RAG is more than just vectors
The article explores Retrieval-Augmented Generation (RAG) as more than a vector store lookup, enhancing Large Language Models (LLMs) by fetching data from diverse sources, expanding capabilities and performance.
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The article discusses the concept of Retrieval-Augmented Generation (RAG) beyond just being a vector store lookup, emphasizing its potential for various data stores beyond vectors. RAG is described as a function call that provides data for Large Language Models (LLMs) to process, enhancing their capabilities. The text explains how RAG adds context for LLMs by fetching data from external sources to inform responses. While RAG is commonly associated with vector databases, the article suggests exploring other possibilities, such as using different data sources like relational databases or graph databases. It highlights the importance of providing interfaces for LLMs to interact with various data sources, enabling a broader range of applications. The article encourages simplifying RAG implementation by leveraging existing knowledge and functions to empower LLMs effectively. Overall, the piece emphasizes the versatility of RAG beyond vector databases and the potential for enhancing LLM performance through diverse data sources and interfaces.
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3 commentsRelated
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