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()'
CODE
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Build Email Q&A App Using Embedchain and Streamlit
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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
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Build LLM RAG App Chat GitHub 30 Lines Python
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Build a LLM app with RAG to chat with GitHub in just 30 lines of Python Code (step-by-step instructions):
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DistBelief trained 10000+ models across architectures
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Distbelief was used internally for training 10,000+ distinct models of all kinds of architectures (convnets, RNNs, LSTMs, feed forward networks, sparse MoEs, etc), with all kinds of different training objectives (supervised, unsupervised, RL, etc).
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Prompt Engineering with LangChain: Free LinkedIn Learning Course
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Prompt Engineering with LangChain This LinkedIn course by @DataScienceHarp is a great resource for getting started It's over 5 hours long, and Harpreet has been a fantastic member of the community since the early days. Big thanks for all the effort! https://
linkedin.com/learning/promp
t-engineering-with-langchain/create-powerful-llm-driven-applications
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Deep Learning v2: What Topics Need Better Coverage?
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Along the way, deep learning has become a lot more popular, with many other sources to learn about it. But are there topics you’d want covered in v2 that aren’t covered well in other sources?
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Why Deep Learning Textbook Lacks Code Examples
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Sometimes people ask me why there isn’t code in the Deep Learning textbook and I say “well, the code would’ve been in pylearn2,” and they say “what’s pylearn2?” and I say “exactly.”