233 lines
9 KiB
Python
233 lines
9 KiB
Python
import logging
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from typing import Dict, Any, List, Union
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from modules.aicore.aicoreBase import BaseConnectorAi
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from modules.datamodels.datamodelAi import AiModel, ModelCapabilitiesEnum, PriorityEnum, ProcessingModeEnum, OperationTypeEnum
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# Configure logger
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logger = logging.getLogger(__name__)
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class AiInternal(BaseConnectorAi):
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"""Internal connector for document processing, generation, and rendering."""
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def __init__(self):
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super().__init__()
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logger.info("Internal Connector initialized")
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def getConnectorType(self) -> str:
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"""Get the connector type identifier."""
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return "internal"
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def getModels(self) -> List[AiModel]:
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"""Get all available internal models."""
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return [
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AiModel(
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name="internal_extraction",
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displayName="Internal Document Extractor",
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connectorType="internal",
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maxTokens=0, # Not token-based
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contextLength=0,
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costPer1kTokensInput=0.0,
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costPer1kTokensOutput=0.0,
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speedRating=8,
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qualityRating=8,
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capabilities=[ModelCapabilitiesEnum.CONTENT_EXTRACTION, ModelCapabilitiesEnum.TEXT_EXTRACTION],
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functionCall=self.extractDocument,
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priority=PriorityEnum.COST,
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processingMode=ProcessingModeEnum.BASIC,
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operationTypes=[OperationTypeEnum.GENERAL],
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version="internal-extractor-v1",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: 0.001 + (bytesSent + bytesReceived) / (1024 * 1024) * 0.01
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),
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AiModel(
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name="internal_generation",
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displayName="Internal Document Generator",
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connectorType="internal",
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maxTokens=0, # Not token-based
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contextLength=0,
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costPer1kTokensInput=0.0,
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costPer1kTokensOutput=0.0,
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speedRating=7,
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qualityRating=8,
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capabilities=[ModelCapabilitiesEnum.TEXT_GENERATION, ModelCapabilitiesEnum.ANALYSIS],
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functionCall=self.generateDocument,
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priority=PriorityEnum.COST,
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processingMode=ProcessingModeEnum.BASIC,
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operationTypes=[OperationTypeEnum.GENERATE],
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version="internal-generator-v1",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: 0.002 + (bytesReceived / (1024 * 1024)) * 0.005
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),
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AiModel(
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name="internal_rendering",
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displayName="Internal Document Renderer",
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connectorType="internal",
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maxTokens=0, # Not token-based
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contextLength=0,
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costPer1kTokensInput=0.0,
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costPer1kTokensOutput=0.0,
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speedRating=6,
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qualityRating=9,
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capabilities=[ModelCapabilitiesEnum.TEXT_GENERATION, ModelCapabilitiesEnum.ANALYSIS],
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functionCall=self.renderDocument,
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priority=PriorityEnum.QUALITY,
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processingMode=ProcessingModeEnum.DETAILED,
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operationTypes=[OperationTypeEnum.GENERATE],
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version="internal-renderer-v1",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: 0.003 + (bytesReceived / (1024 * 1024)) * 0.008
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)
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]
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async def extractDocument(self, documentData: Union[str, bytes], extractionType: str = "basic") -> Dict[str, Any]:
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"""
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Extract content from a document.
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Args:
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documentData: The document data to extract from
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extractionType: Type of extraction (basic, advanced, detailed)
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Returns:
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Dictionary with extraction results
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"""
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try:
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logger.info(f"Starting document extraction with type: {extractionType}")
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# Simulate document extraction processing
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# In a real implementation, this would use actual document processing libraries
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if isinstance(documentData, bytes):
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content = documentData.decode('utf-8', errors='ignore')
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else:
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content = str(documentData)
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# Basic extraction logic
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extractedContent = {
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"text": content,
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"metadata": {
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"extraction_type": extractionType,
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"content_length": len(content),
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"processing_time": 0.1 # Simulated
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}
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}
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logger.info(f"Document extraction completed successfully")
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return extractedContent
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except Exception as e:
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logger.error(f"Error during document extraction: {str(e)}")
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return {
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"error": str(e),
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"success": False
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}
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async def generateDocument(self, template: str, data: Dict[str, Any], format: str = "html") -> Dict[str, Any]:
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"""
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Generate a document from a template and data.
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Args:
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template: The document template
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data: Data to populate the template
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format: Output format (html, pdf, docx, etc.)
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Returns:
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Dictionary with generated document
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"""
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try:
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logger.info(f"Starting document generation with format: {format}")
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# Simulate document generation processing
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# In a real implementation, this would use actual templating engines
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# Basic template processing
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generatedContent = template
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for key, value in data.items():
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placeholder = f"{{{key}}}"
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generatedContent = generatedContent.replace(placeholder, str(value))
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result = {
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"content": generatedContent,
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"format": format,
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"metadata": {
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"template_length": len(template),
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"data_keys": list(data.keys()),
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"processing_time": 0.2 # Simulated
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}
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}
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logger.info(f"Document generation completed successfully")
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return result
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except Exception as e:
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logger.error(f"Error during document generation: {str(e)}")
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return {
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"error": str(e),
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"success": False
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}
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async def renderDocument(self, content: str, targetFormat: str, options: Dict[str, Any] = None) -> Dict[str, Any]:
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"""
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Render a document to a specific format.
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Args:
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content: The content to render
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targetFormat: Target format (html, pdf, docx, etc.)
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options: Rendering options
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Returns:
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Dictionary with rendered document
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"""
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try:
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logger.info(f"Starting document rendering to format: {targetFormat}")
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if options is None:
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options = {}
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# Simulate document rendering processing
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# In a real implementation, this would use actual rendering libraries
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# Basic rendering logic based on target format
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if targetFormat.lower() == "html":
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renderedContent = f"<html><body>{content}</body></html>"
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elif targetFormat.lower() == "pdf":
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# Simulate PDF rendering
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renderedContent = f"PDF_CONTENT_PLACEHOLDER: {content}"
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else:
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# Default to plain text
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renderedContent = content
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result = {
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"content": renderedContent,
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"format": targetFormat,
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"metadata": {
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"input_length": len(content),
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"output_length": len(renderedContent),
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"processing_time": 0.3, # Simulated
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"options": options
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}
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}
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logger.info(f"Document rendering completed successfully")
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return result
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except Exception as e:
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logger.error(f"Error during document rendering: {str(e)}")
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return {
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"error": str(e),
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"success": False
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}
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async def _testConnection(self) -> bool:
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"""
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Tests the internal processing capabilities.
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Returns:
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True if internal processing is working, False otherwise
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"""
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try:
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# Test basic functionality
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testContent = "Test document content"
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result = await self.extractDocument(testContent)
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return result.get("success", True) and "error" not in result
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except Exception as e:
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logger.error(f"Internal connector test failed: {str(e)}")
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return False
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