> For the complete documentation index, see [llms.txt](https://docs.trymito.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.trymito.io/misc/release-notes/november-4th-2024-hello-mito-ai.md). # November 4th, 2024 - Hello Mito AI We've released a new package mito-ai that adds ### Chat Chat is like collaborating with a colleague who sees your code, knows your data, and is a Python expert. Its ChatGPT integrated directly into Jupyter. It knows the current code cell you are editing and the column headers of your data, so it can generate code that runs in your analysis without copying and pasting or editing variable names. New code created by the AI is highlighted in green. Code deleted by the AI is highlighted in red. The new code diffs make it easy to review the code to make sure the AI has not made any unwanted changes and that you understand the new code.

### Smart Debugging Is your code erroring because you typed a variable name incorrectly? Maybe you mixed up your single and double quotes? Or maybe it's something more complicated and nuanced like accidentally running Jupyter code cells out of order. Debugging errors like these can be challenging and frustrating, taking you out of the flow of exploring and analyzing your data. When a code cell errors, click the `Fix Error in AI Chat` button to have Mito AI automatically try to fix your error. It will use the context of the code cell and variables that you have defined to figure out why your code is erroring and suggest a fix.

--- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.trymito.io/misc/release-notes/november-4th-2024-hello-mito-ai.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.