276 lines
14 KiB
Python
276 lines
14 KiB
Python
# Copyright (c) 2025 Patrick Motsch
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# All rights reserved.
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import logging
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import time
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import json
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from typing import Dict, Any, List, Optional
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from modules.datamodels.datamodelChat import ActionResult, ActionDocument
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from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, OperationTypeEnum, ProcessingModeEnum
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from modules.datamodels.datamodelExtraction import ContentPart
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logger = logging.getLogger(__name__)
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async def process(self, parameters: Dict[str, Any]) -> ActionResult:
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try:
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# Init progress logger
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workflowId = self.services.workflow.id if self.services.workflow else f"no-workflow-{int(time.time())}"
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operationId = f"ai_process_{workflowId}_{int(time.time())}"
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# Start progress tracking
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parentOperationId = parameters.get('parentOperationId')
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if not parentOperationId:
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logger.warning(f"ai.process: No parentOperationId provided in parameters. Operation '{operationId}' will appear at root level. Available parameters: {list(parameters.keys())}")
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else:
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logger.debug(f"ai.process: Using parentOperationId '{parentOperationId}' for operation '{operationId}'")
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self.services.chat.progressLogStart(
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operationId,
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"Generate",
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"AI Processing",
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f"Format: {parameters.get('resultType', 'txt')}",
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parentOperationId=parentOperationId
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)
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aiPrompt = parameters.get("aiPrompt")
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logger.info(f"aiPrompt extracted: '{aiPrompt}' (type: {type(aiPrompt)})")
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# Update progress - preparing parameters
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self.services.chat.progressLogUpdate(operationId, 0.2, "Preparing parameters")
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from modules.datamodels.datamodelDocref import DocumentReferenceList
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documentListParam = parameters.get("documentList")
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# Convert to DocumentReferenceList if needed
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if documentListParam is None:
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documentList = DocumentReferenceList(references=[])
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logger.debug(f"ai.process: documentList is None, using empty DocumentReferenceList")
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elif isinstance(documentListParam, DocumentReferenceList):
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documentList = documentListParam
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logger.info(f"ai.process: Received DocumentReferenceList with {len(documentList.references)} references")
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for idx, ref in enumerate(documentList.references):
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logger.info(f" Reference {idx + 1}: documentId={ref.documentId}, type={type(ref).__name__}")
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elif isinstance(documentListParam, str):
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documentList = DocumentReferenceList.from_string_list([documentListParam])
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logger.info(f"ai.process: Converted string to DocumentReferenceList with {len(documentList.references)} references")
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elif isinstance(documentListParam, list):
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documentList = DocumentReferenceList.from_string_list(documentListParam)
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logger.info(f"ai.process: Converted list to DocumentReferenceList with {len(documentList.references)} references")
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else:
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logger.error(f"Invalid documentList type: {type(documentListParam)}")
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documentList = DocumentReferenceList(references=[])
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# Optional: if omitted, formats determined from prompt. Default "txt" is validation fallback only.
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resultType = parameters.get("resultType")
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simpleMode = parameters.get("simpleMode", False)
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if not aiPrompt:
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logger.error(f"aiPrompt is missing or empty. Parameters: {parameters}")
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return ActionResult.isFailure(
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error="AI prompt is required"
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)
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# Handle optional resultType: if None, formats determined from prompt by AI
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if resultType:
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normalized_result_type = (str(resultType).strip().lstrip('.').lower() or "txt")
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output_extension = f".{normalized_result_type}"
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output_format = output_extension.replace('.', '') or 'txt'
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logger.info(f"Using result type: {resultType} -> {output_extension}, simpleMode: {simpleMode}")
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else:
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# No format specified - AI will determine formats from prompt
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normalized_result_type = None
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output_extension = None
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output_format = None
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logger.debug("resultType not provided - formats will be determined from prompt by AI")
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output_mime_type = "application/octet-stream" # Prefer service-provided mimeType when available
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# Phase 7.3: Pass both documentList and contentParts to AI service
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# (Extraction logic removed - handled by AI service)
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contentParts: Optional[List[ContentPart]] = None
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if "contentParts" in parameters:
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contentPartsParam = parameters.get("contentParts")
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if contentPartsParam:
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if isinstance(contentPartsParam, list):
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contentParts = contentPartsParam
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elif hasattr(contentPartsParam, 'parts'):
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# Extract from ContentExtracted if it's an ActionDocument
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contentParts = contentPartsParam.parts
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else:
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logger.warning(f"Invalid contentParts type: {type(contentPartsParam)}, treating as empty")
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contentParts = None
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# Update progress - preparing AI call
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self.services.chat.progressLogUpdate(operationId, 0.4, "Preparing AI call")
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# Build output format for simple mode
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output_format_for_call = output_extension.replace('.', '') if output_extension else (output_format or 'txt')
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# Simple mode: fast path without document generation pipeline
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if simpleMode:
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# Update progress - calling AI (simple mode)
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self.services.chat.progressLogUpdate(operationId, 0.6, "Calling AI (simple mode)")
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# Extract context from documents if provided
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context_text = ""
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if documentList and len(documentList.references) > 0:
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try:
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# Get documents from workflow
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documents = self.services.chat.getChatDocumentsFromDocumentList(documentList)
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context_parts = []
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for doc in documents:
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if hasattr(doc, 'fileId') and doc.fileId:
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# Get file data
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fileData = self.services.interfaceDbComponent.getFileData(doc.fileId)
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if fileData:
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if isinstance(fileData, bytes):
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doc_text = fileData.decode('utf-8', errors='ignore')
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else:
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doc_text = str(fileData)
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context_parts.append(doc_text)
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if context_parts:
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context_text = "\n\n".join(context_parts)
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except Exception as e:
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logger.warning(f"Error extracting context from documents in simple mode: {e}")
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# Use direct AI call without document generation pipeline
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request = AiCallRequest(
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prompt=aiPrompt,
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context=context_text if context_text else None,
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options=AiCallOptions(
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resultFormat=output_format_for_call,
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operationType=OperationTypeEnum.DATA_ANALYSE,
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processingMode=ProcessingModeEnum.BASIC
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)
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)
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aiResponse_obj = await self.services.ai.callAi(request)
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# Convert AiCallResponse to AiResponse format
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from modules.datamodels.datamodelWorkflow import AiResponse, AiResponseMetadata
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aiResponse = AiResponse(
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content=aiResponse_obj.content,
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metadata=AiResponseMetadata(
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additionalData={
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"modelName": aiResponse_obj.modelName,
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"priceCHF": aiResponse_obj.priceCHF,
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"processingTime": aiResponse_obj.processingTime,
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"bytesSent": aiResponse_obj.bytesSent,
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"bytesReceived": aiResponse_obj.bytesReceived,
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"errorCount": aiResponse_obj.errorCount
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}
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),
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documents=[] # Simple mode doesn't generate documents
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)
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else:
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# Full mode: use unified callAiContent method
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# Detect image generation from resultType (if provided)
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imageFormats = ["png", "jpg", "jpeg", "gif", "webp"]
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isImageGeneration = normalized_result_type in imageFormats if normalized_result_type else False
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# Build options with correct operationType
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# resultFormat in options can be None - formats will be determined by AI if not provided
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options = AiCallOptions(
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resultFormat=output_format, # Can be None - formats determined by AI
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operationType=OperationTypeEnum.IMAGE_GENERATE if isImageGeneration else OperationTypeEnum.DATA_GENERATE
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)
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# Get generationIntent from parameters (required for DATA_GENERATE)
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# Default to "document" if not provided (most common use case)
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# For code generation, use ai.generateCode action or explicitly pass generationIntent="code"
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generationIntent = parameters.get("generationIntent", "document")
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# Update progress - calling AI
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self.services.chat.progressLogUpdate(operationId, 0.6, "Calling AI")
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# Use unified callAiContent method with BOTH documentList and contentParts
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# Extraction is handled by AI service - no extraction here
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# outputFormat: Optional - if None, formats determined from prompt by AI
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# Note: ContentExtracted documents (from context.extractContent) are now handled
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# automatically in _extractAndPrepareContent() (Phase 5B)
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logger.info(f"ai.process: Calling callAiContent with {len(documentList.references)} document references")
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if documentList.references:
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from modules.datamodels.datamodelDocref import DocumentListReference, DocumentItemReference
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for idx, ref in enumerate(documentList.references):
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if isinstance(ref, DocumentItemReference):
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logger.info(f" Passing reference {idx + 1}: documentId={ref.documentId}")
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elif isinstance(ref, DocumentListReference):
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logger.info(f" Passing reference {idx + 1}: label={ref.label}")
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else:
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logger.info(f" Passing reference {idx + 1}: {ref}")
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aiResponse = await self.services.ai.callAiContent(
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prompt=aiPrompt,
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options=options,
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documentList=documentList, # Pass documentList - AI service handles extraction
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contentParts=contentParts, # Pass contentParts if provided (or None)
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outputFormat=output_format, # Can be None - AI determines from prompt
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parentOperationId=operationId,
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generationIntent=generationIntent # REQUIRED for DATA_GENERATE
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)
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# Update progress - processing result
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self.services.chat.progressLogUpdate(operationId, 0.8, "Processing result")
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# Extract documents from AiResponse
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if aiResponse.documents and len(aiResponse.documents) > 0:
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action_documents = []
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for doc in aiResponse.documents:
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validationMetadata = {
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"actionType": "ai.process",
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"resultType": normalized_result_type,
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"outputFormat": output_format,
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"hasDocuments": True,
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"documentCount": len(aiResponse.documents)
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}
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action_documents.append(ActionDocument(
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documentName=doc.documentName,
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documentData=doc.documentData,
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mimeType=doc.mimeType or output_mime_type,
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sourceJson=getattr(doc, 'sourceJson', None), # Preserve source JSON for structure validation
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validationMetadata=validationMetadata
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))
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final_documents = action_documents
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else:
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# Text response - create document from content
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# If no extension provided, use "txt" (required for filename)
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extension = output_extension.lstrip('.') if output_extension else "txt"
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meaningful_name = self._generateMeaningfulFileName(
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base_name="ai",
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extension=extension,
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action_name="result"
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)
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validationMetadata = {
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"actionType": "ai.process",
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"resultType": normalized_result_type if normalized_result_type else None,
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"outputFormat": output_format if output_format else None,
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"hasDocuments": False,
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"contentType": "text"
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}
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action_document = ActionDocument(
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documentName=meaningful_name,
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documentData=aiResponse.content,
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mimeType=output_mime_type,
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validationMetadata=validationMetadata
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)
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final_documents = [action_document]
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# Complete progress tracking
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self.services.chat.progressLogFinish(operationId, True)
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return ActionResult.isSuccess(documents=final_documents)
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except Exception as e:
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logger.error(f"Error in AI processing: {str(e)}")
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# Complete progress tracking with failure
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try:
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self.services.chat.progressLogFinish(operationId, False)
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except:
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pass # Don't fail on progress logging errors
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return ActionResult.isFailure(
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error=str(e)
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)
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