LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners

Рет қаралды 692,942

Күн бұрын

In this video, we're going to explore the core concepts of LangChain and understand how the framework can be used to build your own large language model applications.
Code for the video is available here:
github.com/rabbitmetrics/lang...
▬▬▬▬▬▬ V I D E O C H A P T E R S & T I M E S T A M P S ▬▬▬▬▬▬
0:00 Introduction and overview
0:38 Why Langchain?
3:40 The value proposition of Langchain
4:50 Unpacking Langchain
5:42 LLM Wrappers
6:58 Prompts and Prompt Templates
7:45 Chains
9:00 Embeddings and VectorStores
11:40 An example of a Langchain Agent

Пікірлер: 326

@imtanuki4106 Жыл бұрын

90% (or more) of tech tutorials start with code, without providing a conceptual overview, as you have done. This video is phenomenal...

@rabbitmetrics Жыл бұрын

Appreciate it! 🙏 Thanks for watching

@adamgkruger Жыл бұрын

I've noticed a significant lack of comprehensive resources that cover LangChain thoroughly. Your work on the subject is highly valued. Thank you

@artic4873 7 ай бұрын

Yes, there's not enough books on it. The documentation is sparse

@andrewflewelling4294 3 ай бұрын

Agreed. This was the perfect introduction, for me at this time, to Lang chain.

@zerorusher Жыл бұрын

This is the best 101 video I found on the subject. Most of the other videos assume you're already somewhat familiar with the tools or aren't that beginner friendly.

@garratygarret8559 9 ай бұрын

Thank you for the video. I think it gives a really good introduction to the topic without much distraction. Absolutely pleasant to follow even for a non-native speaker.

@maya-akim Жыл бұрын

This was an awesome and very straightforward video. I believe that it's the most useful video about LangChain that exists I've seen so far. Even people that don't know much about programming can follow. Thanks so much!

@ranjithpals 8 ай бұрын

Your video really helps understand the basics of langchain and provides a good context as well. I'm looking forward to more such videos !

@jayhu6075 Жыл бұрын

One of the best QuickStart streaming that I've seen. A clearly explanation in combination with images. Many thanks.

@rabbitmetrics Жыл бұрын

Thank you! 🙏

@chukypedro818 Жыл бұрын

With immediate effect I have subscribe to your awesome channel. Explanation to LangChain was clear and concise. I really learnt a lot in just 12 minutes.

@steve_wk Жыл бұрын

I've been watching a lot of AI videos, this is definitely one the best - well-organized and very clear

@Janeilliams 8 ай бұрын

Wow, this video on lang-chain have all the pieces i have been searching for. Thank you so much for taking time and making this awesome video.

@nickfergis1425 8 ай бұрын

solid instructor. good intro langchain at the right level of depth. For as quick as he rips thru a huge amount of information, he is still pretty easy to follow.

@sitedev Жыл бұрын

Thank you. I have watched a lot of videos that attempt to explain LLM's and LangChain as successfully as you have here but fail to do it as succinctly as you have. I was looking for a video that I can share with my clients that explains what LLM's and LangChain are without being too dumbed down or being too 'over their heads' and this video is perfect for that! So, again - thank you.

@rabbitmetrics Жыл бұрын

Glad it was helpful! I really appreciate the comment, thank you very much 🙏

@dudefromsa 10 ай бұрын

I found this to be very comprehensive and indeed useful.

@ejclearwater 4 ай бұрын

I have been searching and searching for an explanation of how to do this exact thing!! Yasssssss thank yooouuu! ❤

@ernikitamalviya 9 ай бұрын

Thank you so much for covering all the components in just 13 mins. Though, it took an hour to learn and absorb everything :D

@guitarcrax127 9 ай бұрын

Excellent intro, especially for an experienced programmer to start using after a single watch. Learned a lot in a short time with it. Thanks for making.

@rabbitmetrics 8 ай бұрын

You're welcome! Thanks for watching

@danquixote6072 Жыл бұрын

Having read through the LangChain's conceptual documentation, I must say this video is a great accompaniment. Very clear and well presented and for a non coder like myself, easy to understand. (I'd pay for a LangChain manual for 5 year olds!) . Subscribed.

@rabbitmetrics Жыл бұрын

Thank you! 🙏 Glad it was helpful

@lukaskettner3597 Жыл бұрын

Companion*

@4.0.4 Жыл бұрын

The coolest thing about enhancing LLMs like this is that locally-runnable models will be very interesting (no huge API call costs) and smarter than by default.

@ignfishiv Жыл бұрын

I would love local LLMs! Though I doubt that one advanced as GTP-3.5/4 will be able to be run locally for a few years because of the required computational power. I still look forward to the day that it becomes a thing though!

@leonidsdreams3919 Жыл бұрын

The costs are not the advantage. Hosting things on your own hardware is usually more expensive, especially if you need multiple models(embedding model, LLM, maybe a text to speech). The advantage I see is that you could use custom models trained on your data

@oryxchannel Жыл бұрын

Enter neuromorphics: kzfaq.info/get/bejne/e76RgLSWz9mximw.html

@ratral Жыл бұрын

Thank you very much for watching the video, a very well-structured clarification. 👍

@rabbitmetrics Жыл бұрын

Much appreciated! Thanks for watching

@bharatpanchal8582 5 ай бұрын

Thank you for explaining all the components. Highly appreciate it.

@rabbitmetrics 4 ай бұрын

You're welcome! Thanks for watching

@HarshGupta-sf4rj 2 ай бұрын

I never comment on any video but your flawless explanation made me, Thank you for such a masterpiece.

@rabbitmetrics Ай бұрын

Appreciate the kind words! 🙏 Thanks for watching

@ALEJANDV1 9 ай бұрын

Thank you very much, Rabbitmetrics! This tutorial is absolutely a gem for someone looking for a clear and concise overview of the main concepts!

@rabbitmetrics 8 ай бұрын

Thank you! I'm glad it was helpful

@rakeshmr3329 4 ай бұрын

Really fantastic crisp explanation of LLM nothing more nothing less.

@rabbitmetrics 4 ай бұрын

Thank you!

@spicer41282 Жыл бұрын

Your approach on this Langchain vid garnered you a Subscriber! Thanks!

@rabbitmetrics Жыл бұрын

Appreciate the support! Thanks for watching

@Bragheto Жыл бұрын

This is gold! Thank you!❤

@miguelangelromerogutierrez9626 10 ай бұрын

Very good explanation with a simple example to understand how it works! Thanks for this content

@rabbitmetrics 9 ай бұрын

You're welcome! Thanks for watching

@KayYesYouTuber 9 ай бұрын

Simply fantastic. Thank you very much for explaining it so well.

@rabbitmetrics 8 ай бұрын

Appreciate the comment! 🙏 Thanks for watching

@TheAlokgupta83in 11 ай бұрын

This is a cool explanation of how langchain works.

@MrAloha Жыл бұрын

Excellent! I've spent hours looking for this 13 minute tutorial. You fa man! Thanks! 💪😁🌴🤙

@rabbitmetrics Жыл бұрын

Glad you found it! 😊 Thanks for watching

@leventyuksel93 11 ай бұрын

Amazing tutorial and explanation, thank you!

@repairstudio4940 11 ай бұрын

This is a absolutely wonderfuk video on LangChain and its clear and concise. Coukd you do a tutorial for beginners??? 🙏🏼

@dozieweon 7 ай бұрын

This is very insightful and straight to the point.

@rabbitmetrics 5 ай бұрын

Thank you!

@bingolio Жыл бұрын

EXCELLENT OVERVIEW: Pls note Pinecone as of 1 week is NOT allowing new, free accounts to do any operations! PLS CONSIDER DOING SIMILAR VID FOSS end to end, There is a lot of interest. THANK YOU

@hectorprx 11 ай бұрын

Thanks for the clarity , all the best

@anandakumar31 3 ай бұрын

Excellent video for beginners who want to start on Langchain. Well explained.

@rabbitmetrics 3 ай бұрын

Thanks! Glad it was useful

@axelrein9901 Жыл бұрын

This is amazing stuff. Would love to see a deeper dive into it.

@rabbitmetrics Жыл бұрын

Thanks for watching! I'm already working on some deep dive videos

@saddam7008 11 ай бұрын

This video really explains A-Z about langchain. This is damn good man.

@rabbitmetrics 9 ай бұрын

Appreciate the comment! Thanks for watching

@luiscosta9261 9 ай бұрын

Great explanation! I learned a ton with your video

@alaad1009 5 ай бұрын

What a beautiful video. You Sir are a great teacher ! Thank You !

@rabbitmetrics 4 ай бұрын

Thank you!

@RobbieMraz Ай бұрын

Thank you this is the info I was looking for.

@Swanidhi 10 ай бұрын

Great content! Just what someone who just jumped into Gen AI would need to solve diverse use cases. Subscribed!

@rabbitmetrics 9 ай бұрын

Appreciate it! Thanks for watching

@tosinlitics949 5 ай бұрын

Amazing short video packed with knowledge. Just smashed that subscribe button!

@rabbitmetrics 5 ай бұрын

Appreciate the support, thanks for watching!

@pleabargain Жыл бұрын

Fascinating. Thank you for this.

@hardikmehta8308 9 ай бұрын

Fantastic overview of Langchain! Thank you @Rabbitmetrics

@jakobstyrupbrodersen926 11 ай бұрын

Excellent introduction! Thanks a lot :-)

@muhammadhaseeb2895 7 ай бұрын

Absolutely love the way you explained.

@rabbitmetrics 6 ай бұрын

Thank you!

@ciaranryan9485 7 ай бұрын

Hi there, is there a way to combine steps 4 and 5? I assumed you would be using the Agent to answer questions on the autoencoder that we had focused on for the whole video, but then we just used it to do some maths. I think it would be useful if it could answer questions based on the embeddings we have in our index?

@ramp2011 Жыл бұрын

Excellent video. THank you for sharing. Would love to see a video on Langchain Agents. Thank you

@rabbitmetrics Жыл бұрын

You're welcome! Thanks for watching

@noomondai Жыл бұрын

Awesome work thanks a lot!

@user-nk7lx2rw4t 6 ай бұрын

Excellent overview - Thanks!

@rabbitmetrics 6 ай бұрын

You're welcome, thanks for watching!

@CinematicHeartstrings 3 ай бұрын

Thank you very much for the video! Really helpfull to kickstart with LangChain

@rabbitmetrics Ай бұрын

Glad it was helpful!

@xGogita 3 ай бұрын

Brilliant. Structured and clear.

@rabbitmetrics Ай бұрын

Thank you!

@limster5 Жыл бұрын

Thank you for this video. Now I can start work on my Langchain. Have subscribed!

@rabbitmetrics Жыл бұрын

You're welcome! Thanks for watching

@TheOGDesigner 10 ай бұрын

Great explanation, thanks!

@micbab-vg2mu Жыл бұрын

Great video! Thank you.

@emptiness116 Жыл бұрын

Thank you for your contribution through the KZfaq space

@rabbitmetrics Жыл бұрын

Appreciate it! Thanks for watching

@raffdev 9 ай бұрын

Thanks for sharing the knowledge 👍

@daffertube 10 ай бұрын

How do you store a API key in the .env ? I created the .env file in the root and I get error 500 when trying to open the .env and even chatgpt doesn't know why.

@shyama5612 5 ай бұрын

Excellent intro. Harrison would approve!

@rabbitmetrics 5 ай бұрын

Thank you!

@mhm7129 9 ай бұрын

Excellent work!

@jordanchristley1306 10 ай бұрын

Highly appreciated video

@lpanebr Жыл бұрын

Great video! Do you know if pinecone works with other languages? For example to store and then retrieve?

@peralser Жыл бұрын

Wonderful video. Thanks.

@ayhamkanhoush2912 6 ай бұрын

this video was nice and gives a good intro to the topic

@alioraqsa Жыл бұрын

This is really great video!

@roberthuff3122 Жыл бұрын

Subscribed. Others have clamored for the notebook. I do as well. Thank you.

@sujoyroy3157 Жыл бұрын

How is the relevant info (as a vector representation) and question (as a vector representation) combined as a prompt to query the LLM? The example you show is a standard ChatGPT textual prompting scenario. The LLM will spit out what it knows and not what it does not know. So what application will this info be useful for? Also is there any associated paper or benchmark that investigates the performance of extracting "relevant information" using this chunking method or is it implementing some DL based Q/A paper?

@leonardosouzaconradodesant6213 5 ай бұрын

Great!!! Fantastic! Awesome! Thank you for sharing!

@rabbitmetrics 5 ай бұрын

Thanks for watching!

@mwonderlin Жыл бұрын

This is excellent - I have a question re the splitting, lets imagine you have email templates that average like 2000 tokens a piece or IG captions with like 500 tokens - should things like this be embedded as one chunk or what is the advantage to splitting up into say 100 token splits?

@zenfoil 3 ай бұрын

👍 Your explanation is so structure and clear. I can understand how langchain works now even though I don’t know your python codes at all.

@rabbitmetrics 3 ай бұрын

Thanks! 🙏 Glad it was helpful

@henrisiepmann3501 Жыл бұрын

Great explanation!

@kevon217 Жыл бұрын

great overview and slides

@realJeremyZhang 11 ай бұрын

Awesome Explanation

@cymaticchaos2425 7 ай бұрын

Zero clutter. A Guru (remover of darkness) is one who can create chunks of knowledge in a sequence that is easier for the Shishya (student) to learn with ease and get it to their neocortex without having to decode the vectors, that allows for carrying it to their multiple incarnations. Thank you Guru-ji.

@rabbitmetrics 5 ай бұрын

I appreciate the comment - thanks for watching!

@petrkushnir8178 7 ай бұрын

Bloody brilliant!

@andre-le-bone-aparte Жыл бұрын

just found your channel. Excellent Content - another sub for you sir!

@rabbitmetrics Жыл бұрын

Thank you I appreciate the support!

@Stoicbob Жыл бұрын

amazing tutorial. thank you. you are amazing

@zh4842 Жыл бұрын

Great job, what is the soft that you use to draw these magic things?

@felipeblin8616 Жыл бұрын

Great video clear and simple. I wonder is it were possible how can we use this with azure OpenAI

@lee1221ee Жыл бұрын

great! I can use this video to teach my friend

@ilianos Жыл бұрын

Great explanatory video! Would you provide a link to this Jypter notebook?

@venkatkasthala1554 2 ай бұрын

Thanks a lot. Very good explanation.

@rabbitmetrics Ай бұрын

Thanks!

@kailashbalasubramaniyam230 Жыл бұрын

Great video, what is the first app that you were using to explain the diagram ?

@bunnihilator Жыл бұрын

Can these LLM return an entity data with all its attributes, or do they only return conversation text?

@babakbandpey Жыл бұрын

Thanks friend. You answered a lot of questions here and the repo, helped understanding your presentation much better. Please share more. Have a great day.

@rabbitmetrics Жыл бұрын

You're welcome! Thanks for watching

@spacedust8061 10 ай бұрын

thank you a lot, really helped

@youngsdiscovery8909 Жыл бұрын

super helpful. I think langchain engineer could hold significant value in the current job market

@rabbitmetrics Жыл бұрын

I agree!

@Tom.malucao 5 ай бұрын

Really good video!

@stereo_stan 11 ай бұрын

This was so helpful! What are your thoughts on connecting langchain and flutterflow?

@conne637 11 ай бұрын

Can someone explain to me, how the question & and the relevant (personal) data is combined when promting the model? Also, if I understand this correctly, using LangChain after all would enlarge the promt and hence number of tokens needed / cost? Thanks in advance!

@Thisnthat979 5 ай бұрын

Hi I am new to Python, how do I get to the screen at 5:00 to edit the environment file? I installed all the component then stuck, thank you!

@AMYclubNFTs Жыл бұрын

that's so amazing !!!

@johnshaff Жыл бұрын

I inspected Langchain code as soon as it was released, ran some tests and never used it since. Im surprised so many consider its limitations acceptable. Using embedding similarity as a query filter is like trying to answer a prompt by comparing every chunk of text to your prompt. It makes absolutely no sense because often times an answer looks nothing like a question, and/or the data needed to answer a question looks nothing like the question. The purpose of the embedding layer in a transformer neural network is to prepare the prompt tensor for further processing through the remaining model layers. It’s like bringing your prompt to the starting line of a long process to be answered, but instead of bringing just the prompt to the starting line, langchain brings the entire text your asking the question of to the starting line with your question and asking them to look at each other and be like “hey, whoever looks like me, stand over here with me. Ok now the rest of you go away and I’m going to ask chatgpt to see which of you remaining can help answer me”. This is a slight of hand trick, trying to replace everything that happens after the starting line, with chatgpt, but it doesn’t really work for 2 big reasons: (1) chatgpt context is not large enough to transform both the entire text your asking a question of + your prompt, and the same limitation applies to batching (2) your embeddings are incomplete because they were not created by the network, but simply hacking the first layer in a sense

@MeatCatCheesyBlaster Жыл бұрын

Interesting take. I suspect most people don't understand the technology enough to see how it works. Would be helpful if you could make a video explanation

@albertocambronero1326 Жыл бұрын

Biggest limitation right know that we can’t get over with, is chat GPTs context length, there is no way around that unless the contexts is greatly increase by OpenAI themselves or we could train our gpt4 model on large texts

@dendrites Жыл бұрын

@@albertocambronero1326 I agree. It would cool if there was a sort of "short term memory model" that could hold personal data. I don't see expanding context length as a parsimonious solution. Model queries produce the best results when they are sort and poignant. Any time you need to bring a ton of context to the prompt it reduces the relative weight of the primary question. Imagine a patient friend who accepts questions with an unrestricted context length. They have never read the book Great Gadsby (i.e. this would be like your personal data) - so to ask them a question about Jay Gatsby the question must begin by reading them the entire Great Gatsby novel, followed by "thee end... Where did Jay Gatsby go to college?" Then to ask them another Gatsby question it requires reading them the novel, again, and again. It would be awesome if there was a way to side-load a small personalized model that can plug into a LLM for extended capabilities.

@albertocambronero1326 Жыл бұрын

@@dendrites amazing response, I did not know what was going on under the scenes with the context and did not know model queries produce the best results when they are sort and poignant. I believe that if you send the novel it would be stored in the context of the model and then you would be able multiple questions (?) or would the novel be lossing importance (weight) as more and more contexts is added? Referring to the comment that started this thread, the complicated bit about training the model on a certain topic, lets say: we train the existing GPT4 model in the book Great Gadsby it would probably know how to answer questions about the book, but it could not analize the whole book to find linguistic trends in the book (like what is the most talked about topic in the book) unless you ALSO feed the model with an article about "the most talked topic in the book". I mean I want my GPT4 model to read the book and analize the whole picture of what the book is about without needing extra articles about the book. (my use case is to make GPT4 analyze thousands of reviews and answer questions about it, but right now using NLP techniques sounds like a more duable option right now or at least until we have an option to extend GPT4 knowledge)

@ugaaga198 Жыл бұрын

You can't say simply "it doesn't really work". It really depends on the use case. There are true limitations and some creativity might be required to leverage it. The context size might me sufficient for smaller use cases or it might be sufficient to break down bigger questions into smaller questions with their own contexts and then summarize etc.

@PhoebePhuu 2 ай бұрын

Your explanation is super clear to understand for me as a beginner. I want to know brief steps for the code flow as titles just like 1.Creating environment to get keys, 2. etc.,. Can anyone answer it?

@namenl2205 2 ай бұрын

so well explained! :)

@rabbitmetrics Ай бұрын

Thanks!

@HannesSmit-gl7qq Ай бұрын

Hello, I just run your script around 05:53 with python3 and pip3. However it says ` Could not import openai python package. Please install it with `pip install openai`. (type=value_error)`. Which version of that dep should I add to get a coherent project with your code?

@DrAIScience 4 ай бұрын

Very interesting..can we do this for image search? Query and similarity search for image search and image match? Can we see embeddings of images like text that you presented?. Thanks

@peanuts4132 5 ай бұрын

So what do I do when I want to use a chat model + a non-chat-model? I want to have openai chat model with the ability to output audio files. but the audio LLM does not output text which chainlit expects. It outputs a bytes object, which could be converted to base64. Am i supposed to chain it with openaichat or create a BaseTool that calls the other LLM?