3. Building AI Applications on Jetson Nano https://
learn.nvidia.com/courses/course
-detail?course_id=course-v1:DLI+S-IV-02+V2
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TOOLS
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Building Video AI Applications on Jetson Nano Course
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Free Nvidia Courses on Generative AI, Computer Vision and RAG
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Free courses from Nvidia on Generative AI Foundations, Computer Vision and RAG:
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Generative AI Explained: NVIDIA Learning Course
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1. Generative AI Explained: https://
learn.nvidia.com/courses/course
-detail?course_id=course-v1:DLI+S-FX-07+V1
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Full RAG Application Code for GitHub Repository Chat
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Full RAG Application Code to Chat with GitHub Repo
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Build Email Q&A App Using Embedchain and Streamlit
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5. Ask question about your emails and get the answer • Create a text input for the user to enter their question using 'st.text_input()'
• If a question is asked, get the answer from the Embedchain app and display it using 'st.write()' -

Embedchain App Initialization with OpenAI API Integration
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4. Initialize the Embedchain App • If the OpenAI API key is provided, initialize the embedchain app with 'app()'
• The `add` function of the app will accept any valid github query with qualifiers. It only supports loading github code, repository, issues and pull-requests. -

Building Streamlit App with OpenAI API Integration
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3. Set up the Streamlit App Streamlit lets you create user interface with just python code, for this app we will:
• Add a title to the app using 'st.title()'
• Create a text input to enter their OpenAI API key using 'st.text_input()' -

Configure Github Loader with Personal Access Token Setup
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2. Setup the Github loader by configuring the Github account with username and personal access token (PAT). Check out this link to learn how to create a PAT: https://
docs.github.com/en/enterprise-
server@3.6/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens#creating-a-personal-access-token
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Building RAG Web App with Streamlit and Embedchain
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1. Import necessary libraries • Streamlit for building the web app
• Embedchain for the RAG functionality