Building a End to End Multi-Modal RAG System Using AWS Bedrock And Langchain

Learn how to build an end-to-end Retrieval Augmented Generation (RAG) application using AWS Bedrock and Langchain. This project involves loading PDF documents, creating vector embeddings using the Titan model, storing them in a vector store, integrating Cloudy and Llama 2 language models, and building a user-friendly interface with Streamlit. The system efficiently retrieves and summarizes information from PDF files based on user queries.