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

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)

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)

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)

# 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

# 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
)

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]
)

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())

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

# Run tests
pytest tests/

# Code formatting
black .
isort .

# Type checking
mypy .

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]