🔎 Search Engine Gen AI App Using LangChain Tools and Agents

This project demonstrates a search-powered chatbot application built using Streamlit and LangChain, enabling dynamic interactions with multiple information sources such as Wikipedia, Arxiv, and DuckDuckGo. The app leverages LangChain's Agent functionality, integrating powerful tools to enhance user queries by combining information retrieval and language model capabilities.

Features:

• LangChain Integration: Uses LangChain to connect tools for retrieving information from multiple sources.
• Wikipedia API: Queries Wikipedia for concise and informative responses.
• Arxiv API: Extracts relevant academic papers from Arxiv, providing quick and insightful information on research topics.
• DuckDuckGo Search: Executes web searches to provide more general information from the internet.
• Interactive Chat Interface: Implemented using Streamlit, allowing users to input queries and interact with the chatbot directly.
• Groq API: Utilizes the Groq API for large-scale language models, offering powerful, real-time text generation.

How It Works:

1. Input: The user enters a query in the chat interface.
2. Agent Initialization: The app initializes a LangChain Agent with access to various tools like Arxiv, Wikipedia, and DuckDuckGo search.
3. Information Retrieval: Based on the user's input, the agent dynamically selects the appropriate tools to fetch relevant data.
4. Real-Time Response: The response, generated in real-time, is displayed within the chat, along with the agent's thought process and actions using StreamlitCallbackHandler.

Future Enhancements:

• Expand the set of tools and agents for more comprehensive query handling.
• Add more robust error handling for parsing or retrieval issues.
• Improve the UI/UX for a better interactive experience.

Output: