AWS Big Data Blog

Category: Artificial Intelligence

Build a decentralized semantic search engine on heterogeneous data stores using autonomous agents

In this post, we show how to build a Q&A bot with RAG (Retrieval Augmented Generation). RAG uses data sources like Amazon Redshift and Amazon OpenSearch Service to retrieve documents that augment the LLM prompt. For getting data from Amazon Redshift, we use the Anthropic Claude 2.0 on Amazon Bedrock, summarizing the final response based on pre-defined prompt template libraries from LangChain. To get data from Amazon OpenSearch Service, we chunk, and convert the source data chunks to vectors using Amazon Titan Text Embeddings model.

Entity resolution and fuzzy matches in AWS Glue using the Zingg open source library

In this post, we explore how to use Zingg’s entity resolution capabilities within an AWS Glue notebook, which you can later run as an extract, transform, and load (ETL) job. By integrating Zingg in your notebooks or ETL jobs, you can effectively address data governance challenges and provide consistent and accurate data across your organization.

AI recommendations for descriptions in Amazon DataZone for enhanced business data cataloging and discovery is now generally available

In March 2024, we announced the general availability of the generative artificial intelligence (AI) generated data descriptions in Amazon DataZone. In this post, we share what we heard from our customers that led us to add the AI-generated data descriptions and discuss specific customer use cases addressed by this capability. We also detail how the […]

Improve healthcare services through patient 360: A zero-ETL approach to enable near real-time data analytics

Healthcare providers have an opportunity to improve the patient experience by collecting and analyzing broader and more diverse datasets. This includes patient medical history, allergies, immunizations, family disease history, and individuals’ lifestyle data such as workout habits. Having access to those datasets and forming a 360-degree view of patients allows healthcare providers such as claim […]

Exploring real-time streaming for generative AI Applications

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. FMs, as the name suggests, provide the foundation to build more specialized downstream applications, and are unique in their adaptability. They can perform a wide range of different tasks, such as natural language processing, classifying images, […]

Build a RAG data ingestion pipeline for large-scale ML workloads

For building any generative AI application, enriching the large language models (LLMs) with new data is imperative. This is where the Retrieval Augmented Generation (RAG) technique comes in. RAG is a machine learning (ML) architecture that uses external documents (like Wikipedia) to augment its knowledge and achieve state-of-the-art results on knowledge-intensive tasks. For ingesting these […]

Preprocess and fine-tune LLMs quickly and cost-effectively using Amazon EMR Serverless and Amazon SageMaker

Large language models (LLMs) are becoming increasing popular, with new use cases constantly being explored. In general, you can build applications powered by LLMs by incorporating prompt engineering into your code. However, there are cases where prompting an existing LLM falls short. This is where model fine-tuning can help. Prompt engineering is about guiding the […]

Power neural search with AI/ML connectors in Amazon OpenSearch Service

With the launch of the neural search feature for Amazon OpenSearch Service in OpenSearch 2.9, it’s now effortless to integrate with AI/ML models to power semantic search and other use cases. OpenSearch Service has supported both lexical and vector search since the introduction of its k-nearest neighbor (k-NN) feature in 2020; however, configuring semantic search […]

Build scalable and serverless RAG workflows with a vector engine for Amazon OpenSearch Serverless and Amazon Bedrock Claude models

In pursuit of a more efficient and customer-centric support system, organizations are deploying cutting-edge generative AI applications. These applications are designed to excel in four critical areas: multi-lingual support, sentiment analysis, personally identifiable information (PII) detection, and conversational search capabilities. Customers worldwide can now engage with the applications in their preferred language, and the applications […]