Category: Amazon Bedrock Knowledge Bases

The business team in Parcel Perform often needs access to data to answer questions related to merchants’ parcel deliveries, such as “Did we see a spike in delivery delays last week? If so, in which transit facilities were this observed, and what was the primary cause of the issue?” Previously, the data team had to manually form the query and run it to fetch the data. With the new generative AI-powered text-to-SQL capability in Parcel Perform, the business team can self-serve their data needs by using an AI assistant interface. In this post, we discuss how Parcel Perform incorporated generative AI, data storage, and data access through AWS services to make timely decisions.

In this post, we discuss how to make your Amazon Aurora PostgreSQL-Compatible Edition data available for natural language querying through Amazon Bedrock Knowledge Bases while maintaining data freshness.

In this post, we show how to use Amazon Bedrock Knowledge Bases GraphRAG with Amazon Neptune Analytics to build a financial fraud detection solution.

Traditional Retrieval Augmented Generation (RAG) systems consume valuable resources by ingesting and maintaining embeddings for documents that might never be queried, resulting in unnecessary storage costs and reduced system efficiency. This post presents a just-in-time knowledge base solution that reduces unused consumption through intelligent document processing. The solution processes documents only when needed and automatically removes unused resources, so organizations can scale their document repositories without proportionally increasing infrastructure costs.

In this post, we explore five different patterns for implementing LLM-powered structured data query capabilities in AWS, including direct conversational interfaces, BI tool enhancements, and custom text-to-SQL solutions.

In this post, we explore how Amazon Bedrock’s multimodal RAG capabilities revolutionize drug data analysis by efficiently processing complex medical documentation containing text, images, graphs, and tables.

In this post, we demonstrate how agentic workflow patterns such as Retrieval Augmented Generation (RAG), multi-tool orchestration, and conditional routing with LangGraph enable end-to-end solutions that artificial intelligence and machine learning (AI/ML) developers and enterprise architects can adopt and extend. We walk through an example of a financial management AI assistant that can provide quantitative research and grounded financial advice by analyzing both the earnings call (audio) and the presentation slides (images), along with relevant financial data feeds.

Developing robust text-to-SQL capabilities is a critical challenge in the field of natural language processing (NLP) and database management. The complexity of NLP and database management increases in this field, particularly while dealing with complex queries and database structures. In this post, we introduce a straightforward but powerful solution with accompanying code to text-to-SQL using a custom agent implementation along with Amazon Bedrock and Converse API.

This post provides instructions to configure a structured data retrieval solution, with practical code examples and templates. It covers implementation samples and additional considerations, empowering you to quickly build and scale your conversational data interfaces.

Adobe partnered with the AWS Generative AI Innovation Center, using Amazon Bedrock Knowledge Bases and the Vector Engine for Amazon OpenSearch Serverless. This solution dramatically improved their developer support system, resulting in a 20% increase in retrieval accuracy. In this post, we discuss the details of this solution and how Adobe enhances their developer productivity.