Keyoxide: aspe:keyoxide.org:MWU7IK7RMUTL3AP6U6UWCF4LHY

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Cake day: June 15th, 2023

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  • A lot of the answers here are short or quippy. So, here’s a more detailed take. LLMs don’t “know” how good a source is. They are word association machines. They are very good at that. When you use something like Perplexity, an external API feeds information from the search queries into the LLM, and then it summarizes that text in (hopefully) a coherent way. There are ways to reduce hallucination rate and check factualness of sources, e.g. by comparing the generated text against authoritative information. But how much of that is employed by Perplexity et al I have no idea.



  • Had a team lead that kept requesting nitpicky changes, going in a FULL CIRCLE about what we should change or not, to the point that changes would take weeks to get merged. Then he had the gall to say that changes were taking too long to be merged and that we couldn’t just leave code lying around in PRs.

    Jesus fucking Christ.

    There’s a reason that team imploded…








  • A vector search converts your query into magic numbers, and then searches the database for other magic numbers that are “similar” (closet to it in the vector space, which is basically an N-dimensional graph of points and directions). These results are then returned as snippets to the LLM and it does stuff from that point.

    The effectiveness of the vector search depends on how Open WebUI breaks up the documents into smaller sections, and how good the embeddings are.

    I’m not exactly sure what you want to achieve, but you might have success in using an LLM to summarize the documents beforehand, using a specific prompt to extract the info you want, then feed that into the vector DB. This would require some scripting, of course.

    The easiest thing to do is try it. See if Open WebUI’s vector search is able to handle it. Make sure to use a good embedding model like nomic-embed-text (can be found on ollama.com). You can change the vector search settings in the documents settings from the workspace on OpenWebUI.

    Edit: https://ollama.com/library/nomic-embed-text