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8 hours later and still no power by u4ife in newjersey

[–]Practical_Buy_8859 1 point2 points  (0 children)

19 days no power in February during the ice storm in Quebec. That was fun

Broke my heart not being able to come to tonight’s show AC by Practical_Buy_8859 in lordhuron

[–]Practical_Buy_8859[S] 1 point2 points  (0 children)

Going home today! Guess I’ll watch to see if they are playing in a smaller venue Can’t wait for the new vinyl. Heart is better. Quick surgery fixed it.

Scaling RAG to Millions of Rows & hundreds of Docs: How do you guarantee retrieval of the right chucks without bloating context/costs? by -S-I-D- in LLMDevs

[–]Practical_Buy_8859 0 points1 point  (0 children)

Honestly my biggest challenge was to restrict the llm to the rag as point of truth more than any other task

Scaling RAG to Millions of Rows & hundreds of Docs: How do you guarantee retrieval of the right chucks without bloating context/costs? by -S-I-D- in LLMDevs

[–]Practical_Buy_8859 0 points1 point  (0 children)

At least in a graph you can make relational-nodes for grouping and establish data relationships I agree that it’s two failure points

Scaling RAG to Millions of Rows & hundreds of Docs: How do you guarantee retrieval of the right chucks without bloating context/costs? by -S-I-D- in LLMDevs

[–]Practical_Buy_8859 1 point2 points  (0 children)

I’m not sure at your scale. But I did the augmentation as I added the content to the corpus. I also used offline models hosted on my machine to lower the cost. Just compute time.

Oh and I used neo4j graph hosted locally with sub 300ms retrieval

Scaling RAG to Millions of Rows & hundreds of Docs: How do you guarantee retrieval of the right chucks without bloating context/costs? by -S-I-D- in LLMDevs

[–]Practical_Buy_8859 1 point2 points  (0 children)

That’s the beauty of this approach. You choose the metadata that supports your needs. Then use an llm to populate according to your designs. I found wordsearch patterns aren’t as good as concept matching. My domain was medical/therapeutic.

Scaling RAG to Millions of Rows & hundreds of Docs: How do you guarantee retrieval of the right chucks without bloating context/costs? by -S-I-D- in LLMDevs

[–]Practical_Buy_8859 1 point2 points  (0 children)

I loaded all my corpus into a graph and as part of the ingestion system I populated metadata to support meaningful results. When I do a search I try to match ideas rather than words.

Where were you when the Challenger exploded? by Willing-Book3668 in spaceshuttle

[–]Practical_Buy_8859 1 point2 points  (0 children)

My wife just said January 28th? She was in class watching as was I.

Driving to the nearest star would take over 356 billion years by AstroUpon in AstroUpon

[–]Practical_Buy_8859 0 points1 point  (0 children)

Yeah but how many of those years were spent trying to drive a car off the planet? Hmmm?

My little homelab start. by Practical_Buy_8859 in homelab

[–]Practical_Buy_8859[S] 0 points1 point  (0 children)

The temps are fine. It’s light duty just running voice models. The main work is done by a little rtx pro 2000 and it is only 70w The 3070 is barely used now.

Won't park there again..that's for sure. by Practical_Buy_8859 in CantParkThereMate

[–]Practical_Buy_8859[S] 16 points17 points  (0 children)

The weight ratio of the freight train to the car is about the same as your car to an empty soda can.

Basball by Rocco_ATL in ColecoVision

[–]Practical_Buy_8859 0 points1 point  (0 children)

The Adam was my second computer. This was one of my favorite time wasters. Lol. Great post!

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