This project showcases a sophisticated news retrieval and question-answering (Q&A) system using Gradio, LangChain, and OpenAI's GPT models. The system is designed to process user-provided URLs of news articles, analyze their content, and deliver contextually relevant answers to user queries.

• Dynamic URL Processing: Users can input multiple URLs, which the system processes to extract relevant news content.
• Advanced Text Splitting: Implements a recursive character text splitter to manage large documents, ensuring comprehensive analysis.
• AI-Driven Q&A Engine: Utilizes LangChain and OpenAI's language models to interpret user questions and retrieve accurate answers.
• Efficient Data Retrieval: Leverages FAISS (Facebook AI Similarity Search) for efficient indexing and retrieval of text data from news sources.
• Interactive Chat Interface: Offers a Gradio-based chat interface for user interaction, enabling real-time question submission and response generation.
• Environmental Variable Management: Uses dotenv for secure and efficient management of environment variables.

• Python
• Gradio for UI
• LangChain for language model workflows
• OpenAI API for advanced language models
• FAISS for efficient similarity search in large datasets
• Dotenv for environment variable management