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Notebooks: Tools for LLMs

Notebooks: Tools for LLMs

Lightning talk about Notebooks for LLMs at AI Dev Day, hosted by AICamp.

This talk shows how we integrated computational notebooks, specifically Jupyter, with Large Language Models (LLMs). This talk will explore how the Noteable Plugin has been designed to enable LLMs to write literate computational notebooks, allowing models to write prose, code, and plot. An unexpected discovery, once GPT models had access to notebooks, they unlocked the ability to debug and reason in a paired interactive computing environment. Through some live demos and videos, we will walk through doing data science with our LLM partner. We will also discuss the tradeoffs and risks associated with temporary and durable computation for chat models. This session aims to inspire the audience to explore the untapped potential of LLMs and notebooks in their data analysis tasks, paving the way for a future of collaborative analysis that includes not just human peers but also artificial intelligence.

Kyle Kelley

July 26, 2023

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  1. Kyle Kelley at AI Dev Day, July 25th, 2023
    Notebooks: Tools for LLMs
    ⚡ Talk at AI Dev Day

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  2. I'm Kyle Kelley
    • Chief Architect at Noteable

    • IPython & Jupyter Developer

    • Previously Notebooks Platform
    at Net

    • Containers, DevOps, and UX

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  3. What are Notebooks



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  5. In[1]
    import openai

    import noteable

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  6. https://www.youtube.com/watch?v=Xd-VPCC_WKI&t=584s
    Choose your own adventure
    Video from I versus AI Live Demo!

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  7. Make literate computational documents
    Data Driven Documents

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  8. How?

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  9. Headless Execution
    From Scheduled Jobs to Realtime Notebooks
    • Original scheduled notebook jobs:

    • Interactive compute with realtime updates

    • Both interactive and batched

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  10. Headless Execution
    Close your tabs

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  12. Comparisons
    Noteable Plugin, OpenAI Code Interpreter, and Jupyter Extensions
    • Ephemeral compute: Fast to spin up

    • Durable Compute: access to data
    les, environment variables/secrets, and

    • In all cases, ability to debug, reason about outputs, and run code

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  13. From: Please use the IMDb Data Source
    with the `imdb` schema to determine
    which years had the most horror
    To: Which years had the most horror

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  14. Conclusion
    Untapped Potential of LLMs and Notebooks for Data Analysis
    • Longer form blog post of this talk:


    • Augment Notebooks with app.noteable.io

    • Try the Plugin with ChatGPT Plus!

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