Really wanna appreciate this video. There are so many langchain videos but nothing clarifies the basics this well. Also given langchain gets updated very frequently to be clear with the core this is a beautiful video. Please make a series out of these. Cover all the basics needed , new approaches etc.
Dude I've been learning langchain for months and this is the tightest explanation of the basics I have seen. I like how you mention the things that might be confusing while learning langchain. Really puts it in perspective.
Awesome curated information, your contribution is much appreciated. It's 4 am here and woke up by chance, I was sleepy and even so it was easy to follow you, you are a natural-born teacher. Cheers.
@@decoder-sh Waiting for your series impatiently. please prefer depth when teaching, otherwise , you know that there is a lot of stuff on langchain. You have done some great work in this video. So, please maintain the quality of content!
this was great, thanks. I'm hoping to build a chat bot combining, LLM, fine-tuned, Rag, and a (not sure the best production ready method) to parse ancient texts to seek wisdom and deeper understanding of those texts. but I'm new to each part of this so I'm not sure where to start quite yet. your videos are very helpful, and I feel like I'll figure it out at some point.
Thanks for watching! Mind if I ask some questions about your project? What is the source of dataset? Is it image or text? Is it already translated into a modern language?
@@decoder-sh Sure! Modern Language in .txt . Ideally after proof of concept, long term I'd like to do original languages but start with English and move from there. Greek and Hebrew are the first two older languages I would want to start with to see if I could retrieve any additional context from the words. I would be starting with the Bible since it's such a well-studied book already and has a lot of resources around it for free.
@decoder-sh the more I dive into this project the more it looks like the first iteration ideally would be an advanced rag pipeline. based off your experience, do you have a recommended direction you would advise or recommend? or a video series for a specific advanced rag pipeline format that might be a good starting point for something as large as the Bible? I believe the Bible is roughly a half a million words and a 66 different books so there's quite a bit of variables there to consider. any help, or pointing to an already existing tutorial would be very helpful. Thanks so much for what you do!
Great work... One issue tho... You need to post weekly instead of monthly.... Coz you're posting smaller digestible videos and waiting a month to get the next video is too long.... really enjoying your content... really appreciate it...
I'm working on it! Weekly would be my ideal cadence, but I've been traveling this summer and am still optimizing my recording + editing flow. Rapidity is a top priority though 🫡
Thank you for your video. I have been practicing using langchain. In my research(reddit) devs are moving away from Langchain and have some cristisms. Could you give your opinion? Is langchain an introductory tool?
People on reddit really do seem to love to hate on LangChain. I think it was possibly the first framework to gain major popularity, so it's unsurprising parts of it were build for a more type of use case. Part of what I wanted to address in this video was some of the feedback that I read on Reddit which was that there isn't one canonical way of doing things, and the docs are a bit all over the place. I do think that LangChain gives you a lot of capabilities. They also give you a ton of abstractions that don't always do exactly what you want them to do, or appear to be magic. But if you really want, you can always rebuild whatever you want with LCEL. So I don't think that LangChain is a bad tool to start building with, however I don't yet have much experience with LlamaIndex. Are there any other frameworks you think I should be looking at?
@@decoder-sh agree on the documentation. Does feel a bit all over the place and sometimes incomplete. Folks hate on it as it's not recommended to be used in production. Which then causes issues when frameworks like crewai choose to use it. I personally really like it, as long as you understand what you're using. I do really like langgraph too
@@madhudson1 Yeah I think langchain's document loaders are a great example of having both pros and cons. Pros are that you can load and parse a directory full of PDFs in one line of code. Cons are that there are a million ways to parse a PDF, and the default parser only takes you so far and its a little unclear what levers you're actually able to pull via langchain. With that said, document loaders are just API wrappers in a sense, and are used all over the place x.com/Decoder_sh/status/1780249955875144159
You are doing an amazing job. Looks like you are not feeling motivated to make more videos. I see you're spending too much time editing hence the turn off. Just produce raw videos, people will excuse small mistakes. You've some great talent, spread the knowledge around. Can I request you to make similar video on crewai? thanks
Hey thanks for the comment! You rightfully noticed my absence, however I’ve actually been spending that time moving to a new city and building a tool to help me edit much faster! It’s not quite ready for prime yet yet but I am looking for alpha testers - it’s matcha.video Anyway, more videos coming soon, crewai and related tools are on my list. Thanks for watching :)
can you please add a video/short in which you tell that how to display the name what i want using open web ui i mean i write something in open web ui localhost which you have tell and i want that when i send the message its format is Me: bla bla bla the the llm reply like this [the name i want] : bla bla bla please 🥺🥺
Really wanna appreciate this video. There are so many langchain videos but nothing clarifies the basics this well. Also given langchain gets updated very frequently to be clear with the core this is a beautiful video. Please make a series out of these. Cover all the basics needed , new approaches etc.
Dude I've been learning langchain for months and this is the tightest explanation of the basics I have seen. I like how you mention the things that might be confusing while learning langchain. Really puts it in perspective.
Thank you so much, I really appreciate you saying that! Keep at it 🫡
really appreciated. well made video, now i understood the crux of LangChain.
Dude just made my doubts clear before I finished my tea.
Awesome curated information, your contribution is much appreciated. It's 4 am here and woke up by chance, I was sleepy and even so it was easy to follow you, you are a natural-born teacher. Cheers.
Good morning, thanks for watching my video! I'm looking forward to building more with LangChain
Thank you very much for this clear and comprehensive tutorial.
Very thorough explanation, thank you!
This was awesome! it would be lovely to have a similar tutorial about agents and tool calling explaining the different langchain abstractions!
I would be happy to! Are there any specific abstractions that you're curious about?
@@decoder-sh Something about creating chains of agents and tools vs the AgentExecutors abstraction would be great! P.s. thank you for responding!
Amazing video, super clear, helped me understand and debug my code! Thank you for sharing this.
Thank you Nat!
Thanks for the video. Keep going. Your explanations are on point!
I appreciate it!
How simply he explains the concept Chaining and Piping 👏
But I have a question, Is it a RAG model that you've developed ....??
I love that your video is up to date with the latest Langchain imports 👍👍 Are you planning a series on LangChain ?
I would like to! A few videos on langchain, then a few videos on llamaindex
Another great video. You're a terrific teacher!
Thank you kindly Dr Mikey!
great series. Would love for it to continue
And it will! Thanks for watching
Excellent intro!! Thanks for share!
My pleasure, thanks for watching!
Are you planning to do a full series on Langchain?
Yes I would love to explore more with langchain, and also do a series on llamaindex
@@decoder-sh Waiting for your series impatiently. please prefer depth when teaching, otherwise , you know that there is a lot of stuff on langchain. You have done some great work in this video. So, please maintain the quality of content!
would like to see a video on pretraining and fine-tuning models
PLEASE show us how to create local agents for tasks: research, create sumarises, grab data, and decorate in html in near real time. Thanks!! ❤
this was great, thanks. I'm hoping to build a chat bot combining, LLM, fine-tuned, Rag, and a (not sure the best production ready method) to parse ancient texts to seek wisdom and deeper understanding of those texts. but I'm new to each part of this so I'm not sure where to start quite yet.
your videos are very helpful, and I feel like I'll figure it out at some point.
Thanks for watching! Mind if I ask some questions about your project? What is the source of dataset? Is it image or text? Is it already translated into a modern language?
@@decoder-sh Sure! Modern Language in .txt .
Ideally after proof of concept, long term I'd like to do original languages but start with English and move from there. Greek and Hebrew are the first two older languages I would want to start with to see if I could retrieve any additional context from the words. I would be starting with the Bible since it's such a well-studied book already and has a lot of resources around it for free.
@decoder-sh the more I dive into this project the more it looks like the first iteration ideally would be an advanced rag pipeline. based off your experience, do you have a recommended direction you would advise or recommend? or a video series for a specific advanced rag pipeline format that might be a good starting point for something as large as the Bible? I believe the Bible is roughly a half a million words and a 66 different books so there's quite a bit of variables there to consider. any help, or pointing to an already existing tutorial would be very helpful.
Thanks so much for what you do!
Fantastic! Thank you!
Great work... One issue tho... You need to post weekly instead of monthly.... Coz you're posting smaller digestible videos and waiting a month to get the next video is too long.... really enjoying your content... really appreciate it...
I'm working on it! Weekly would be my ideal cadence, but I've been traveling this summer and am still optimizing my recording + editing flow. Rapidity is a top priority though 🫡
thanks - great content
Thank you for your video. I have been practicing using langchain. In my research(reddit) devs are moving away from Langchain and have some cristisms. Could you give your opinion? Is langchain an introductory tool?
People on reddit really do seem to love to hate on LangChain. I think it was possibly the first framework to gain major popularity, so it's unsurprising parts of it were build for a more type of use case. Part of what I wanted to address in this video was some of the feedback that I read on Reddit which was that there isn't one canonical way of doing things, and the docs are a bit all over the place.
I do think that LangChain gives you a lot of capabilities. They also give you a ton of abstractions that don't always do exactly what you want them to do, or appear to be magic. But if you really want, you can always rebuild whatever you want with LCEL. So I don't think that LangChain is a bad tool to start building with, however I don't yet have much experience with LlamaIndex.
Are there any other frameworks you think I should be looking at?
@@decoder-sh agree on the documentation. Does feel a bit all over the place and sometimes incomplete. Folks hate on it as it's not recommended to be used in production. Which then causes issues when frameworks like crewai choose to use it.
I personally really like it, as long as you understand what you're using.
I do really like langgraph too
@@madhudson1 Yeah I think langchain's document loaders are a great example of having both pros and cons. Pros are that you can load and parse a directory full of PDFs in one line of code. Cons are that there are a million ways to parse a PDF, and the default parser only takes you so far and its a little unclear what levers you're actually able to pull via langchain.
With that said, document loaders are just API wrappers in a sense, and are used all over the place
x.com/Decoder_sh/status/1780249955875144159
You are doing an amazing job. Looks like you are not feeling motivated to make more videos. I see you're spending too much time editing hence the turn off. Just produce raw videos, people will excuse small mistakes. You've some great talent, spread the knowledge around. Can I request you to make similar video on crewai? thanks
Hey thanks for the comment! You rightfully noticed my absence, however I’ve actually been spending that time moving to a new city and building a tool to help me edit much faster! It’s not quite ready for prime yet yet but I am looking for alpha testers - it’s matcha.video
Anyway, more videos coming soon, crewai and related tools are on my list. Thanks for watching :)
can you please add a video/short in which you tell that how to display the name what i want using open web ui
i mean i write something in open web ui localhost which you have tell and i want that when i send the message its format is
Me: bla bla bla
the the llm reply like this [the name i want] : bla bla bla
please 🥺🥺
LlamaIndex please?
On the list! Thanks for watching