Built an AI-powered Historical Event

Built an AI-powered Historical Event Analysis Assistant using RAG (Retrieval-Augmented Generation).

The application allows users to ask questions about historical documents and generates answers based on relevant retrieved content rather than relying only on the LLM’s general knowledge.

Built with: Python, LangChain, RAG, Qdrant/FAISS, BM25, Hugging Face embeddings, Groq, and Streamlit.

The project demonstrates document processing, semantic and keyword retrieval, context-based generation, and an interactive Streamlit interface.