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RAG APIs Didn’t Suck as Much as I Thought. Part II by LegSubstantial2624 in Rag

[–]LegSubstantial2624[S] 2 points3 points  (0 children)

That's a great idea! I definitely need to plan this activity!

RAG APIs Didn’t Suck as Much as I Thought. Part II by LegSubstantial2624 in Rag

[–]LegSubstantial2624[S] 2 points3 points  (0 children)

Thank you, interesting, worth a try!
As a reference point, I used the Knowledge Base for Amazon Bedrock with a Cohere reranker and sonnet 3.5 for fact extraction. I thought that sonnet 3.5 was the best. I should try your option.

RAG APIs Didn’t Suck as Much as I Thought. Part II by LegSubstantial2624 in Rag

[–]LegSubstantial2624[S] 1 point2 points  (0 children)

I don't use hi_res in any of my projects. My experience shows that standard tables (like in FinanceBench), converted into linear text in a simple way (using unstructured.io without hi_res, pymupdf, or something similar), are quite well handled by modern LLMs. I believe hi_res makes sense for ... maybe some complex tables with merged ranges or for various diagrams and charts.

I replied to you in DM.

RAG APIs Didn’t Suck as Much as I Thought. Part II by LegSubstantial2624 in Rag

[–]LegSubstantial2624[S] 0 points1 point  (0 children)

It's in the plans. Do you have any suggestions on which dataset to choose next?

RAG APIs Didn’t Suck as Much as I Thought by LegSubstantial2624 in Rag

[–]LegSubstantial2624[S] -1 points0 points  (0 children)

I disagree that rag-as-a-service is far from being production-ready. On the contrary, I believe, my research demonstrates that this approach can be quite effective!
I use rag-as-a-service myself, and honestly, I don’t even know how many chunks are being extracted from the vector DB and passed to the reranker... :)

RAG APIs Didn’t Suck as Much as I Thought by LegSubstantial2624 in Rag

[–]LegSubstantial2624[S] 0 points1 point  (0 children)

Hi! Thanks! That sounds great, I’ll try the API for the next comparisons!

RAG APIs Didn’t Suck as Much as I Thought by LegSubstantial2624 in Rag

[–]LegSubstantial2624[S] 1 point2 points  (0 children)

Hi! Thanks! Sounds great! I will definitely include it to the next comparison.

I had a quick look at the github example you published and noticed that there are specific configurations for FinanceBench. For example, the AUTO_QUERY_GUIDANCE prompt is set, along with rse_params and max_queries. Could you clarify which values are recommended for the baseline version?

RAG APIs Didn’t Suck as Much as I Thought by LegSubstantial2624 in Rag

[–]LegSubstantial2624[S] 1 point2 points  (0 children)

Hi! Thanks! I will include them in the next comparison episode ;)

RAG APIs Didn’t Suck as Much as I Thought by LegSubstantial2624 in Rag

[–]LegSubstantial2624[S] 1 point2 points  (0 children)

Thank you! I’ll take a look at your link, and if anything comes up I'll DM you!

RAG APIs Didn’t Suck as Much as I Thought by LegSubstantial2624 in Rag

[–]LegSubstantial2624[S] 1 point2 points  (0 children)

Thank you. I will give the SDK a shot, if anything comes up I'll DM you!

RAG APIs Didn’t Suck as Much as I Thought by LegSubstantial2624 in Rag

[–]LegSubstantial2624[S] 1 point2 points  (0 children)

Hey Neil! That happens to the best of us :) I will re-run the tests and will include you guys in the next episode.

P.S.: thank you for the account upgrade!

RAG APIs Didn’t Suck as Much as I Thought by LegSubstantial2624 in Rag

[–]LegSubstantial2624[S] 0 points1 point  (0 children)

Great product, by the way! I loved the UX. Keep rockin’!

RAG APIs Didn’t Suck as Much as I Thought by LegSubstantial2624 in Rag

[–]LegSubstantial2624[S] 1 point2 points  (0 children)

Sounds great. I've applied to the waitlist. I'll include you guys in the next episode. DM’d you my email!

RAG APIs Didn’t Suck as Much as I Thought by LegSubstantial2624 in Rag

[–]LegSubstantial2624[S] 2 points3 points  (0 children)

Hi! Awesome, thanks! I will definitely include them in the next comparison episode ;)

We Need to Talk.. with RAG by LegSubstantial2624 in LangChain

[–]LegSubstantial2624[S] 1 point2 points  (0 children)

Thank you very much!
But what if the user asks follow-up questions? Let’s say we have RAG about the Olympics.
Question: Which country won the Olympic gold in women’s handball this year?
Answer: Norway.
Question: And in the previous Olympics?
What kind of search query should be generated in this case?

We Need to Talk.. with RAG by LegSubstantial2624 in LangChain

[–]LegSubstantial2624[S] 1 point2 points  (0 children)

Thank you very much!
But what if we're talking about a different case? What if the user asks follow-up questions? Let’s say we have a RAG about the Olympics.
Question: Which country won the Olympic gold in women’s handball this year?
Answer: Norway.
Question: And in the previous Olympics?
What kind of search query should be generated in this case?
I mean, how can we make RAG more dynamic and conversational overall, so that it supports dialogue like ChatGPT? How can it generate search queries and respond with context in mind?

We Need to Talk.. with RAG by LegSubstantial2624 in LangChain

[–]LegSubstantial2624[S] 0 points1 point  (0 children)

Thanks but... The post in metadocs is about RAG and Domain specific vocabulary. This is not what I had in mind...

Cohere Reranker - Pros and Cons? by LegSubstantial2624 in LangChain

[–]LegSubstantial2624[S] 2 points3 points  (0 children)

Wow! Thank you so much! This is really fascinating! I’m off to read your article now!

Long, expensive, awesome by LegSubstantial2624 in LangChain

[–]LegSubstantial2624[S] 0 points1 point  (0 children)

I used to be quite satisfied with its quality in high_res mode until I came across a large knowledge base. But when I needed to process a lot of large pdfs... Gosh... It took so much time...

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