llm-magics 1.2.1

LLM Magics for Ipython created by kmkolasinski

LLM Magics for Jupyter Notebooks

Interact seamlessly with Large Language Models (LLMs) like OpenAI's GPT models directly from your Jupyter Notebook using IPython magics.

https://pypi.org/project/llm-magics/

pip install llm-magics

Features

• Interactive Chat Interface: Engage in a conversational exchange with LLMs within your notebook cells.
• Customizable Models: Switch between different OpenAI chat models (e.g., gpt-3.5-turbo, gpt-4).
• Set System Messages: Define system prompts to guide the behavior of the LLM.
• Persistent Chat History: Maintain context across multiple interactions.
• Rich Rendering: Receive responses with proper formatting, including syntax-highlighted code blocks.
• Easy Clearing: Reset the conversation when needed.

Installation

Prerequisites

• Python 3.10 or higher
• Jupyter Notebook or JupyterLab
• An OpenAI API key

Steps

1. Clone the Repository

   git clone https://github.com/kmkolasinski/ipython-llm-magics
   cd llm-magics
   

2. Install Dependencies

   Install the required Python packages:

   pip install -r requirements.txt
   

3. Install the Package

   pip install .
   

Configuration

Setting the OpenAI API Key

Before using the magics, ensure your OpenAI API key is accessible to the application.

Option 1: Environment Variable

Set the OPENAI_API_KEY environment variable in your shell:

export OPENAI_API_KEY='your-api-key-here'

Option 2: Within the Notebook

Alternatively, set the API key within your notebook:

import os
os.environ['OPENAI_API_KEY'] = 'your-api-key-here'

Usage

Loading the Extension

In your Jupyter Notebook, load the llm_magics extension:

%load_ext llm_magics

Setting the Model

Specify the OpenAI model you want to use:

%llm_set_model gpt-4o

Available models include gpt-3.5-turbo, gpt-4o, etc.

Setting a System Message (Optional)

Define a system message to guide the assistant's behavior:

%llm_set_system_message "You are a helpful assistant."

Starting a Conversation

Use the %%llm_chat cell magic to send a message to the LLM:

%%llm_chat

Write a Python function that generates a random integer between 1 and 100.

Response:

The assistant will provide a Python function as per your request.

Continuing the Conversation

Maintain context across multiple %%llm_chat cells:

%%llm_chat

Now modify the function to generate a random floating-point number between 0 and 1.

Response:

The assistant will update the function accordingly.

Inserting local variables into the chat

not_sorted_list = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]
expected_output = sorted(not_sorted_list)

%%llm_chat

Write python code to sort the list in the descending order using merge sort algorithm.
So that I can write:
input = $not_sorted_list
assert $expected_output == my_merge_sort(input)

Clearing the Conversation History

Reset the chat history when needed:

%llm_clear

Rendering and Syntax Highlighting

The extension includes rich rendering of the LLM's responses:

• Code blocks are syntax-highlighted using Prism.js and Highlight.js.
• Copy buttons are added to code blocks for convenience.
• Markdown formatting is preserved in the responses.

History

• v1.1.0: Initial release with basic chat functionality and rendering.