239 lines
11 KiB
Python
239 lines
11 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 AiCallOptions
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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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elif isinstance(documentListParam, DocumentReferenceList):
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documentList = documentListParam
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elif isinstance(documentListParam, str):
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documentList = DocumentReferenceList.from_string_list([documentListParam])
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elif isinstance(documentListParam, list):
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documentList = DocumentReferenceList.from_string_list(documentListParam)
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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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resultType = parameters.get("resultType", "txt")
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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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# 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_mime_type = "application/octet-stream" # Prefer service-provided mimeType when available
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logger.info(f"Using result type: {resultType} -> {output_extension}")
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# Phase 7.3: Extract content first if documents provided, then use contentParts
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# Check if contentParts are already provided (preferred path)
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contentParts: Optional[List[ContentPart]] = None
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if "contentParts" in parameters:
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contentParts = parameters.get("contentParts")
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if contentParts and not isinstance(contentParts, list):
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# Try to extract from ContentExtracted if it's an ActionDocument
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if hasattr(contentParts, 'parts'):
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contentParts = contentParts.parts
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else:
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logger.warning(f"Invalid contentParts type: {type(contentParts)}, treating as empty")
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contentParts = None
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# If contentParts not provided but documentList is, extract content first
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if not contentParts and documentList.references:
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self.services.chat.progressLogUpdate(operationId, 0.3, "Extracting content from documents")
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# Get ChatDocuments
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chatDocuments = self.services.chat.getChatDocumentsFromDocumentList(documentList)
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if not chatDocuments:
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logger.warning("No documents found in documentList")
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else:
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logger.info(f"Extracting content from {len(chatDocuments)} documents")
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# Prepare extraction options (use defaults if not provided)
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from modules.datamodels.datamodelExtraction import ExtractionOptions, MergeStrategy
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extractionOptions = parameters.get("extractionOptions")
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if not extractionOptions:
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extractionOptions = ExtractionOptions(
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prompt="Extract all content from the document",
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mergeStrategy=MergeStrategy(
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mergeType="concatenate",
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groupBy="typeGroup",
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orderBy="id"
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),
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processDocumentsIndividually=True
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)
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# Extract content using extraction service
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extractedResults = self.services.extraction.extractContent(chatDocuments, extractionOptions)
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# Combine all ContentParts from all extracted results
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contentParts = []
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for extracted in extractedResults:
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if extracted.parts:
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contentParts.extend(extracted.parts)
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logger.info(f"Extracted {len(contentParts)} content parts from {len(extractedResults)} documents")
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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
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imageFormats = ["png", "jpg", "jpeg", "gif", "webp"]
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isImageGeneration = normalized_result_type in imageFormats
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# Build options with correct operationType
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output_format = output_extension.replace('.', '') or 'txt'
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from modules.datamodels.datamodelAi import OperationTypeEnum
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options = AiCallOptions(
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resultFormat=output_format,
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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
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generationIntent = parameters.get("generationIntent")
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# For DATA_GENERATE, generationIntent is REQUIRED
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# If not provided, default to "document" for document formats (xlsx, docx, pdf, txt, html, etc.)
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# This is format-based defaulting, not prompt-based auto-detection
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if options.operationType == OperationTypeEnum.DATA_GENERATE and not generationIntent:
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# Document formats (default to document generation)
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documentFormats = ["xlsx", "docx", "pdf", "txt", "md", "html", "csv", "xml", "json", "pptx"]
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# Code formats (should use ai.generateCode instead, but default to code if ai.process is used)
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codeFormats = ["py", "js", "ts", "java", "cpp", "c", "go", "rs", "rb", "php", "swift", "kt"]
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if normalized_result_type in documentFormats:
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generationIntent = "document"
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logger.info(f"Defaulting generationIntent to 'document' for resultType '{normalized_result_type}'")
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elif normalized_result_type in codeFormats:
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generationIntent = "code"
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logger.info(f"Defaulting generationIntent to 'code' for resultType '{normalized_result_type}'")
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else:
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# Unknown format - default to document (most common use case)
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generationIntent = "document"
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logger.warning(
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f"Unknown resultType '{normalized_result_type}', defaulting generationIntent to 'document'. "
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f"For code generation, use ai.generateCode action or explicitly pass generationIntent='code'."
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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 contentParts (extraction is now separate)
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# ContentParts are already extracted above (or None if no 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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contentParts=contentParts, # Already extracted (or None if no documents)
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outputFormat=output_format,
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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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extension = output_extension.lstrip('.')
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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,
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"outputFormat": output_format,
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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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