aiwand 0.4.15

A simple AI toolkit for text processing using OpenAI and Gemini APIs

AIWand 🪄

One API to rule them all - Unified OpenAI and Gemini interface with automatic provider switching and structured data extraction from anywhere.

🎯 Two Powerful Features, One Simple API

1. call_ai - Unified AI Interface

Drop-in replacement for OpenAI and Gemini - Same code works with both providers, automatic model detection, and structured output magic.

import aiwand
from pydantic import BaseModel

# Works with any model - provider auto-detected
response = aiwand.call_ai(
    model="gpt-4o",              # or "gemini-2.0-flash"
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)

# Structured output with Pydantic - no JSON parsing needed!
class BlogPost(BaseModel):
    title: str
    content: str
    tags: list[str]

blog = aiwand.call_ai(
    model="gemini-2.0-flash",
    messages=[{"role": "user", "content": "Write a blog about AI"}],
    response_format=BlogPost    # Returns BlogPost object directly!
)
print(blog.title)  # Direct access, no parsing

2. extract - Smart Data Extraction

Pass any content or links - Extracts structured data from text, URLs, files, or mixed sources. Creates or uses your Pydantic models.

# Extract from text
contact = aiwand.extract(content="John Doe, [e-mail], [phone]")

# Extract from URLs or files
data = aiwand.extract(links=["https://company.com/about", "resume.pdf"])

# Mix content + links with custom structure
from pydantic import BaseModel

class ContactInfo(BaseModel):
    name: str
    email: str
    phone: str

result = aiwand.extract(
    content="Meeting notes: call John tomorrow",
    links=["https://company.com/team", "business_card.txt"],
    response_format=ContactInfo  # Get ContactInfo object back
)

🚀 Quick Start

Installation & Setup

pip install aiwand

# Set your API key (either one works)
export OPENAI_API_KEY="your-key"     # or
export GEMINI_API_KEY="your-key"     # or both

Core Usage

import aiwand

# 1. Basic AI calls - auto provider selection
response = aiwand.call_ai(
    model="gpt-4o",  # Uses OpenAI
    messages=[{"role": "user", "content": "Hello!"}]
)

response = aiwand.call_ai(
    model="gemini-2.0-flash",  # Uses Gemini  
    messages=[{"role": "user", "content": "Hello!"}]
)

# 2. Extract data from anywhere
contact = aiwand.extract(content="Dr. Sarah Johnson, [e-mail]")
webpage_data = aiwand.extract(links=["https://example.com"])
mixed_data = aiwand.extract(
    content="Meeting notes...",
    links=["document.pdf", "https://site.com"]
)

✨ Key Benefits

Feature	Benefit
🔄 Provider Agnostic	Same code works with OpenAI and Gemini
🏗️ Structured Output	Get Pydantic objects directly, no JSON parsing
🧠 Smart Detection	Automatic provider selection based on model
📄 Universal Extraction	Extract from text, URLs, files - anything
⚡ Zero Configuration	Works with just API keys
🎯 Drop-in Ready	Minimal code changes from existing AI code

🔧 Advanced Examples

Smart Provider Switching

# Automatic provider detection
responses = []
for model in ["gpt-4o", "gemini-2.0-flash", "o3-mini"]:
    response = aiwand.call_ai(
        model=model,  # Auto-detects OpenAI vs Gemini
        messages=[{"role": "user", "content": "Compare yourself to other AI models"}]
    )
    responses.append(f"{model}: {response}")

# Force specific provider for custom models
response = aiwand.call_ai(
    model="my-custom-model",
    provider="gemini",  # Explicit override
    messages=[{"role": "user", "content": "Test custom model"}]
)

Structured Data Extraction

from pydantic import BaseModel
from typing import List

class CompanyInfo(BaseModel):
    name: str
    founded: int
    employees: int
    technologies: List[str]
    headquarters: str

# Extract from company website
company = aiwand.extract(
    links=["https://company.com/about"],
    response_format=CompanyInfo
)

# Extract from mixed sources
analysis = aiwand.extract(
    content="Research on tech companies in 2024",
    links=[
        "https://techcrunch.com/company-news",
        "market_report.pdf",
        "/path/to/notes.txt"
    ],
    response_format=CompanyInfo
)

Built-in Convenience Functions

# High-level functions for common tasks
summary = aiwand.summarize("Long article text...", style="bullet-points")
response = aiwand.chat("What is machine learning?")
story = aiwand.generate_text("Write a poem about coding")

# Classification and grading
grader = aiwand.create_binary_classifier(criteria="correctness")
result = grader(question="What is 2+2?", answer="4", expected="4")
print(f"Score: {result.score}")

🎨 CLI Usage

# Direct chat (quoted for multi-word)
aiwand "Explain quantum computing in simple terms"

# Extract from content
aiwand extract "John Doe, [e-mail]" --json

# Extract from URLs/files  
aiwand extract --links https://company.com resume.pdf

# Other functions
aiwand summarize "Long text..." --style bullet-points
aiwand chat "Hello there!"

📚 Documentation

• API Reference - Complete function docs
• CLI Guide - Command line usage
• Installation - Setup details
• Development - Contributing guide

🔑 Why AIWand?

Before AIWand - Provider-specific code, manual JSON parsing, complex setup:

# OpenAI specific
import openai
response = openai.chat.completions.create(...)
result = json.loads(response.choices[0].message.content)  # Manual parsing

# Gemini specific  
import google.generativeai as genai
response = genai.generate_content(...)
result = parse_json_response(response.text)  # Different API

After AIWand - One API, automatic everything:

import aiwand
result = aiwand.call_ai(model="any-model", response_format=MyModel, ...)
# result is already a MyModel object! ✨

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

📝 License

MIT License - see LICENSE file for details.

---

Star this repo if you find it useful!

Made with ❤️ by Aman Kumar