Author: Mani Khanuja

In this post, we explore an innovative approach that uses LLMs on Amazon Bedrock to intelligently extract metadata filters from natural language queries. By combining the capabilities of LLM function calling and Pydantic data models, you can dynamically extract metadata from user queries. This approach can also enhance the quality of retrieved information and responses generated by the RAG applications.

This post explores the new enterprise-grade features for Amazon Bedrock Knowledge Bases and how they align with the AWS Well-Architected Framework. With Amazon Bedrock Knowledge Bases, you can quickly build applications using Retrieval Augmented Generation (RAG) for use cases like question answering, contextual chatbots, and personalized search.

At AWS re:Invent 2023, we announced the general availability of Amazon Bedrock Knowledge Bases. With a knowledge base, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for fully managed Retrieval Augmented Generation (RAG). In a previous post, we described how Amazon Bedrock Knowledge Bases manages the end-to-end RAG workflow […]

Time series forecasting is a common problem in machine learning (ML) and statistics. Some common day-to-day use cases of time series forecasting involve predicting product sales, item demand, component supply, service tickets, and all as a function of time. More often than not, time series data follows a hierarchical aggregation structure. For example, in retail, […]

Proteins, the key fundamental macromolecules governing in biological bodies, are composed of amino acids. These 20 essential amino acids, each represented by a capital letter, combine to form a protein sequence, which can be used to predict the subcellular localization (the location of protein in a cell) and structure of proteins. Figure 1: Protein Sequence […]