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For the past year, I’ve been exploring how LLMs can be used as retrieval and recommendation agents, and I put together this blog post as a simple introduction to the topic. My goal is to keep it clear and accessible, so even if you don’t have a technical background, don’t worry — this post is written for you too. I have tried to explain the core concepts as simply as possible, use intuitive examples from different research papers, and, on top of that, provide a short script with code showing how to use LLMs as retrieval agents with vLLM and Hugging Face models (it’s in part 2). Press enter or click to view image in full size Retrieval is the process of finding and returning the most relevant items from a large collection, given a query. That collection could be all the webpages on the internet, the books in a library, or the products in an online store. Examples are everywhere: Google surfaces relevant URLs from billions of pages in response to your search query, YouTube and Spotify pu
Motivation For the past year, I’ve been exploring how LLMs can be used as retrieval and recommendation agents, and I put together this blog post as a simple introduction to the topic. My goal is to keep it clear and accessible, so even if you don’t have a technical background, don’t worry — this post is written for you too. I have tried to explain the core concepts as simply as possible, use intuitive examples from different research papers, and, on top of that, provide a short script with code showing how to use LLMs as retrieval agents with vLLM and Hugging Face models (it’s in part 2).…
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