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LangChain_Intro.srt
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Welcome to this short course on
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LandChain for large language model application development.
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By prompting an llm or large language model,
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it's now possible to develop AI applications much faster than ever before.
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But an application can require prompting an llm multiple times and parsing as output.
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So there's a lot of glue code that needs to be written.
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LandChain created by Harrison Chase makes this development process much easier.
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I'm thrilled to have Harrison here,
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who had built this short course in collaboration with
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deeplearning.ai to teach how to use this amazing tool.
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Thanks for having me. I'm really excited to be here.
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LandChain started as an open source framework for building all on applications.
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It came about when I was talking to a bunch of folks in the field who were
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building more complex applications and saw
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some common abstractions in terms of how they were being developed.
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We've been really thrilled at the community adoption of LandChain so far.
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So look forward to sharing it with everyone
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here and look forward to seeing what people build with it.
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In fact, as a sign of LandChain's momentum,
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not only does it have numerous users,
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but there are also many hundreds of contributors to the open source.
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This has been instrumental for its rapid rate of development.
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This team really shifts code and features at an amazing pace.
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So hopefully, after this short course,
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you'll be able to quickly put together some really cool applications using LandChain.
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And who knows, maybe you even decide to
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contribute back to the open source LandChain effort.
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LandChain is an open source development framework for building applications.
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We have two different packages,
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a Python one and a JavaScript one.
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They're focused on composition and modularity.
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So they have a lot of individual components that can be
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used in conjunction with each other or by themselves.
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And so that's one of the key value adds.
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And then the other key value add is a bunch of different use cases.
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So chains of ways of combining these modular components into
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more end-to-end applications and making it
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very easy to get started with those use cases.
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In this class, we'll cover the common components of LandChain.
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So we'll talk about models.
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We'll talk about prompts, which are how you get
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models to do useful and interesting things.
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We'll talk about indexes,
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which are ways of ingesting data so that you can combine it with models.
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And then we'll talk about chains,
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which are more end-to-end use cases along with agents,
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which are a very exciting type of end-to-end use case,
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which uses the model as a reasoning engine.
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We're also grateful to Ankush Gholar,
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who is the co-founder of LandChain alongside Harrison Chase,
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for also putting a lot of thoughts into
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these materials and helping with the creation of this short course.
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And on the deep learning.ai side,
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Jeff Ludwig, Eddie Hsu, and Diala Ezzedine,
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have also contributed to these materials.
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And so with that, let's go on to the next video where we'll learn
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about LandChain's models, prompts, and pauses.