272 lines
No EOL
12 KiB
Python
272 lines
No EOL
12 KiB
Python
# contentValidator.py
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# Content validation for adaptive React mode
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import logging
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import json
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import re
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from typing import List, Dict, Any
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logger = logging.getLogger(__name__)
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class ContentValidator:
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"""Validates delivered content against user intent"""
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def __init__(self, services=None):
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self.services = services
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async def validateContent(self, documents: List[Any], intent: Dict[str, Any]) -> Dict[str, Any]:
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"""Validates delivered content against user intent using AI"""
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try:
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# Use AI for comprehensive validation
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return await self._validateWithAI(documents, intent)
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except Exception as e:
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logger.error(f"Error validating content: {str(e)}")
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return self._createFailedValidationResult(str(e))
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def _extractContent(self, doc: Any) -> str:
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"""Extracts content from a document"""
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try:
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if hasattr(doc, 'documentData'):
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data = doc.documentData
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if isinstance(data, dict) and 'content' in data:
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return str(data['content'])
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else:
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return str(data)
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return ""
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except Exception:
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return ""
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def _createFailedValidationResult(self, error: str) -> Dict[str, Any]:
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"""Creates a failed validation result"""
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return {
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"overallSuccess": False,
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"qualityScore": 0.0,
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"validationDetails": [],
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"improvementSuggestions": [f"NEXT STEP: Fix validation error - {error}. Check system logs for more details and retry the operation."]
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}
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def _isValidJsonResponse(self, response: str) -> bool:
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"""Checks if response contains valid JSON structure"""
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try:
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import re
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# Look for JSON with expected structure
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json_match = re.search(r'\{[^{}]*"overallSuccess"[^{}]*\}', response, re.DOTALL)
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if json_match:
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json.loads(json_match.group(0))
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return True
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return False
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except:
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return False
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def _extractFallbackValidationResult(self, response: str) -> Dict[str, Any]:
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"""Extracts validation result from malformed AI response"""
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try:
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import re
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# Extract key values using regex patterns
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overall_success = re.search(r'"overallSuccess"\s*:\s*(true|false)', response, re.IGNORECASE)
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quality_score = re.search(r'"qualityScore"\s*:\s*([0-9.]+)', response)
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gap_analysis = re.search(r'"gapAnalysis"\s*:\s*"([^"]*)"', response)
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# Determine overall success from context if not found
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if not overall_success:
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# Look for positive/negative indicators in the text
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if any(word in response.lower() for word in ['success', 'complete', 'fulfilled', 'satisfied']):
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overall_success = True
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elif any(word in response.lower() for word in ['failed', 'incomplete', 'missing', 'error']):
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overall_success = False
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else:
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overall_success = False
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return {
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"overallSuccess": overall_success if isinstance(overall_success, bool) else (overall_success.group(1).lower() == 'true' if overall_success else False),
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"qualityScore": float(quality_score.group(1)) if quality_score else 0.5,
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"validationDetails": [{
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"documentName": "AI Validation (Fallback)",
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"gapAnalysis": gap_analysis.group(1) if gap_analysis else "Unable to parse detailed analysis",
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"successCriteriaMet": [False] # Conservative fallback
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}],
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"improvementSuggestions": ["NEXT STEP: AI response was malformed - retry the operation for better results"]
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}
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except Exception as e:
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logger.error(f"Fallback extraction failed: {str(e)}")
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return None
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async def _validateWithAI(self, documents: List[Any], intent: Dict[str, Any]) -> Dict[str, Any]:
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"""AI-based comprehensive validation - single main function"""
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try:
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if not hasattr(self, 'services') or not self.services or not hasattr(self.services, 'ai'):
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return self._createFailedValidationResult("AI service not available")
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# Extract content from all documents
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documentContents = []
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for doc in documents:
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content = self._extractContent(doc)
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documentContents.append({
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"name": getattr(doc, 'documentName', 'Unknown'),
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"content": content[:2000] # Limit content for AI processing
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})
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# Create comprehensive AI validation prompt
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validationPrompt = f"""
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You are a comprehensive task completion validator. Analyze if the delivered content fulfills the user's request.
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USER REQUEST: {intent.get('primaryGoal', 'Unknown')}
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EXPECTED DATA TYPE: {intent.get('dataType', 'unknown')}
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EXPECTED FORMAT: {intent.get('expectedFormat', 'unknown')}
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SUCCESS CRITERIA: {intent.get('successCriteria', [])}
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DELIVERED CONTENT:
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{json.dumps(documentContents, indent=2)}
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Perform comprehensive validation:
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1. Check if content matches expected data type
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2. Check if content matches expected format
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3. Verify success criteria are met
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4. Assess overall quality and completeness
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5. Identify specific gaps and issues
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6. Provide actionable next steps
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CRITICAL: You MUST respond with ONLY the JSON object below. NO TEXT ANALYSIS. NO EXPLANATIONS. NO OTHER CONTENT.
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RESPOND WITH THIS EXACT JSON FORMAT:
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{{
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"overallSuccess": false,
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"qualityScore": 0.5,
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"dataTypeMatch": false,
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"formatMatch": false,
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"successCriteriaMet": [false, false],
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"gapAnalysis": "Content does not match expected format and lacks required elements",
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"improvementSuggestions": ["NEXT STEP: Create proper content in expected format", "NEXT STEP: Ensure all success criteria are met"],
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"validationDetails": [
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{{
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"documentName": "Content Validation",
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"issues": ["Format mismatch", "Missing required elements"],
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"suggestions": ["NEXT STEP: Fix format", "NEXT STEP: Add missing elements"]
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}}
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]
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}}
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"""
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# Call AI service for validation
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from modules.datamodels.datamodelAi import AiCallOptions, OperationType
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request_options = AiCallOptions()
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request_options.operationType = OperationType.GENERAL
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response = await self.services.ai.callAi(
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prompt=validationPrompt,
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documents=None,
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options=request_options
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)
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# If first attempt fails, try with more explicit prompt
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if response and not self._isValidJsonResponse(response):
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logger.debug("First AI validation attempt failed, retrying with explicit JSON-only prompt")
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explicitPrompt = f"""
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VALIDATE AND RETURN JSON ONLY - NO TEXT ANALYSIS
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Request: {intent.get('primaryGoal', 'Unknown')}
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Data Type: {intent.get('dataType', 'unknown')}
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Format: {intent.get('expectedFormat', 'unknown')}
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Criteria: {intent.get('successCriteria', [])}
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Content: {json.dumps(documentContents, indent=2)}
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RESPOND WITH THIS EXACT JSON FORMAT - NO OTHER TEXT:
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{{
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"overallSuccess": false,
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"qualityScore": 0.3,
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"dataTypeMatch": false,
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"formatMatch": false,
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"successCriteriaMet": [false, false],
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"gapAnalysis": "Content does not match expected format and lacks required elements",
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"improvementSuggestions": ["NEXT STEP: Create proper content in expected format", "NEXT STEP: Ensure all success criteria are met"],
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"validationDetails": [
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{{
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"documentName": "Content Validation",
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"issues": ["Format mismatch", "Missing required elements"],
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"suggestions": ["NEXT STEP: Fix format", "NEXT STEP: Add missing elements"]
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}}
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]
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}}
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"""
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response = await self.services.ai.callAi(
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prompt=explicitPrompt,
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documents=None,
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options=request_options
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)
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if not response or not response.strip():
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logger.warning("AI validation returned empty response")
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return self._createFailedValidationResult("AI validation failed - empty response")
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# Clean and extract JSON from response
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result = response.strip()
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logger.debug(f"AI validation response length: {len(result)}")
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# Try to find JSON in the response with multiple strategies
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import re
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# Strategy 1: Look for JSON in markdown code blocks
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json_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', result, re.DOTALL)
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if json_match:
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result = json_match.group(1)
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logger.debug(f"Extracted JSON from markdown code block: {result[:200]}...")
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else:
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# Strategy 2: Look for JSON object with proper structure
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json_match = re.search(r'\{[^{}]*"overallSuccess"[^{}]*\}', result, re.DOTALL)
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if not json_match:
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# Strategy 3: Look for any JSON object
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json_match = re.search(r'\{.*\}', result, re.DOTALL)
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if json_match:
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result = json_match.group(0)
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logger.debug(f"Extracted JSON directly: {result[:200]}...")
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else:
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logger.debug(f"No JSON found in AI response, trying fallback extraction: {result[:200]}...")
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logger.debug(f"Full AI response: {result}")
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# Try fallback extraction for text responses
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fallback_result = self._extractFallbackValidationResult(result)
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if fallback_result:
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logger.info("Using fallback text extraction for validation")
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return fallback_result
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logger.warning("All AI validation attempts failed - no JSON found and fallback extraction failed")
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return self._createFailedValidationResult("AI validation failed - no JSON in response")
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try:
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aiResult = json.loads(result)
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logger.info("AI validation JSON parsed successfully")
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return {
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"overallSuccess": aiResult.get("overallSuccess", False),
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"qualityScore": aiResult.get("qualityScore", 0.0),
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"validationDetails": aiResult.get("validationDetails", [{
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"documentName": "AI Validation",
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"gapAnalysis": aiResult.get("gapAnalysis", ""),
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"successCriteriaMet": aiResult.get("successCriteriaMet", [False])
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}]),
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"improvementSuggestions": aiResult.get("improvementSuggestions", [])
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}
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except json.JSONDecodeError as json_error:
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logger.warning(f"All AI validation attempts failed - invalid JSON: {str(json_error)}")
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logger.debug(f"JSON content: {result}")
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# Try to extract key information from malformed response
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fallbackResult = self._extractFallbackValidationResult(result)
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if fallbackResult:
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logger.info("Using fallback validation result from malformed JSON")
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return fallbackResult
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return self._createFailedValidationResult(f"AI validation failed - invalid JSON: {str(json_error)}")
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return self._createFailedValidationResult("AI validation failed - no response")
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except Exception as e:
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logger.error(f"AI validation failed: {str(e)}")
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return self._createFailedValidationResult(f"AI validation error: {str(e)}") |