miniai 0.1.0

A minimalist Python library for byte-sized AI tasks

MiniAI

A minimalist Python library for byte-sized AI tasks. No complex chains, no confusing abstractions, just AI that works.

The Problem

Using AI models for simple tasks comes with too much overhead:

• Writing boilerplate API calls
• Managing prompts and message lists
• Configuring providers and models
• Learning complex frameworks like LangChain

These barriers make it difficult to quickly prototype and integrate AI capabilities into your workflow.

The Solution

MiniAI provides a dead-simple interface for common AI tasks:

from miniai import ai

# Simple tasks with just one line
text = ai.ask("Write a haiku about Python")
category = ai.classify("I love this product!", ["positive", "negative", "neutral"])
entities = ai.extract("Apple was founded by Steve Jobs in 1976", ["people", "organizations", "dates"])
summary = ai.summarize("Very long text goes here...")
translated = ai.translate("Hello world", to="spanish")
answer = ai.ask("Who was Ada Lovelace?")
image_analysis = ai.ask("What's in this image?", images=["Logo.png"])
image_url_analysis = ai.ask("What's in this image?", images=["https://example.com/image.jpg"])

# Configure once, use anywhere
ai.set_api_key("openai", "your-api-key-here")  # Or use environment variables
ai.use("anthropic")  # Switch providers dynamically
ai.set_model("gpt-4-turbo")  # Set model for current provider

# Mock mode for testing without API keys
ai.config.mock(True)
result = ai.ask("This will return a mock response")  # No API key needed

Turn Any Function into an AI Function

The most powerful feature is the function decorator:

@ai.function
def generate_poem(topic, style):
    """Generate a poem about {topic} in the style of {style}."""

poem = generate_poem("autumn leaves", "haiku")
print(poem)

# With system prompt
@ai.function(system_prompt="You are a professional software engineer.")
def write_code(task, language):
    """Write {language} code to {task}. Include comments."""

code = write_code("sort a list", "python")

Installation

pip install miniai

Why Choose MiniAI?

• 🚀 Simple API: Just one import, intuitive methods
• 🔧 Zero configuration: Works out of the box (with environment variables)
• 🧠 Smart defaults: Uses appropriate models for each task
• 🔄 Model agnostic: Works with OpenAI, Anthropic, and more
• 📦 Lightweight: No heavy dependencies
• 🧩 Extensible: Easy to add new providers and tasks
• 🧪 Test-friendly: Mock mode for development without API keys

API Reference

Core Functions

• ai.ask(question, format_instructions=None, images=None, raw_response=False, **kwargs) - Answer a question, optionally with images and format instructions
• ai.classify(text, categories, raw_response=False, **kwargs) - Classify text into categories
• ai.extract(text, entities, raw_response=False, **kwargs) - Extract entities from text
• ai.summarize(text, raw_response=False, **kwargs) - Summarize text
• ai.translate(text, to, raw_response=False, **kwargs) - Translate text to another language
• ai.embedding(text, raw_response=False, **kwargs) - Get embedding vector for text
• ai.text_to_speech(text, raw_response=False, **kwargs) - Convert text to speech (OpenAI only)
• ai.speech_to_text(audio_data, raw_response=False, **kwargs) - Convert speech to text (OpenAI only)

Configuration

• ai.set_api_key(provider, key) - Set API key for a provider
• ai.use(provider) - Switch to a different provider
  • ai.use('mock') - Use the mock provider for testing
• ai.set_model(model, provider=None) - Set model for current or specified provider
• ai.get_active_provider() - Get current provider
• ai.get_available_providers() - List all available providers

Response Format

By default, all functions return the processed response that makes sense for the task (e.g. a string for text generation, a list of entities for entity extraction, etc.).

All functions also support a raw_response parameter that returns a Response object with:

• content: The processed response
• raw_response: The complete response from the provider

Example:

response = ai.ask("What is the capital of France?", raw_response=True)
print(response.content)  # The text answer
print(response.raw_response)  # Full provider response

More examples in the examples directory.

Decorator

• @ai.function(system_prompt=None) - Turn any function into an AI function

License

MIT