课程: Creating Advanced AI Applications with Python, APIs, and GitHub Models
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Solution: Function calling
课程: 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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Using GitHub Codespaces3 分钟 32 秒
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Using ExchangeRate-API.com4 分钟 56 秒
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Challenge: Get_exchange_rate21 秒
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Solution: Get_exchange_rate5 分钟 46 秒
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Challenge: Choose an LLM supporting multiple languages24 秒
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Solution: Choose an LLM supporting multiple languages3 分钟 27 秒
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Challenge: Create an app layout37 秒
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Solution: Create an app layout3 分钟 56 秒
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Challenge: LLM API call58 秒
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Solution: LLM API call6 分钟 28 秒
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Function calling: Add function calling code1 分钟 43 秒
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Challenge: Function calling1 分钟 12 秒
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Solution: Function calling8 分钟 57 秒
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Challenge: Using tools when required52 秒
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Solution: Using tools when required8 分钟 44 秒
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Deploy the app to production6 分钟 10 秒
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Working with LangSmith8 分钟 22 秒
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