fix:rebase from int
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commit
b6724a797f
2 changed files with 106 additions and 59 deletions
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@ -54,8 +54,9 @@ async def process(self, parameters: Dict[str, Any]) -> ActionResult:
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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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resultType = parameters.get("resultType", "txt")
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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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@ -63,18 +64,11 @@ async def process(self, parameters: Dict[str, Any]) -> ActionResult:
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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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# Determine output extension and default MIME type without duplicating service logic
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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}")
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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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logger.info(f"Using result type: {resultType} -> {output_extension}, simpleMode: {simpleMode}")
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output_mime_type = "application/octet-stream" # Prefer service-provided mimeType when available
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@ -96,37 +90,98 @@ async def process(self, parameters: Dict[str, Any]) -> ActionResult:
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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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# 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
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output_format = output_extension.replace('.', '') or 'txt'
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# Build options with correct operationType
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from modules.datamodels.datamodelAi import OperationTypeEnum
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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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# 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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from modules.datamodels.datamodelAi import AiCallRequest, OperationTypeEnum, ProcessingModeEnum
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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,
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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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# 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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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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"priceUsd": aiResponse_obj.priceUsd,
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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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options = AiCallOptions(
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resultFormat=output_format
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)
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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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# Use unified callAiContent method
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# If contentParts provided (pre-extracted), use them directly
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# Otherwise, pass documentList and let callAiContent handle Phases 5A-5E internally
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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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if contentParts:
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# Pre-extracted ContentParts - use them directly
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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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contentParts=contentParts, # Pre-extracted ContentParts
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outputFormat=output_format,
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parentOperationId=operationId
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)
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else:
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# Pass documentList - callAiContent handles Phases 5A-5E internally
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# This includes automatic detection of ContentExtracted documents
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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, # callAiContent macht Phasen 5A-5E
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outputFormat=output_format,
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parentOperationId=operationId
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)
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# Update progress - processing result
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@ -60,24 +60,16 @@ class MethodAi(MethodBase):
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frontendOptions=["txt", "json", "md", "csv", "xml", "html", "pdf", "docx", "xlsx", "pptx", "png", "jpg"],
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required=False,
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default="txt",
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description="Output file extension. Optional: if omitted, formats are determined from prompt by AI. Default \"txt\" is validation fallback only. With per-document format determination, AI can determine different formats for different documents based on prompt."
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description="Output file extension. All output documents will use this format"
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),
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"generationIntent": WorkflowActionParameter(
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name="generationIntent",
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type="str",
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frontendType=FrontendType.SELECT,
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frontendOptions=["document", "code", "image"],
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"simpleMode": WorkflowActionParameter(
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name="simpleMode",
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type="bool",
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frontendType=FrontendType.CHECKBOX,
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required=False,
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default="document",
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description="Explicit generation intent (\"document\" | \"code\" | \"image\"). Required for DATA_GENERATE operations. Defaults to \"document\" if not provided. For code generation, use ai.generateCode action or explicitly pass generationIntent=\"code\". For IMAGE_GENERATE operations, this parameter is ignored."
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),
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"contentParts": WorkflowActionParameter(
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name="contentParts",
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type="List[ContentPart]",
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frontendType=FrontendType.HIDDEN,
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required=False,
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description="Pre-extracted content parts (internal parameter, typically passed between actions). If provided, these will be used instead of extracting from documentList. Can be a list of ContentPart objects or an object with a 'parts' attribute."
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),
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default=False,
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description="If true, uses fast simple AI call without document generation pipeline. Use for chatbot responses and simple text generation."
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)
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},
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execute=process.__get__(self, self.__class__)
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),
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