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LOL I think this entire video is made with an A.I. clone of you :P

Really Informative 😊

I have a use case with a haven't seen anywhere: I create a private GPT that has documents as contexts. This documents has criteria for a specific subject I give it system instructions so its function is to evaluate a user document that it's attached as a part of the prompt, to see if the document complies with the criteria in the context documents, and give detail response on the result of evaluation and the justification that includes, the content of the user document and the criteria in the context documents. I want to do that in LangChain but I don't know hot to add a user document as a part of the prompt for the RAG.. It would be great if you can you explain how to approach this implementation. Thanks you for the content!! Keep the good work.

pricing, pricing, pricing always pricing.

Great Insights

Hey! Does the 4o version use something similar?

Rip to semantic search for large datasets 😂! But interesting approach.

Love how straight to the point the explanation was

8:52 Thou shan't troll the beginners for you were one too. - Pro Code, Rule 4.

Nicely done

This is an amazing professional content! it hits the point directly

This is an insanely detailed from first principles tutorial. Thank you for taking the time to put this together.

While running the code elements=partition_pdf(filename=filename,strategy="hi_res",infer_table_structure=True,model_name="yolox") in my Jupyter notebook, I encountered errors such as TesseractNotFoundError. If anyone has faced this issue or knows how to solve it, please guide me as soon as possible. Great work, sir. I can confidently say that such a combination of content and explanation is unparalleled in the RUclips world. @DataIndependent

My question is pinecone only stores vectors and not text files, how do I get the texts in my program

Love this! I'm working with transcripts where semantically generated chunks can be quite large. These chunks need to be further divided to fit the limits of the embedding model. Given this, isn't semantic chunking unnecessary if we ultimately have to recursively break down the larger chunks into smaller ones?

You Good sir are not a Guy who talks the talk. I learnt more about Langchain from you in this half hour than anybody else I have listened to on the last 3months. The veritable quarter to be exact. You are a Guy who walks the walk 🫡

Liked this semantic splitting! Cool stuff you´ve done there!! Also agentic chunking. Pretty cool!!!

great video, thanks you

Hey Greg, thanks for this video! Since, there is a limit to access open ai api key without paying, how can the above implementation be carried out with other open source LLMs ?

Fantastic tutorial! It would be great to see another tutorial using "transformers" instead of openai with chroma or any local database... and how will you save the extracted information.. does Kor tokenize that information, etc?

Great video! it helped me clarify the past and present of all the chunks. I have a question, in agent chunking, there can be an issue of having too much content on a single topic. In extreme cases, an entire book might be about one topic. How should we further break it down in such situations?

That was really helpful, thank you for information you're sharing!

was working then: RuntimeError: Task <Task pending name='Task-10' coro=<AsyncLiveClient.send() running at C:\Users\Sarah\.conda\envs\deepgram\lib\site-packages\deepgram\clients\live\v1\async_client.py:251> cb=[_chain_future.<locals>._call_set_state() at C:\Users\Sarah\.conda\envs\deepgram\lib\asyncio\futures.py:392]> got Future <Future pending> attached to a different loop

Hey Greg Thanks for an amazing tutorial, really loved all the strategies. I have couple of doubts. So if we do agentic chunking wont it cost too much and also considering the scale of the data we have and also i believe it would be very costly to update with a new item aswell every time. What about retrival strategies, Also how to tackle retrival strategies in QA chat setup. Thank you once again.

Excellent, clear video, inspired me to check out more of your content - cheers! On an aside, anyone ever mentioned that you kinda look like Dennis from Always Sunny? 😅

I also never thought of purpose built chunking or semantic proxies like that. Adding question hypotheticals as the embedding could be extrapolated to other use cases.

Imagine langsmith for chunking. Something like the agentic flow you have but all chunk titles, summaries ECT... Are kept and able to be used for future tuning and other iterations.

Nice one! how to insert list in df[list].mean()?

Greg your video tutorials are making the world a better place, I just wanted to remind you of that!!! They are perfect, feels like I hired a mercenary to better understand lang chain and the ai ecosystem. As a full-stack typescript developer I feel confident I can combine your tutorials with my startup idea and run with it. Thanks!

Just Awesome .. Thanks a lot for making and sharing this video ..

this is quite helpful.can you help me with some references code/links how to extract transcribe from anyvideo, not just youtube links, any link and do al these analysis after it?

amazing video ! very helpful ! thanks !

I just reduced summarization time on our Gen AI product to a maximum of 10% and also 10% the cost using BRV on large document! Thank you so much! You have a Patreon?

Could you also add the latency for Audio to text of your voice?

So, it is possible that some Twitter profiles that are commenting and responding on people's tweets are also AI bots?

Imagine taking anything written by Booz Allen Hamilton seriously. Their devs in 2024 still struggling with printing “Hello, world!”

I got hooked to your videos - came to watch one and now binging! quite nice :)

Yuppppp

Great explanation ! Just one blip - s/n ratio reduction. It happens to all of us 😊

Great explanation - Just one blip - s/n ratio reduction. It happens to all of us 😊

29:13 Now there is GPT4-O ... So you were right with the prediction 😂

No mention of costs? Is it free?

Hi Greg, appreciate the fantastic breakdown of text splitting for LLM. Personally I keep finding LLM's having trouble retrieving whole chapters or pages. I was wondering why we wouldn't split based on page number instead of paragraph, would that slow down the LLM ? I'm temped to train a local LLM on chunking based on user input.

good video but you speak so fast

It's awesome, very interesting. but you teach and speak too much fast. I think it's better to explain codes and steps more.

Came for a quickstart guide - got a 50% guide. Was good information, but the things i came here to understand you said: "Already did that, so I won't do it." :( Otherwise Thanks!

Would LOVE to see an end-to-end setup for these 🙏

Very clear. Thank you!

These videos are great, thank you!