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Getting enterprise data into large language models (LLMs) is a critical task for enabling the success of enterprise AI deployments.
That's where retrieval augmented generation (RAG) fits in, which is an area where many vendors have offered various solutions. Today at AWS re:invent 2024 the company announced a series of new services and updates designed to help make it easier for enterprises to get both structured and unstructured data into RAG pipelines. Making structured data accessible for RAG requires more than just looking up a single row in a table. It involves translating natural language queries into complex SQL queries to filter, join tables and aggregate data.The challenges are further compounded for unstructured data, where by definition there is no structure for the data.
To help solve those challenges AWS announced new services for structured data retrieval support, ETL (extract, transform and load) for unstructured data, data automation and knowledge base support.
"Retrieval augmented generation (RAG) is a very popular technique for customizing your data, but one of the challenges with retrieval augmented generation is it's historically been mostly for text data," Swami Sivasubramanian, VP of AI and Data at AWS, told VentureBeat. " And if you see enterprises, most of the data, especially operational, is sitting in data lakes and data warehouses, and that has never been ready for RAG, per se."
Improving structured data retrieval support with Amazon Bedrock Knowledge Bases
Why isn't structured data ready for RAG? Sivasubramanian provided a few scenarios.
"To build a highly accurate, secure system, you've got to actually understand the schema, build a custom schema embedding, and then actually understand the historical query log, and then keep up with the changes and schemas," Sivasubramanian said.
During his keynote at re:invent Sivasubramanian explained that the Amazon Bedrock Knowledge Bases service is a fully managed RAG capability that enables enterpris ...