How to build a RAG app on AWS!The visual below shows the exact flow of how a simple RAG system works inside AWS, using services you already know.At its core, RAG is a two-stage pattern:- Ingestion (prepare knowledge)- Querying (use knowledge)Below is how each stage works in practice.> Ingestion: Turning raw data into searchable knowledge- Your documents live in S3 or any internal data source.- Whenever something new is added, a Lambda ingestion function kicks in.- It cleans, processes, and chunks the file i ...

There are a lot of really hard infrastructure problems that go into supporting EVELs in the first place. Some of the problems that we solve are things like um how to ingest megabytes per second of data that you know systems generate and guess what agents just generate more data. Another problem that we solve is um running evals that might take a really really long period of time um sometimes days and accumulating the data in a way that's easy to visualize. And again, um, being able to run and orchestrate so ...

Data Ingestion Efficiency - Data ingestion process improved from approximately 3 weeks to less than a day with Llama Cloud [1] - In-house solution previously took weeks to incorporate new data into agents [2] AI Solution & Integration - AI is used to improve maintenance, supply chain optimization, smart operations, health and safety, and commercial efforts [2] - Llama Index integrates seamlessly with internal systems for building tools and agents [3] - Llama Index integrates well with existing stack such as Databricks and MLflow [5] Performance Improvement - Observed a 20% improvement in chunk relevance and accuracy without changes to downstream agents [4] - Llama Index helps to extract and structure data from high technical dense documents [4] Customization & Development - Llama Index provides a level of abstraction and customization that allows extension with in-house modules without disrupting development [5]