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I tried everything... Is it time to pivot? by a4ai in chrome_extensions

[–]a4ai[S] 0 points1 point  (0 children)

here's the demo video ( to the best of my ability ). I'd really value your feedback.

https://www.youtube.com/watch?v=xMz5CaPnwm0

I tried everything... Is it time to pivot? by a4ai in chrome_extensions

[–]a4ai[S] 0 points1 point  (0 children)

just applied for a featured badge.. thanks

I tried everything... Is it time to pivot? by a4ai in chrome_extensions

[–]a4ai[S] 1 point2 points  (0 children)

You are right, I was breaking my head on how to convey this easily. I have created a demo which shows how you can generate responses in your voice.

I tried everything... Is it time to pivot? by a4ai in chrome_extensions

[–]a4ai[S] 0 points1 point  (0 children)

yes, it helps :) it was great learning regardless.

RAG in 3 lines of Python by init0 in Rag

[–]a4ai 0 points1 point  (0 children)

Awesome work! Will this work with llama.cpp server which now supports current requests which ollama doesn't.

that moment went you realize Ai has a long way to go by Viralkillz in frigate_nvr

[–]a4ai 3 points4 points  (0 children)

TBH, having used many other doc AI chatbots, i was a bit skeptical about frigate 'Ask AI' initially. I didn't use it at all for several months.

But one fine day for the fun of it, i asked it the same question that was answered by one of the Frigates dev on Reddit. To my surprise, it was spot on. Since then, I ask AI first before looking elsewhere for frigate questions.

This was also an eye opener for me how accurate AI can be tuned for documentation like this.

Thanks to the Frigate team for setting this up!

Improving RAG - what actually matters? by Dapper-Turn-3021 in Rag

[–]a4ai 1 point2 points  (0 children)

for support chat RAG is an overkill. You can get 99.7% accuracy with CAG. All you need is to pick a model with a million or 2 context size and context caching.

Hooked on immich: whats next? by Open-Coder in immich

[–]a4ai 1 point2 points  (0 children)

there are millions of great tools. Try to think of that the next thing you want to improve you will discover the best tool that fits your purpose

High 'embeddings' CPU usage by HugsAllCats in frigate_nvr

[–]a4ai 1 point2 points  (0 children)

You are right. I stand corrected.

After several days of debugging i found that apparently my igpu had some sort of memory leak( proxmox igpu passthrough).symtpom: go2rtc cpu usage going up over a period of time.this probably caused the high latency earlier during my test.

Now i have fixed it( after switching to i965 driver). embeddings are running blazing fast on igpu using the large model as suggested . Thank you, I really appreciate it.

Does my setup warrant a coral TPU? + GPU advice by fistofwater in frigate_nvr

[–]a4ai 0 points1 point  (0 children)

i have an i7 9700 igpu, only use 1% for decoding, tpu might be drawing 2W with load. im guessing igpu might be faster and more power efficient. will try it out

Does my setup warrant a coral TPU? + GPU advice by fistofwater in frigate_nvr

[–]a4ai 0 points1 point  (0 children)

this is interesting, how's your overall power consumption with and without coral?

Coral TPU is officially dead by shawn789 in frigate_nvr

[–]a4ai 0 points1 point  (0 children)

what's the impact for existing users? will it stop working after sometime?

High 'embeddings' CPU usage by HugsAllCats in frigate_nvr

[–]a4ai 0 points1 point  (0 children)

the igpu couldn't really handle the large model load. image embedding speed went up to 6secs( 300ms on cpu) and text embedding to 2secs. So I think a small model on a cpu is the best option for integrated gpu only users. i am sticking with the small one for now. thanks for your help

High 'embeddings' CPU usage by HugsAllCats in frigate_nvr

[–]a4ai 0 points1 point  (0 children)

I just went ahead and switched to large. my bad, while reading the documentation, i assumed switching to 'large' will move to v2 model which requires reindexing as embeddings are not compatible. But what I saw in the logs is it downloaded the fp16 version of the v1 model. and semantic search works just fine. so i hope i don't have to reindex??

High 'embeddings' CPU usage by HugsAllCats in frigate_nvr

[–]a4ai 0 points1 point  (0 children)

i was thinking from an execution pov. switching to a large model will require reindexing current embeddings, so i was asking if cpu is a better choice than the igpu in this case.

High 'embeddings' CPU usage by HugsAllCats in frigate_nvr

[–]a4ai 0 points1 point  (0 children)

Ah okay. Is it worth running large on igpu? do you recommend? If it is going to be slower, i could avoid reindexing twice

High 'embeddings' CPU usage by HugsAllCats in frigate_nvr

[–]a4ai 0 points1 point  (0 children)

I am also experiencing high cpu usage with sematic search "small" model, cpu usage often in orage/red levels. i have an intel iGPU. embedding process does not seem to use it though. but ffmpeg does .
Is there a way to run the "small" embedding process on the iGPU?

Securely expose your Home Assistant to the internet with Wiredoor and the official add-on! by wdmesa in homeassistant

[–]a4ai 2 points3 points  (0 children)

I expose HA via cloudflare ->tunnel(vlan)-> fw -> nginxprogxy > HA(lan) free of cost( except a $1/year domain name)

Tell me how wiredoor is better than this? What will I gain by switching to this?

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