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Knowledge Bases for Amazon Bedrock - Code samples for building RAG applications

Contents

Contains following folders:

  • 00-zero-setup-chat-with-your-document
  • 01-rag-concepts
  • 02-optimizing-accuracy-retrieved-results
  • 03-advanced-concepts
  • 04-infrastructure

00-zero-setup-chat-with-your-document

  • 0_chat_with_document_kb.ipynb - Enables you to chat with your document without setting up any vector database. You can either upload the document or simply point to the document in your S3 location.

01-rag-concepts

  • 01_create_ingest_documents_test_kb_multi_ds.ipynb - creates necessary role and policies required using the utility.py file. It uses the roles and policies to create Open Search Serverless vector index, knowledge base, data source, and then ingests the documents to the vector store. You can add multiple data sources to the knowledge base such as Amazon S3, Confluence, Sharepoint, Salesforce, & Web crawler. Once the documents are ingested it will then test the knowledge base using RetrieveAndGenerate API for question answering, and Retrieve API for fetching relevant documents. Finally, it deletes all the resources. If you want to continue with other notebooks, you can choose not to delete the resources and move to other notebooks. Please note, that if you do not delete the resources, you may be incurred cost of storing data in OpenSearch Serverless, even if you are not using it. Therefore, once you are done with trying out the sample code, make sure to delete all the resources.

  • 02_managed_rag_custom_prompting_and_no_of_results.ipynb - Code sample for managed retrieval augmented generation (RAG) using RetrieveAndGenerate API from Knowledge Bases for Amazon Bedrock.

  • 03_customized-rag-retreive-api-hybrid-search-claude-3-sonnet-langchain.ipynb - If you want to customize your RAG workflow, you can use the retrieve API provided by Knowledge Bases for Amazon Bedrock. You can either performa semantic or hybrid search over your vector store. This notebook, provides sample code for hybrid search using Claude 3 models as well as demonstrates LangChain integraion with Knowledge Bases for Amazon Bedrock.

  • 04_customized-rag-retreive-api-langchain-claude-evaluation-ragas.ipynb - If you are interested in evaluating your RAG application, try this sample code for evaluating the response using RAGAS as assesment framework.

  • 05_observability-evaluation-langfuse-ragas.ipynb -

    Langfuse aims to provide granular visibility into model invocation traces and metrics like accuracy, latency and cost per query.

    In this notebook, we will use RAGAS to run the evaluations and Langfuse for observability. This gives you better idea of how each call to RAG pipelines is performing. You can read more about the Langfuse here.

    Vidoe : Langfuse Dashboard and Traces view

    Lanfuse.Dashboard.and.Traces.view.recording.mp4

02-optimizing-accuracy-retrieved-results

  • advanced_chunking_options.ipynb - This notebook provides sample code for various advance chunking strategies (Fixed, Semantic, & Hierarchical) offered Knowledge Bases for Amazon Bedrock for building optimum RAG applcation.

  • query_reformulation.ipynb - This notebook provides sample code for query reformulation which takes a complex input query and break it into multiple sub-queries. These sub-queries will then separately go through their own retrieval steps to find relevant chunks. In this process, the subqueries having less semantic complexity might find more targeted chunks. These chunks will then be pooled and ranked together before passing them to the FM to generate a response.

03-advanced-concepts

  • Provides sample code that demonstrates advanced concepts that could be applied to improve retrieval quality/results for Knowledge Base on Amazon Bedrock..

04-infrastructure

  • Provides sample code for creating knowledge base and associated data sources using AWS CloudFormation templates.

Note

If you use the notebook - 01_create_ingest_documents_test_kb_multi_ds.ipynb for creating the knowledge bases and do not delete the resources, you may be incurred cost of storing data in OpenSearch Serverless, even if you are not using it. Therefore, once you are done with trying out the sample code, make sure to delete all the resources.

Contributing

We welcome community contributions! Please ensure your sample aligns with AWS best practices, and please update the Contents section of this README file with a link to your sample, along with a description..