article thumbnail

Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

Flipboard

One effective way to improve context relevance is through metadata filtering, which allows you to refine search results by pre-filtering the vector store based on custom metadata attributes. By combining the capabilities of LLM function calling and Pydantic data models, you can dynamically extract metadata from user queries.

Metadata 159
article thumbnail

Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

Flipboard

Metadata can play a very important role in using data assets to make data driven decisions. Generating metadata for your data assets is often a time-consuming and manual task. This post shows you how to enrich your AWS Glue Data Catalog with dynamic metadata using foundation models (FMs) on Amazon Bedrock and your data documentation.

Metadata 145
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

9 data governance strategies that will unlock the potential of your business data

IBM Journey to AI blog

Establishing standardized definitions and control measures builds a solid foundation that evolves as the framework matures. Data owners manage data domains, help to ensure quality, address data-related issues, and approve data definitions, promoting consistency across the enterprise.

Metadata 189
article thumbnail

Achieve your AI goals with an open data lakehouse approach

IBM Journey to AI blog

Also, a lakehouse can introduce definitional metadata to ensure clarity and consistency, which enables more trustworthy, governed data. Watsonx.data enables users to access all data through a single point of entry, with a shared metadata layer deployed across clouds and on-premises environments.

Metadata 247
article thumbnail

Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes

AWS Machine Learning Blog

This configuration takes the form of a Directed Acyclic Graph (DAG) represented as a JSON pipeline definition. The DevOps engineer can then use the Kubernetes APIs provided by ACK to submit the pipeline definition and initiate one or more pipeline runs in SageMaker. This entire workflow is shown in the following solution diagram.

DevOps 104
article thumbnail

Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 2

AWS Machine Learning Blog

When creating agents that use action groups , you can specify your function definitions as a JSON object to the agent or provide an API schema in the OpenAPI schema format. If you’re starting with no existing schema, the simplest way to provide tool metadata for your agent is to use simple JSON function definitions.

article thumbnail

How we achieved 89% accuracy on contract question answering

Snorkel AI

This helped to better organize the chunks and enhance them with relevant metadata. The metadata included: Identification of the document section where a paragraph was located. Detection of whether a paragraph was providing legal definitions. Recognition of whether a paragraph was discussing a date. We built them in a single day.