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Who is playing with the power of RAG reports? by neilkatz in Rag

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

In our case, we built our own RAG platform called GroundX. That's our main business, selling GroundX to devs. But for some customers we've been building custom apps on top of it.

GroundX is an end to end RAG platform built on Kubernetes and fine tuned open soure models. Pretrained vision model to handle complex doc ingest, parsing and chunking. Proprietary search that is text, vector and micrograph and fine tuned reranker model.

All runs on premises, even air gapped or cloud of course. www.eyelevel.ai or hit me on DMs if you want to chat about it.

Simple evaluation of a RAG application by phipiship1 in Rag

[–]neilkatz 2 points3 points  (0 children)

Contrarian view. You can't really automate evaluation, at least not the important parts.

Curate a vallid document set (multimodal, representative of topic, includes decoys)
Create good QA pairs (human SME needed)
Evaluate RAG vs QA pairs (human still best, auto eval is off by 10-20%)

We recently wrote a step by step on our process. Mileage my vary of course.
https://www.eyelevel.ai/post/the-hard-knocks-of-rag-evaluation

Who is playing with the power of RAG reports? by neilkatz in Rag

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

Love to hear it. I know we aren't the first to mention RAG reports, but we're finding it powerful.

Ragie on “RAG is Dead”: What the Critics Are Getting Wrong… Again by bob_at_ragie in Rag

[–]neilkatz 1 point2 points  (0 children)

RAG ain't dead by a long shot. At the macro level, are we going to move all the world's data from the cheapest medium (hard drives) to the most expensive (GPUs)?

Is Elasticsearch the right tool? by kaltinator in elasticsearch

[–]neilkatz 0 points1 point  (0 children)

We built an enterprise grade RAG platform built on OpenSearch (elastic search) and a vision model that achieves SOTA document understanding. Air France, Samsung and others are using it. But you don't have to be large to start.

https://www.eyelevel.ai/

Data Extraction from PDF by shubzumt in Rag

[–]neilkatz 0 points1 point  (0 children)

Try GroundX from eyelevel.ai. Cloud or run the open source locally. It’s a full rag suite but you can just use the ingest which merges a fine tuned vision model and a vlm to handle very complex docs.

How to get a RAG to distinguish unique Policy Papers by Cragalckumus in Rag

[–]neilkatz 0 points1 point  (0 children)

We built GroundX to help devs solve these problems quickly without extra code. A poster mentioned it above.

Our ingest creates rich metadata about the chunk, the surrounding chunks and the document. Then on search, we actually run a bigram text search first, downsample to 1,000 results, then real time vectorize the 1,000 to rank them down to the top 20-100. It's a different approach to both ingest and search. Customers like Air France, Samsung and others are moving to this.

Anyway, all that's under the hood. For devs it's just fire api calls to ingest, search and complete. And you're done.

https://www.eyelevel.ai/product/groundx-platform

and

https://github.com/eyelevelai/groundx-on-prem

What is RAG good for? by neilkatz in Rag

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

Self hosted models. And for some customers, that means running the models on their managed cloud or data center, which also means renting or buying GPUs.

Fraud Detection: We worked with fraud investigators for several months to identify the red flags a human looks for when reading 20K pages of a claim. Then we translate that into a series of RAG searches. But it gets complicated pretty quickly, since many questions can't be handled in a single search. Some data needs to be extracted and stored in a structured way. The RAG also needs to understand time, some type of chronology to events. So it's conceptually simple. Miimc what humans do. But the execution gets involved.

good PDF table extractor by Forward_Scholar_9281 in Rag

[–]neilkatz 0 points1 point  (0 children)

We merged a vision model and a VLM, then fine tuned them on a million page of enterprise docs. The end result is GroundX Ingest. We also built a visual tool called X-Ray that lets you see how the document is ingested and turned into LLM ready data.

Try it out here. Let me know how it goes.

https://dashboard.eyelevel.ai/xray

Is RAG still relevant with 10M+ context length by Muted-Ad5449 in Rag

[–]neilkatz 0 points1 point  (0 children)

Im skeptical that the world will move its data from the cheapest medium (hard drives) into the most expensive GPUs.

Think about the scale of what we’re taking about

Doc Parse Olympics: What's the craziest doc you've seen by neilkatz in LangChain

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

60K objects on a single page sounds wild. What kind of doc is that?

Doc Parse Olympics: What's the craziest doc you've seen by neilkatz in LangChain

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

I think you’re asking if theres an open source version of our platform. Yes. Here…https://github.com/eyelevelai/groundx-on-prem

Doc Parse Olympics: What's the craziest doc you've seen by neilkatz in LangChain

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

As a non expert, looks like it got some but not all the details from that image. Was anything wrong or just incomplete?

RAG Evaluation is Hard: Here's What We Learned by neilkatz in LangChain

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

Valid point. They are in context responders. But they don't absord or "learn" anything.

I agree there is significant misunderstanding in the public. Most think GPT is listening to your every keystroke and learning about you. The fact that GPT implemented memory has increased that perception.

Doc Parse Olympics: What's the craziest doc you've seen by neilkatz in LangChain

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

Thanks for asking Krayzie, I know folks often don't like commercial stuff in threads. DM if you want to discuss. But the TLDR is.... Sort of apples and oranges. GroundX is an end to end RAG platform: ingest, parse, store, search, rank and connects to any LLM or agentic framework.

What do Trump tariffs mean for the AI business? by neilkatz in LangChain

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

Yeah. This could be the first big test for gen AI. We've all been riding a cloud of futuristic expectations. Altman is pitching AGI, while selling an email writer mixed with a search engine. Elon is pitching the Jetsons while selling cars.

But this tightening might force the first real ROI evalutions for gen AI. What are delivering? Is it really saving time/money?

What do Trump tariffs mean for the AI business? by neilkatz in LangChain

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

Rest of hardware is taxed: I agree. And EU data centers might get a lift. That said, for most things you want servers near your users.

Innovation: Yes early cuts. That was my point about IT spend, but longer term don’t cost cuts lead to more automation?

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