To develop an intelligent, multi-agent document analysis platform powered by Large Language Models (LLMs) that can process a variety of document types and video content, extract key insights, and enable semantic search and interactive querying.

This project leverages LLMs to build a versatile document analysis system capable of handling diverse inputs, including PDFs, Word documents, plain text files, and YouTube videos. Uploaded files are parsed into meaningful chunks for precise and efficient processing, while YouTube videos are transcribed from audio to text for analysis. The processed content is stored in a vector database, enabling semantic search and Retrieval-Augmented Generation (RAG)-style querying.
A multi-agent architecture powers the platform, with specialized agents responsible for handling specific file formats or analytical tasks such as summarization, Q&A, sentiment analysis, and keyword extraction. Users can upload content, query for specific details, and receive AI-generated summaries or answers, making the platform highly adaptable for research, reporting, and content analysis.
By combining flexible content ingestion, advanced NLP, and agent-based task specialization, the system offers a powerful, scalable solution for extracting meaningful insights from unstructured documents and multimedia content.