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by appstorelottery
813 days ago
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I've been disciplined (perhaps obsessive at times) with keeping a daily diary for many years and I was interested in being able to query my diary locally via AI. I found a solution that works surprisingly well using GPT4ALL. I found GPT4ALL (https://gpt4all.io) to have a nice-enough GUI, and it runs reasonably quickly on my M1 MacBook Air with 8Gb of ram, and it can be setup to be a completely local solution - not sending your data to the Goliaths. GPT4ALL has an option to access local documents, via the Sbert text embedding model (RAG). My specific results have been as follows; using the Nous Hermes 2 Mistral DPO and Sbert - I indexed 153 days of my daily writing (most days I write between 2 and 3 thousand words). Asking a simple question like "what are the challenges faced by the author?" provides remarkable, almost spooky results (which I won't share here) - which in my opinion are spot-on regarding my own challenges over that the period - and Sbert provides references to which documents it used to generate the answer. Options are available to reference an arbitrary number of documents, however the default is 10. Ideally I'd like to have it reference all 153 documents in the query - I'm not sure if it's a ram or a token issue, however increasing the value of documents referenced has resulted in machine lock-ups. Anyhow - that's my experience - hope it's helpful to someone. |
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This is regular embeddings + LLM.
At the end of the day, you are basically just adding a preprompt to a search. Not to mention, the Mistral models are barely useful for logic.
I'm not really sure what you are getting out of it. I'm wondering if you are reading some mostly generic Mistral output with a few words from your pre-prompt/embedding.