This Project is made of Langcahin can be useful in using different models and differnt tyoes of chat bot's as well

Agentic AI Chatbot with Groq and Tavily

This project demonstrates how to build agentic AI applications starting from a simple Python foundation. The chatbot is powered by Groq open-source models and extended with the Tavily API for web-enabled search and real-time data fetching.

It includes:

• A basic chatbot directly connected to an LLM
• A web-enabled chatbot that fetches and summarizes real-world information
• An AI news summarization tool with daily, weekly, and monthly updates

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🚀 Features

1. Basic Chatbot

Uses Groq LLMs to answer simple queries.
Example: A basic question on machine learning.

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2. Web-Enabled Chatbot

Integrates the Tavily API to perform live searches.
Tested by searching my own name, showcasing real-time data retrieval.

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3. AI News Summarization

Automatically generates summaries of AI news with daily, weekly, and monthly frequencies.

• Daily AI News

• Weekly AI News

• Monthly AI News

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🛠️ Tech Stack

• Python
• LangGraph
• Groq LLMs
• Tavily API for real-time search
• Streamlit for UI

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📂 Project Structure

├── app.py # Main Streamlit application ├── requirements.txt # Project dependencies ├── utils/ # Utility functions │ ├── chatbot.py # LLM chatbot logic │ ├── web_agent.py # Web-enabled chatbot with Tavily │ └── news_agent.py # AI news summarization └── README.md # Project documentation

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⚙️ Setup Instructions

1. Clone the repository:

   git clone https://github.com/<your-username>/agentic-ai-chatbot.git
   cd agentic-ai-chatbot
   

2.install dependencides:

pip install -r requirements.txt

3.Add api_keys:

export GROQ_API_KEY="your_groq_api_key"

export TAVILY_API_KEY="your_tavily_api_key"

4.Run the Application:

streamlit run app.py

🙌 Acknowledgements

Groq for open-source LLMs

Tavily for real-time search APIs

LangChain / LangGraph for building agentic AI workflows

Special thanks to Krish Naik for mentorship and guidance