RAG implementation using AWS Bedrock and LlamaIndex/LangChain

Basic RAG application to query our PDFs and fetch relevant answers. You need to have a basic understanding of Python for this tutorial.

Frameworks/Technologies we will use:

• AWS Bedrock (a fully managed service that makes leading foundation models available through a Unified API)
• LlamaIndex/LangChain (a data framework for LLMs that helps developers work with data)
• Streamlit (a free, open-source framework that allows users to create and share web apps from Python scripts)

The processing of this application involves two components:

1. Prepare the documents (VectorStore)

2. Retrieve the relevant documents and frame the response using LLM.

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• Retrieval of Relevant Documents: The model searches a large corpus of documents to find the most relevant to the input query. 

• Response Generation Using LLM (Large Language Model): Once the relevant documents are retrieved, a large language model uses the information from these documents to generate a coherent and contextually appropriate response. 

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