Build Your Own 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 strong instrument for builders. On this article, we’ll learn 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 rather more, as proven within the screenshot beneath.

⚠️ Jupyter AI continues to be below heavy growth, so some options could break. As of writing this text, I’ve examined the setup to substantiate it really works, however count on potential changes because the mission evolves. Additionally the efficiency of the assistant depends upon 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 title suggests, Jupyter AI is a JupyterLab extension for generative AI. This highly effective instrument transforms your normal Jupyter notebooks or JupyterLab setting right into a generative AI playground. One of the best 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 a wide range of mannequin suppliers (each open and closed supply) proper inside your Jupyter setting. 

Establishing the setting includes three predominant 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 carried out accurately. It’s due to this fact important you comply with the steps accurately.

1. Putting in the Jupyter AI Extension

It’s beneficial to create a new environment particularly for Jupyter AI to maintain your current setting clear and organised. As soon as carried out comply with the following 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 best technique for set up because it contains all supplier dependencies (so it helps Hugging Face, Ollama, and many others., out of the field). Up to now, Jupyter AI helps the next model providers : 

In case you encounter errors in the course of the Jupyter AI set up, manually set up Jupyter AI utilizing pip with out the [all] optionally available dependency group. This manner you may management which fashions can be found in your Jupyter AI setting. For instance, to put in Jupyter AI with solely added help for Ollama fashions, use the next:

pip set up jupyter-ai langchain-ollama

The dependencies depend on the mannequin suppliers (see desk above).  Subsequent, restart your JupyterLab occasion. In case you see a chat icon on the left sidebar, this implies every little thing has been put in completely. With Jupyter AI, you may chat with fashions or use inline magic instructions straight 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 straight, some fashions may not work properly. As a substitute, Ollama gives a extra dependable approach to load fashions regionally.

Ollama is a helpful instrument for operating Large Language Models regionally. It helps 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 website. After set up, confirm that it’s arrange accurately by operating:

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

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

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

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

Choice 1: Utilizing Pre-Configured Fashions

Ollama gives a library of pre-trained fashions that you could 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.

Choice 2: Loading a Customized Mannequin

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

Choice 3: Operating GGUF Fashions straight from Hugging Face

Ollama now helps GGUF models directly from the Hugging Face Hub, together with each private and non-private fashions. This implies if you wish to use GGUF mannequin straight from Hugging Face Hub you are able to do so with out requiring a customized Mannequin File as talked about in Choice 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 Local Apps settings.

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

We’re virtually there. In JupyterLab, open the Jupyter AI chat interface on the sidebar. On the high of the chat panel or in its settings (gear icon), there’s a dropdown or discipline to pick the Mannequin supplier and mannequin ID. Select Ollama because the supplier, and enter the mannequin title 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 primarily based on the fashions of your selection.
• Save the settings and return to the chat interface.

This configuration hyperlinks Jupyter AI to the regionally operating mannequin through 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 may choose Allow completions by Jupyternaut to activate the characteristic.

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

Coding assistant through Chat interface

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

It’s also possible to ask the AI to generate code for a job from scratch, simply by describing what you want in pure language. Here’s a Python perform that returns all prime numbers as much as a given constructive integer N, generated by Jupyternaut.

Coding assistant through pocket book cell or IPython shell:

It’s also possible to work together with fashions straight inside a Jupyter pocket book. First, load the IPython extension:

%load_ext jupyter_ai_magics

Now, you should utilize 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 may consult with the official documentation.

As you may gauge from this text, Jupyter AI makes it simple to arrange a coding assistant, offered you’ve got the precise installations and setup in place. I used a comparatively small mannequin, however you may select from a wide range of fashions supported by Ollama or Hugging Face. The important thing benefit right here is that utilizing a neighborhood mannequin provides important advantages: it enhances privateness, reduces latency, and reduces dependence on proprietary mannequin suppliers. Nonetheless, operating large fashions regionally with Ollama might be resource-intensive so guarantee that you’ve ample RAM. With the fast tempo at which open-source fashions are enhancing, you may obtain comparable efficiency even with these options.