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Question Answering Generative AI application with Large Language Models (LLMs) and Amazon OpenSearch Service using Haystack

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QA with LLMs and RAG (Retrieval Augmented Generation)

❗ This project is a updated version based on the original project, Haystack Retrieval-Augmented Generative QA Pipelines with SageMaker JumpStart

This project is a Question Answering application with Large Language Models (LLMs) and Amazon OpenSearch Service. An application using the RAG(Retrieval Augmented Generation) approach retrieves information most relevant to the user’s request from the enterprise knowledge base or content, bundles it as context along with the user’s request as a prompt, and then sends it to the LLM to get a GenAI response.

LLMs have limitations around the maximum word count for the input prompt, therefore choosing the right passages among thousands or millions of documents in the enterprise, has a direct impact on the LLM’s accuracy.

In this project, Amazon OpenSearch Service is used for knowledge base.

The overall architecture is like this:

rag_with_opensearch_arch

Overall Workflow

  1. Deploy the cdk stacks (For more information, see here).
    • A SageMaker Endpoint for text generation.
    • An Amazon OpenSearch cluster for storing embeddings.
    • Opensearch cluster's access credentials (username and password) stored in AWS Secrets Mananger as a name such as OpenSearchMasterUserSecret1-xxxxxxxxxxxx.
  2. Open SageMaker Studio and then open a new terminal.
  3. Run the following commands on the terminal to clone the code repository for this project:
    git clone https://github.com/ksmin23/rag-with-haystack-and-amazon-opensearch.git
    
  4. Open data_ingestion_to_opensearch notebook and Run it. (For more information, see here)
  5. Run Streamlit application. (For more information, see here)

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