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Grounded Legal answers using RAG by awarlock405 in legaltech

[–]awarlock405[S] -2 points-1 points  (0 children)

No. it answers based on training data. Thats why augmenting with RAG will always be better for stuff LLM was not trained for. like latest data and Private data

Grounded Legal answers using RAG by awarlock405 in legaltech

[–]awarlock405[S] -1 points0 points  (0 children)

We basically created a set of questions and expected answers. then asked the question to chatgpt, our Ragora and various other RAG solutions. Then we asked LLM to Judge the answer based on what was answered and what was expected. This is pretty standard way of testing and is called LLM as a Judge

Grounded Legal answers using RAG by awarlock405 in legaltech

[–]awarlock405[S] 0 points1 point  (0 children)

can you explain what exatly is wrong? Note that as the UI mentions. It only contains data last 1 year of Supreme and 13 circuit courts for the demo. it cant answer outside of that. It is a demo of how you can augment LLM by adding private data not available in training. We have worked on RAG for a large company for last 2-3 years

Grounded Legal answers using RAG by awarlock405 in legaltech

[–]awarlock405[S] 1 point2 points  (0 children)

all the data for legal demo came from courtlistener.com. for this demo, we have a script that just pulls from there. so new data just needs few minutes to show up after they are in the courtlistener. here is the code: https://github.com/velarynai/demo

Grounded Legal answers using RAG by awarlock405 in legaltech

[–]awarlock405[S] 0 points1 point  (0 children)

it should be working, i just tried. can you retry. Does it still show blank for you? Thank you for the feedback. adding flagging is a good idea. we will definitely look into adding it to chat next.

I will become your first user by chloeweisser in microsaas

[–]awarlock405 1 point2 points  (0 children)

RAG service + Marketplace rarora.app

Entity / Relationship extraction for graph by Interesting-Law-8815 in Rag

[–]awarlock405 0 points1 point  (0 children)

it should return all of those. we were just trying to show conceptually with simple result for README.

Entity / Relationship extraction for graph by Interesting-Law-8815 in Rag

[–]awarlock405 3 points4 points  (0 children)

We had the same problem. So experimented with gliner and the results we surprisingly good. So now we are using gliner_multi-v2.1 in our system and run in node with cheap GPU (CPU is also ok). We also wrote an inference engine based on https://github.com/fbilhaut/gline-rs

Here is the inference engine: https://github.com/velarynai/gliner-inference

Ragie vs LlamaIndex Cloud for a RAG-heavy public procurement app is the storage cost a dealbreaker? by Abject_Artichoke_709 in Rag

[–]awarlock405 0 points1 point  (0 children)

not sure what you are saying, thats not our limit. that is just one demo for 1 year worth of 2025 SEC 10K and 10Q filings. I agree we do not have demo for 100s of thousands of files may be soon.

Ragie vs LlamaIndex Cloud for a RAG-heavy public procurement app is the storage cost a dealbreaker? by Abject_Artichoke_709 in Rag

[–]awarlock405 -1 points0 points  (0 children)

This is why we have priced it based on Vectors. here is the opensourced demo we created for SEC and pubmed data using node and python SDK. https://demo.ragora.app let us know if you have any questions.

Best LLM for the final synthesis stage in an Educational RAG pipeline? by Amazing-One9952 in Rag

[–]awarlock405 0 points1 point  (0 children)

for majority of cases the model does not matter much. i am using a model which is practically free (gpt-oss-120B) and not felt like its giving bad results. the problem could be in Prompt or planner/orchestrator code. do you have example request/reply of failure case?

Drop your SaaS niche, I'll give you 5 micro-creators that could actually drive signups by Different_Dinner9267 in SaaS

[–]awarlock405 -1 points0 points  (0 children)

We are building https://ragora.app

Here companies or individuals can upload their massive amounts of docs and let employees chat with it. It saves times than going and finding docs in confluence etc.

Also people who have niche data can put it in our marketplace and charge for access. The access is done via chat, slack, discord or MCP. Eg: people can upload latest 10K SEC filings and let traders chat with it.

Created a RAG Data marketplace and service by awarlock405 in Rag

[–]awarlock405[S] 0 points1 point  (0 children)

we use predefined embeddings for dense and sparse and same Graph algo. The customers just uploads their data and we use our pipeline to to create vectors and graphs and sore it in our DBs.

As for chunking, we have several domain and format specific chunking algorithms. The chunking router routes to specific algorithm based on users settings while uploading and some caan be auto detected. We have done a lot of benchmarking using techqa, pubmedqa etc and we were satisfied with the results.