Build scalable RAG applications using Amazon Bedrock Knowledge Bases (AIM305)

Here is a detailed summary of the video transcription in markdown format:

Overview of Knowledge Bases

  • What is RAG (Retrieval Augmented Generation)?

    • Customizing models by adding your own proprietary data or context to be retrieved and used to generate a response.
    • Can be used for text, visuals, or other multimodal outputs.
    • Involves creating a data ingestion pipeline to ingest documents, represent them as embeddings, and use for context-aware search and response generation.
  • Amazon Bedrock Knowledge Bases

    • Launched a year ago as an end-to-end managed service for creating RAG workflows.
    • Developed over 50+ features based on customer feedback and requirements.
    • Key features include hybrid search, manual metadata filters, connectors, chunking strategies, entity extraction, custom prompts, and more.

Recent Announcements and Capabilities

  • Structured Data Retrieval

    • Allows natively integrating structured data sources into knowledge bases.
    • Generates SQL queries from natural language, runs them against the database, and summarizes the results.
  • Autogenerated Query Filters

    • Automatically generates filters from the user's natural language query.
  • Ranking API

    • Performs post-processing on retrieved chunks to prioritize the most relevant content.
  • Graph RAG

    • Supports using Neptune graph databases as a knowledge base source.
    • Enables understanding relationships between entities across documents.
  • Streaming Responses

    • Starts returning response tokens as soon as the language model begins generating, improving latency.
  • RAG Evaluation and LLM Judging

    • Allows evaluating the quality of generated responses and iterating on the application.

Methodology for Building Scalable RAG Applications

  1. Data Strategy

    • Properly handle different data types (structured, unstructured) and prepare them for ingestion.
    • Determine chunking strategies based on the data and use case.
  2. RAG Infrastructure

    • Select appropriate models and vector databases based on the requirements.
  3. Start Small and Iterate

    • Begin with a simple proof-of-concept and gradually add complexity.
    • Use RAG Evaluation to validate responses and gather user feedback.
    • Continuously improve the application based on user behavior and performance.

Enverus Journey

  • Enverus is a leading SaaS provider for the energy industry, processing over $200 billion in spend.

  • Their "Instant Analyst" product aims to unlock the value in energy data by providing intelligent connections.

  • They followed a 5-phase methodology to build a scalable and flexible RAG-based solution:

    1. Planning and Strategy
    2. Application Development and Integration
    3. Testing and Validation
    4. Security and Optimization
    5. Platform Building
  • Key challenges included handling chunking, data synchronization, and retrieval accuracy, which were addressed using Amazon Bedrock Knowledge Bases.

  • Enverus leveraged features like infrastructure as code, A/B testing, and the hybrid search capabilities of Bedrock to improve their solution.

Advanced Techniques

  • Multimodal Data Processing

    • Bedrock Knowledge Bases support processing of text, images, tables, and other data types within the same knowledge base.
    • Automatically extracts and surfaces relevant information from multimodal sources.
  • Structured Data Retrieval

    • Bedrock generates SQL queries from natural language questions, runs them against databases, and summarizes the results.
    • Provides APIs to access just the generated query or the full response.
  • Graph RAG

    • Leverages Neptune graph databases to understand relationships between entities across documents.
    • Fully managed by Bedrock, relieving the burden of building the knowledge graph.

Challenge and Next Steps

  • The challenge is to get started building your own scalable RAG applications, or reach out to your AWS account team to further develop your existing solutions.
  • By partnering with the Bedrock team, you can provide feedback and help shape the future development of the service.

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