This is prob one of the best RAG video I've seen, so many learnings in 20 mins

Man, your videos keep getting better every time I look. You have a great mind and your presentation is excellent. Thank you very much, again, for sharing!

@magicismagic123

13 күн бұрын

he is much better than 99.9% wanna be over hyped ai gurus on youtubu, twitter and linkedin!

One of the most informative RAG videos I’ve seen. Can’t wait to see more from your channel.

Firecrawl boosted our RAG accuracy at our company. fast + provided good markdown format. Llama parse also super helpful too! Amazing video Jason! This is gold! Edit: thanks for the likes :)

@rafaelmiller9147

21 күн бұрын

The search api is just insane on firecrawl

00:05 AI can revolutionize Knowledge Management 01:46 Llama3 can process precise knowledge with fast inference 05:27 Market strategy for AI startups 07:16 Convert PDF files to markdown format for enhanced accuracy and control 10:47 Finding the optimal chunk size through experiments 12:34 Hybrid search combines Vector search and keyword search for better results 16:12 Building a local agentic RAG with llama3 17:48 Running Llama3 model on local machine and using Visual Studio Code 20:53 Setting up key components for Llama3 performance 22:20 Creating a complex agentic RAG workflow for document retrieval and answering

Excellent video! I don't have much experience with RAG and this was sooo helpful!

1) The link for the corrective RAG agent had an extra URL attached at the end which caused it to fail; manually tracing the link got me to the proper location 2) LlamaParse looks like a wonderful tool, since I have a lot of documents with equations, and I really need it to grab equations, if for no other reason than to return them. Unfortunately, LlamaParse requires an API key and seems to send PDFs off for processing, something that others have noted and there is an open issue from 2 weeks ago. As of 3 hours ago, it's still an open issue - clearly most companies don't want to send internal docs out of house. Hopefully this gets resolved soon. 3) Really liked your presentation - easy to follow every step with the provided materials.

@dennou2012

19 күн бұрын

Hopefully we will have more better options for local use - shame it's not a local only pipeline yet

@yunxinglu4020

17 күн бұрын

yes - I have found this issue too. LlamaParse seems use OpenAI llm to process the pdf and it leads to the privacy concerns.

Holy crap! This gave me such amazing background knowledge, love it! Now, what would be extra cool, would be if you could do a real "hands-on" type of workshop to go through it all by setting up the environment completely, including the actual training/RAG implementation of a set of various document types (PDF, excel, website etc..) to extend a locally running llama 3 instance 😊

Yet again an amazing tutorial, thanks so much Jason!

This is the best RAG video on the internet, awesome job, no fluff, high complexity but easy to understand, nice work

Great content! Thanks for putting in the effort. Will use this.

Really great tutorial, teaches a lot in very short time! Thanks!

What a great video! Thanks for sharing your knowledge

Jason, I watch a lot of AI videos but I learn the most from yours. I am actually excited everytime i see you have put another one out. Keep up the great work!

You always amaze me by the amount of knowledge I get from your videos

dude... great video! Thanks for the knowledge!

Didn't know about the Agentic RAG techniques, thanks for sharing!! That's definitely a trade off between speed & quality, but good to have the option

This is extremely helpful! Awesome!

Amazing tutorial! Thank you

right when i needed it, thank you man! also, just finished watching and i understood the theory behind it but kinda got lost during the code explanation, i might watching again and again

Amazing info shared -. Thank you!

Great tutorial! Thank you

Awesome content!

You're the man 💯👏

Keep this up. This answered to loads of questions I have had previously, and were not answered in any of the HuggingFace tutorials!

Subscribed, dont have an AI company since I'm still a poor student... this video was very informative, the man speaks at two times speed just like my professor. I respect it 😁

wow nice work thanks!

Great timing! Why do you always read my mind JASON!!?! lol

many many thanks, bro!

This is a great trick, thanks

Thanks... Awesome video

great video keep making these please.. only "criticism" / advice if you can call if that is to keep things focused on local / open source solutions as much as possible.. love the use of Ollama here for example.. things that perhaps don't require API keys, subscriptions, external integrations / dependencies help people like me understand more of what's going on in a workflow like this! thanks again!

Solid video Jason

Great video, thanks

Such a bait and switch. Thumbnail promises fine tuning tutorial. Delivers best improve-your-RAG video on the internet. Excellent work.

awesome jason thank you

I am literally using this technique now in my internship for a project. I went through so many approaches and ended up on my version of this one. Wish you released this video about 2 months ago lol

Thanks! It's so fascinating how these programs 'think.' Even if I don't install one, concepts like chunking seem to translate to humans as well.

Thanks!

I prefer finetuning to RAG first then RAG on top of the finetuned model. Just a simple QLORA is all you need. It really helps a ton.

@helix8847

20 күн бұрын

How would you go about doing that, as in just do it backwards from the video?

The corrective RAG schema explains why AI often tries to bring results from the web even when you tell them not to in prompt. If it doesn't understand the source properly it will look elsewhere. This was insightful, thank you.

You are relevant, Subscribing to your channel!

OG Jin Yang from Silicon Valley.. Amazing video 🎉

This is epic! keep up...

Platform agnostic LLM space overview videos from Jason are the best on AI YT

I have to say, it is great :D

Thanks Jason, great video, this explains RAG pretty well. Subscribed!

Best video💯

Clicked that BELL too! 🔔

Amazing lesson! I learned a lot in just 20 min!

You got a sub. Finally, an AI channel that actually teaches.

Amazinnnnggggg🎉🎉🎉🎉

thx

I thought we were gonna fine tune llama3 😢 but the fire crawl implementation looks unreal I’ll have to check that out and add it to my rags. I don’t know how well it’ll work for RAGs but people have extended the context window like crazy and still can do the needle in haystack to around 130k. If you have 64gb on the Mac you can try out the 256k context window Llama 3 released by Eric Hartford. Would love to see a side by side with both of them using the same embeddings.

great video jason! quick question, im wondering if a knowledge graph in place of vector database would be better since it mitigates the lost in the middle problem?

Great video, thanks. New subscriber (and like) here. I had a couple of questions though: why use langchain? It seems unnecessary from what I have read. Would also love a demo ipynb/copy of code.

This answer a lot of questions why my chat with PDF doesn't work, llama parser & firecrawl looks so freaking good!

Came to train my 3 Llamas... Now I'm a full stack developer.

The speaker in the transcript discusses the use of AI, particularly large language models, in knowledge management. They highlight that AI can provide value in managing vast amounts of documentation and meeting notes, which can be overwhelming for humans to process. The speaker also mentions the potential disruption of traditional search engines like Google by large language models, which can provide hyper-personalized answers based on their extensive knowledge. The speaker then introduces the concept of a retrieval augmented generation (RAG) pipeline, which involves extracting information from real data sources, converting them into a vector database, and retrieving relevant information to answer user queries. However, they also note the challenges in building a production-ready RAG application, including dealing with messy real-world data, accurately retrieving relevant information, and handling complex queries that may involve multiple data sources. The speaker also discusses various tactics to mitigate these challenges, such as better data preprocessing, optimal chunk size, relevance-based retrieval, and hybrid search methods. They also mention the use of agentic RAG, which utilizes agents' dynamic and reasoning abilities to decide the optimal RAG pipeline and improve the answer quality. The speaker concludes by expressing their curiosity about how AI-native startups operate and embed AI into their business processes. They recommend a research document on the subject for those interested. In summary, the speaker's points are: 1. AI, particularly large language models, can provide significant value in knowledge management. 2. Traditional search engines could potentially be disrupted by large language models. 3. Retrieval augmented generation (RAG) pipelines can be used to answer user queries based on private knowledge. 4. Building a production-ready RAG application is complex due to challenges like messy real-world data, accurate retrieval of relevant information, and handling complex queries. 5. Various tactics can mitigate these challenges, including better data preprocessing, optimal chunk size, relevance-based retrieval, and hybrid search methods. 6. Agentic RAG can further improve answer quality by utilizing agents' dynamic and reasoning abilities. 7. The speaker is interested in how AI-native startups operate and embed AI into their business processes, and recommends a research document on the subject.

@pithlyx9576

18 күн бұрын

Dead internet thory is getting closer and closer every day

I watch lots of AI videos and 99% of them are just a waste of time. As an AI engineer, this channel is hands down the BEST yet KEEP UP👏🏼

here come dat boi!!!!!!

Awesome content Jason. A Question. I need to create an AI psychologist and store college data, but this college data is a guide of what to speak, not the content itself. In that case, what is the best approach, RAG or Fine-tuning?

Hey Jason thanks for the video, I think it helps a lot. Can I apply on GPT as well?

Hi brilliant session , do you have a link for the notebook ?

I don’t understand everything but I can feel the gold penetrating my ears

Great video. Thanks! A lot of very good tips!

would be great to get a video on best methods for data extraction from these pdfs

Thank you. Can you say a little about your hardware setup for this work? This information is missing from a lot of online sources.

too many api calls here - do it local with no api calls - better and the model has to be able to crawl more doc formats - people will probably do p2p, real time and uncensored models for 'real' open source ai that has no limiting factors like api calls or tokens - this is where things need to go in order to take off, gain relevance and leverage economies of scale, of course cxl and better i/o will help but those are on the way. real open source ai will hit smb mkt in about 4-5 years and there will be more innovation and discovery - exciting times as we all watch the development curve

great content! why wouldn't you use groq to speed up the agent response?

Interesting. Someone needs to create a wrapper which works out the best way to answer questions / queries, based on the input and question/query. I think intelligence of system could then be increased.

I'm a simple man. I see a new AI Jason video, I click.

Good rag video, the thumbnail taking about "training llama3" is hurting my brain tho

Great thanks. Can we get the repo and link to the colab notebook?

Thanks, Jason, incredible as always! Would you consider sharing the code from the walkthrough? 🙏

@AIJasonZ

15 күн бұрын

Thanks mate, appreciate it! Code is in the description link!

@yashsrivastava677

9 күн бұрын

@@AIJasonZ Link is not there

Goddamn it Jian Yang

Great video Jason, I only missed routing as a technique to determine if your question should really go through the RAG. James Briggs has done a few good videos on “semantic routing”. Is your example notebook available somewhere?

@christenjacquottet9799

5 күн бұрын

I'm wondering the same thing. Don't see a link to a github repo

What about preparing data, for exemple as question / response, the response would be used to generate embedding and the response would be the data retrieved ?

Very usefull, thank you! Is it posible for the model to retrieve images or graphs from a PDF, or it's only text?

Great video. How do I add PDF documents and llama_parse to the python notebook?

I like using gemini for getting quick up to date answers, and chat gpt for stuff that doesn't require up to date stuff

amazing as always. could you share the notebook please

great video! Is there a github location with the code?

We been trying to build a middleware that connects with any inventory ERP to be able to have real time data information about inventory data for the chatbot

Hi, what are the areas current LLMs excel at? I am new to this world of AI, but not IT (familiar with infra). It is good that people are trying out things to see what it can do. But my naïve thoughts are that as a language tool, it just looks for patterns of words that appear close together, and knows enough of the formation of language that it produces text that is not only readable, but also relevant. But this surely must have limits, if it does not actually understand? Would it be serving up answers from a well vetted and written sources such as internal KMS by using this RAG method? Our team was thinking about it use for education / learning - perhaps tied into custom flashcard and evaluation of human provided answers. Alongside the still very useful text summarisation, alternative wording suggestions.

👍👍

4:36 girlfriend walks casually in the background

Would you have plans to create a tutorial that connects what ur teaching here and running thing on something like AnythingLLM that allows document reading to create embeddings.

thanks Jason, can i use llama on API and train PDf files in a specify directory train to respond

4:36 Someone walks into the void and disappears

How would you go about parsing documents of all kinds of types? PDFs, Excels, Word etc...Is there a way to achieve this with only one parser? Or how would you go on about this issue?

Curious how this workflow changes with bigger context length. Gradient just released Llama-3 8B with a 1M context length

Is those steps and advices are explained on your website ? It would be amazing if you could share the code 😮

Can we also finetune the 70B model? Even if its not local

Can you share the code in the video?

@basedmuslimbooks

18 күн бұрын

I was hoping that was the case since it's a "simple" workflow

@pollywops9242

17 күн бұрын

The code is personal you need to apply for a download link with meta and it will provide the code to copy / paste

@christenjacquottet9799

5 күн бұрын

@@pollywops9242 apply where? I don’t see it

@joesmoo9254

Күн бұрын

​@@pollywops9242😂

Damn it Jin yiang 😂

Would there be a way to automate this with Obsidian? I sporadically log everything in Obsidian and it would be amazing to find a way to do this with Obsidian

is there a good parser for powerpoint?

Fucking dope bra

Can u create end to end custome fine tuning LLM LLAMA with API