/rag-aws-bedrock

RAG implementation using AWS Bedrock and LlamaIndex/LangChain

Primary LanguagePython

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)

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  1. 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.