ScrapeGen

ScrapeGen 🚀 is a powerful Python library that combines web scraping with AI-driven data extraction to collect and structure information from websites efficiently. It leverages Google's Gemini models for intelligent data processing and provides a flexible, configurable framework for web scraping operations.

✨ Features

• 🤖 AI-Powered Data Extraction: Utilizes Google's Gemini models for intelligent parsing
• ⚙️ Configurable Web Scraping: Supports depth control and flexible extraction rules
• 📊 Structured Data Modeling: Uses Pydantic for well-defined data structures
• 🛡️ Robust Error Handling: Implements retry mechanisms and detailed error reporting
• 🔧 Customizable Scraping Configurations: Adjust settings dynamically based on needs
• 🌐 Comprehensive URL Handling: Supports both relative and absolute URLs
• 📦 Modular Architecture: Ensures clear separation of concerns for maintainability

📥 Installation

pip install scrapegen

📌 Requirements

• Python 3.7+
• Google API Key (for Gemini models)
• Required Python packages:
  • requests
  • beautifulsoup4
  • langchain
  • langchain-google-genai
  • pydantic

🚀 Quick Start with Custom Prompts

from scrapegen import ScrapeGen, CompanyInfo, CompaniesInfo

# Initialize ScrapeGen with your Google API key
scraper = ScrapeGen(api_key="your-google-api-key", model="gemini-1.5-pro")

# Define target URL and custom prompt
url = "https://example.com"
custom_prompt = """
Analyze the website content and extract:
- Company name
- Core technologies
- Industry focus areas
- Key product features
"""

# Scrape with custom prompt and model
companies_data = scraper.scrape(
    url=url,
    prompt=custom_prompt,
    base_model=CompaniesInfo
)

# Display extracted data
for company in companies_data.companies:
    print(f"🏢 {company.company_name}")
    print(f"🔧 Technologies: {', '.join(company.core_technologies)}")
    print(f"📈 Focus Areas: {', '.join(company.industry_focus)}")

⚙️ Configuration

🔹 ScrapeConfig Options

from scrapegen import ScrapeConfig

config = ScrapeConfig(
    max_pages=20,      # Max pages to scrape per depth level
    max_subpages=2,    # Max subpages to scrape per page
    max_depth=1,       # Max depth to follow links
    timeout=30,        # Request timeout in seconds
    retries=3,         # Number of retry attempts
    user_agent="Mozilla/5.0 (compatible; ScrapeGen/1.0)",
    headers=None       # Additional HTTP headers
)

🔄 Updating Configuration

scraper = ScrapeGen(api_key="your-api-key", model="gemini-1.5-pro", config=config , verbose=False)

# Dynamically update configuration
scraper.update_config(max_pages=30, timeout=45)

📌 Custom Data Models

Define Pydantic models to structure extracted data:

from pydantic import BaseModel
from typing import Optional, List

class CustomDataModel(BaseModel):
    title: str
    description: Optional[str]
    date: str
    tags: List[str]

class CustomDataCollection(BaseModel):
    items: List[CustomDataModel]

# Scrape using the custom model
data = scraper.scrape(url, CustomDataCollection)

🤖 Supported Gemini Models

• gemini-1.5-flash-8b
• gemini-1.5-pro
• gemini-2.0-flash-exp
• gemini-1.5-flash

🆕 Custom Prompt Engineering Guide

1️⃣ Basic Prompt Structure

basic_prompt = """
Extract the following details from the content:
- Company name
- Founding year
- Headquarters location
- Main products/services
"""

2️⃣ Tech-Focused Extraction

tech_prompt = """
Identify and categorize technologies mentioned in the content:
1. AI/ML Technologies
2. Cloud Infrastructure
3. Data Analytics Tools
4. Cybersecurity Solutions
Include version numbers and implementation details where available.
"""

3️⃣ Multi-Level Extraction

multi_level_prompt = """
Perform hierarchical extraction:
1. Company Overview:
   - Name
   - Mission statement
   - Key executives
2. Technical Capabilities:
   - Core technologies
   - Development stack
   - Infrastructure
3. Market Position:
   - Competitors
   - Market share
   - Growth metrics
"""

📌 Specialized Prompt Examples

🔍 Competitive Analysis Prompt

competitor_prompt = """
Identify and compare key competitors:
- Competitor names
- Feature differentiators
- Pricing models
- Market positioning
Output as a comparison table with relative strengths.
"""

🌱 Sustainability Focused Prompt

green_prompt = """
Extract environmental sustainability information:
1. Green initiatives
2. Carbon reduction targets
3. Eco-friendly technologies
4. Sustainability certifications
5. Renewable energy usage
Prioritize quantitative metrics and timelines.
"""

💡 Innovation Tracking Prompt

innovation_prompt = """
Analyze R&D activities and innovations:
- Recent patents filed
- Research partnerships
- New product launches (last 24 months)
- Technology breakthroughs
- Investment in R&D (% of revenue)
"""

🛠️ Prompt Optimization Tips

1. Be Specific: Clearly define required fields and formats

   "Format output as JSON with 'company_name', 'employees', 'revenue' keys"
   

2. Add Context:

   "Analyze content from CEO interviews for strategic priorities"
   

3. Define Output Structure:

   "Categorize findings under 'basic_info', 'tech_stack', 'growth_metrics'"
   

4. Set Priorities:

   "Focus on technical specifications over marketing content"
   

⚠️ Error Handling

ScrapeGen provides specific exception classes for detailed error handling:

• ❗ ScrapeGenError: Base exception class
• ⚙️ ConfigurationError: Errors related to scraper configuration
• 🕷️ ScrapingError: Issues encountered during web scraping
• 🔍 ExtractionError: Problems with AI-driven data extraction

Example usage:

try:
    data = scraper.scrape(
        url=url,
        prompt=complex_prompt,
        base_model=MarketAnalysis
    )
except ExtractionError as e:
    print(f"🔍 Extraction failed with custom prompt: {e}")
    print(f"🧠 Prompt used: {complex_prompt}")
except ScrapingError as e:
    print(f"🌐 Scraping error: {str(e)}")

🏗️ Architecture

ScrapeGen follows a modular design for scalability and maintainability:

1. 🕷️ WebsiteScraper: Handles core web scraping logic
2. 📑 InfoExtractorAi: Performs AI-driven content extraction
3. 🤖 LlmManager: Manages interactions with language models
4. 🔗 UrlParser: Parses and normalizes URLs
5. 📥 ContentExtractor: Extracts structured data from HTML elements

✅ Best Practices

1️⃣ Rate Limiting

• ⏳ Use delays between requests
• 📜 Respect robots.txt guidelines
• ⚖️ Configure max_pages and max_depth responsibly

2️⃣ Error Handling

• 🔄 Wrap scraping operations in try-except blocks
• 📋 Implement proper logging for debugging
• 🔁 Handle network timeouts and retries effectively

3️⃣ Resource Management

• 🖥️ Monitor memory usage for large-scale operations
• 📚 Implement pagination for large datasets
• ⏱️ Adjust timeout settings based on expected response times

🤝 Contributing

Contributions are welcome! 🎉 Feel free to submit a Pull Request to improve ScrapeGen.

✨ Features

• 🤖 AI-Powered Data Extraction: Utilizes Google's Gemini models for intelligent parsing.
• ⚙️ Configurable Web Scraping: Supports depth control and flexible extraction rules.
• 📊 Structured Data Modeling: Uses Pydantic for well-defined data structures.
• 🛡️ Robust Error Handling: Implements retry mechanisms and detailed error reporting.
• 🔧 Customizable Scraping Configurations: Adjust settings dynamically based on needs.
• 🌐 Comprehensive URL Handling: Supports both relative and absolute URLs.
• 📦 Modular Architecture: Ensures clear separation of concerns for maintainability.

📥 Installation

pip install scrapegen  # Package name may vary

📌 Requirements

• Python 3.7+
• Google API Key (for Gemini models)
• Required Python packages:
  • requests
  • beautifulsoup4
  • langchain
  • langchain-google-genai
  • pydantic

🚀 Quick Start

from scrapegen import ScrapeGen, CompanyInfo, CompaniesInfo

# Initialize ScrapeGen with your Google API key
scraper = ScrapeGen(api_key="your-google-api-key", model="gemini-1.5-pro")

# Define the target URL
url = "https://example.com"

# Scrape and extract company information
companies_data = scraper.scrape(url, CompaniesInfo)

# Display extracted data
for company in companies_data.companies:
    print(f"🏢 Company Name: {company.company_name}")
    print(f"📄 Description: {company.company_description}")

⚙️ Configuration

🔹 ScrapeConfig Options

from scrapegen import ScrapeConfig

config = ScrapeConfig(
    max_pages=20,      # Max pages to scrape per depth level
    max_subpages=2,    # Max subpages to scrape per page
    max_depth=1,       # Max depth to follow links
    timeout=30,        # Request timeout in seconds
    retries=3,         # Number of retry attempts
    user_agent="Mozilla/5.0 (compatible; ScrapeGen/1.0)",
    headers=None       # Additional HTTP headers
)

🔄 Updating Configuration

scraper = ScrapeGen(api_key="your-api-key", model="gemini-1.5-pro", config=config)

# Dynamically update configuration
scraper.update_config(max_pages=30, timeout=45)

📌 Custom Data Models

Define Pydantic models to structure extracted data:

from pydantic import BaseModel
from typing import Optional, List

class CustomDataModel(BaseModel):
    title: str
    description: Optional[str]
    date: str
    tags: List[str]

class CustomDataCollection(BaseModel):
    items: List[CustomDataModel]

# Scrape using the custom model
data = scraper.scrape(url, CustomDataCollection)

🤖 Supported Gemini Models

• gemini-1.5-flash-8b
• gemini-1.5-pro
• gemini-2.0-flash-exp
• gemini-1.5-flash

⚠️ Error Handling

ScrapeGen provides specific exception classes for detailed error handling:

• ❗ ScrapeGenError: Base exception class.
• ⚙️ ConfigurationError: Errors related to scraper configuration.
• 🕷️ ScrapingError: Issues encountered during web scraping.
• 🔍 ExtractionError: Problems with AI-driven data extraction.

Example usage:

try:
    data = scraper.scrape(url, CustomDataCollection)
except ConfigurationError as e:
    print(f"⚙️ Configuration error: {e}")
except ScrapingError as e:
    print(f"🕷️ Scraping error: {e}")
except ExtractionError as e:
    print(f"🔍 Extraction error: {e}")

🏗️ Architecture

ScrapeGen follows a modular design for scalability and maintainability:

1. 🕷️ WebsiteScraper: Handles core web scraping logic.
2. 📑 InfoExtractorAi: Performs AI-driven content extraction.
3. 🤖 LlmManager: Manages interactions with language models.
4. 🔗 UrlParser: Parses and normalizes URLs.
5. 📥 ContentExtractor: Extracts structured data from HTML elements.

✅ Best Practices

1️⃣ Rate Limiting

• ⏳ Use delays between requests.
• 📜 Respect robots.txt guidelines.
• ⚖️ Configure max_pages and max_depth responsibly.

2️⃣ Error Handling

• 🔄 Wrap scraping operations in try-except blocks.
• 📋 Implement proper logging for debugging.
• 🔁 Handle network timeouts and retries effectively.

3️⃣ Resource Management

• 🖥️ Monitor memory usage for large-scale operations.
• 📚 Implement pagination for large datasets.
• ⏱️ Adjust timeout settings based on expected response times.

🤝 Contributing

Contributions are welcome! 🎉 Feel free to submit a Pull Request to improve ScrapeGen.