My name is Dan. I am a developer advocate at Databricks. I focus on MLops and LLMops.
Before starting at Databricks, I worked at bit.io, which offered the fastest way to get a cloud PostgreSQL database. In my role, I used bit.io databases to manage the data in various data science research projects. I've analyzed data and written about topics such as methane emissions, Bayesian statistics, labor turnover, the size of the House of Representatives, and much more. Read more…
I find that I tend to re-use a few prompt patterns for specific tasks. Yasnippet provides a great
way to create prompt templates made up of some fixed component with placeholders
for user input. I can easily insert these prompt templates when working with
chatgpt-shell to gain easy access to reusable, task-specific prompts. This post
describes how to start using Yasnippet for prompt templates for use with
Since this start of this year, I've been working on and writing about AI tools for working with Postgres databases. Most of this work has involved finding different ways to integrate ChatGPT (and previously Codex) with other tools and workflows. I wanted to collect and share some of that writing here, as it's related to a lot of the other things I write about on my personal blog.
One of the things that makes working with the ChatGPT API a little different
from working with, e.g., the
davinci-text-003 model api is the need to maintain
the history of a given chat session. A Julia
Struct containing the chat
history, coupled with a function that acts on that Struct, provides a good way
to work with the ChatGPT API.
For the basics of working with the ChatGPT API, check out part 1.
This brief post shows the basics of using the Julia
HTTP library to interact
with the OpenAI ChatGPT API, which was made public a few days ago. This post
will only include the minimum necessary detail for getting started with the
API. Future posts will go into a little more detail on how to send message
histories and engage more interactively with the API.