Nvidia NIM GenAI Application

This project, developed using Streamlit, serves as a demonstration of NVIDIA's advanced LLM capabilities for document embedding and context-based question answering. The app leverages LangChain's NVIDIA endpoints and FAISS vector store to create a high-performance document retrieval system. Users can upload document directories (in this case, US Census documents), which are split into smaller chunks and embedded as vectors to facilitate efficient search and retrieval.

Key Features:
- Document Ingestion and Chunking: Uses PDF directory loaders and chunking techniques to preprocess documents, enabling efficient and relevant search.
- Vector Storage with FAISS: Creates embeddings from document chunks and stores them in FAISS, a high-speed vector store, for fast and accurate retrieval.
- NVIDIA-powered LLMs for QA: Integrates NVIDIA’s large language models, such as Meta Llama, to provide accurate answers based on context from stored documents.
- Contextual Similarity Search: Displays document chunks relevant to the query, enhancing transparency and comprehension.

Ideal for users and organizations needing robust document search and Q&A capabilities, this app provides a reliable, user-friendly way to interact with large document sets and extract precise information quickly.