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Thanks! Great video!

please make a video about chatbot using fastapi with memory .

Cool, because of your videos I also switched to Langgraph and it is so much nicer in my opinion. However when i tested CRAG I realized that especially the document grade takes very long, which is not suitable for a production environment. What would you do to make this faster? Could also be slow for me because i am using mixtral 8x7b from Groq and not an OpenAi model

As far as I understand, this does not work with Ollama at the moment, does it?

I use LangGraph almost exclusively and on a daily basis, it makes the code more readable. Debugging also works much better. But sometimes I use LCEL as a one-liner for simple things (prompt template | llm | output parser). This can also make the code more readable, because otherwise you have some kind of subgraph again instead of a one-liner. Conclusion: Your opinion is tried and tested 👍

did they stop with the developer mode toggle on the signup page? cant seem to get a developer account

Awesome video! Would you consider adding a module to discuss how to do tool calling with other LLMs (such as Llama3 70B via Groq or Mistral)?

thanks a lot for the great video! I especially like the Design Patterns part. Just a feedback: I wish you first explained what you want to achieve with each example and then implemented the code and briefly went over why each element is necessary. For example why the super class Observer is needed. Coming from a C programming background, every problem I encounter during coding, seems like it will be solvable by functions. I never understood the real need of making classes. It would be nice to make another video going a bit deeper in design patterns part, explaining the chain of thought of "why" you choose classes for specific problems. Thanks a lot!

Excellent demo - only thing I would say is show at the beginning what the outcome/you are trying to build so you can see how it all ties together.

That is a really interesting implementation! I wonder if this could help reducing time on the retriever.add_documents operation, as I'm trying to do a RAG with around 100 pdfs and when testing ParentDocument retriever this is delaying too much. Do you know any solution for this?

Using astream, the response from the LLM has words that are split for example the word "hippopotamus" comes as 2 chunks "hippo" and "potamus". When creating an app, how to recognize and combine the 2 split parts into a single word for front-end?

I think you make excellent videos. My only request for future content would be for you to make a quick recap on your agent related projects to point out the changes that have to be made to make them work locally without OpenAI. Thank you for all of the content you produce. I have learned a lot from you.

Is this subject to loss in the middle problem?

Hi, is line 157 in the code meant to come before the iteration summaries loop or after in line 165? i.e. are we updating the all_summaries field with the previous cluster texts or does it not matter? Otherwise we would be updating "iteration summaries["texts"]" with the same value as "iteration summaries["summaries"]"

Great Video, Thanks for sharing!

Great video !

Great video! I would like to see a video about a customer support chatbot(with Q&A and RAG)

hi, could you help me out with the errors in this code: drive.google.com/file/d/1qlzGwMjaewy0Ni32Cpo7pZ_nz5x-AgTq/view?usp=drivesdk I have been struggling with integrating guardrails in my code since a long time. your help would be much appreciated

Nice video! Is there some way to retrieve the metadata as well with the multivector retriever? Such as page number or file name?

i really want to see a langgraph fastapi version from you ser

is there a github repo for us to see this code and walk through it?

This is a good video. Thank you.

Thanks for the video! Is there a way to evaluate the performance of the Guardrails? Basically I got dataset of Jailbreak prompts and want to look how many of those prompts jailbreak the model before and after the implementation of guardrails? Thank you for your help!

This is great, but will the source code be made public?

in 24:11 why self.arrows == self.arrows?? for the rest you did self.(variablename) == other.(variablename)

Thank you for such great video, as always! How can I find your Udemy courses?

Im sure this is production ready with some changes in identifying current logged in user. But I’m curious how we gonna make it work if want to integrate in WhatsApp or other social handles. You always unlock endless possibilities with your videos 🦾🫶🏻

Great tutorial! I've got one question, though: in 6:02 respectively 6:54: Does the model decide which tool to use on basis of the doc string?

Wait what, I thought FAISS didnt support metadata filters ? Weird that TimeWaited works with it no ?

I don't know the umap library, its very interesting. Good explanation about RAG advanced techniques, sucess for you!

A god among men

First comment I have also purchased your Udemy course , found it really useful

I tried this code, and everytime got exception "OpenAI function call failed: You exceeded your current quota, please check your plan and billing details". my credit remaining is $0.00 but in Usage Project's Monthly Bill is $0.00/$5.00 limit does anyone know how to solve it?

Which would you recommend for creating complex, production-ready agents with more than 30 nodes, with cycling and branching capabilities: Haystack, LangGraph, or a custom-built framework? I haven't seen a comparison between Haystack and LangGraph for real product development. What is your opinion?

why dont you make project videos like chatbot etc?

route conversation is giving error "401 Unauthorised". How to debug the same.

If possible I would give 1k likes! that solved hours of studying!

nice video. I can echo the sentiment of others. Most videos miss out on important things or basics for a non experienced beginner. Do you think you could add a video in actual production (live production). Second thing just a feedback , the data flow and handshake of services if it is shown in numbers and different colors ( the flow basically ) would be great.

On your Github Site, there is a repository for an "Advanced RAG with Langchain" Course - I cannot find it on Udemy. Is it already live? When can I expect it online?

Excellent tutorial. But what do you need the `from langchain_openai import ChatOpenAI` for in the first cycles example?

This is just great!

LLMChain() is deprecated and the output_parser in the examples also cause json output error. Would be nice, if you could update the github code. Thank you If anyone having issue with json output, here is a fix: from langchain_core.output_parsers import BaseOutputParser class LineList(BaseModel): lines: list[str] = Field(description="Lines of text") class LineListOutputParser(BaseOutputParser[LineList]): def __init__(self) -> None: super().__init__(pydantic_object=LineList) def parse(self, text: str) -> list[str]: lines = text.strip().split(" ") return lines

Very good tutorial. Thank you.

how would you store a chat history here? So e.g. saving the last 2 qa pairs

Nice work! few new methods of Langchain I was not aware of :)

video doesn't have implementation example of map rerank right