feat: integration of scrapegraph APIs

[VinciGit00]

I added the ScrapegraphAI's apis

[joaomdmoura]

Disclaimer: This review was made by a crew of AI Agents.

Code Review Comment: ScrapegraphScrapeTool Implementation

Overview

The implementation introduces ScrapegraphScrapeTool, a new tool leveraging the Scrapegraph AI API for web scraping. The PR includes the main class for scraping functionality, usage documentation, and project configuration updates.

Code Quality Findings

1. scrapegraph_scrape_tool.py

Positive Aspects:

• Type Hints: The use of type hints throughout the code significantly increases clarity and maintainability.
• Error Handling: Effective error handling around the API key enhances security and ensures proper usage.
• Class Structure: The implementation adheres to solid OOP principles through a clean class structure and inheritance.
• Pydantic Utilization: The use of Pydantic for schema validation is commendable, ensuring the integrity of input data.

Specific Improvements:

1. Enhanced Error Handling
   Currently, there is a lack of comprehensive error handling for API responses. Here is a recommended approach:

   try:
       response = sgai_client.smartscraper(
           website_url=website_url,
           user_prompt=user_prompt,
       )
       if not response or "result" not in response:
           raise ValueError("Invalid response from Scrapegraph API")
       return response["result"]
   except Exception as e:
       raise RuntimeError(f"Scraping failed: {str(e)}")
   finally:
       sgai_client.close()

2. Input Validation
   Implement URL validation to ensure that the input meets expected formats:

   from urllib.parse import urlparse
   
   def validate_url(url: str) -> bool:
       try:
           result = urlparse(url)
           return all([result.scheme, result.netloc])
       except:
           return False
   
   if not validate_url(website_url):
       raise ValueError("Invalid URL format")

3. Documentation Improvements
   Enhance the documentation to better inform users about potential exceptions:

   class ScrapegraphScrapeTool(BaseTool):
       """
       A tool that uses Scrapegraph AI to intelligently scrape website content.
       
       Raises:
           ValueError: If API key or website URL is missing.
           RuntimeError: If scraping operation fails.
       """

2. README.md

Positive Aspects:

• Clear Installation Instructions: Easy-to-follow steps for installation help new users get started quickly.
• Usage Examples: Practical examples help convey how to utilize the tool correctly.

Recommendations:

1. Error Handling Example
   Include an example demonstrating how to handle errors properly:

   ## Error Handling Example
   ```python
   try:
       tool = ScrapegraphScrapeTool(api_key="your_api_key")
       result = tool.run(
           website_url="https://www.example.com",
           user_prompt="Extract the main heading"
       )
   except ValueError as e:
       print(f"Configuration error: {e}")
   except RuntimeError as e:
       print(f"Scraping error: {e}")

2. Rate Limiting Guidance
   Inform users about the API's rate limits:

   ## Rate Limiting
   Note: The Scrapegraph API has rate limits. Implement appropriate delays between requests when processing multiple URLs.

3. pyproject.toml

Recommendations:

• Version Constraints:
  Specify version constraints for dependencies to ensure compatibility:

  dependencies = [
      "scrapegraph-py>=1.8.0,<2.0.0",
  ]

General Suggestions

• Unit Tests: Encourage the creation of unit tests covering key scenarios such as successful scraping, invalid inputs, and API error handling.
• Logging Integration: Implement logging throughout the tool to aid in debugging and operational monitoring:

  import logging
  logger = logging.getLogger(__name__)

Security and Performance Considerations

• API Key Management: Consider employing a secret management system to protect API keys.
• URL Sanitization: Implement sanitization to mitigate risks from potentially malicious inputs.
• Connection Pooling: Enhance performance by utilizing connection pooling for repeated requests.

Conclusion

These recommendations aim to improve the reliability, maintainability, and usability of the ScrapegraphScrapeTool. Continuous enhancements and adherence to best practices will ensure that this tool remains effective and user-friendly.