bitbuffet 0.1.3

Python SDK for the bitbuffet API - BitBuffet

BitBuffet Python SDK

A powerful Python SDK for the BitBuffet API that allows you to extract structured data from any web content using Pydantic models in under two seconds.

🚀 Features

• Universal: Works with any website or web content (url, image, video, audio, youtube, pdf, etc.)
• Type-safe: Built with Pydantic for complete type safety and validation
• Fast: Extract structured data in under 2 seconds
• Flexible: Support for custom prompts and reasoning levels
• Easy to use: Simple, intuitive API
• Well-tested: Comprehensive test suite with integration tests

📦 Installation

pip install bitbuffet
# or
poetry add bitbuffet
# or
uv add bitbuffet

🏃‍♂️ Quick Start

from bitbuffet import BitBuffet
from pydantic import BaseModel, Field
from typing import List, Optional

# Define your data structure with Pydantic
class Article(BaseModel):
    title: str = Field(description="The main title of the article")
    author: str = Field(description="The author of the article")
    publish_date: str = Field(description="When the article was published")
    content: str = Field(description="The main content/body of the article")
    tags: List[str] = Field(description="Article tags or categories")
    summary: str = Field(description="A brief summary of the article")

# Initialize the client with your API key
client = BitBuffet(api_key="your-api-key-here") # Get your API key from https://bitbuffet.dev

# Extract structured data from any URL
try:
    result: Article = client.extract(
        url="https://example.com/article",
        schema_class=Article
    )
    
    print(f"Title: {result.title}")
    print(f"Author: {result.author}")
    print(f"Published: {result.publish_date}")
    print(f"Tags: {', '.join(result.tags)}")
except Exception as error:
    print(f"Extraction failed: {error}")

⚙️ Configuration Options

Customize the extraction process with various options:

result = client.extract(
    url="https://example.com/complex-page",
    schema_class=Article,
    timeout=30,  # Timeout in seconds (default: 30)
    reasoning_effort="high",  # 'medium' | 'high' - Higher effort for complex pages
    prompt="Focus on extracting the main article content, ignoring ads and navigation",
    temperature=0.1,  # Lower for more consistent results (0.0 - 1.5)
    # OR use top_p instead of temperature
    # top_p=0.9
)

📚 Advanced Examples

E-commerce Product Extraction

from pydantic import BaseModel, Field, HttpUrl
from typing import List, Optional

class Product(BaseModel):
    name: str
    price: float
    currency: str
    description: str
    images: List[HttpUrl]
    in_stock: bool
    rating: Optional[float] = Field(None, ge=0, le=5)
    reviews: Optional[int] = None

product = client.extract(
    url="https://shop.example.com/product/123",
    schema_class=Product,
    reasoning_effort="high"
)

print(f"Product: {product.name}")
print(f"Price: {product.price} {product.currency}")
print(f"In Stock: {product.in_stock}")

News Article with Nested Models

class Author(BaseModel):
    name: str
    bio: Optional[str] = None

class RelatedArticle(BaseModel):
    title: str
    url: HttpUrl

class NewsArticle(BaseModel):
    headline: str
    subheadline: Optional[str] = None
    author: Author
    published_at: str
    category: str
    content: str
    related_articles: Optional[List[RelatedArticle]] = None

article = client.extract(
    url="https://news.example.com/breaking-news",
    schema_class=NewsArticle
)

print(f"Headline: {article.headline}")
print(f"Author: {article.author.name}")
print(f"Category: {article.category}")

Batch Processing Multiple URLs

import asyncio
from concurrent.futures import ThreadPoolExecutor

def extract_article(url: str) -> Article:
    return client.extract(url=url, schema_class=Article)

# Process multiple URLs concurrently
urls = [
    "https://example.com/article1",
    "https://example.com/article2",
    "https://example.com/article3"
]

with ThreadPoolExecutor(max_workers=3) as executor:
    articles = list(executor.map(extract_article, urls))

for article in articles:
    print(f"Extracted: {article.title}")

🔧 API Reference

BitBuffet Class

Constructor

BitBuffet(api_key: str, base_url: str = None, timeout: int = 30)

Methods

extract(url: str, schema_class: Type[BaseModel], **kwargs) -> BaseModel

Extracts structured data from a URL using the provided Pydantic model.

Parameters:

• url: The URL to extract data from
• schema_class: Pydantic model class defining the expected data structure
• timeout: Request timeout in seconds (default: 30)
• reasoning_effort: 'medium' | 'high' (default: 'medium')
• prompt: Custom extraction prompt (optional)
• temperature: Sampling temperature 0.0-1.0 (optional)
• top_p: Alternative to temperature (optional)

Returns: Instance of the provided Pydantic model with extracted data

🛠️ Development

# Install dependencies with uv (recommended)
uv sync

# Or with pip
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=bitbuffet

# Run integration tests
pytest -m integration

# Build the package
python -m build

📋 Requirements

• Python >= 3.9
• pydantic >= 2.11.7
• requests >= 2.32.5

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔗 Links

• Complete API Documentation - Full API reference and guides
• GitHub Repository
• PyPI Package
• Report Issues

💡 Need Help?

For detailed documentation, examples, and API reference, visit our complete documentation.

If you encounter any issues or have questions, please open an issue on GitHub.