Add AgentQL query_data tool

[desi003]

This PR introduces AgentQL's query_data tool, where CrewAI users can extract structured data from websites by using either precise AgentQL queries for targeted extraction or natural language prompts for more flexible scraping needs.

Tested with example.py by running it with a simple query.

[joaomdmoura]

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

Code Review Comment for AgentQL Query Data Tool PR

Overview

The pull request introduces a new tool for querying website data using AgentQL, encompassing the implementation of AgentQLQueryDataTool, its documentation, and example usage. Below are detailed insights.

Code Quality Findings

1. AgentQLQueryDataTool Implementation (agentql_query_data_tool.py)

• Strengths:

  • Exceptional error handling with specific messages enhances user experience.
  • Clear documentation in schema fields supports maintainability.
  • Timeout handling for API requests indicates thoughtful design.
  • Well-structured code promotes readability and ease of use.

• Specific Code Improvements:

  • Environment Variable Handling:

    API_KEY = os.getenv("AGENTQL_API_KEY")
    if not API_KEY:
        raise ValueError("AGENTQL_API_KEY environment variable is required")
  • Constants Organization:
    • Consider relocating constants to a configuration file for better organization.
  • Type Hints:
    • Improve type hints for the _run function for better clarity:

    from typing import Dict, Any
    def _run(self, url: str, query: Optional[str] = None, prompt: Optional[str] = None) -> Dict[str, Any]:
  • Response Validation Logic:
    • Implement comprehensive response validation to handle unexpected API outputs:

    json = response.json()
    if 'data' not in json:
        raise ValueError("Unexpected response format from AgentQL API")
    return json["data"]

2. README.md

• Strengths:

  • Comprehensive installation instructions help users set up the tool.
  • Usage examples provide clarity.

• Improvement Suggestions:

  • Error Handling Example:
    Include error handling snippets to guide users in practical scenarios:

    ## Error Handling Example
    ```python
    try:
        tool = AgentQLQueryDataTool(url='https://example.com')
        result = tool._run(url=tool.url)
    except ValueError as e:
        print(f"Configuration error: {e}")
    except Exception as e:
        print(f"Query execution error: {e}")

3. Example.py Usage

• Strengths:

  • Offers a clear demonstration of the main functionalities of the query tool.

• Suggestions for Enhancement:

  • Add more complex examples to showcase the tool’s capabilities effectively.

    def demonstrate_complex_query():
        tool = AgentQLQueryDataTool()
        complex_query = """
        {
          navigation_menu[] {
            link_text
            url
            sub_items[] {
              text
              url
            }
          }
        }
        """
        result = tool._run(url="https://example.com", query=complex_query)
        return result

Historical Context and Lessons Learned

Past PR discussions highlighted the importance of robust error handling and comprehensive documentation in library tools. Emphasizing user guidance can significantly improve adoption and user satisfaction.

Additionally, reviewing related tools often brings to light patterns of code smells, such as hardcoding sensitive information, which this PR addresses well by using environment variables.

General Recommendations

1. Enhance Documentation:

   • Include detailed docstrings for classes and methods alongside API response schema documentation to bolster understanding.

2. Increase Testing Coverage:

   • Implement unit tests and integration tests with mock responses to ensure robustness against varied inputs, especially for edge cases.

3. Security Practices:

   • Tighten URL validation to mitigate potential security vulnerabilities.

4. Logging:

   • Consider adding logging for better debug visibility and operational monitoring.

By implementing these recommendations, the project will achieve increased reliability and maintainability, greatly benefiting the end-users. Thank you for the opportunity to review this PR!