From the course: Creating Advanced AI Applications with Python, APIs, and GitHub Models
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Challenge: Using tools when required
From the course: Creating Advanced AI Applications with Python, APIs, and GitHub Models
Challenge: Using tools when required
(bright electronic music) - [Instructor] In this challenge, we're going to continue working on our Moneychanger App. Now, one of the problems with our app is that if we were to now ask a non-exchange-related question, it would fail. So, let me give you an example. So, Why is the sky blue, for example? And you can see we get a TypeError, because we aren't able to pass the output that's been returned. So, using the documentation for the large language model provider of your choice, ensure that your app uses the function calling tools for exchange-related questions, and provides a response from the large language model for non-exchange-related questions. So, using moneychanger.py, make these changes in the run_pipeline() function.
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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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Challenge: Create an app layout37s
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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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Function calling: Add function calling code1m 43s
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Challenge: Function calling1m 12s
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Solution: Function calling8m 57s
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Challenge: Using tools when required52s
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Solution: Using tools when required8m 44s
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Deploy the app to production6m 10s
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(Locked)
Working with LangSmith8m 22s
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