I did not find before a simpler and more practical explanation of running local model and using Langchain. Congrats
@ahmed_hefnawy18115 ай бұрын
is that solution solve multihop question that happend in basic RAG !?
@jim023777 ай бұрын
That is one of best explanations of using local models, langchain and langGraph that I have seen. Awesome job!
@priyanshugarg61757 ай бұрын
Hi. Would we need a high end pc to run models locally ? My computer has only 4gb RAM which I suspect would not be enough.
@jim023777 ай бұрын
It won't be. I ran it on an i5 with 16GB of RAM and it can process one token per second. It is better on a the same machine running 32GB but still a bit slow. Trying it on a M2 mac next and an old server with 80GBytes of RAM @@priyanshugarg6175
@JJN6316 ай бұрын
@@priyanshugarg6175you need at least 8 ram and 8vram
@roscatres6 ай бұрын
@@priyanshugarg6175 Yes, you do need a powerful computer. As a rule of thumb, you can run models that are smaller than your VRAM (not RAM, the memory of your GPU). There are exceptions and other ways, but yes, you need powerful computers for this.
@aladinmovies6 ай бұрын
@@priyanshugarg6175with linux it's enough for small models not more than 1Gb
@joshuacunningham79126 ай бұрын
I love Lance’s teaching style. He makes this topic so accessible! 👏
@michaelwallace47577 ай бұрын
Great job explaining the process and keeping it understandable to a non-programmer!
@SolidBuildersInc4 ай бұрын
Ok, this was a jewel of a find and I couldn't have said it better than you did in your closing. Up until now I was looking for a local model solution to perform the agents and only could find OpenAI for LainGraph. Your description of how each node is performing a specific discpline with an ability to have a Boolean to proceed to the next step is really simplifying the experience. I have been saying to myself how this is really just writing code with some functions so why is it so exciting? It's basically integrating the AI into our everyday coding sandbox that we are so familiar with and giving it that Python Syntactic sugar experience. Anyway I really really appreciate you sharing this approach. It is so straightforward and clean to just Build whatever you want and now maybe reuse the code for other models instead of agents. It's endless
@JoshuaMcQueen7 ай бұрын
Your videos continue to be pure gold. Keep 'em coming!
@Egorfreeman7 ай бұрын
This is a very helpful and practical video. And I would be interested in seeing an implementation of a chat application using Langraph and FastAPI.
@jofus5214 ай бұрын
Thank you. Wow. This example is even in the TS/JS repo. That is so awesome
@donb55217 ай бұрын
Thanks for another great video and notebook driven tutorial. Appreciate the heads up on JSON mode in Ollama - a lot of great functionality built into their API.
@jim023777 ай бұрын
Same question on the different configurations (The Windows ll PC is actually an i7). "Why is the sky blue". It took 3 minutes on the I7, 45 seconds on the old server and 17 seconds on the Mac.
@roscatres6 ай бұрын
I assume only the Mac has a GPU?
@jim023776 ай бұрын
That is correct. I can't get a card for the old server and the battery life with an NVIDIA card in my laptop would have been terrible. I have heard the Mac has a GPU and I have heard it has a neural processor. I am not sure what the M2 has but it works well. Especially for only having 8gigs of RAM@@roscatres
@kenchang34565 ай бұрын
Thanks for this. I see the value and now I have a way to toy around with it and learn.
@MikewasG7 ай бұрын
Thank you very much for your efforts, your videos are very helpful to me!
@aladinmovies6 ай бұрын
Good explanation, only about the topic
@TournamentPoker7 ай бұрын
excellent video! Thanks for sharing!
@nguyenanhnguyen76583 ай бұрын
Rerank model that is trained on relevant dataset will help, and that is the most important
@kevinkawchak4 ай бұрын
Thank you for the discussion.
@shakilkhan43067 ай бұрын
Fantastic job bro.. Thank you.
@preben017 ай бұрын
This was sooooo usefull, thank you so much for this.
@jim023777 ай бұрын
I ran it on a i5 PC running windows 11 with 16GB of RAM and it can process 1 token per second. It is better with 32GB of RAM. I am trying an M2 Mac with 8GB next and an old i5 server with 80 GB of RAM.
@r.lancemartin79927 ай бұрын
Got it. I run on an m2 with 32GB of ram.
@jofus5214 ай бұрын
How about i7 with 128gb ram and 48 gb vram?
@nityasingh37 ай бұрын
Awesome explaination
@pich13377 ай бұрын
Awesome! Thanks!
@howechan48186 ай бұрын
I really appreciate.
@wuhaipeng7 ай бұрын
great sharing!
@lucianst.24447 ай бұрын
Awesome explanation. Thank you! But I have one question. I’m pretty new in this field. Why did you choose to use mistral instruct on behalf of mistral?
@shashank10627 ай бұрын
version of the model is fine-tuned for conversation and question answering.
@klncgty2 ай бұрын
For example I want to set up a rag sistei . Thanks to this RAG and LLM, it will calculate the data in my query according to the data in the PDF. For this, I get different results every time. How can I make this consistent?
@harshgupta36415 ай бұрын
Great explanation, why we used graph in the end to make the process works , can we do this through procedural or functional way ?
@mohamedkeddache42025 ай бұрын
how can i see the source of the web search ? in langsmith i see the documents that are retrieved, but i didn't find the source of the web site that was retrieved from.
@hari85682 ай бұрын
Is web search called when even one context is irrelevant?or does it have to be that all are irrelevant?
@ganeshkamath895 ай бұрын
I think the function should have returned Yes / No mapped to specific question instead of an overall Yes / No. Then the calling function should have done web search only for the questions which had got a No.
@jma78897 ай бұрын
In production env, what kind of GPU machine do you recommend for low usage case? Can it run with CPU only machine? Thank you!
@aladinmovies6 ай бұрын
Only with cpu you can run, 8gb ram nice, but 16 and more is good
@MidSeasonGroup7 ай бұрын
How close can RAG enabled agent automate actual software development. PDF review and summation is great but actually application development is what I want.
@r.lancemartin79927 ай бұрын
Do you mean that you want a coding assistant? We have a project coming out soon focused on this - can ingest a codebase and perform question-answering w/ executable code.
@MidSeasonGroup7 ай бұрын
Hi Lance @@r.lancemartin7992 thanks for getting back to me. That’s correct, an effective coding assistant for new and existing codebases. So many of our applications need to be refactored or rewritten altogether. I’m glad this is on your roadmap and looking forward to the beta.
@MidSeasonGroup7 ай бұрын
@@r.lancemartin7992 Yes, when is the project in beta.
@eointolster7 ай бұрын
Just starting the video but can we do rag on documents with check boxes. Like can we retrieve an answer as to whether a check box is ticked. Thank you
@r.lancemartin79926 ай бұрын
(This is Lance from the video) I've never tested check box state in documents. If it's in an image, multi-modal RAG would work.
@binstitus39097 ай бұрын
Can we build this application in production using the same approach as you do, downloading the model MistralAI? Can I use this in my production, or should I use APIs to access the model?
@choiswimmer6 ай бұрын
If you are here asking langchain this question means you fundamentally don't know or understand what it takes to deploy these models
@r.lancemartin79926 ай бұрын
(This is Lance from the video.) In production, it is typically user to use API unless you are deploying the model yourself.
@laurenstaples77785 ай бұрын
is anyone else trying this out on a Windows machine? I'm having an error "RuntimeError: Failed to generate embeddings: locale::facet::_S_create_c_locale name not valid" on the Index section code, seems to be a windows issue....
@laurenstaples77785 ай бұрын
I ended up installing Windows SubSystem Linux and running in the Linux environment and was able to get it to compile there!
@Jeganbaskaran7 ай бұрын
Any idea why langchain not integrate with LLingua. Is there any roadmap to implement this ??
@GeoffLadwig7 ай бұрын
nice
@chitrakshakaushik32667 ай бұрын
It doesn't work on windows as when we import WebBaseLoader it throw an error no module named PWD
@drm20056 ай бұрын
How can i use an api of langchaine to my RAG i found only open ai api ?
@akshatsingh60367 ай бұрын
You are not using format_docs function in generate fun. ===================== your code: generation = rag_chain.invoke({"context": documents, "question": question}) ===================== Should be this: generation = rag_chain.invoke({"context": format_docs(documents), "question": question})
@r.lancemartin79926 ай бұрын
(This is Lance from the video) Thanks, I'm updating the notebook and fixing.
@roi3656 ай бұрын
How is it local if we need api keys?
@r.lancemartin79926 ай бұрын
(This is Lance from the video) API keys are only needed if you are using Mistral API. Not for local.
@jawadmansoor60647 ай бұрын
what's with the weird logo? i liked the parrot now it is pirate ninja
@EddyLeeKhane6 ай бұрын
anyone else has the issue that it runs indefinetly in jupyter notebook when asking bigger questions or letting it compare? my prompt: "Find us two more related papers and give a short summary for each"
@r.lancemartin79926 ай бұрын
(This is Lance from the video.) Interesting, I haven't seen that. I'm going to update the notebook w/ a few things. If easy to share your code, I can have a look.