From the course: LLaMa for Developers

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Using schemas with LLaMA

Using schemas with LLaMA - Llama Tutorial

From the course: LLaMa for Developers

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Using schemas with LLaMA

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- [Instructor] In this video, we're going to learn how to be more structured with LLaMa. Our goal is to be able to use schemas in order to structure our data. For this video under 05_04, our goal will be to get a Python object back from LLaMa. The steps are pretty straightforward. We're going to define a pydantic schema. We're then going to define a langchain series of prompts. And finally, we're going to get a Python object in return. So let's go ahead and install our dependencies. We need langchain-groq and LangChain. Going to hit Enter on that. If you're not familiar with LangChain, it's a prompting framework, which has lots of useful packages. So we're going to import our prompt templates and ChatGroq. We'll be using the API key we used in previous videos. You can find the key on the left-hand side, clicking on this key icon. So let's import all these dependencies and we'll get to defining our pydantic schema. So we're using the PydanticOutputParser. First, we'll define a class…

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