429 lines
No EOL
21 KiB
Python
429 lines
No EOL
21 KiB
Python
import logging
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import uuid
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from typing import Any, Dict, List, Optional
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from datetime import datetime, UTC
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import re
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from modules.shared.timezoneUtils import get_utc_timestamp
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from modules.datamodels.datamodelChat import ChatDocument
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from modules.services.serviceGeneration.subDocumentUtility import (
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getFileExtension,
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getMimeTypeFromExtension,
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detectMimeTypeFromContent,
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detectMimeTypeFromData,
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convertDocumentDataToString
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)
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logger = logging.getLogger(__name__)
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class GenerationService:
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def __init__(self, serviceCenter=None):
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# Directly use interfaces from the provided service center (no self.service calls)
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self.serviceCenter = serviceCenter
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self.interfaceDbComponent = getattr(serviceCenter, 'interfaceDbComponent', None) if serviceCenter else None
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self.interfaceDbChat = getattr(serviceCenter, 'interfaceDbChat', None) if serviceCenter else None
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self.workflow = getattr(serviceCenter, 'workflow', None) if serviceCenter else None
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def processActionResultDocuments(self, action_result, action, workflow) -> List[Dict[str, Any]]:
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"""
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Process documents produced by AI actions and convert them to ChatDocument format.
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This function handles AI-generated document data, not document references.
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Returns a list of processed document dictionaries.
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"""
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try:
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# Read documents from the standard documents field (not data.documents)
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documents = action_result.documents if action_result and hasattr(action_result, 'documents') else []
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if not documents:
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logger.info(f"No documents found in action_result.documents for {action.execMethod}.{action.execAction}")
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return []
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logger.info(f"Processing {len(documents)} documents from action_result.documents")
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# Process each document from the AI action result
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processed_documents = []
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for doc in documents:
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processed_doc = self.processSingleDocument(doc, action)
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if processed_doc:
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processed_documents.append(processed_doc)
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logger.info(f"Successfully processed {len(processed_documents)} documents")
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return processed_documents
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except Exception as e:
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logger.error(f"Error processing action result documents: {str(e)}")
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return []
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def processSingleDocument(self, doc: Any, action) -> Optional[Dict[str, Any]]:
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"""Process a single document from action result with simplified logic"""
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try:
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# ActionDocument objects have documentName, documentData, and mimeType
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mime_type = doc.mimeType
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if mime_type == "application/octet-stream":
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content = doc.documentData
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# Detect MIME without relying on a service center
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mime_type = detectMimeTypeFromContent(content, doc.documentName)
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return {
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'fileName': doc.documentName,
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'fileSize': len(str(doc.documentData)),
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'mimeType': mime_type,
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'content': doc.documentData,
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'document': doc
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}
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except Exception as e:
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logger.error(f"Error processing single document: {str(e)}")
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return None
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def createDocumentsFromActionResult(self, action_result, action, workflow, message_id=None) -> List[Any]:
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"""
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Create actual document objects from action result and store them in the system.
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Returns a list of created document objects with proper workflow context.
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"""
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try:
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logger.info(f"Creating documents from action result for {action.execMethod}.{action.execAction}")
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logger.info(f"Action result documents count: {len(action_result.documents) if action_result.documents else 0}")
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processed_docs = self.processActionResultDocuments(action_result, action, workflow)
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logger.info(f"Processed {len(processed_docs)} documents")
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created_documents = []
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for i, doc_data in enumerate(processed_docs):
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try:
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document_name = doc_data['fileName']
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document_data = doc_data['content']
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mime_type = doc_data['mimeType']
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logger.info(f"Creating document {i+1}: {document_name} (mime: {mime_type}, content length: {len(str(document_data))})")
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# Convert document data to string content
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content = convertDocumentDataToString(document_data, getFileExtension(document_name))
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# Skip empty or minimal content
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minimal_content_patterns = ['{}', '[]', 'null', '""', "''"]
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if not content or content.strip() == "" or content.strip() in minimal_content_patterns:
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logger.warning(f"Empty or minimal content for document {document_name}, skipping")
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continue
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logger.info(f"Document {document_name} has content: {len(content)} characters")
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# Normalize file extension based on mime type if missing or incorrect
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try:
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mime_to_ext = {
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"application/vnd.openxmlformats-officedocument.wordprocessingml.document": ".docx",
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"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet": ".xlsx",
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"application/vnd.openxmlformats-officedocument.presentationml.presentation": ".pptx",
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"application/pdf": ".pdf",
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"text/html": ".html",
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"text/markdown": ".md",
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"text/plain": ".txt",
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"application/json": ".json",
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}
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expected_ext = mime_to_ext.get(mime_type)
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if expected_ext:
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if not document_name.lower().endswith(expected_ext):
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# Append/replace extension to match mime type
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if "." in document_name:
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document_name = document_name.rsplit(".", 1)[0] + expected_ext
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else:
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document_name = document_name + expected_ext
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except Exception:
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pass
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# Decide if content is base64-encoded binary (e.g., docx/pdf) or plain text
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base64encoded = False
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try:
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binary_mime_types = {
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"application/vnd.openxmlformats-officedocument.wordprocessingml.document",
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"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
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"application/vnd.openxmlformats-officedocument.presentationml.presentation",
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"application/pdf",
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}
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if isinstance(document_data, str) and mime_type in binary_mime_types:
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base64encoded = True
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except Exception:
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base64encoded = False
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# Create document with file in one step using interfaces directly
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document = self._createDocument(
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fileName=document_name,
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mimeType=mime_type,
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content=content,
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base64encoded=base64encoded,
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messageId=message_id
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)
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if document:
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# Set workflow context on the document if possible
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self._setDocumentWorkflowContext(document, action, workflow)
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created_documents.append(document)
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logger.info(f"Successfully created ChatDocument: {document_name} (ID: {document.id if hasattr(document, 'id') else 'N/A'}, fileId: {document.fileId if hasattr(document, 'fileId') else 'N/A'})")
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else:
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logger.error(f"Failed to create ChatDocument object for {document_name}")
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except Exception as e:
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logger.error(f"Error creating document {doc_data.get('fileName', 'unknown')}: {str(e)}")
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continue
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logger.info(f"Successfully created {len(created_documents)} documents")
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return created_documents
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except Exception as e:
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logger.error(f"Error creating documents from action result: {str(e)}")
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return []
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def _setDocumentWorkflowContext(self, document, action, workflow):
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"""Set workflow context on a document for proper routing and labeling"""
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try:
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# Get current workflow context directly from workflow object
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workflow_context = self._getWorkflowContext(workflow)
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workflow_stats = self._getWorkflowStats(workflow)
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current_round = workflow_context.get('currentRound', 0)
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current_task = workflow_context.get('currentTask', 0)
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current_action = workflow_context.get('currentAction', 0)
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# Try to set workflow context attributes if they exist
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if hasattr(document, 'roundNumber'):
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document.roundNumber = current_round
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if hasattr(document, 'taskNumber'):
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document.taskNumber = current_task
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if hasattr(document, 'actionNumber'):
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document.actionNumber = current_action
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if hasattr(document, 'actionId'):
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document.actionId = action.id if hasattr(action, 'id') else None
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# Set additional workflow metadata if available
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if hasattr(document, 'workflowId'):
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document.workflowId = workflow_stats.get('workflowId', workflow.id if hasattr(workflow, 'id') else None)
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if hasattr(document, 'workflowStatus'):
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document.workflowStatus = workflow_stats.get('workflowStatus', workflow.status if hasattr(workflow, 'status') else 'unknown')
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logger.debug(f"Set workflow context on document: Round {current_round}, Task {current_task}, Action {current_action}")
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logger.debug(f"Document workflow metadata: ID={document.workflowId if hasattr(document, 'workflowId') else 'N/A'}, Status={document.workflowStatus if hasattr(document, 'workflowStatus') else 'N/A'}")
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except Exception as e:
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logger.warning(f"Could not set workflow context on document: {str(e)}")
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def _createDocument(self, fileName: str, mimeType: str, content: str, base64encoded: bool = True, messageId: str = None) -> Optional[ChatDocument]:
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"""Create file and ChatDocument using interfaces without service indirection."""
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try:
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if not self.interfaceDbComponent:
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logger.error("Component interface not available for document creation")
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return None
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# Convert content to bytes
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if base64encoded:
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import base64
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content_bytes = base64.b64decode(content)
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else:
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content_bytes = content.encode('utf-8')
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# Create file and store data
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file_item = self.interfaceDbComponent.createFile(
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name=fileName,
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mimeType=mimeType,
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content=content_bytes
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)
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self.interfaceDbComponent.createFileData(file_item.id, content_bytes)
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# Collect file info
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file_info = self._getFileInfo(file_item.id)
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if not file_info:
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logger.error(f"Could not get file info for fileId: {file_item.id}")
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return None
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# Build ChatDocument
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document = ChatDocument(
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id=str(uuid.uuid4()),
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messageId=messageId or "",
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fileId=file_item.id,
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fileName=file_info.get("fileName", fileName),
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fileSize=file_info.get("size", 0),
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mimeType=file_info.get("mimeType", mimeType)
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)
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# Ensure document can access component interface later
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if hasattr(document, 'setComponentInterface') and self.interfaceDbComponent:
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try:
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document.setComponentInterface(self.interfaceDbComponent)
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except Exception:
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pass
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return document
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except Exception as e:
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logger.error(f"Error creating document: {str(e)}")
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return None
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def _getFileInfo(self, fileId: str) -> Optional[Dict[str, Any]]:
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try:
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if not self.interfaceDbComponent:
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return None
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file_item = self.interfaceDbComponent.getFile(fileId)
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if file_item:
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return {
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"id": file_item.id,
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"fileName": file_item.fileName,
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"size": file_item.fileSize,
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"mimeType": file_item.mimeType,
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"fileHash": getattr(file_item, 'fileHash', None),
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"creationDate": getattr(file_item, 'creationDate', None)
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}
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return None
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except Exception as e:
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logger.error(f"Error getting file info for {fileId}: {str(e)}")
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return None
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def _getWorkflowContext(self, workflow) -> Dict[str, int]:
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try:
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return {
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'currentRound': getattr(workflow, 'currentRound', 0),
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'currentTask': getattr(workflow, 'currentTask', 0),
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'currentAction': getattr(workflow, 'currentAction', 0)
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}
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except Exception:
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return {'currentRound': 0, 'currentTask': 0, 'currentAction': 0}
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def _getWorkflowStats(self, workflow) -> Dict[str, Any]:
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try:
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context = self._getWorkflowContext(workflow)
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return {
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'currentRound': context['currentRound'],
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'currentTask': context['currentTask'],
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'currentAction': context['currentAction'],
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'totalTasks': getattr(workflow, 'totalTasks', 0),
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'totalActions': getattr(workflow, 'totalActions', 0),
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'workflowStatus': getattr(workflow, 'status', 'unknown'),
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'workflowId': getattr(workflow, 'id', 'unknown')
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}
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except Exception:
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return {
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'currentRound': 0,
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'currentTask': 0,
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'currentAction': 0,
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'totalTasks': 0,
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'totalActions': 0,
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'workflowStatus': 'unknown',
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'workflowId': 'unknown'
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}
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async def renderReport(self, extractedContent: Dict[str, Any], outputFormat: str, title: str, userPrompt: str = None, aiService=None) -> tuple[str, str]:
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"""
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Render extracted JSON content to the specified output format.
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Args:
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extractedContent: Structured JSON document from AI extraction
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outputFormat: Target format (html, pdf, docx, txt, md, json, csv, xlsx)
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title: Report title
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userPrompt: User's original prompt for report generation
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aiService: AI service instance for generation prompt creation
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Returns:
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tuple: (rendered_content, mime_type)
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"""
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try:
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# Validate JSON input
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if not isinstance(extractedContent, dict):
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raise ValueError("extractedContent must be a JSON dictionary")
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if "sections" not in extractedContent:
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raise ValueError("extractedContent must contain 'sections' field")
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# DEBUG: dump renderer input to diagnose JSON structure TODO REMOVE
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try:
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import os
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import json
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ts = datetime.now(UTC).strftime("%Y%m%d-%H%M%S")
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debug_root = "./test-chat/ai"
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debug_dir = os.path.join(debug_root, f"render_input_{ts}")
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os.makedirs(debug_dir, exist_ok=True)
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with open(os.path.join(debug_dir, "meta.txt"), "w", encoding="utf-8") as f:
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f.write(f"title: {title}\nformat: {outputFormat}\ncontent_type: {type(extractedContent).__name__}\n")
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with open(os.path.join(debug_dir, "extracted_content.json"), "w", encoding="utf-8") as f:
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json.dump(extractedContent, f, indent=2, ensure_ascii=False)
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except Exception:
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pass
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# Get the appropriate renderer for the format
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renderer = self._getFormatRenderer(outputFormat)
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if not renderer:
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raise ValueError(f"Unsupported output format: {outputFormat}")
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# Generate AI-based generation prompt if AI service is available
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generationPrompt = userPrompt # Default to user prompt
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if aiService and userPrompt:
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try:
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from .subPromptBuilder import buildGenerationPrompt
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generationPrompt = await buildGenerationPrompt(
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outputFormat=outputFormat,
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userPrompt=userPrompt,
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title=title,
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aiService=aiService
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)
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except Exception as e:
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logger.warning(f"Failed to generate AI-based generation prompt: {str(e)}, using user prompt")
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generationPrompt = userPrompt
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# Render the JSON content with AI-generated prompt
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renderedContent, mimeType = await renderer.render(extractedContent, title, generationPrompt, aiService)
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# DEBUG: dump rendered output
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try:
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import os
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with open(os.path.join(debug_dir, "rendered_output.txt"), "w", encoding="utf-8") as f:
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f.write(renderedContent or "")
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except Exception:
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pass
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logger.info(f"Successfully rendered JSON report to {outputFormat} format: {len(renderedContent)} characters")
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return renderedContent, mimeType
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except Exception as e:
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logger.error(f"Error rendering JSON report to {outputFormat}: {str(e)}")
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raise
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async def getExtractionPrompt(self, outputFormat: str, userPrompt: str, title: str, aiService=None) -> str:
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"""
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Get the format-specific extraction prompt for AI content extraction.
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Args:
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outputFormat: Target format (html, pdf, docx, txt, md, json, csv, xlsx)
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userPrompt: User's original prompt for report generation
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title: Report title
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aiService: AI service instance for intent extraction
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Returns:
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str: Format-specific prompt for AI extraction
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"""
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try:
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# Get the appropriate renderer for the format
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renderer = self._getFormatRenderer(outputFormat)
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if not renderer:
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raise ValueError(f"Unsupported output format: {outputFormat}")
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# Build centralized prompt with generic rules + format-specific guidelines
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from .subPromptBuilder import buildExtractionPrompt
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extractionPrompt = await buildExtractionPrompt(
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outputFormat=outputFormat,
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renderer=renderer,
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userPrompt=userPrompt,
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title=title,
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aiService=aiService
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)
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logger.info(f"Generated {outputFormat}-specific extraction prompt: {len(extractionPrompt)} characters")
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return extractionPrompt
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except Exception as e:
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logger.error(f"Error getting extraction prompt for {outputFormat}: {str(e)}")
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raise
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def _getFormatRenderer(self, output_format: str):
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"""Get the appropriate renderer for the specified format using auto-discovery."""
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try:
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from .renderers.registry import get_renderer
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renderer = get_renderer(output_format)
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if renderer:
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return renderer
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# Fallback to text renderer if no specific renderer found
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logger.warning(f"No renderer found for format {output_format}, falling back to text")
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fallback_renderer = get_renderer('text')
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if fallback_renderer:
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return fallback_renderer
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logger.error("Even text renderer fallback failed")
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return None
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except Exception as e:
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logger.error(f"Error getting renderer for {output_format}: {str(e)}")
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return None |