RAG End-to-End Application - Speak with your Unstructured Data

Learn to build an end-to-end RAG (Retrieval-Augmented Generation) application that enables conversational interaction with unstructured data including audio, video, images, PDFs, Excel files, CSV files, and HTML documents. Master the integration of LangChain with multiple technologies to create a comprehensive data chat system using Gemini Pro Fast, Microsoft Phi3.5 Mini, and LLama 3.2 3B language models, with the latter two hosted on Nvidia NIM. Explore various vector storage solutions including Elastic, Chroma, Faiss, and Wilvus for efficient data retrieval. Implement embedding models from Gemini and Nvidia to transform your data into searchable vectors. Develop a user-friendly interface using Streamlit as both the UX tool and application server. Deploy your application using Docker and Docker Compose for containerized deployment, ensuring scalability and portability across different environments.