Create a RAG Chain using LangChain 0.1 (New version)

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In this video we explore a crash course of the new Langchain version (0.1.0) in python. This will allow you to create RAG chains in Langchain to chat with your documents.

We will be showcasing LangChain, a revolutionary Python library that empowers developers to create context-aware and reasoning-driven applications using powerful language models.

In the video, we will create several chains using the new version of LangChain to chat with a website. The final chain that we build here is a history-aware chain that takes the history of the conversation into account to answer your questions.

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🔗 What is LangChain?
LangChain is a framework designed to elevate your applications to new heights. It enables the creation of context-aware applications by connecting language models to various sources of context, allowing them to reason and provide intelligent responses.

🚨 Quickstart Highlights:
👉 1. Get Set Up with LangChain: Learn how to seamlessly set up the LangChain ecosystem to kickstart your development journey.

👉 2. Basic Components Mastery: Explore the fundamental components of LangChain, including prompt templates, models, and output parsers. Harness the power of these components to enhance your applications.

👉 3. LangChain Expression Language: Delve into the protocol that serves as the backbone of LangChain. Discover how the LangChain Expression Language (LCEL) facilitates component chaining, enabling seamless integration and communication between different elements.

👉 4. Build Your First Chains: Follow our step-by-step guide to construct a set of simple yet powerful chains using LangChain. Witness firsthand the capabilities that this innovative library brings to your projects.

🛠️ Why LangChain?
LangChain opens up a world of possibilities, allowing you to create intelligent applications that understand context and make informed decisions. Whether you're a seasoned developer or a coding enthusiast, LangChain is your gateway to building the next generation of language-powered applications.

👨‍💻 Who is This For?
This Quickstart tutorial is perfect for developers looking to harness the potential of language models in their applications. No matter your experience level, this guide provides a straightforward introduction to LangChain's capabilities.

🚀 Level Up Your Development Game with LangChain!
Don't miss out on this opportunity to revolutionize your application development process. Join us in this Quickstart tutorial and unlock the true potential of language models. Get ready to code smarter, reason better, and create applications that stand out!

⏰ Timestamps
0:00 Intro
1:19 Installing Langchain
4:06 Get API Keys and Initialize LLM
6:40 Create Your First Chain
10:22 Add Output Parser to Your Chain
13:21 What is a RAG Chain
14:55 Load Text From a Website
19:35 Create a Vector Store
22:44 Create a Document Chain
29:04 Create a RAG Chain
33:24 Retriever Chain with History
43:06 RAG Chain with History
50:35 Conclusion

🚀 #LangChain #PythonLibrary #LanguageModels #DeveloperTutorial

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Комментарии

Thanks for the update of Langchain. Quite a lot changes in the syntax. Looking forward with open source llm and embeddings with agents using the new Langchain👍

henkhbit

Awesome. I'm excited that you are back !!!
Thanks
Desperately waiting for the next chapter 😀

guruprasannasuresh

Thanks so much bro for all your great videos! I got to know your channel only 2 weeks ago, and since then, I have been watching and practising your tutorials from early 2023. Please don't stop thw great work!
Can't wait to watch the app version of thia RAG tutorial with agent 😃

samcavalera

I am impressed with your video. it was Simple, practical, and easy to follow, I've been watching tutorials on how to use Langchain but this is the best I've seen so far. I'm waiting for the app version. Keep doing the good work Alejandro.

reubensolomon

Great Video! I actually coded along with the whole thing! I've been trying to get to grips with the new update and the LCEL syntax. Some topics I would love to see videos for are: 1. Runnables, RunnableParallel, RunnablePassthrough(), Runnable protocol... So many runnable things! : ) 2. Interface, is that like a wrapper for things you chain together? like Prompt | LLM | etc... 3. I'm still confused about the difference between a Chain and an Agent and how/when they work together, like can you use chains with agents or vice versa... 4. Finally, I'd love to see a video for a Conversational Agent that does function calling/tools, where the chat history is sent to a vector db and can then be retrieved as context, so that the agent can learn things over time. Thats my wish list! Thanks again.

jacobgoldenart

thanks for making these awesome videos, it helps alot to understand the concepts and you are very clear n concise. keep it up!🎉

mygicarskrsk

Dude, I love your content. Your work addresses real world problems which is what I have been looking for. Thank you! Also you are very good at explaining these advanced terms to dumb it down for us beginners ❤. Can you make some videos about image processing with langchain?

arashkoushkebaghi

Great Tutorial! How can we modify this so that we get context from all the hyperlinks inside a website! Is it possible??

shivamrawat

Thank you for this. With the recent changes it's been so hard to find updated tutorials.

Sarkkoth

super, i was doig the same thing yesterday and then yt showed me your video:).... exellent work, WATING FOR THE NEXT CHAPTER

cheattube

"I come from across the ocean, where we lack video tutorials, so I'm really fortunate to have found such high-quality videos. More importantly, I hope everything goes well for the creator😉"

oooooohmygoood-xunm

Awesome videos….just wondered why you used colab instead of the python runtime environment explained in some video before? Presumably to execute the code samples on the fly? Can you explain when to use either of these? Not sure I totally grasped the Faiss step? Anyhow would love some video’s in future on training your own models and some on the use of hugging face? Keep up the good work

rmjjanssen

Thank you for this great Tutorial!
As far as I know, FAISS uses the inner product (dot product) and L2 (Euclidean) distance as standard metrics for similarity search. However, I'm curious if it's possible to use cosine similarity with FAISS instead. Would utilizing cosine similarity be more beneficial, especially considering its advantages with higher-dimensional vectors?

moonly

Thanks for the video, man!! It's great! Your content is very good and you also provide a great explanation!! Keep going!! Also, could you create a tutorial with a RAG agent with this new version of langchain? 😊

danielmacedo

Thanks AO - looking forward to your next video!

bwilliams

Awesome. You have always somehting great to offer us.

SanjeevKumar-drqj

Hey Please upoad the Agent and other stuffs video too, its very helpful!!
Also a request to cover Langsmith and Langserve !! Itll give a upperhand

meetvasa

Thank you brother. Truely saved my time.

swiftmindai

Loving the content! Thanks! Also, Can you create this with a streamlit interface?

neilmcd

Alejandro, thank you. Excellent work.

ratral

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