596 lines
28 KiB
Python
596 lines
28 KiB
Python
# contentValidator.py
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# Content validation for adaptive Dynamic mode
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# Generic, document-aware validation system
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import logging
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import json
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import base64
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import re
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from typing import List, Dict, Any, Optional
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logger = logging.getLogger(__name__)
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# Configuration constants
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MAX_CONTENT_SIZE_FOR_FULL_PREVIEW = 50 * 1024 # 50KB threshold
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PREVIEW_SAMPLE_SIZE = 1024 # 1KB preview for large documents
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class ContentValidator:
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"""Validates delivered content against user intent - generic and document-aware"""
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def __init__(self, services=None, learningEngine=None):
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self.services = services
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self.learningEngine = learningEngine
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async def validateContent(self, documents: List[Any], intent: Dict[str, Any], taskStep: Optional[Any] = None, actionName: Optional[str] = None) -> Dict[str, Any]:
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"""Validates delivered content against user intent using AI (single attempt; parse-or-fail)
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Args:
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documents: List of documents to validate
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intent: Workflow-level intent dict (for format requirements)
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taskStep: Optional TaskStep object (preferred source for objective)
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actionName: Optional action name (e.g., "ai.process", "ai.webResearch") that created the documents
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"""
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return await self._validateWithAI(documents, intent, taskStep, actionName)
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def _analyzeDocuments(self, documents: List[Any]) -> List[Dict[str, Any]]:
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"""Generic document analysis - create simple summaries with metadata."""
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summaries = []
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for doc in documents:
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try:
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data = getattr(doc, 'documentData', None)
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name = getattr(doc, 'documentName', 'Unknown')
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mimeType = getattr(doc, 'mimeType', 'unknown')
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formatExt = self._detectFormat(doc)
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sizeInfo = self._calculateSize(doc)
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# Simple preview: if it's dict/list, dump JSON; otherwise use string
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preview = None
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if data is not None:
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if isinstance(data, (dict, list)):
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preview = json.dumps(data, indent=2, ensure_ascii=False)
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# Truncate if too large
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if len(preview) > MAX_CONTENT_SIZE_FOR_FULL_PREVIEW:
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preview = preview[:PREVIEW_SAMPLE_SIZE] + f"\n\n[Truncated - {self._formatBytes(sizeInfo['bytes'])} total]"
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else:
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text = str(data)
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if len(text) > MAX_CONTENT_SIZE_FOR_FULL_PREVIEW:
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preview = text[:PREVIEW_SAMPLE_SIZE] + f"\n\n[Truncated - {self._formatBytes(sizeInfo['bytes'])} total]"
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else:
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preview = text
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summary = {
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"name": name,
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"mimeType": mimeType,
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"format": formatExt,
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"size": sizeInfo["readable"],
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"preview": preview
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}
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summaries.append(summary)
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except Exception as e:
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logger.warning(f"Error analyzing document {getattr(doc, 'documentName', 'Unknown')}: {str(e)}")
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summaries.append({
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"name": getattr(doc, 'documentName', 'Unknown'),
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"mimeType": getattr(doc, 'mimeType', 'unknown'),
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"format": "unknown",
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"size": "0 B",
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"preview": None,
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"error": str(e)
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})
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return summaries
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def _calculateAvailablePromptSpace(self, basePromptSizeBytes: int) -> int:
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"""Calculate available space for document summaries based on model context length."""
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try:
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from modules.aicore.aicoreModelRegistry import modelRegistry
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from modules.aicore.aicoreModelSelector import modelSelector
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from modules.datamodels.datamodelAi import AiCallOptions, OperationTypeEnum
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# Get available models
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availableModels = modelRegistry.getAvailableModels()
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# Create options for PLAN operation (what validation uses)
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# Use default values for priority and processingMode (will use defaults from AiCallOptions)
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from modules.datamodels.datamodelAi import PriorityEnum, ProcessingModeEnum
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options = AiCallOptions(
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operationType=OperationTypeEnum.PLAN,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.BASIC
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)
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# Get failover model list to find the model that will be used
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failoverModels = modelSelector.getFailoverModelList("", "", options, availableModels)
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if not failoverModels:
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# Fallback: assume 16K tokens context (conservative)
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logger.warning("No models available for space calculation, using fallback: 16K tokens")
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maxBytes = 16 * 1024 * 4 # 16K tokens * 4 bytes per token
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else:
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# Use the first (best) model
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model = failoverModels[0]
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# Calculate 80% of context length in bytes (tokens * 4 bytes per token)
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maxBytes = int(model.contextLength * 0.8 * 4)
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# Available space = max - base prompt - safety margin (10%)
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availableBytes = int((maxBytes - basePromptSizeBytes) * 0.9)
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# Ensure minimum available space (at least 1KB)
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availableBytes = max(availableBytes, 1024)
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logger.debug(f"Prompt space calculation: base={basePromptSizeBytes} bytes, max={maxBytes} bytes, available={availableBytes} bytes")
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return availableBytes
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except Exception as e:
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logger.warning(f"Error calculating available prompt space: {str(e)}, using fallback: 8KB")
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# Fallback: assume 8KB available
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return 8 * 1024
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def _summarizeJsonStructure(self, jsonData: Any) -> Dict[str, Any]:
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"""Summarize JSON document structure for validation - extracts main objects, statistics, captions, and IDs."""
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try:
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if not isinstance(jsonData, dict):
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return {"type": "non-dict", "preview": str(jsonData)[:200]}
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summary = {
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"metadata": {},
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"sections": [],
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"statistics": {}
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}
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# Extract metadata
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metadata = jsonData.get("metadata", {})
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if metadata:
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summary["metadata"] = {
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"title": metadata.get("title"),
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"split_strategy": metadata.get("split_strategy"),
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"extraction_method": metadata.get("extraction_method")
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}
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# Extract documents array (if present)
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documents = jsonData.get("documents", [])
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if documents:
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summary["statistics"]["documentCount"] = len(documents)
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# Process first document (most common case)
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if len(documents) > 0:
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doc = documents[0]
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docSections = doc.get("sections", [])
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summary["statistics"]["sectionCount"] = len(docSections)
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# Summarize sections
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for section in docSections:
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sectionSummary = {
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"id": section.get("id"),
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"content_type": section.get("content_type"),
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"title": section.get("title"),
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"order": section.get("order")
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}
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# For tables: extract caption and statistics
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if section.get("content_type") == "table":
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elements = section.get("elements", [])
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if elements and isinstance(elements, list) and len(elements) > 0:
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tableElement = elements[0]
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sectionSummary["caption"] = tableElement.get("caption")
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headers = tableElement.get("headers", [])
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rows = tableElement.get("rows", [])
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sectionSummary["columnCount"] = len(headers)
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sectionSummary["rowCount"] = len(rows)
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sectionSummary["headers"] = headers # Include headers for context
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# For lists: extract item count
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elif section.get("content_type") == "list":
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elements = section.get("elements", [])
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if elements and isinstance(elements, list) and len(elements) > 0:
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listElement = elements[0]
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items = listElement.get("items", [])
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sectionSummary["itemCount"] = len(items)
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# For paragraphs/headings: extract text preview
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elif section.get("content_type") in ["paragraph", "heading"]:
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elements = section.get("elements", [])
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if elements and isinstance(elements, list) and len(elements) > 0:
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textElement = elements[0]
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text = textElement.get("text", "")
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if text:
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sectionSummary["textPreview"] = text[:100] + ("..." if len(text) > 100 else "")
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summary["sections"].append(sectionSummary)
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else:
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# Fallback: check for sections directly in root
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sections = jsonData.get("sections", [])
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if sections:
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summary["statistics"]["sectionCount"] = len(sections)
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for section in sections:
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sectionSummary = {
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"id": section.get("id"),
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"content_type": section.get("content_type"),
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"title": section.get("title")
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}
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if section.get("content_type") == "table":
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elements = section.get("elements", [])
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if elements and isinstance(elements, list) and len(elements) > 0:
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tableElement = elements[0]
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sectionSummary["caption"] = tableElement.get("caption")
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headers = tableElement.get("headers", [])
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rows = tableElement.get("rows", [])
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sectionSummary["columnCount"] = len(headers)
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sectionSummary["rowCount"] = len(rows)
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sectionSummary["headers"] = headers
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summary["sections"].append(sectionSummary)
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return summary
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except Exception as e:
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logger.warning(f"Error summarizing JSON structure: {str(e)}")
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return {"error": str(e), "type": "error"}
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def _analyzeDocumentsWithSizeLimit(self, documents: List[Any], maxTotalBytes: int) -> List[Dict[str, Any]]:
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"""
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Analyze documents for validation - includes metadata AND JSON structure summary.
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JSON summary provides structure information (sections, tables with captions, IDs) without full content.
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"""
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if not documents:
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return []
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summaries = []
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for doc in documents:
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try:
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name = getattr(doc, 'documentName', 'Unknown')
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mimeType = getattr(doc, 'mimeType', 'unknown')
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formatExt = self._detectFormat(doc)
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sizeInfo = self._calculateSize(doc)
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summary = {
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"name": name,
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"mimeType": mimeType,
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"format": formatExt,
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"size": sizeInfo["readable"]
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}
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# Extract JSON structure summary - prioritize sourceJson for rendered documents
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sourceJson = getattr(doc, 'sourceJson', None)
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data = getattr(doc, 'documentData', None)
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if sourceJson and isinstance(sourceJson, dict):
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# Use source JSON for structure analysis (for rendered documents like xlsx/docx/pdf)
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jsonSummary = self._summarizeJsonStructure(sourceJson)
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summary["jsonStructure"] = jsonSummary
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elif data is not None:
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# Fallback: try to parse documentData as JSON (for non-rendered documents)
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if isinstance(data, dict):
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# Summarize JSON structure
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jsonSummary = self._summarizeJsonStructure(data)
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summary["jsonStructure"] = jsonSummary
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elif isinstance(data, list) and len(data) > 0 and isinstance(data[0], dict):
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# Handle list of documents
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jsonSummary = self._summarizeJsonStructure(data[0])
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summary["jsonStructure"] = jsonSummary
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summaries.append(summary)
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except Exception as e:
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logger.warning(f"Error analyzing document {getattr(doc, 'documentName', 'Unknown')}: {str(e)}")
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summaries.append({
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"name": getattr(doc, 'documentName', 'Unknown'),
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"mimeType": getattr(doc, 'mimeType', 'unknown'),
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"format": "unknown",
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"size": "0 B",
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"error": str(e)
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})
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return summaries
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def _detectFormat(self, doc: Any) -> str:
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"""Extract format from filename extension (always use extension)"""
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try:
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docName = getattr(doc, 'documentName', '')
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# Extract from filename extension
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if docName and '.' in docName:
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ext = docName.rsplit('.', 1)[1].lower()
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return ext
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return 'unknown'
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except Exception as e:
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logger.warning(f"Error detecting format: {str(e)}")
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return 'unknown'
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def _calculateSize(self, doc: Any) -> Dict[str, Any]:
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"""Calculate document size in bytes and human-readable format"""
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try:
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if not hasattr(doc, 'documentData') or doc.documentData is None:
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return {"bytes": 0, "readable": "0 B"}
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data = doc.documentData
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size_bytes = 0
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if isinstance(data, str):
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size_bytes = len(data.encode('utf-8'))
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elif isinstance(data, bytes):
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size_bytes = len(data)
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elif isinstance(data, (dict, list)):
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# Estimate JSON size
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try:
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json_str = json.dumps(data)
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size_bytes = len(json_str.encode('utf-8'))
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except:
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size_bytes = len(str(data).encode('utf-8'))
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else:
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size_bytes = len(str(data).encode('utf-8'))
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# Convert to human-readable format
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readable = self._formatBytes(size_bytes)
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return {"bytes": size_bytes, "readable": readable}
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except Exception as e:
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logger.warning(f"Error calculating size: {str(e)}")
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return {"bytes": 0, "readable": "0 B"}
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def _formatBytes(self, bytes: int) -> str:
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"""Format bytes to human-readable string"""
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for unit in ['B', 'KB', 'MB', 'GB']:
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if bytes < 1024.0:
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return f"{bytes:.1f} {unit}"
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bytes /= 1024.0
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return f"{bytes:.1f} TB"
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def _isFormatCompatible(self, deliveredFormat: str, expectedFormat: str) -> bool:
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"""
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Generic format compatibility check.
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- txt/md/html are text formats (compatible with each other)
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- pdf/docx/xlsx are document formats (not compatible with each other)
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- json/xml are structured formats
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- images are image formats
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"""
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deliveredLower = deliveredFormat.lower()
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expectedLower = expectedFormat.lower()
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# Exact match
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if deliveredLower == expectedLower:
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return True
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# Text formats are interchangeable
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textFormats = ['txt', 'md', 'html', 'text', 'plain']
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if deliveredLower in textFormats and expectedLower in textFormats:
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return True
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# Structured formats
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if deliveredLower in ['json', 'xml'] and expectedLower in ['json', 'xml']:
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return True
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# Document formats are NOT compatible with each other
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documentFormats = ['pdf', 'docx', 'xlsx', 'pptx']
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if deliveredLower in documentFormats and expectedLower in documentFormats:
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return False # pdf ≠ docx
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return False
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async def _validateWithAI(self, documents: List[Any], intent: Dict[str, Any], taskStep: Optional[Any] = None, actionName: Optional[str] = None) -> Dict[str, Any]:
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"""AI-based comprehensive validation - generic approach"""
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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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# Use taskStep.objective if available, otherwise fall back to intent.primaryGoal
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taskObjective = None
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if taskStep and hasattr(taskStep, 'objective'):
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taskObjective = taskStep.objective
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elif taskStep and isinstance(taskStep, dict):
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taskObjective = taskStep.get('objective')
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# Use taskStep format fields if available, otherwise fall back to intent
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dataType = None
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expectedFormats = None
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if taskStep:
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if hasattr(taskStep, 'dataType') and taskStep.dataType:
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dataType = taskStep.dataType
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elif isinstance(taskStep, dict):
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dataType = taskStep.get('dataType')
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if hasattr(taskStep, 'expectedFormats') and taskStep.expectedFormats:
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expectedFormats = taskStep.expectedFormats
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elif isinstance(taskStep, dict):
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expectedFormats = taskStep.get('expectedFormats')
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# Fallback to intent if taskStep format fields not available
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if not dataType:
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dataType = intent.get('dataType', 'unknown')
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if not expectedFormats:
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expectedFormats = intent.get('expectedFormats', [])
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# Determine objective text and label
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objectiveText = taskObjective if taskObjective else intent.get('primaryGoal', 'Unknown')
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objectiveLabel = "TASK OBJECTIVE" if taskObjective else "USER REQUEST"
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# Build prompt base WITHOUT document summaries first
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# Use success criteria from taskStep if available, otherwise from intent
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successCriteria = []
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if taskStep and hasattr(taskStep, 'successCriteria') and taskStep.successCriteria:
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successCriteria = taskStep.successCriteria
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elif taskStep and isinstance(taskStep, dict):
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successCriteria = taskStep.get('successCriteria', [])
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else:
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successCriteria = intent.get('successCriteria', [])
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criteriaCount = len(successCriteria)
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# Build action name context with human-readable description
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actionContext = ""
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if actionName:
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# Convert action name to human-readable format
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actionDescription = actionName.replace("ai.", "").replace(".", " ").title()
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if "convert" in actionName.lower():
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actionDescription = "Document format conversion"
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elif "generate" in actionName.lower() or "create" in actionName.lower():
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actionDescription = "Document generation"
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elif "extract" in actionName.lower():
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actionDescription = "Content extraction"
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elif "process" in actionName.lower():
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actionDescription = "Content processing"
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actionContext = f"\nDOCUMENTS CREATED BY: {actionDescription} ({actionName})"
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# Format success criteria for display
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criteriaDisplay = json.dumps(successCriteria, ensure_ascii=False) if successCriteria else "[]"
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# Build successCriteriaMet example - show proper array format
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criteriaMetExample = json.dumps([False] * criteriaCount) if criteriaCount > 0 else "[]"
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promptBase = f"""TASK VALIDATION
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{objectiveLabel}: '{objectiveText}'
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EXPECTED DATA TYPE: {dataType}
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EXPECTED FORMATS: {expectedFormats if expectedFormats else ['any']}
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SUCCESS CRITERIA ({criteriaCount} items): {criteriaDisplay}{actionContext}
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VALIDATION CONTEXT:
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You have METADATA (filename, format, size, mimeType) and STRUCTURE SUMMARY (if available: sections, tables, captions, IDs, statistics).
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VALIDATION PRINCIPLES:
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1. Format compatibility: Match delivered format to expected format
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2. Structure validation: Use structure summary to verify requirements (section count, table captions, IDs, section types, etc.)
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3. Filename appropriateness: Check if filename suggests correct content type
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4. Document count: Verify number matches expectations
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5. Size sanity: Only flag if clearly wrong (<1KB for complex content or suspiciously large)
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LIMITATIONS:
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- Cannot validate: Content accuracy, data correctness, formatting details, or requirements requiring full content reading
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- If structure summary unavailable, validate only metadata (format, filename, count, size)
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SCORING GUIDELINES:
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- Format matches + reasonable structure → qualityScore: 0.8-1.0
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- Format matches but structure issues → qualityScore: 0.7-0.8
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- Format mismatch → qualityScore: <0.7
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- Only suggest improvements for CLEAR metadata/structure issues
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OUTPUT FORMAT (JSON only):
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{{
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"overallSuccess": false,
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"qualityScore": 0.0,
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"dataTypeMatch": false,
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"formatMatch": false,
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"documentCount": {len(documents)},
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"successCriteriaMet": {criteriaMetExample},
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"gapAnalysis": "Brief description of gaps based on metadata/structure only. If validation is limited, state this clearly.",
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"improvementSuggestions": [],
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"validationDetails": [
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{{
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"documentName": "document.ext",
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"issues": ["Issue inferred from metadata/structure only"],
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"suggestions": ["Specific fix based on metadata/structure analysis"]
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}}
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]
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}}
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DELIVERED DOCUMENTS ({len(documents)} items):
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"""
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# Calculate available space for document summaries
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# Get the model that will be used for validation
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basePromptSize = len(promptBase.encode('utf-8'))
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availableBytes = self._calculateAvailablePromptSpace(basePromptSize)
|
|
|
|
# Analyze documents with size constraints
|
|
documentSummaries = self._analyzeDocumentsWithSizeLimit(documents, availableBytes)
|
|
|
|
# Build final prompt with summaries at the end
|
|
documentsJson = json.dumps(documentSummaries, indent=2, ensure_ascii=False)
|
|
validationPrompt = promptBase + documentsJson
|
|
|
|
# Call AI service for validation
|
|
response = await self.services.ai.callAiPlanning(
|
|
prompt=validationPrompt,
|
|
placeholders=None,
|
|
debugType="contentvalidation"
|
|
)
|
|
|
|
if not response or not response.strip():
|
|
logger.warning("AI validation returned empty response")
|
|
raise ValueError("AI validation failed - empty response")
|
|
|
|
# Clean and extract JSON from response using proper JSON extraction utility
|
|
# This handles nested structures and markdown code blocks correctly
|
|
result = response.strip()
|
|
logger.debug(f"AI validation response length: {len(result)}")
|
|
|
|
# Extract JSON first
|
|
extractedJson = self.services.utils.jsonExtractString(result)
|
|
if not extractedJson:
|
|
logger.debug(f"No JSON found in AI response: {result[:200]}...")
|
|
logger.debug(f"Full AI response: {result}")
|
|
raise ValueError("AI validation failed - no JSON in response")
|
|
|
|
# Proactively fix Python-style booleans (False/True -> false/true) BEFORE parsing
|
|
# This handles booleans in any context: standalone, in lists, in dicts, etc.
|
|
import re
|
|
# Use word boundaries but also handle cases where booleans are in brackets/arrays
|
|
# Replace False/True regardless of context (word boundary handles string matching correctly)
|
|
normalizedJson = re.sub(r'\bFalse\b', 'false', extractedJson)
|
|
normalizedJson = re.sub(r'\bTrue\b', 'true', normalizedJson)
|
|
|
|
logger.debug(f"Extracted JSON (before normalization): {extractedJson[:200]}...")
|
|
logger.debug(f"Normalized JSON (after boolean fix): {normalizedJson[:200]}...")
|
|
|
|
# Now try to parse the normalized JSON
|
|
try:
|
|
aiResult = json.loads(normalizedJson)
|
|
logger.info("AI validation JSON parsed successfully")
|
|
except json.JSONDecodeError as json_error:
|
|
logger.warning(f"AI validation invalid JSON after normalization: {str(json_error)}")
|
|
logger.debug(f"JSON content that failed: {normalizedJson[:500]}...")
|
|
raise ValueError(f"AI validation failed - invalid JSON: {str(json_error)}")
|
|
|
|
overall = aiResult.get("overallSuccess")
|
|
quality = aiResult.get("qualityScore")
|
|
details = aiResult.get("validationDetails")
|
|
gap = aiResult.get("gapAnalysis", "")
|
|
criteria = aiResult.get("successCriteriaMet")
|
|
improvements = aiResult.get("improvementSuggestions", [])
|
|
|
|
# Normalize while keeping failures explicit
|
|
normalized = {
|
|
"overallSuccess": overall if isinstance(overall, bool) else None,
|
|
"qualityScore": float(quality) if isinstance(quality, (int, float)) else None,
|
|
"documentCount": len(documentSummaries),
|
|
"gapAnalysis": gap if gap else "",
|
|
"validationDetails": details if isinstance(details, list) else [{
|
|
"documentName": "AI Validation",
|
|
"gapAnalysis": gap,
|
|
"successCriteriaMet": criteria if isinstance(criteria, list) else []
|
|
}],
|
|
"improvementSuggestions": improvements,
|
|
"schemaCompliant": True,
|
|
"originalType": "json",
|
|
"missingFields": []
|
|
}
|
|
|
|
if normalized["overallSuccess"] is None:
|
|
normalized["missingFields"].append("overallSuccess")
|
|
if normalized["qualityScore"] is None:
|
|
normalized["missingFields"].append("qualityScore")
|
|
if normalized["missingFields"]:
|
|
normalized["schemaCompliant"] = False
|
|
|
|
return normalized
|
|
|
|
except Exception as e:
|
|
logger.error(f"AI validation failed: {str(e)}")
|
|
raise
|
|
|
|
def _createFailedValidationResult(self, errorMessage: str) -> Dict[str, Any]:
|
|
"""Create a standardized failed validation result"""
|
|
return {
|
|
"overallSuccess": False,
|
|
"qualityScore": 0.0,
|
|
"dataTypeMatch": False,
|
|
"formatMatch": False,
|
|
"documentCount": 0,
|
|
"successCriteriaMet": [],
|
|
"gapAnalysis": errorMessage,
|
|
"improvementSuggestions": [],
|
|
"validationDetails": [],
|
|
"schemaCompliant": True,
|
|
"originalType": "error",
|
|
"missingFields": [],
|
|
"error": errorMessage
|
|
}
|