AI Magic

Direct access to AI models with fine-grained control over providers, formats, and conversation context. Use %ai and %%ai to call any supported model from a notebook cell.

Loading the Extension

%load_ext jupyter_ai_magics

Cell Magic (`%%ai`)

Generate content from AI models with full control over output format.

Basic Syntax

%%ai provider:model [-f format]
Your prompt here

Output Formats

Format	Flag	Use Case
`markdown`	`-f markdown`	Documentation, explanations (default)
`code`	`-f code`	Executable code blocks
`html`	`-f html`	Rich HTML output
`math`	`-f math`	LaTeX mathematical expressions
`json`	`-f json`	Structured data
`text`	`-f text`	Plain text
`image`	`-f image`	Image generation (HuggingFace)

Examples

Code Generation

%%ai claude -f code
Write a Python class for a binary search tree with insert,
search, and delete methods. Include docstrings.

Output:

class BinarySearchTree:
    """A binary search tree implementation."""

    def __init__(self):
        """Initialize an empty BST."""
        self.root = None

    def insert(self, value):
        """Insert a value into the BST."""
        # ... implementation

Mathematical Expressions

%%ai gpt4o -f math
Derive the quadratic formula from ax² + bx + c = 0

Output (rendered LaTeX): $$x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a}$$

HTML Generation

%%ai claude -f html
Create an SVG of a simple bar chart showing sales data:
Q1: 100, Q2: 150, Q3: 120, Q4: 200

Output: Interactive SVG rendered in notebook

Structured Data

%%ai gpt4o -f json
Generate a JSON schema for a user profile with name,
email, age, and preferences (array of strings)

Output:

{
  "type": "object",
  "properties": {
    "name": {"type": "string"},
    "email": {"type": "string", "format": "email"},
    "age": {"type": "integer", "minimum": 0},
    "preferences": {
      "type": "array",
      "items": {"type": "string"}
    }
  },
  "required": ["name", "email"]
}

Variable Interpolation

Reference Python variables in your prompts using curly braces:

language = "Python"
task = "web scraping"

%%ai claude -f code
Write a {language} script for {task} using BeautifulSoup

Special Variables

Access notebook cells directly:

# Reference input from cell 5
%%ai claude
Explain this code: {In[5]}

# Reference output from cell 3
%%ai claude
Analyze these results: {Out[3]}

# Reference an error
%%ai claude
Fix this error: {Err[7]}

Output Redirection

Capture AI output into variables for further processing:

%%ai claude -f code -o my_function
Write a function to validate email addresses

# Now use the generated code
exec(my_function)
is_valid = validate_email("test@example.com")

Chaining Outputs

# Generate data
%%ai gpt4o -f json -o sample_data
Generate 5 sample user records with name, email, age

# Process the data
import json
users = json.loads(sample_data)
for user in users:
    print(f"Processing {user['name']}...")

Building Pipelines

# Step 1: Generate analysis code
%%ai claude -f code -o analysis_code
Write pandas code to load 'sales.csv' and calculate
monthly revenue trends

# Step 2: Execute and capture results
exec(analysis_code)

# Step 3: Get AI interpretation
%%ai claude
Interpret these results: {Out[-1]}
What business insights can we draw?

Conversation Context

AI maintains conversation history within a session:

%%ai claude
I'm building a REST API for a todo app.
What endpoints should I create?

%%ai claude
Now show me the Flask code for those endpoints

%%ai claude
Add authentication middleware to protect these routes

Reset Context

%ai reset

Configure History Length

%config AiMagics.max_history = 6  # Keep last 6 exchanges

Model Providers

Supported Providers

Provider	Model Examples	Best For
Anthropic	`claude`, `claude-3-opus`	Complex reasoning, long context
OpenAI	`gpt4o`, `gpt4o-mini`	General purpose, reliable
Google	`gemini`	Fast, multimodal
Mistral	`codestral`, `mistral-large`	Code generation
Bedrock	`bedrock-claude`	AWS integration
Ollama	`ollama:llama3`	Local, private
HuggingFace	`huggingface_hub:model-id`	Specialized models

List Available Models

%ai list

%ai list anthropic  # List models from specific provider

Model Shortcuts

Pre-configured shortcuts for common models:

Shortcut	Model
`claude`	Claude 3.5 Sonnet
`gpt4o`	GPT-4o
`gpt4o-mini`	GPT-4o Mini
`gemini`	Gemini 2.0 Flash
`bedrock-claude`	Claude via AWS Bedrock

Advanced Configuration

Set Default Model

%config AiMagics.default_language_model = "anthropic:claude-3-5-sonnet-20241022"

Register Model Aliases

# Create a shortcut
%ai register mymodel anthropic:claude-3-5-sonnet-20241022

# Use the shortcut
%%ai mymodel -f code
Write a sorting algorithm

# Update an alias
%ai update mymodel openai:gpt-4o

# Delete an alias
%ai delete mymodel

Error Handling

Explain Errors

When a code cell fails, get AI help:

# After an error occurs
%ai error claude

The AI will analyze the traceback and suggest fixes.

Fix Errors Automatically

In the chat sidebar, use:

/fix

Select the error cell, and AI will propose corrections.

SageMaker Endpoints

Connect to custom models deployed on AWS SageMaker:

%%ai sagemaker-endpoint:my-endpoint-name \
    --region-name us-east-1 \
    --request-schema {"text_inputs": "<prompt>"} \
    --response-path generated_texts.[0] \
    -f code
Write a data validation function

Documentation

Cell Magic

%%ai syntax and output formats

Line Magic

%ai subcommands and configuration