git clone https://github.com/itanishqshelar/langchain-ai-agent...

🤖 LangChain AI Agent

Intelligent Conversational AI with Multi-Tool Integration

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📚 Tech Stack & Documentation

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🚀 Features

• Multi-tool Integration: Wikipedia search, web search, file saving, and time utilities
• Conversation Memory: Maintains chat history for contextual responses
• Extensible Architecture: Easy to add new tools and customize behavior
• Error Handling: Robust error handling and graceful degradation
• Configuration Management: Environment-based configuration
• Clean CLI Interface: User-friendly command-line interface

📋 Prerequisites

• Python 3.8+
• Google API Key (for Gemini models) or OpenAI API Key

🛠️ Installation

Clone the repository

git clone https://github.com/itanishqshelar/langchain-ai-agent.git
cd langchain-ai-agent

Create a virtual environment

python -m venv venv

# Windows
.\venv\Scripts\activate

# macOS/Linux
source venv/bin/activate

Install dependencies

pip install -r requirements.txt

Set up environment variables

Create a .env file in the project root:

# Required: At least one API key
GOOGLE_API_KEY=your_google_api_key_here
# OPENAI_API_KEY=your_openai_api_key_here

# Optional: Model Configuration
DEFAULT_MODEL=gemini-2.0-flash-exp
TEMPERATURE=0.7
MAX_TOKENS=2048

# Optional: Agent Settings
MAX_ITERATIONS=5
VERBOSE=True
MAX_HISTORY_LENGTH=10

# Optional: Storage
OUTPUT_DIR=outputs

🎯 Usage

Basic Usage

Run the chatbot:

python main.py

Available Commands

While chatting:

• Ask questions: Just type your question naturally
• clear: Clear chat history
• history: View conversation history
• quit or exit: Exit the chatbot

Example Interactions

You: What is LangChain?
AI: [Searches Wikipedia and provides detailed explanation]

You: Search for the latest AI news
AI: [Uses web search to find current information]

You: Save that information to a file
AI: [Saves the previous response to a timestamped file]

You: What time is it?
AI: [Returns current date and time]

🏗️ Project Structure

langchain-ai-agent/
├── main.py              # Main chatbot application
├── tools.py             # Tool definitions and implementations
├── config.py            # Configuration settings
├── requirements.txt     # Python dependencies
├── .env                 # Environment variables (create this)
├── .gitignore          # Git ignore rules
├── README.md           # This file
└── outputs/            # Generated output files (auto-created)

🔧 Customization

Adding New Tools

Define your tool function in tools.py:

def my_custom_tool(input: str) -> str:
"""Your tool implementation"""
return result

Create a Tool instance:

custom_tool = Tool(
name="MyTool",
func=my_custom_tool,
description="Description of what your tool does"
)

Add to the tools list:

all_tools = [wiki_tool, search_tool, save_tool, time_tool, custom_tool]

Changing the LLM Model

Edit .env:

# For Google Gemini
DEFAULT_MODEL=gemini-2.0-flash-exp

# For OpenAI (requires OPENAI_API_KEY)
# DEFAULT_MODEL=gpt-4-turbo-preview

Update main.py to use OpenAI:

from langchain_openai import ChatOpenAI

# In ChatBot.__init__:
self.llm = ChatOpenAI(
model=model_name,
temperature=temperature
)

Adjusting Response Style

Modify the system prompt in main.py:

prompt = ChatPromptTemplate.from_messages([
(
"system",
"""Your custom system prompt here..."""
),
# ... rest of the template
])

📚 Available Tools

Tool	Description	Input Example
Wikipedia	Search Wikipedia for detailed information	“Python programming language”
WebSearch	Search the web for current information	“latest AI developments 2024”
SaveToFile	Save content to a text file	“content to save”
CurrentTime	Get current date and time	(no input needed)

🧪 Testing

Run a quick test:

python -c "from main import ChatBot; bot = ChatBot(); print(bot.chat('Hello!'))"

🔒 Security Best Practices

• Never commit .env files to version control
• Rotate API keys regularly
• Use environment variables for all sensitive data
• Limit tool permissions based on your use case
• Monitor API usage to avoid unexpected costs

📝 Configuration Options

Edit .env to customize:

Variable	Default	Description
GOOGLE_API_KEY	-	Google Gemini API key (required)
OPENAI_API_KEY	-	OpenAI API key (optional)
DEFAULT_MODEL	gemini-2.0-flash-exp	LLM model to use
TEMPERATURE	0.7	Response creativity (0.0-1.0)
MAX_TOKENS	2048	Maximum response length
MAX_ITERATIONS	5	Max tool calling iterations
VERBOSE	True	Show detailed agent logs
MAX_HISTORY_LENGTH	10	Chat history size
OUTPUT_DIR	outputs	Directory for saved files

🤝 Contributing

Contributions are welcome! Please:

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

📄 License

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

🆘 Troubleshooting

“No API key found”

• Ensure .env file exists and contains valid API keys
• Check that python-dotenv is installed

“Module not found” errors

• Run pip install -r requirements.txt
• Ensure virtual environment is activated

Tool execution errors

• Check internet connection (required for Wikipedia and web search)
• Verify API rate limits haven’t been exceeded

Import errors

• Ensure all dependencies are up to date: pip install --upgrade -r requirements.txt

🔗 Resources

• LangChain Documentation
• Google Gemini API
• OpenAI API

📧 Support

For issues and questions:

• Open an issue on GitHub
• Check existing issues for solutions

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Made with ❤️ using LangChain