From the course: Hands-On AI: Building LLM-Powered Apps

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Solution: Introduction to Chainlit solution

Solution: Introduction to Chainlit solution - Python Tutorial

From the course: Hands-On AI: Building LLM-Powered Apps

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Solution: Introduction to Chainlit solution

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- [Instructor] All right. Hopefully you have fun implementing a basic trended scaffold. So the first question is, will you need to add a proper decorator? So this main function will get called whenever Chainlit receives the message. So we use the on_message decorator and then we will try to echo back the content of the message back to the user. And this completes the scaffold. So to see the application, we can now type chainlit run app/app.py -w from the terminal, and this should pull up an application from GitHub Codespaces. Cool. Now in this application you can type in anything and channel it or echo back whatever we send it. So as an example, when I type, "I love LLM apps," it will echo back the same sentences. Next up, we will try to understand some basics about instructing and prompting Large Language Models.

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