Unlocking power of structured data with Amazon Bedrock Knowledge Bases-AIM396-NEW

Summary

Introduction

  • The presenters, Ron and Sacket, introduced the topic of the session - unlocking the power of structured data with Amazon Bedrock knowledge bases.
  • They aimed to provide context for those new to the topic, contrast the approaches for unstructured and structured data, and dive deep into the structured data use case.

Retrieval Augmented Generation (RAG)

  • RAG is a technique that provides foundational models access to the latest information from traditional data sources, such as documents, S3, and databases.
  • It reduces hallucination, increases accuracy, and focuses on particular tasks by retrieving relevant content and augmenting it to the user prompt before passing it to the foundational model.

Structured Data Retrieval

  • Unstructured data can be handled well by semantic search, but structured data requires a different approach.
  • The presenters believe that the best approach for structured data is natural language to SQL conversion, which Bedrock knowledge bases support.

Challenges of Natural Language to SQL

  • The presenters discussed the challenges of natural language to SQL, including the need for personalization to the database schema, data formatting, and SQL dialect.

Bedrock Knowledge Bases for Structured Data Retrieval

  • Bedrock knowledge bases tap into the database metadata, leverage past queries, and understand the SQL dialect to generate customized SQL queries.
  • The platform also offers query configuration options for further customization and supports conversational interaction with secure access to the data.

Demo

  • The presenters demonstrated the creation and configuration of a Bedrock knowledge base for structured data retrieval.
  • They showed how to use the retrieval API to generate SQL queries and execute them, as well as how to customize the query generation process using descriptions and curated queries.
  • They also highlighted the security features, such as the detection and blocking of data modification queries.

Conclusion

  • The presenters emphasized the importance of working backwards from customer needs and customizing AI experiences.
  • They encouraged the audience to try out Bedrock knowledge bases and provided opportunities for further discussions and feedback.

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