l0rinc

I just noticed the same while trying to figure out https://github.com/hwchase17/langchain/issues/7427, the code should do a `1 - score`

Thanks a lot guys for checking, appreciate it! So the culprit for the mismatched expectations was the OpenAI embeddings - I wonder why the direct Chroma way works so much...

@Bearnardd, @Guidosalimbeni is there a way for me to tip you guys for your help?

Let me return the favor somehow, you guys were really helpful!

These searches are working a lot better now, just a note that `all-MiniLM-L6-v2` seems to require a lot more memory, the pod was suddenly crashing with OOM.

My understanding is that embeddings and retraining (fine-tuning) are different. If you just want extra info, you can embed, if you want new knowledge or style, you probably need to...

I saw a few posts about it, e.g. https://github.com/nomic-ai/gpt4all/issues/173#issuecomment-1496681937 My understanding is you can use gpt4all with langchain https://python.langchain.com/en/latest/modules/models/llms/integrations/gpt4all.html and use indexes as https://python.langchain.com/en/latest/modules/indexes/getting_started.html I personally have to retrain, since...

Yes, we can use a combination of retraining, fine tuning and embedding, each having different effect. I'm currently fine-tuning one in a Colab Pro+ notebook - it requires >40GB video...

Not (yet) an expert either, but it's probably cheaper and more convenient to rent those GPUs for the duration of the training only - you won't really be able to...

Look what I just found: ~https://github.com/lxe/simple-llm-finetuner~ https://github.com/zetavg/LLaMA-LoRA-Tuner With slight modification you can get a public link in colab to a UI where you can just add your data and fine-tune...