Category: Artificial Intelligence

Organizations are collecting and storing vast amounts of structured and unstructured data like reports, whitepapers, and research documents. By consolidating this information, analysts can discover and integrate data from across the organization, creating valuable data products based on a unified dataset. This post shows how to integrate Amazon Bedrock with the AWS Serverless Data Analytics Pipeline architecture using Amazon EventBridge, AWS Step Functions, and AWS Lambda to automate a wide range of data enrichment tasks in a cost-effective and scalable manner.

While the potential of generative artificial intelligence (AI) is increasingly under evaluation, organizations are at different stages in defining their generative AI vision. In many organizations, the focus is on large language models (LLMs), and foundation models (FMs) more broadly. This is just the tip of the iceberg, because what enables you to obtain differential […]

In this blog post, we will highlight how ZS Associates used multiple AWS services to build a highly scalable, highly performant, clinical document search platform. This platform is an advanced information retrieval system engineered to assist healthcare professionals and researchers in navigating vast repositories of medical documents, medical literature, research articles, clinical guidelines, protocol documents, […]

In this post, instead of using the BM25 algorithm, we introduce sparse vector retrieval. This approach offers improved term expansion while maintaining interpretability. We walk through the steps of integrating sparse and dense vectors for knowledge retrieval using Amazon OpenSearch Service and run some experiments on some public datasets to show its advantages.

The current scaling approach of Amazon Redshift Serverless increases your compute capacity based on the query queue time and scales down when the queuing reduces on the data warehouse. However, you might need to automatically scale compute resources based on factors like query complexity and data volume to meet price-performance targets, irrespective of query queuing. […]

Amazon Redshift ML is a feature of Amazon Redshift that enables you to build, train, and deploy machine learning (ML) models directly within the Redshift environment. Now, you can use pretrained publicly available large language models (LLMs) in Amazon SageMaker JumpStart as part of Redshift ML, allowing you to bring the power of LLMs to analytics. You can use pretrained publicly available LLMs from leading providers such as Meta, AI21 Labs, LightOn, Hugging Face, Amazon Alexa, and Cohere as part of your Redshift ML workflows. By integrating with LLMs, Redshift ML can support a wide variety of natural language processing (NLP) use cases on your analytical data, such as text summarization, sentiment analysis, named entity recognition, text generation, language translation, data standardization, data enrichment, and more. Through this feature, the power of generative artificial intelligence (AI) and LLMs is made available to you as simple SQL functions that you can apply on your datasets. The integration is designed to be simple to use and flexible to configure, allowing you to take advantage of the capabilities of advanced ML models within your Redshift data warehouse environment.

A protein is a sequence of amino acids that, when chained together, creates a 3D structure. This 3D structure allows the protein to bind to other structures within the body and initiate changes. This binding is core to the working of many drugs. A common workflow within drug discovery is searching for similar proteins, because […]

Data streaming enables generative AI to take advantage of real-time data and provide businesses with rapid insights. This post looks at how to integrate generative AI capabilities when implementing a streaming architecture on AWS using managed services such as Managed Service for Apache Flink and Amazon Kinesis Data Streams for processing streaming data and Amazon Bedrock to utilize generative AI capabilities. We include a reference architecture and a step-by-step guide on infrastructure setup and sample code for implementing the solution with the AWS Cloud Development Kit (AWS CDK). You can find the code to try it out yourself on the GitHub repo.

This post takes a step-by-step approach to showcase how you can use Retrieval Augmented Generation (RAG) to reference real-time tweets as a context for large language models (LLMs). RAG is the process of optimizing the output of an LLM so it references an authoritative knowledge base outside of its training data sources before generating a response. LLMs are trained on vast volumes of data and use billions of parameters to generate original output for tasks such as answering questions, translating languages, and completing sentences.

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.