Large language models (LLMs), such as GPT-4, use the power of vector embeddings and databases to address challenges posed by evolving data. These embeddings, when combined with a vector database or search algorithm, offer a way for LLMs to gain access to an up-to-date and ever-expanding knowledge base. This ensures LLMs remain capable of generating accurate and contextually appropriate outputs, even in the face of constantly changing information. This approach is sometimes called Retrieval Augmented Generation (RAG). In this lightning talk, learn about RAG and the benefits of using Redis Enterprise as a vector database with Amazon Bedrock. This presentation is brought to you by Redis, an AWS Partner.
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Негізгі бет Ғылым және технология AWS re:Invent 2023 - Real-time RAG: How to augment LLMs with Redis and Amazon Bedrock (DAT101)
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