Replies: 2 comments
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Thanks for the kind works @robertgro:
No requirements for phidata, just whatever models you run. For example if you have 16gb RAM you'll probably run llama3 8b comfortably.
Yes we plan to support every LLM, if we're missing any please share and we'll add it within a week
With phidata you control the input, chunks, how its parsed. Everything is open source so you can tune your Assistant to your needs. + you can add custom tools (like making api calls, searching youtube etc). I think it all depends on how much you want to customize your Assistant. We're here to help :)
Yes you can easily query much larger documents, mainly because you control the vector db -- so add as much as you'd like. With PgVector you can easily go upto 5-10 GB but if you hit 10GB+ the retrieval needs tuning.
@robertgro in this case we first "retrieve relevant chunks" from the PDF and then send to the model for synthesis, so we can QnA over much larger documents even with smaller context window. Thank you for the questions, let me know if there's anything else i can help with |
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Thank you for your fast response @ashpreetbedi.
Sounds great! I'm already familiar with running models locally so that I know what my hardware is capable of.
In case I'm missing any LLM, I'll report back to you. For now, your support seems to be pretty comprehensive and enough for my needs!
Customization is a huge plus in my opinion. And I'll definitely keep an eye on your project.
Sounds promising. By the time writing, I haven't had a chance to try it out yet, but I'm excited to figure out if Phidata and PgVector are able to deliver reliable results when querying my documents.
Thank you too, I will! |
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Hi,
at first, thank you for your efforts. I really appreciate projects like this in general and your documentation for it's simplicity especially. Still, I got some questions left:
Kind Regards!
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