597 lines
27 KiB
Python
597 lines
27 KiB
Python
import logging
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from typing import Dict, Any, List, Optional, Tuple, Union
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from modules.datamodels.datamodelChat import PromptPlaceholder, ChatDocument
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from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, ModelCapabilities, OperationType, Priority
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from modules.interfaces.interfaceAiObjects import AiObjects
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logger = logging.getLogger(__name__)
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class SubCoreAi:
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"""Core AI operations including image analysis, text generation, and planning calls."""
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def __init__(self, services, aiObjects):
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"""Initialize core AI operations.
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Args:
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services: Service center instance for accessing other services
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aiObjects: Initialized AiObjects instance
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"""
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self.services = services
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self.aiObjects = aiObjects
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# AI Processing Call
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async def callAi(
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self,
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prompt: str,
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documents: Optional[List[ChatDocument]] = None,
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placeholders: Optional[List[PromptPlaceholder]] = None,
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options: Optional[AiCallOptions] = None,
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outputFormat: Optional[str] = None,
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title: Optional[str] = None,
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documentProcessor=None,
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documentGenerator=None
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) -> Union[str, Dict[str, Any]]:
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"""
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Unified AI call interface that automatically routes to appropriate handler.
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Args:
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prompt: The main prompt for the AI call
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documents: Optional list of documents to process
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placeholders: Optional list of placeholder replacements for planning calls
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options: AI call configuration options
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outputFormat: Optional output format (html, pdf, docx, txt, md, json, csv, xlsx) for document generation
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title: Optional title for generated documents
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documentProcessor: Document processing service instance
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documentGenerator: Document generation service instance
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Returns:
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AI response as string, or dict with documents if outputFormat is specified
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Raises:
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Exception: If all available models fail
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"""
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if options is None:
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options = AiCallOptions()
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# Normalize placeholders from List[PromptPlaceholder]
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placeholders_dict: Dict[str, str] = {}
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placeholders_meta: Dict[str, bool] = {}
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if placeholders:
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placeholders_dict = {p.label: p.content for p in placeholders}
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placeholders_meta = {p.label: bool(getattr(p, 'summaryAllowed', False)) for p in placeholders}
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# Auto-determine call type based on documents and operation type
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call_type = self._determineCallType(documents, options.operationType)
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options.callType = call_type
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try:
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# Build the full prompt that will be sent to AI
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if placeholders:
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full_prompt = prompt
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for p in placeholders:
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placeholder = f"{{{{KEY:{p.label}}}}}"
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full_prompt = full_prompt.replace(placeholder, p.content)
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else:
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full_prompt = prompt
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# Timestamp-only prompt debug writing removed
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except Exception:
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pass
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# Handle document generation with specific output format
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if outputFormat and documentGenerator:
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result = await documentGenerator.callAiWithDocumentGeneration(prompt, documents, options, outputFormat, title)
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# Log AI response for debugging
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try:
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if isinstance(result, dict) and 'content' in result:
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self._writeAiResponseDebug(
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label='ai_document_generation',
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content=result['content'],
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partIndex=1,
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modelName=None, # Document generation doesn't return model info
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continuation=False
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)
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except Exception:
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pass
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return result
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if call_type == "planning":
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result = await self._callAiPlanning(prompt, placeholders_dict, placeholders_meta, options)
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# Log AI response for debugging
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try:
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self._writeAiResponseDebug(
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label='ai_planning',
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content=result or "",
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partIndex=1,
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modelName=None, # Planning doesn't return model info
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continuation=False
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)
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except Exception:
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pass
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return result
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else:
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# Set processDocumentsIndividually from the legacy parameter if not set in options
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if options.processDocumentsIndividually is None and documents:
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options.processDocumentsIndividually = False # Default to batch processing
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# For text calls, we need to build the full prompt with placeholders here
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# since _callAiText doesn't handle placeholders directly
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if placeholders_dict:
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full_prompt = self._buildPromptWithPlaceholders(prompt, placeholders_dict)
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else:
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full_prompt = prompt
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if documentProcessor and documents:
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result = await documentProcessor.callAiText(full_prompt, documents, options)
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else:
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# Fallback to direct AI call if no document processor available
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request = AiCallRequest(
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prompt=full_prompt,
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context="",
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options=options
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)
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response = await self.aiObjects.call(request)
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result = response.content
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# Emit stats for direct AI call
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self.services.workflow.storeWorkflowStat(
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self.services.workflow,
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response,
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f"ai.call.{options.operationType}"
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)
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# Log AI response for debugging (additional logging for text calls)
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try:
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self._writeAiResponseDebug(
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label='ai_text_main',
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content=result or "",
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partIndex=1,
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modelName=None, # Text calls already log internally
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continuation=False
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)
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except Exception:
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pass
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return result
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# AI Image Analysis
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async def readImage(
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self,
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prompt: str,
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imageData: Union[str, bytes],
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mimeType: str = None,
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options: Optional[AiCallOptions] = None,
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) -> str:
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"""Call AI for image analysis using interface.callImage()."""
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try:
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# Check if imageData is valid
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if not imageData:
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error_msg = "No image data provided"
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self.services.utils.debugLogToFile(f"Error in AI image analysis: {error_msg}", "AI_SERVICE")
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logger.error(f"Error in AI image analysis: {error_msg}")
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return f"Error: {error_msg}"
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self.services.utils.debugLogToFile(f"readImage called with prompt, imageData type: {type(imageData)}, length: {len(imageData) if imageData else 0}, mimeType: {mimeType}", "AI_SERVICE")
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logger.info(f"readImage called with prompt, imageData type: {type(imageData)}, length: {len(imageData) if imageData else 0}, mimeType: {mimeType}")
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# Always use IMAGE_ANALYSIS operation type for image processing
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if options is None:
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options = AiCallOptions(operationType=OperationType.IMAGE_ANALYSIS)
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else:
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# Override the operation type to ensure image analysis
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options.operationType = OperationType.IMAGE_ANALYSIS
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self.services.utils.debugLogToFile(f"Calling aiObjects.callImage with operationType: {options.operationType}", "AI_SERVICE")
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logger.info(f"Calling aiObjects.callImage with operationType: {options.operationType}")
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response = await self.aiObjects.callImage(prompt, imageData, mimeType, options)
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# Emit stats for image analysis
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self.services.workflow.storeWorkflowStat(
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self.services.workflow,
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response,
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f"ai.image.{options.operationType}"
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)
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# Debug the result
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self.services.utils.debugLogToFile(f"Raw AI result type: {type(response)}, value: {repr(response)}", "AI_SERVICE")
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# Extract content from response
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result = response.content if hasattr(response, 'content') else str(response)
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# Check if result is valid
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if not result or (isinstance(result, str) and not result.strip()):
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error_msg = f"No response from AI image analysis (result: {repr(result)})"
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self.services.utils.debugLogToFile(f"Error in AI image analysis: {error_msg}", "AI_SERVICE")
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logger.error(f"Error in AI image analysis: {error_msg}")
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return f"Error: {error_msg}"
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self.services.utils.debugLogToFile(f"callImage returned: {result[:200]}..." if len(result) > 200 else result, "AI_SERVICE")
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logger.info(f"callImage returned: {result[:200]}..." if len(result) > 200 else result)
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return result
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except Exception as e:
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self.services.utils.debugLogToFile(f"Error in AI image analysis: {str(e)}", "AI_SERVICE")
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logger.error(f"Error in AI image analysis: {str(e)}")
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return f"Error: {str(e)}"
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# AI Image Generation
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async def generateImage(
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self,
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prompt: str,
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size: str = "1024x1024",
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quality: str = "standard",
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style: str = "vivid",
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options: Optional[AiCallOptions] = None,
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) -> Dict[str, Any]:
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"""Generate an image using AI using interface.generateImage()."""
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try:
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response = await self.aiObjects.generateImage(prompt, size, quality, style, options)
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# Emit stats for image generation
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self.services.workflow.storeWorkflowStat(
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self.services.workflow,
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response,
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f"ai.generate.image"
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)
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# Convert response to dict format for backward compatibility
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if hasattr(response, 'content'):
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return {
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"success": True,
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"content": response.content,
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"modelName": response.modelName,
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"priceUsd": response.priceUsd,
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"processingTime": response.processingTime
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}
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else:
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return response
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except Exception as e:
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logger.error(f"Error in AI image generation: {str(e)}")
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return {"success": False, "error": str(e)}
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def _determineCallType(self, documents: Optional[List[ChatDocument]], operation_type: str) -> str:
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"""
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Determine call type based on documents and operation type.
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Criteria: no documents AND operationType is "generate_plan" -> planning
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All other cases -> text
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"""
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has_documents = documents is not None and len(documents) > 0
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is_planning_operation = operation_type == OperationType.GENERATE_PLAN
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if not has_documents and is_planning_operation:
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return "planning"
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else:
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return "text"
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async def _callAiPlanning(
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self,
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prompt: str,
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placeholders: Optional[Dict[str, str]],
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placeholdersMeta: Optional[Dict[str, bool]],
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options: AiCallOptions
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) -> str:
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"""
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Handle planning calls with placeholder system and selective summarization.
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"""
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# Build full prompt with placeholders; if too large, summarize summaryAllowed placeholders proportionally
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effective_placeholders = placeholders or {}
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full_prompt = self._buildPromptWithPlaceholders(prompt, effective_placeholders)
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if options.compressPrompt and placeholdersMeta:
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# Determine model capacity
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try:
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caps = self._getModelCapabilitiesForContent(full_prompt, None, options)
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max_bytes = caps.get("maxContextBytes", len(full_prompt.encode("utf-8")))
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except Exception:
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max_bytes = len(full_prompt.encode("utf-8"))
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current_bytes = len(full_prompt.encode("utf-8"))
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if current_bytes > max_bytes:
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# Compute total bytes contributed by allowed placeholders (approximate by content length)
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allowed_labels = [l for l, allow in placeholdersMeta.items() if allow]
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allowed_sizes = {l: len((effective_placeholders.get(l) or "").encode("utf-8")) for l in allowed_labels}
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total_allowed = sum(allowed_sizes.values())
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overage = current_bytes - max_bytes
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if total_allowed > 0 and overage > 0:
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# Target total for allowed after reduction
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target_allowed = max(total_allowed - overage, 0)
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# Global ratio to apply across allowed placeholders
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ratio = target_allowed / total_allowed if total_allowed > 0 else 1.0
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ratio = max(0.0, min(1.0, ratio))
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reduced: Dict[str, str] = {}
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for label, content in effective_placeholders.items():
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if label in allowed_labels and isinstance(content, str) and len(content) > 0:
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old_len = len(content)
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# Reduce by proportional ratio on characters (fallback if empty)
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reduction_factor = ratio if old_len > 0 else 1.0
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reduced[label] = self._reduceText(content, reduction_factor)
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else:
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reduced[label] = content
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effective_placeholders = reduced
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full_prompt = self._buildPromptWithPlaceholders(prompt, effective_placeholders)
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# If still slightly over, perform a second-pass fine adjustment with updated ratio
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current_bytes = len(full_prompt.encode("utf-8"))
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if current_bytes > max_bytes and total_allowed > 0:
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overage2 = current_bytes - max_bytes
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# Recompute allowed sizes after first reduction
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allowed_sizes2 = {l: len((effective_placeholders.get(l) or "").encode("utf-8")) for l in allowed_labels}
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total_allowed2 = sum(allowed_sizes2.values())
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if total_allowed2 > 0 and overage2 > 0:
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target_allowed2 = max(total_allowed2 - overage2, 0)
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ratio2 = target_allowed2 / total_allowed2
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ratio2 = max(0.0, min(1.0, ratio2))
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reduced2: Dict[str, str] = {}
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for label, content in effective_placeholders.items():
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if label in allowed_labels and isinstance(content, str) and len(content) > 0:
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old_len = len(content)
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reduction_factor = ratio2 if old_len > 0 else 1.0
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reduced2[label] = self._reduceText(content, reduction_factor)
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else:
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reduced2[label] = content
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effective_placeholders = reduced2
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full_prompt = self._buildPromptWithPlaceholders(prompt, effective_placeholders)
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# Make AI call using AiObjects (let it handle model selection)
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request = AiCallRequest(
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prompt=full_prompt,
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context="", # Context is already included in the prompt
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options=options
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)
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response = await self.aiObjects.call(request)
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try:
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logger.debug(f"AI model selected (planning): {getattr(response, 'modelName', 'unknown')}")
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except Exception:
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pass
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return response.content
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async def _callAiDirect(
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self,
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prompt: str,
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documents: Optional[List[ChatDocument]],
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options: AiCallOptions,
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documentProcessor=None
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) -> Dict[str, Any]:
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"""
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Call AI directly with prompt and documents for JSON output.
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Used for multi-file generation - uses the existing generation pipeline.
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"""
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# Use the existing generation pipeline that already works
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# This ensures proper document processing and content extraction
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logger.info(f"Using existing generation pipeline for {len(documents) if documents else 0} documents")
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if documentProcessor:
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# Process documents with JSON merging using the existing pipeline
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result = await documentProcessor.processDocumentsPerChunkJson(documents, prompt, options)
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else:
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# Fallback to simple AI call
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request = AiCallRequest(
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prompt=prompt,
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context="",
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options=options
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)
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response = await self.aiObjects.call(request)
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result = {"metadata": {"title": "AI Response"}, "sections": [{"id": "section_1", "content_type": "paragraph", "elements": [{"text": response.content}]}]}
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# Convert single-file result to multi-file format if needed
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if "sections" in result and "documents" not in result:
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logger.info("Converting single-file result to multi-file format")
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# This is a single-file result, convert it to multi-file format
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return {
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"metadata": result.get("metadata", {"title": "Converted Document"}),
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"documents": [{
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"id": "doc_1",
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"title": result.get("metadata", {}).get("title", "Document"),
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"filename": "document.txt",
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"sections": result.get("sections", [])
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}]
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}
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return result
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def _getModelCapabilitiesForContent(self, prompt: str, documents: Optional[List[ChatDocument]], options: AiCallOptions) -> Dict[str, int]:
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"""
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Get model capabilities for content processing, including appropriate size limits for chunking.
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"""
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# Estimate total content size
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prompt_size = len(prompt.encode('utf-8'))
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document_size = 0
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if documents:
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# Rough estimate of document content size
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for doc in documents:
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document_size += doc.fileSize or 0
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total_size = prompt_size + document_size
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# Use AiObjects to select the best model for this content size
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# We'll simulate the model selection by checking available models
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from modules.interfaces.interfaceAiObjects import aiModels
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# Find the best model for this content size and operation
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best_model = None
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best_context_length = 0
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for model_name, model_info in aiModels.items():
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context_length = model_info.get("contextLength", 0)
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# Skip models with no context length or too small for content
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if context_length == 0:
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continue
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# Check if model supports the operation type
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capabilities = model_info.get("capabilities", [])
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if options.operationType == OperationType.IMAGE_ANALYSIS and "image_analysis" not in capabilities:
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continue
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elif options.operationType == OperationType.IMAGE_GENERATION and "image_generation" not in capabilities:
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continue
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elif options.operationType == OperationType.WEB_RESEARCH and "web_search" not in capabilities:
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continue
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elif "text_generation" not in capabilities:
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continue
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# Prefer models that can handle the content without chunking, but allow chunking if needed
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if context_length >= total_size * 0.8: # 80% of content size
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if context_length > best_context_length:
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best_model = model_info
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best_context_length = context_length
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elif best_model is None: # Fallback to largest available model
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if context_length > best_context_length:
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best_model = model_info
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best_context_length = context_length
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# Fallback to a reasonable default if no model found
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if best_model is None:
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best_model = {
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"contextLength": 128000, # GPT-4o default
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"llmName": "gpt-4o"
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}
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# Calculate appropriate sizes
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# Convert tokens to bytes (rough estimate: 1 token ≈ 4 characters)
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context_length_bytes = int(best_model["contextLength"] * 4)
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max_context_bytes = int(context_length_bytes * 0.9) # 90% of context length
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text_chunk_size = int(max_context_bytes * 0.7) # 70% of max context for text chunks
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image_chunk_size = int(max_context_bytes * 0.8) # 80% of max context for image chunks
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logger.debug(f"Selected model: {best_model.get('llmName', 'unknown')} with context length: {best_model['contextLength']}")
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logger.debug(f"Content size: {total_size} bytes, Max context: {max_context_bytes} bytes")
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logger.debug(f"Text chunk size: {text_chunk_size} bytes, Image chunk size: {image_chunk_size} bytes")
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return {
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"maxContextBytes": max_context_bytes,
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"textChunkSize": text_chunk_size,
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"imageChunkSize": image_chunk_size
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}
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def _getModelsForOperation(self, operation_type: str, options: AiCallOptions) -> List[ModelCapabilities]:
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"""
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Get models capable of handling the specific operation with capability filtering.
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"""
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# Use the actual AI objects model selection instead of hardcoded default
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if hasattr(self, 'aiObjects') and self.aiObjects:
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# Let AiObjects handle the model selection
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return []
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else:
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# Fallback to default model if AiObjects not available
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default_model = ModelCapabilities(
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name="default",
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maxTokens=4000,
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capabilities=["text", "reasoning"] if operation_type == "planning" else ["text"],
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costPerToken=0.001,
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processingTime=1.0,
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isAvailable=True
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)
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return [default_model]
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def _buildPromptWithPlaceholders(self, prompt: str, placeholders: Optional[Dict[str, str]]) -> str:
|
|
"""
|
|
Build full prompt by replacing placeholders with their content.
|
|
Uses the new {{KEY:placeholder}} format.
|
|
"""
|
|
if not placeholders:
|
|
return prompt
|
|
|
|
full_prompt = prompt
|
|
for placeholder, content in placeholders.items():
|
|
# Replace both old format {{placeholder}} and new format {{KEY:placeholder}}
|
|
full_prompt = full_prompt.replace(f"{{{{{placeholder}}}}}", content)
|
|
full_prompt = full_prompt.replace(f"{{{{KEY:{placeholder}}}}}", content)
|
|
|
|
return full_prompt
|
|
|
|
def _writeAiResponseDebug(self, label: str, content: Any, partIndex: int = 1, modelName: str = None, continuation: bool = None) -> None:
|
|
"""Disabled verbose debug writing; only minimal files elsewhere."""
|
|
return
|
|
|
|
def _exceedsTokenLimit(self, text: str, model: ModelCapabilities, safety_margin: float) -> bool:
|
|
"""
|
|
Check if text exceeds model token limit with safety margin.
|
|
"""
|
|
# Simple character-based estimation (4 chars per token)
|
|
estimated_tokens = len(text) // 4
|
|
max_tokens = int(model.maxTokens * (1 - safety_margin))
|
|
return estimated_tokens > max_tokens
|
|
|
|
def _reducePlanningPrompt(
|
|
self,
|
|
full_prompt: str,
|
|
placeholders: Optional[Dict[str, str]],
|
|
model: ModelCapabilities,
|
|
options: AiCallOptions
|
|
) -> str:
|
|
"""
|
|
Reduce planning prompt size by summarizing placeholders while preserving prompt structure.
|
|
"""
|
|
if not placeholders:
|
|
return self._reduceText(full_prompt, 0.7)
|
|
|
|
# Reduce placeholders while preserving prompt
|
|
reduced_placeholders = {}
|
|
for placeholder, content in placeholders.items():
|
|
if len(content) > 1000: # Only reduce long content
|
|
reduction_factor = 0.7
|
|
reduced_content = self._reduceText(content, reduction_factor)
|
|
reduced_placeholders[placeholder] = reduced_content
|
|
else:
|
|
reduced_placeholders[placeholder] = content
|
|
|
|
return self._buildPromptWithPlaceholders(full_prompt, reduced_placeholders)
|
|
|
|
def _reduceTextPrompt(
|
|
self,
|
|
prompt: str,
|
|
context: str,
|
|
model: ModelCapabilities,
|
|
options: AiCallOptions
|
|
) -> str:
|
|
"""
|
|
Reduce text prompt size using typeGroup-aware chunking and merging.
|
|
"""
|
|
max_size = int(model.maxTokens * (1 - options.safetyMargin))
|
|
|
|
if options.compressPrompt:
|
|
# Reduce both prompt and context
|
|
target_size = max_size
|
|
current_size = len(prompt) + len(context)
|
|
reduction_factor = (target_size * 0.7) / current_size
|
|
|
|
if reduction_factor < 1.0:
|
|
prompt = self._reduceText(prompt, reduction_factor)
|
|
context = self._reduceText(context, reduction_factor)
|
|
else:
|
|
# Only reduce context, preserve prompt integrity
|
|
max_context_size = max_size - len(prompt)
|
|
if len(context) > max_context_size:
|
|
reduction_factor = max_context_size / len(context)
|
|
context = self._reduceText(context, reduction_factor)
|
|
|
|
return prompt + "\n\n" + context if context else prompt
|
|
|
|
def _extractTextFromContentParts(self, extracted_content) -> str:
|
|
"""
|
|
Extract text content from ExtractionService ContentPart objects.
|
|
"""
|
|
if not extracted_content or not hasattr(extracted_content, 'parts'):
|
|
return ""
|
|
|
|
text_parts = []
|
|
for part in extracted_content.parts:
|
|
if hasattr(part, 'typeGroup') and part.typeGroup in ['text', 'table', 'structure']:
|
|
if hasattr(part, 'data') and part.data:
|
|
text_parts.append(part.data)
|
|
|
|
return "\n\n".join(text_parts)
|
|
|
|
def _reduceText(self, text: str, reduction_factor: float) -> str:
|
|
"""
|
|
Reduce text size by the specified factor.
|
|
"""
|
|
if reduction_factor >= 1.0:
|
|
return text
|
|
|
|
target_length = int(len(text) * reduction_factor)
|
|
return text[:target_length] + "... [reduced]"
|