Summarize the following. Use a bulleted list for all key point. Do not include introductory material. If code is explained, use speech to text and extract the code into a code block. If you can Identify what models, tokenizers, embeddings or other files mentioned, create a list in the footer.
Title: “What is LangChain ? | LangChain Intro #1”
Transcript: “hello guys I’m sudhasan koirala and welcome back to data science basics I am going to create a series of videos in launching so why this series is because as you have also noticed there are many Frameworks in software development but in large language models I don’t see that many Frameworks and Langston has evolved as a framework for developing applications powered by language models there has been many language models being released and there are many variants that are being fine-tuned on top of that and it’s really difficult to keep up with all those things and interacting with those stops my goal is to educate myself in these topics and I decided to create a video also out of it so that as I progress on studying or educating myself you will also get most out of it this is the introductory video where we will just go through what is line chain and I will show you a small demo more of how we can use it to interact with the last language model I hope you are familiar with software engineering things in software development we use different Frameworks right for different languages there is react view very Django and there is spring there is laravel symphony and all the different Frameworks the main goal of those Frameworks is to make things easier and create web applications easily right Lang Syne is exactly doing the same thing where we use this framework which is powered by this models meaning that we create the applications powered by language models and the good part of this is that as it says in the documentation we believe that the most powerful and differentiated applications will not only call out to a language model via an API but also it is Data aware meaning that we can connect or large language model to other sources of data and also be agentic meaning that we allow a language model to interact with its environment this is really good and actually the Langston is well documented and I see here that they have the introduction page and there is python docs and with JavaScript and typescript Docs I will be mainly focusing on the python docs in my upcoming videos and there is actually just two main things that you need to remember there is components and the huge cases to create the applications and what you need to mainly focus is that we have components and we can change those components to create good apps that is what we will be focusing on I will go through each and every of these different aspects or different topics because I want to educate myself in all of these topics as I go and explore these topics I will create a video how it works is host in each videos I will explain you the theoretical part of that particular topic and then I will create a demo out of it so that I will show you how that can be implemented in the code itself and between the videos I will also create some good applications where applications to show and demonstrate you what kind of cool applications we can make out of it and by the way there is a Blog here with luncheon and before going here if you scroll all the way down there is the page create which Community Discord and Twitter you can join there there is the GitHub there is Python and JavaScript RP script and there is home page and blog I was going through the blog it says announcing our US dollar 10 million seed round by led by benzmark by the way this is just as you can see here it was only six months ago that we released that the first version of Langston but it seems like several years it is actually October 24th when Harrison who is the creator of this framework just tweet it something right and why they want to raise money because this all is started as an open source side project but now it is gaining attraction by just seeing these numbers you can see here it has 20K or more over 20k it starts on GitHub 10K active Discord members 30k followers on Twitter and most importantly over 350 contributors I see that this is going to be a huge framework for large language models you can create cool applications as you can see here in use cases you can create a personal assistant question answering over docs chatbots acquiring table or data interacting with the apis extracts and evaluation summarization and all these things but in this introductory video I will just show you a quick glimpse of what Lang chain can do so first we need to install right let me make it right here so you can first installing chain I have already done this so it must not take that much of time we need to set the environment variables right because installing land chain itself is not enough because as I said here using Lang chain will usually require integration with one or more model providers data sources apis and all these different things in this case I am going to use open AI we can just install the open AI here in order to interact with open AI we need to have the open AI API key right for that you can go to this URL platform.openai.com accounts API Keys you need to have a account of open AI if you don’t have already and then you can just go to this API Keys Tab and then create a new secret key I have already created and I have already pasted here you need to create a API key and then paste it here and you are good to go I have already run this so I will not run that again now we will be building the last language models now we have Lang chain and we have openai API key right now we can create a simple demo here I just said okay from Land chain dot llms import open AI we just want to do some kind of predictions now what we need to do is we need to initialize the wrapper so for that we can create an instance or variable what you call it llm equals to open Ai and there we go and what we want to do now is just say a text let’s say that what would be a good company name for a company that makes colorful sorts I have that text right now I will pass that text into open AIS llm model so here I can just say llm and then I can pass that text inside there and if I run this it will provide me it says Vivid threads interesting and if you run this again it will provide another answer okay bright face and sorts you can see it relates to that right this is how quickly you can interact with open AI I don’t think before it was that easy to have output or to do a simple prediction this easily yeah this is how easily we can interact with Lantern I find it really interesting and helpful okay that’s all for this video I hope you find it helpful or you learn something new today as I said I will be creating a series of videos in the future if you like the video please give the thumbs up if you haven’t subscribed please do so and to get notified when I upload the video you know what to do thanks for watching and see you in the next video”

– LangChain is a framework for developing applications powered by language models.
– It aims to make it easier to create web applications using language models.
– LangChain allows language models to interact with their environment and connect to other data sources.
– It is well-documented, with Python, JavaScript, and TypeScript documentation available.
– The main focus is on using components and use cases to create applications.
– The creator of LangChain announced a $10 million seed round led by Benzmark.
– LangChain has gained significant attention with over 20k stars on GitHub, 10k active Discord members, 30k Twitter followers, and 350 contributors.
– Use cases of LangChain include personal assistants, question answering, chatbots, data extraction, summarization, and more.
– In the introductory video, a quick demonstration of LangChain’s capabilities is shown.
– To use LangChain, you need to install it and set up environment variables.
– Integration with model providers, data sources, and APIs is usually required.
– OpenAI is used in the demo, and an OpenAI API key is needed for interaction.
– The code example demonstrates how to initialize the LangChain wrapper and make predictions using the OpenAI language model.
– The video concludes by encouraging viewers to like, subscribe, and stay tuned for future videos.

Models used:
– OpenAI language model

Tokenizers used:
– Not mentioned

Embeddings used:
– Not mentioned