LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners

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

[@imtanuki4106]

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

[@rabbitmetrics]

Appreciate it! 🙏 Thanks for watching

[@ThangTran-hi3es]

Totally agree with this. I love the way this guy teaching the conceptual

[@adamgkruger]

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

[@artic4873]

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

[@andrewflewelling4294]

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

[@zerorusher]

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.

[@chukypedro818]

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.

[@ranjithpals]

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

[@garratygarret8559]

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.

[@jayhu6075]

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

[@rabbitmetrics]

Thank you! 🙏

[@Janeilliams]

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.

[@maya-akim]

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!

[@nickfergis1425]

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.

[@steve_wk]

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

[@ejclearwater]

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

[@ernikitamalviya]

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]

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]

You're welcome! Thanks for watching

[@dudefromsa]

I found this to be very comprehensive and indeed useful.

[@sitedev]

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]

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

[@danquixote6072]

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]

Thank you! 🙏 Glad it was helpful

[@lukaskettner3597]

Companion*

[@repairstudio4940]

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

[@hectorprx]

Thanks for the clarity , all the best

[@Bragheto]

This is gold! Thank you!❤

[@TheAlokgupta83in]

This is a cool explanation of how langchain works.

[@leventyuksel93]

Amazing tutorial and explanation, thank you!

[@bharatpanchal8582]

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

[@rabbitmetrics]

You're welcome! Thanks for watching

[@RobbieMraz]

Thank you this is the info I was looking for.

[@ratral]

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

[@rabbitmetrics]

Much appreciated! Thanks for watching

[@luiscosta9261]

Great explanation! I learned a ton with your video

[@spicer41282]

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

[@rabbitmetrics]

Appreciate the support! Thanks for watching

[@miguelangelromerogutierrez9626]

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

[@rabbitmetrics]

You're welcome! Thanks for watching

[@rakeshmr3329]

Really fantastic crisp explanation of LLM nothing more nothing less.

[@rabbitmetrics]

Thank you!

[@MrAloha]

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

[@rabbitmetrics]

Glad you found it! 😊 Thanks for watching

[@pleabargain]

Fascinating. Thank you for this.

[@ALEJANDV1]

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]

Thank you! I'm glad it was helpful

[@HarshGupta-sf4rj]

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

[@rabbitmetrics]

Appreciate the kind words! 🙏 Thanks for watching

[@anandakumar31]

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

[@rabbitmetrics]

Thanks! Glad it was useful

[@jakobstyrupbrodersen926]

Excellent introduction! Thanks a lot :-)

[@saddam7008]

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

[@rabbitmetrics]

Appreciate the comment! Thanks for watching

[@hardikmehta8308]

Fantastic overview of Langchain! Thank you @Rabbitmetrics

[@dozieweon]

This is very insightful and straight to the point.

[@rabbitmetrics]

Thank you!

[@KayYesYouTuber]

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

[@rabbitmetrics]

Appreciate the comment! 🙏 Thanks for watching

[@noomondai]

Awesome work thanks a lot!

[@ramp2011]

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

[@rabbitmetrics]

You're welcome! Thanks for watching

[@raffdev]

Thanks for sharing the knowledge 👍

[@micbab-vg2mu]

Great video! Thank you.

[@TheOGDesigner]

Great explanation, thanks!

[@alaad1009]

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

[@rabbitmetrics]

Thank you!

[@Swanidhi]

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

[@rabbitmetrics]

Appreciate it! Thanks for watching

[@axelrein9901]

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

[@rabbitmetrics]

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

[@peralser]

Wonderful video. Thanks.

[@ayhamkanhoush2912]

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

[@mhm7129]

Excellent work!

[@jordanchristley1306]

Highly appreciated video

[@roberthuff3122]

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

[@muhammadhaseeb2895]

Absolutely love the way you explained.

[@rabbitmetrics]

Thank you!

[@limster5]

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

[@rabbitmetrics]

You're welcome! Thanks for watching

[@CinematicHeartstrings]

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

[@rabbitmetrics]

Glad it was helpful!

[@tosinlitics949]

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

[@rabbitmetrics]

Appreciate the support, thanks for watching!

[@user-nk7lx2rw4t]

Excellent overview - Thanks!

[@rabbitmetrics]

You're welcome, thanks for watching!

[@alioraqsa]

This is really great video!

[@realJeremyZhang]

Awesome Explanation

[@henrisiepmann3501]

Great explanation!

[@kevon217]

great overview and slides

[@Stoicbob]

amazing tutorial. thank you. you are amazing

[@xGogita]

Brilliant. Structured and clear.

[@rabbitmetrics]

Thank you!

[@petrkushnir8178]

Bloody brilliant!

[@leonardosouzaconradodesant6213]

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

[@rabbitmetrics]

Thanks for watching!

[@lee1221ee]

great! I can use this video to teach my friend

[@zenfoil]

👍 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]

Thanks! 🙏 Glad it was helpful

[@spacedust8061]

thank you a lot, really helped

[@shyama5612]

Excellent intro. Harrison would approve!

[@rabbitmetrics]

Thank you!

[@emptiness116]

Thank you for your contribution through the CZcams space

[@rabbitmetrics]

Appreciate it! Thanks for watching

[@4.0.4]

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]

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]

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]

Enter neuromorphics: czcams.com/video/EXaMQejsMZ8/video.html

[@bingolio]

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

[@mwonderlin]

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?

[@ciaranryan9485]

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?

[@stereo_stan]

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

[@lpanebr]

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

[@ilianos]

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

[@AMYclubNFTs]

that's so amazing !!!

[@felipeblin8616]

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

[@Tom.malucao]

Really good video!

[@venkatkasthala1554]

Thanks a lot. Very good explanation.

[@rabbitmetrics]

Thanks!

[@andre-le-bone-aparte]

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

[@rabbitmetrics]

Thank you I appreciate the support!

[@chavann]

very nice
thank you

[@alanwunsche-official]

Great. Would love to have access to the code as well. Thanks!

[@robertof.8174]

Impressive video, thanks! I will subscribe to your channel!

[@sujoyroy3157]

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?

[@kailashbalasubramaniyam230]

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

[@davidoludepo]

Thank you

[@skyforever1000]

good instruction ...

[@zh4842]

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

[@stevehu6511]

great video !

[@youngsdiscovery8909]

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

[@rabbitmetrics]

I agree!

[@PhoebePhuu]

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?

[@vieiradev6881]

Great Video!

[@rabbitmetrics]

Thanks!

[@nonomnismoriar9601]

Great video

[@babakbandpey]

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]

You're welcome! Thanks for watching

[@bunnihilator]

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

[@namenl2205]

so well explained! :)

[@rabbitmetrics]

Thanks!

[@vikasgupta1828]

Thanks

[@vikaspoddar9456]

🎉🎉🎉 Great overview of LangChain, can you do similar video on using LangChain on open_assistant and weiviate vector database

[@rabbitmetrics]

Thanks! That’s a good idea for a video