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| from typing import Optional, Dict, Any, Type | |
| from crewai.tools import BaseTool | |
| from pydantic import Field, BaseModel | |
| # Define the input schema as a separate class | |
| class ContentAnalyzerArgs(BaseModel): | |
| query: str = Field( | |
| ..., | |
| description="The search query to compare content against" | |
| ) | |
| content: str = Field( | |
| ..., | |
| description="The content to analyze for relevance and factuality" | |
| ) | |
| class ContentAnalyzerTool(BaseTool): | |
| """ | |
| A tool for analyzing content relevance and factuality. | |
| This tool uses LLM to judge the relevance and factual accuracy of content | |
| in relation to a specific query. | |
| """ | |
| name: str = Field( | |
| default="Content Analyzer", | |
| description="Name of the content analysis tool" | |
| ) | |
| description: str = Field( | |
| default=( | |
| "Use this tool to analyze the relevance and factuality of content " | |
| "in relation to a specific query. " | |
| "It helps filter out irrelevant or potentially non-factual information." | |
| ), | |
| description="Description of what the content analyzer does" | |
| ) | |
| # Define args_schema as a class attribute | |
| args_schema: Type[BaseModel] = ContentAnalyzerArgs | |
| def _run(self, query: str, content: str) -> Dict[str, Any]: | |
| """ | |
| Analyze the content for relevance and factuality. | |
| Args: | |
| query: The original search query | |
| content: The content to analyze | |
| Returns: | |
| Dict with analysis results including: | |
| - relevance_score: A score from 0-10 indicating relevance | |
| - factuality_score: A score from 0-10 indicating factual reliability | |
| - filtered_content: The processed content with irrelevant parts removed | |
| - analysis: Brief explanation of the judgment | |
| """ | |
| # The actual implementation will use the agent's LLM | |
| # via CrewAI's mechanism, returning the placeholders | |
| # for now which will be replaced during execution | |
| prompt = f""" | |
| You are a strict content judge evaluating web search results. | |
| QUERY: {query} | |
| CONTENT: {content} | |
| Analyze the content above with these criteria: | |
| 1. Relevance to the query (score 0-10) | |
| 2. Factual accuracy and reliability (score 0-10) | |
| 3. Information quality | |
| For content scoring below 5 on relevance, discard it entirely. | |
| For content with factuality concerns, flag these specifically. | |
| PROVIDE YOUR ANALYSIS IN THIS FORMAT: | |
| {{ | |
| "relevance_score": [0-10], | |
| "factuality_score": [0-10], | |
| "filtered_content": "The filtered and cleaned content, removing irrelevant parts", | |
| "analysis": "Brief explanation of your judgment" | |
| }} | |
| ONLY RETURN THE JSON, nothing else. | |
| """ | |
| # This method will be handled by CrewAI's internal mechanism | |
| # For placeholder purposes during direct testing, we return example data. | |
| # In a real CrewAI run, the agent's LLM would process the prompt. | |
| return { | |
| "relevance_score": 7, # Placeholder | |
| "factuality_score": 8, # Placeholder | |
| "filtered_content": content, # Placeholder | |
| "analysis": "This is a placeholder analysis. The real analysis will be performed during execution." | |
| } | |
| class Config: | |
| """Pydantic config for the tool""" | |
| arbitrary_types_allowed = True | |
| def run(self, query: str, content: str) -> Dict[str, Any]: | |
| """Public method to run content analysis""" | |
| return self._run(query, content) |