From the course: Creating Advanced AI Applications with Python, APIs, and GitHub Models
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Solution: Function calling
From the course: Creating Advanced AI Applications with Python, APIs, and GitHub Models
Solution: Function calling
(upbeat music) - [Instructor] Now in order to add function calling capability to our large language model, we need to spend some time looking at the documentation for our model provider. Now in this case, I'm going to be using OpenAI. Unfortunately, since I've recorded this video, the documentation at OpenAI around function calling has changed. So head over to the GitHub repo for this course and copy the code from there. And so do take some time to take a look at this documentation. Right, so you can see that in function calling, it looks like we have an additional tool section that we need to add. And this is the definition of the tools. I scroll down a little bit further, there's an image that explains what function calling is all about. And then I've got the advanced usage and a couple of examples that I can look at. So spend a couple of minutes to work your way through this documentation. Now it looks like the first section that we'll need is "Tools." So let me go ahead and grab…
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Contents
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Using GitHub Codespaces3m 32s
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Using ExchangeRate-API.com4m 56s
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(Locked)
Challenge: Get_exchange_rate21s
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(Locked)
Solution: Get_exchange_rate5m 46s
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(Locked)
Challenge: Choose an LLM supporting multiple languages24s
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Solution: Choose an LLM supporting multiple languages3m 27s
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(Locked)
Challenge: Create an app layout37s
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(Locked)
Solution: Create an app layout3m 56s
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Challenge: LLM API call58s
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Solution: LLM API call6m 28s
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(Locked)
Function calling: Add function calling code1m 43s
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Challenge: Function calling1m 12s
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(Locked)
Solution: Function calling8m 57s
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Challenge: Using tools when required52s
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(Locked)
Solution: Using tools when required8m 44s
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(Locked)
Deploy the app to production6m 10s
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(Locked)
Working with LangSmith8m 22s
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