From the course: Prompt Flow: Hands-On
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Adding required Python dependencies before deployment - Azure AI Services Tutorial
From the course: Prompt Flow: Hands-On
Adding required Python dependencies before deployment
- [Tutor] When the flow for your app is fully built out and tested, and you are happy with how it works, it's time to deploy it so it's a standalone application and other people can use it. In the next video, we'll go through the actual deployment process, but before we do that, we have to make sure that all the necessary information sits inside our current project folder. Here's what I mean by that. In my flow, I have a bunch of different Python tools, and these different Python tools are using different Python dependencies. Now so far, because of how I've set up my entire environment, all of those dependencies were loaded in when you originally set up the code space. So here you see, we have beautiful soup, we have key rings, we have mistral, we have Prompt flow, and Python.end and requests. But when we deploy our reader app, it'll only pull information from the reader folder. That means each of your prompt flow folders needs to contain a requirements.text file with all of the…
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Idea: Build an article discussion app for kids2m 32s
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Creating a new Prompt Flow from scratch2m 15s
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Creating a connection to access AI services2m 17s
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Testing the flow1m 39s
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Adding custom Python to the flow4m 24s
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Continuous incremental testing1m 40s
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Adding LLM tools to the flow3m
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Adding new input fields to LLM tools4m 26s
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Debugging the flow3m 10s
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Creating conditional flow patterns7m 22s
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Adding required Python dependencies before deployment2m 40s
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Building a flow using Docker3m 5s
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Create a Docker image for a flow59s
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Run the Docker image3m 10s
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Bonus: Add Mistral AI as a custom connection5m 10s
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