Construct Your Personal AI Coding Assistant in JupyterLab with Ollama and Hugging Face

Jupyter AI brings generative AI capabilities proper into the interface. Having a neighborhood AI assistant ensures privateness, reduces latency, and gives offline performance, making it a robust instrument for builders. On this article, we’ll discover ways to arrange a neighborhood AI coding assistant in JupyterLab utilizing Jupyter AI, Ollama and Hugging Face. By the tip of this text, you’ll have a totally useful coding assistant in JupyterLab able to autocompleting code, fixing errors, creating new notebooks from scratch, and far more, as proven within the screenshot under.

⚠️ Jupyter AI continues to be below heavy improvement, so some options might break. As of writing this text, I’ve examined the setup to substantiate it really works, however anticipate potential modifications because the undertaking evolves. Additionally the efficiency of the assistant will depend on the mannequin that you choose so be sure you select the one that’s match to your use case.

First issues first — what’s Jupyter AI? Because the identify suggests, Jupyter AI is a JupyterLab extension for generative AI. This highly effective instrument transforms your customary Jupyter notebooks or JupyterLab setting right into a generative AI playground. The most effective half? It additionally works seamlessly in environments like Google Colaboratory and Visible Studio Code. This extension does all of the heavy lifting, offering entry to quite a lot of mannequin suppliers (each open and closed supply) proper inside your Jupyter setting. 

Establishing the setting includes three essential parts:

• JupyterLab
• The Jupyter AI extension
• Ollama (for Native Mannequin Serving)
• [Optional] Hugging Face (for GGUF fashions)

Actually, getting the assistant to resolve coding errors is the simple half. What is hard is making certain all of the installations have been completed accurately. It’s subsequently important you comply with the steps accurately.

1. Putting in the Jupyter AI Extension

It’s really useful to create a new setting particularly for Jupyter AI to maintain your present setting clear and organised. As soon as completed comply with the subsequent steps. Jupyter AI requires JupyterLab 4.x or Jupyter Pocket book 7+, so be sure you have the newest model of Jupyter Lab put in​. You’ll be able to set up/improve JupyterLab with pip or conda:

# Set up JupyterLab 4 utilizing pip
pip set up jupyterlab~=4.0

Subsequent, set up the Jupyter AI extension as follows.

pip set up "jupyter-ai[all]"

That is the simplest methodology for set up because it contains all supplier dependencies (so it helps Hugging Face, Ollama, and many others., out of the field). To this point, Jupyter AI helps the next mannequin suppliers : 

Should you encounter errors throughout the Jupyter AI set up, manually set up Jupyter AI utilizing pip with out the [all] elective dependency group. This fashion you possibly can management which fashions can be found in your Jupyter AI setting. For instance, to put in Jupyter AI with solely added assist for Ollama fashions, use the next:

pip set up jupyter-ai langchain-ollama

The dependencies rely on the mannequin suppliers (see desk above).  Subsequent, restart your JupyterLab occasion. Should you see a chat icon on the left sidebar, this implies all the things has been put in completely. With Jupyter AI, you possibly can chat with fashions or use inline magic instructions instantly inside your notebooks.

2. Setting Up Ollama for Native Fashions

Now that Jupyter AI is put in, we have to configure it with a mannequin. Whereas Jupyter AI integrates with Hugging Face fashions instantly, some fashions might not work correctly. As an alternative, Ollama gives a extra dependable strategy to load fashions regionally.

Ollama is a useful instrument for working Giant Language Fashions regionally. It permits you to obtain pre-configured AI fashions from its library. Ollama helps all main platforms (macOS, Home windows, Linux)​, so select the strategy to your OS and obtain and set up it from the official web site. After set up, confirm that it’s arrange accurately by working:

Ollama --version
------------------------------
ollama model is 0.6.2

Additionally, be certain that your Ollama server should be working which you’ll test by calling ollama serve on the terminal:

$ ollama serve
Error: pay attention tcp 127.0.0.1:11434: bind: tackle already in use

If the server is already energetic, you will notice an error like above confirming that Ollama is working and in use.

Possibility 1: Utilizing Pre-Configured Fashions

Ollama gives a library of pre-trained fashions you can obtain and run regionally. To start out utilizing a mannequin, obtain it utilizing the pull command. For instance, to make use of qwen2.5-coder:1.5b, run:

ollama pull qwen2.5-coder:1.5b

This can obtain the mannequin in your native setting. To verify if the mannequin has been downloaded, run:

ollama record

This can record all of the fashions you’ve downloaded and saved regionally in your system utilizing Ollama.

Possibility 2: Loading a Customized Mannequin

If the mannequin you want isn’t out there in Ollama’s library, you possibly can load a customized mannequin by making a Mannequin File that specifies the mannequin’s supply.For detailed directions on this course of, discuss with the Ollama Import Documentation.

Possibility 3: Working GGUF Fashions instantly from Hugging Face

Ollama now helps GGUF fashions instantly from the Hugging Face Hub, together with each private and non-private fashions. This implies if you wish to use GGUF mannequin instantly from Hugging Face Hub you are able to do so with out requiring a customized Mannequin File as talked about in Possibility 2 above.

For instance, to load a 4-bit quantized Qwen2.5-Coder-1.5B-Instruct mannequin from Hugging Face:

1. First, allow Ollama below your Native Apps settings.

2. On the mannequin web page, select Ollama from the Use this mannequin dropdown as proven under.

We’re nearly there. In JupyterLab, open the Jupyter AI chat interface on the sidebar. On the prime of the chat panel or in its settings (gear icon), there’s a dropdown or subject to pick out the Mannequin supplier and mannequin ID. Select Ollama because the supplier, and enter the mannequin identify precisely as proven by Ollama record within the terminal (e.g. qwen2.5-coder:1.5b). Jupyter AI will connect with the native Ollama server and cargo that mannequin for queries​. No API keys are wanted since that is native.

• Set Language mannequin, Embedding mannequin and inline completions fashions based mostly on the fashions of your alternative.
• Save the settings and return to the chat interface.

This configuration hyperlinks Jupyter AI to the regionally working mannequin by way of Ollama. Whereas inline completions ought to be enabled by this course of, if that doesn’t occur, you are able to do it manually by clicking on the Jupyternaut icon, which is situated within the backside bar of the JupyterLab interface to the left of the Mode indicator (e.g., Mode: Command). This opens a dropdown menu the place you possibly can choose Allow completions by Jupyternaut to activate the function.

As soon as arrange, you need to use the AI coding assistant for varied duties like code autocompletion, debugging assist, and producing new code from scratch. It’s necessary to notice right here you can work together with the assistant both by the chat sidebar or instantly in pocket book cells utilizing %%ai magic instructions. Let’s have a look at each the methods.

Coding assistant by way of Chat interface

That is fairly easy. You’ll be able to merely chat with the mannequin to carry out an motion. As an illustration, right here is how we are able to ask the mannequin to clarify the error within the code after which subsequently repair the error by choosing code within the pocket book.

You can too ask the AI to generate code for a activity from scratch, simply by describing what you want in pure language. Here’s a Python operate that returns all prime numbers as much as a given constructive integer N, generated by Jupyternaut.

Coding assistant by way of pocket book cell or IPython shell:

You can too work together with fashions instantly inside a Jupyter pocket book. First, load the IPython extension:

%load_ext jupyter_ai_magics

Now, you need to use the %%ai cell magic to work together along with your chosen language mannequin utilizing a specified immediate. Let’s replicate the above instance however this time throughout the pocket book cells.

For extra particulars and choices you possibly can discuss with the official documentation.

As you possibly can gauge from this text, Jupyter AI makes it straightforward to arrange a coding assistant, supplied you will have the suitable installations and setup in place. I used a comparatively small mannequin, however you possibly can select from quite a lot of fashions supported by Ollama or Hugging Face. The important thing benefit right here is that utilizing a neighborhood mannequin gives important advantages: it enhances privateness, reduces latency, and reduces dependence on proprietary mannequin suppliers. Nonetheless, working large fashions regionally with Ollama might be resource-intensive so guarantee that you’ve got enough RAM. With the speedy tempo at which open-source fashions are enhancing, you possibly can obtain comparable efficiency even with these options.