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

To start your project effectively with AWS and Python, you must set up the AWS CLI in the appropriate region where your model is accessible.

The first step in our project involves loading PDF documents using the `PyPDF` library, which prepares them for further processing with Langchain's `TextSplitter`.

Creating a vector store using the Titan embedding model from AWS Bedrock allows for efficient similarity searches and retrieval of relevant information from our document set.

Read at Medium