src/tools/search_tool.py

# Search tool for finding relevant scientific information

import logging
from typing import Dict, Any, List, Optional
from .base_tool import BaseTool

class SearchTool(BaseTool):
    """Tool for searching scientific literature and databases.
    
    This tool allows agents to search for relevant scientific information
    to support hypothesis generation, evaluation, and refinement.
    """
    
    def __init__(self):
        """Initialize the search tool."""
        super().__init__(
            name="search",
            description="Search scientific literature and databases for relevant information"
        )
        self.logger = logging.getLogger("tool.search")
    
    def execute(self, query: str, source: Optional[str] = None, max_results: int = 5) -> List[Dict[str, Any]]:
        """Execute a search for scientific information.
        
        Args:
            query: The search query
            source: Optional specific source to search (e.g., 'pubmed', 'arxiv', 'general')
            max_results: Maximum number of results to return
            
        Returns:
            A list of search results, each containing title, source, summary, and relevance score
        """
        self.logger.info(f"Executing search: {query} (source: {source or 'all'}, max_results: {max_results})")
        
        # TODO: Implement actual search functionality using appropriate APIs
        # This would connect to scientific databases, search engines, or APIs
        
        # Mock implementation for now
        mock_results = [
            {
                "title": f"Scientific Paper about {query} #{i}",
                "authors": ["Author A", "Author B"],
                "year": 2023,
                "source": source or "Scientific Database",
                "summary": f"This paper discusses various aspects of {query} with significant findings.",
                "relevance_score": 0.95 - (i * 0.1),
                "url": f"https://example.org/paper/{i}"
            }
            for i in range(min(max_results, 10))
        ]
        
        return mock_results
    
    def get_parameters_schema(self) -> Dict[str, Any]:
        """Get the JSON schema for the tool's parameters.
        
        Returns:
            A dictionary containing the JSON schema
        """
        return {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "The search query for scientific information"
                },
                "source": {
                    "type": "string",
                    "description": "Optional specific source to search (e.g., 'pubmed', 'arxiv', 'general')"
                },
                "max_results": {
                    "type": "integer",
                    "description": "Maximum number of results to return",
                    "default": 5
                }
            },
            "required": ["query"]
        }