783 lines
35 KiB
Python
783 lines
35 KiB
Python
from typing import Dict, Any, List, Optional
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import logging
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import uuid
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import asyncio
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import json
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from modules.datamodels.datamodelChat import (
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UserInputRequest,
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ChatMessage,
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ChatWorkflow,
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ChatDocument,
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WorkflowModeEnum
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)
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from modules.datamodels.datamodelChat import TaskContext
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from modules.workflows.processing.workflowProcessor import WorkflowProcessor
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from modules.workflows.processing.shared.stateTools import WorkflowStoppedException, checkWorkflowStopped
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logger = logging.getLogger(__name__)
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class WorkflowManager:
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"""Manager for workflow processing and coordination"""
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def __init__(self, services):
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self.services = services
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self.workflowProcessor = None
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# Exported functions
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async def workflowStart(self, userInput: UserInputRequest, workflowMode: WorkflowModeEnum, workflowId: Optional[str] = None) -> ChatWorkflow:
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"""Starts a new workflow or continues an existing one, then launches processing."""
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try:
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# Debug log to check workflowMode parameter
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logger.info(f"WorkflowManager received workflowMode: {workflowMode}")
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currentTime = self.services.utils.timestampGetUtc()
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if workflowId:
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workflow = self.services.chat.getWorkflow(workflowId)
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if not workflow:
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raise ValueError(f"Workflow {workflowId} not found")
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# Store workflow in services for reference (this is the ChatWorkflow object)
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self.services.workflow = workflow
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if workflow.status == "running":
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logger.info(f"Stopping running workflow {workflowId} before processing new prompt")
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workflow.status = "stopped"
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workflow.lastActivity = currentTime
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self.services.chat.updateWorkflow(workflowId, {
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"status": "stopped",
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"lastActivity": currentTime
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})
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self.services.chat.storeLog(workflow, {
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"message": "Workflow stopped for new prompt",
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"type": "info",
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"status": "stopped",
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"progress": 100
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})
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newRound = workflow.currentRound + 1
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self.services.chat.updateWorkflow(workflowId, {
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"status": "running",
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"lastActivity": currentTime,
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"currentRound": newRound,
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"workflowMode": workflowMode # Update workflow mode for existing workflows
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})
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# Reflect updates on the in-memory object without reloading
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workflow.status = "running"
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workflow.lastActivity = currentTime
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workflow.currentRound = newRound
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workflow.workflowMode = workflowMode
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self.services.chat.storeLog(workflow, {
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"message": f"Workflow resumed (round {workflow.currentRound}) with mode: {workflowMode}",
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"type": "info",
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"status": "running",
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"progress": 0
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})
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else:
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workflowData = {
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"name": "New Workflow",
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"status": "running",
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"startedAt": currentTime,
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"lastActivity": currentTime,
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"currentRound": 1,
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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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"mandateId": self.services.user.mandateId,
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"messageIds": [],
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"workflowMode": workflowMode,
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"maxSteps": 5 if workflowMode == WorkflowModeEnum.WORKFLOW_DYNAMIC else 1, # Set maxSteps for Dynamic mode
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}
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workflow = self.services.chat.createWorkflow(workflowData)
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logger.info(f"Created workflow with mode: {getattr(workflow, 'workflowMode', 'NOT_SET')}")
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logger.info(f"Workflow data passed: {workflowData.get('workflowMode', 'NOT_IN_DATA')}")
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# Store workflow in services (this is the ChatWorkflow object)
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self.services.workflow = workflow
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# Start workflow processing asynchronously
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asyncio.create_task(self._workflowProcess(userInput))
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return workflow
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except Exception as e:
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logger.error(f"Error starting workflow: {str(e)}")
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raise
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async def workflowStop(self, workflowId: str) -> ChatWorkflow:
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"""Stops a running workflow."""
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try:
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workflow = self.services.chat.getWorkflow(workflowId)
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if not workflow:
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raise ValueError(f"Workflow {workflowId} not found")
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# Store workflow in services (this is the ChatWorkflow object)
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self.services.workflow = workflow
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workflow.status = "stopped"
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workflow.lastActivity = self.services.utils.timestampGetUtc()
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self.services.chat.updateWorkflow(workflowId, {
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"status": "stopped",
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"lastActivity": workflow.lastActivity
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})
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self.services.chat.storeLog(workflow, {
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"message": "Workflow stopped",
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"type": "warning",
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"status": "stopped",
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"progress": 100
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})
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return workflow
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except Exception as e:
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logger.error(f"Error stopping workflow: {str(e)}")
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raise
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# Main processor
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async def _workflowProcess(self, userInput: UserInputRequest) -> None:
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"""Process a workflow with user input"""
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try:
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# Store the current user prompt in services for easy access throughout the workflow
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self.services.rawUserPrompt = userInput.prompt
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self.services.currentUserPrompt = userInput.prompt
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# Reset progress logger for new workflow
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self.services.chat._progressLogger = None
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self.workflowProcessor = WorkflowProcessor(self.services)
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await self._sendFirstMessage(userInput)
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task_plan = await self._planTasks(userInput)
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await self._executeTasks(task_plan)
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await self._processWorkflowResults()
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except WorkflowStoppedException:
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self._handleWorkflowStop()
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except Exception as e:
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self._handleWorkflowError(e)
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# Helper functions
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async def _sendFirstMessage(self, userInput: UserInputRequest) -> None:
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"""Send first message to start workflow"""
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try:
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workflow = self.services.workflow
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checkWorkflowStopped(self.services)
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# Create initial message using interface
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# For first user message, include round info in the user context label
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roundNum = workflow.currentRound
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contextLabel = f"round{roundNum}_usercontext"
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messageData = {
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"workflowId": workflow.id,
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"role": "user",
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"message": userInput.prompt,
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"status": "first",
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"sequenceNr": 1,
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"publishedAt": self.services.utils.timestampGetUtc(),
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"documentsLabel": contextLabel,
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"documents": [],
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# Add workflow context fields
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"roundNumber": workflow.currentRound,
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"taskNumber": 0,
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"actionNumber": 0,
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# Add progress status
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"taskProgress": "pending",
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"actionProgress": "pending"
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}
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# Analyze the user's input to detect language, normalize request, extract intent, and offload bulky context into documents
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createdDocs = []
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try:
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analyzerPrompt = (
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"You are an input analyzer. From the user's message, perform ALL of the following in one pass:\n"
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"1) detectedLanguage: detect ISO 639-1 language code (e.g., de, en).\n"
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"2) normalizedRequest: full, explicit restatement of the user's request in the detected language; do NOT summarize; preserve ALL constraints and details.\n"
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"3) intent: concise single-paragraph core request in the detected language for high-level routing.\n"
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"4) contextItems: supportive data blocks to attach as separate documents if significantly larger than the intent (large literal content, long lists/tables, code/JSON blocks, transcripts, CSV fragments, detailed specs). Keep URLs in the intent unless they embed large pasted content.\n\n"
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"Rules:\n"
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"- If total content (intent + data) is < 10% of model max tokens, do not extract; return empty contextItems and keep intent compact and self-contained.\n"
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"- If content exceeds that threshold, move bulky parts into contextItems; keep intent short and clear.\n"
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"- Preserve critical references (URLs, filenames) in intent.\n"
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"- Normalize to the primary detected language if mixed-language.\n\n"
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"Return ONLY JSON (no markdown) with this shape:\n"
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"{\n"
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" \"detectedLanguage\": \"de|en|fr|it|...\",\n"
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" \"normalizedRequest\": \"Full explicit instruction in detected language\",\n"
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" \"intent\": \"Concise normalized request...\",\n"
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" \"contextItems\": [\n"
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" {\n"
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" \"title\": \"User context 1\",\n"
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" \"mimeType\": \"text/plain\",\n"
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" \"content\": \"Full extracted content block here\"\n"
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" }\n"
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" ]\n"
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"}\n\n"
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f"User message:\n{self.services.utils.sanitizePromptContent(userInput.prompt, 'userinput')}"
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)
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# Call AI analyzer (planning call - will use static parameters)
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aiResponse = await self.services.ai.callAiPlanning(
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prompt=analyzerPrompt,
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placeholders=None,
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debugType="userintention"
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)
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detectedLanguage = None
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normalizedRequest = None
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intentText = userInput.prompt
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contextItems = []
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# Parse analyzer response (JSON expected)
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try:
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jsonStart = aiResponse.find('{') if aiResponse else -1
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jsonEnd = aiResponse.rfind('}') + 1 if aiResponse else 0
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if jsonStart != -1 and jsonEnd > jsonStart:
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parsed = json.loads(aiResponse[jsonStart:jsonEnd])
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detectedLanguage = parsed.get('detectedLanguage') or None
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normalizedRequest = parsed.get('normalizedRequest') or None
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if parsed.get('intent'):
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intentText = parsed.get('intent')
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contextItems = parsed.get('contextItems') or []
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except Exception:
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contextItems = []
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# Update services state
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if detectedLanguage and isinstance(detectedLanguage, str):
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self._setUserLanguage(detectedLanguage)
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try:
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setattr(self.services, 'currentUserLanguage', detectedLanguage)
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except Exception:
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pass
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self.services.currentUserPrompt = intentText or userInput.prompt
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try:
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if normalizedRequest:
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setattr(self.services, 'currentUserPromptNormalized', normalizedRequest)
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if contextItems is not None:
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setattr(self.services, 'currentUserContextItems', contextItems)
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except Exception:
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pass
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# Create documents for context items
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if contextItems and isinstance(contextItems, list):
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for idx, item in enumerate(contextItems):
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try:
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title = item.get('title') if isinstance(item, dict) else None
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mime = item.get('mimeType') if isinstance(item, dict) else None
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content = item.get('content') if isinstance(item, dict) else None
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if not content:
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continue
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fileName = (title or f"user_context_{idx+1}.txt").strip()
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mimeType = (mime or "text/plain").strip()
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# Neutralize content before storing if neutralization is enabled
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contentBytes = content.encode('utf-8')
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contentBytes = await self._neutralizeContentIfEnabled(contentBytes, mimeType)
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# Create file in component storage
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fileItem = self.services.interfaceDbComponent.createFile(
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name=fileName,
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mimeType=mimeType,
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content=contentBytes
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)
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# Persist file data
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self.services.interfaceDbComponent.createFileData(fileItem.id, contentBytes)
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# Collect file info
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fileInfo = self.services.chat.getFileInfo(fileItem.id)
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from modules.datamodels.datamodelChat import ChatDocument
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doc = ChatDocument(
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fileId=fileItem.id,
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fileName=fileInfo.get("fileName", fileName) if fileInfo else fileName,
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fileSize=fileInfo.get("size", len(contentBytes)) if fileInfo else len(contentBytes),
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mimeType=fileInfo.get("mimeType", mimeType) if fileInfo else mimeType
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)
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createdDocs.append(doc)
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except Exception:
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continue
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except Exception as e:
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logger.warning(f"Prompt analysis failed or skipped: {str(e)}")
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# Process user-uploaded documents (fileIds) and combine with context documents
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if userInput.listFileId:
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try:
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userDocs = await self._processFileIds(userInput.listFileId, None)
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if userDocs:
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createdDocs.extend(userDocs)
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except Exception as e:
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logger.warning(f"Failed to process user fileIds: {e}")
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# Finally, persist and bind the first message with combined documents (context + user)
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self.services.chat.storeMessageWithDocuments(workflow, messageData, createdDocs)
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except Exception as e:
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logger.error(f"Error sending first message: {str(e)}")
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raise
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async def _planTasks(self, userInput: UserInputRequest):
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"""Generate task plan for workflow execution"""
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workflow = self.services.workflow
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handling = self.workflowProcessor
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# Generate task plan first (shared for both modes)
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taskPlan = await handling.generateTaskPlan(userInput.prompt, workflow)
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if not taskPlan or not taskPlan.tasks:
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raise Exception("No tasks generated in task plan.")
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workflowMode = getattr(workflow, 'workflowMode')
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logger.info(f"Workflow object attributes: {workflow.__dict__ if hasattr(workflow, '__dict__') else 'No __dict__'}")
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logger.info(f"Executing workflow mode={workflowMode} with {len(taskPlan.tasks)} tasks")
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return taskPlan
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async def _executeTasks(self, taskPlan) -> None:
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"""Execute all tasks in the task plan and update workflow status."""
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workflow = self.services.workflow
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handling = self.workflowProcessor
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totalTasks = len(taskPlan.tasks)
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allTaskResults: List = []
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previousResults: List[str] = []
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for idx, taskStep in enumerate(taskPlan.tasks):
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currentTaskIndex = idx + 1
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logger.info(f"Task {currentTaskIndex}/{totalTasks}: {taskStep.objective}")
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# Build TaskContext (mode-specific behavior is inside WorkflowProcessor)
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taskContext = TaskContext(
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taskStep=taskStep,
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workflow=workflow,
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workflowId=workflow.id,
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availableDocuments=None,
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availableConnections=None,
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previousResults=previousResults,
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previousHandover=None,
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improvements=[],
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retryCount=0,
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previousActionResults=[],
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previousReviewResult=None,
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isRegeneration=False,
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failurePatterns=[],
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failedActions=[],
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successfulActions=[],
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criteriaProgress={
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'met_criteria': set(),
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'unmet_criteria': set(),
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'attempt_history': []
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}
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)
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taskResult = await handling.executeTask(taskStep, workflow, taskContext, currentTaskIndex, totalTasks)
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handoverData = await handling.prepareTaskHandover(taskStep, [], taskResult, workflow)
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allTaskResults.append({
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'taskStep': taskStep,
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'taskResult': taskResult,
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'handoverData': handoverData
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})
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if taskResult.success and taskResult.feedback:
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previousResults.append(taskResult.feedback)
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# Mark workflow as completed; error/stop cases update status elsewhere
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workflow.status = "completed"
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return None
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async def _processWorkflowResults(self) -> None:
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"""Process workflow results based on workflow status and create appropriate messages"""
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try:
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workflow = self.services.workflow
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try:
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checkWorkflowStopped(self.services)
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except WorkflowStoppedException:
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logger.info(f"Workflow {workflow.id} was stopped during result processing")
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# Create final stopped message
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stoppedMessage = {
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"workflowId": workflow.id,
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"role": "assistant",
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"message": "🛑 Workflow stopped by user",
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"status": "last",
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"sequenceNr": len(workflow.messages) + 1,
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"publishedAt": self.services.utils.timestampGetUtc(),
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"documentsLabel": "workflow_stopped",
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"documents": [],
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# Add workflow context fields
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"roundNumber": workflow.currentRound,
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"taskNumber": 0,
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"actionNumber": 0,
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# Add progress status
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"taskProgress": "stopped",
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"actionProgress": "stopped"
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}
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self.services.chat.storeMessageWithDocuments(workflow, stoppedMessage, [])
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# Update workflow status to stopped
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workflow.status = "stopped"
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workflow.lastActivity = self.services.utils.timestampGetUtc()
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self.services.chat.updateWorkflow(workflow.id, {
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"status": "stopped",
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"lastActivity": workflow.lastActivity
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})
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return
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if workflow.status == 'stopped':
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# Create stopped message
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stopped_message = {
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"workflowId": workflow.id,
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"role": "assistant",
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"message": "🛑 Workflow stopped by user",
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"status": "last",
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"sequenceNr": len(workflow.messages) + 1,
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"publishedAt": self.services.utils.timestampGetUtc(),
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"documentsLabel": "workflow_stopped",
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"documents": [],
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# Add workflow context fields
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"roundNumber": workflow.currentRound,
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"taskNumber": 0,
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"actionNumber": 0,
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# Add progress status
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"taskProgress": "stopped",
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"actionProgress": "stopped"
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}
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self.services.chat.storeMessageWithDocuments(workflow, stopped_message, [])
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# Update workflow status to stopped
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workflow.status = "stopped"
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workflow.lastActivity = self.services.utils.timestampGetUtc()
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self.services.chat.updateWorkflow(workflow.id, {
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"status": "stopped",
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"lastActivity": workflow.lastActivity,
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"totalTasks": workflow.totalTasks,
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"totalActions": workflow.totalActions
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})
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# Add stopped log entry
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self.services.chat.storeLog(workflow, {
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"message": "Workflow stopped by user",
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"type": "warning",
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"status": "stopped",
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"progress": 100
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})
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return
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elif workflow.status == 'failed':
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# Create error message
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errorMessage = {
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"workflowId": workflow.id,
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"role": "assistant",
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"message": f"Workflow failed: {'Unknown error'}",
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"status": "last",
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"sequenceNr": len(workflow.messages) + 1,
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"publishedAt": self.services.utils.timestampGetUtc(),
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"documentsLabel": "workflow_failure",
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"documents": [],
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# Add workflow context fields
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"roundNumber": workflow.currentRound,
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"taskNumber": 0,
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"actionNumber": 0,
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# Add progress status
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"taskProgress": "fail",
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"actionProgress": "fail"
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}
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self.services.chat.storeMessageWithDocuments(workflow, errorMessage, [])
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# Update workflow status to failed
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workflow.status = "failed"
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workflow.lastActivity = self.services.utils.timestampGetUtc()
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self.services.chat.updateWorkflow(workflow.id, {
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"status": "failed",
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"lastActivity": workflow.lastActivity,
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"totalTasks": workflow.totalTasks,
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"totalActions": workflow.totalActions
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})
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# Add failed log entry
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self.services.chat.storeLog(workflow, {
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"message": "Workflow failed: Unknown error",
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"type": "error",
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"status": "failed",
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"progress": 100
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})
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return
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# For successful workflows, send detailed completion message
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await self._sendLastMessage()
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except Exception as e:
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logger.error(f"Error processing workflow results: {str(e)}")
|
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# Create error message
|
|
error_message = {
|
|
"workflowId": workflow.id,
|
|
"role": "assistant",
|
|
"message": f"Error processing workflow results: {str(e)}",
|
|
"status": "last",
|
|
"sequenceNr": len(workflow.messages) + 1,
|
|
"publishedAt": self.services.utils.timestampGetUtc(),
|
|
"documentsLabel": "workflow_error",
|
|
"documents": [],
|
|
# Add workflow context fields
|
|
"roundNumber": workflow.currentRound,
|
|
"taskNumber": 0,
|
|
"actionNumber": 0,
|
|
# Add progress status
|
|
"taskProgress": "fail",
|
|
"actionProgress": "fail"
|
|
}
|
|
self.services.chat.storeMessageWithDocuments(workflow, error_message, [])
|
|
|
|
# Update workflow status to failed
|
|
workflow.status = "failed"
|
|
workflow.lastActivity = self.services.utils.timestampGetUtc()
|
|
self.services.chat.updateWorkflow(workflow.id, {
|
|
"status": "failed",
|
|
"lastActivity": workflow.lastActivity,
|
|
"totalTasks": workflow.totalTasks,
|
|
"totalActions": workflow.totalActions
|
|
})
|
|
|
|
async def _sendLastMessage(self) -> None:
|
|
"""Send last message to complete workflow (only for successful workflows)"""
|
|
try:
|
|
workflow = self.services.workflow
|
|
# Safety check: ensure this is only called for successful workflows
|
|
if workflow.status in ['stopped', 'failed']:
|
|
logger.warning(f"Attempted to send last message for {workflow.status} workflow {workflow.id}")
|
|
return
|
|
|
|
# Generate feedback
|
|
feedback = await self._generateWorkflowFeedback()
|
|
|
|
# Create last message using interface
|
|
messageData = {
|
|
"workflowId": workflow.id,
|
|
"role": "assistant",
|
|
"message": feedback,
|
|
"status": "last",
|
|
"sequenceNr": len(workflow.messages) + 1,
|
|
"publishedAt": self.services.utils.timestampGetUtc(),
|
|
"documentsLabel": "workflow_feedback",
|
|
"documents": [],
|
|
# Add workflow context fields
|
|
"roundNumber": workflow.currentRound,
|
|
"taskNumber": 0,
|
|
"actionNumber": 0,
|
|
# Add progress status
|
|
"taskProgress": "success",
|
|
"actionProgress": "success"
|
|
}
|
|
|
|
# Create message using interface
|
|
self.services.chat.storeMessageWithDocuments(workflow, messageData, [])
|
|
|
|
# Update workflow status to completed
|
|
workflow.status = "completed"
|
|
workflow.lastActivity = self.services.utils.timestampGetUtc()
|
|
|
|
# Update workflow in database
|
|
self.services.chat.updateWorkflow(workflow.id, {
|
|
"status": "completed",
|
|
"lastActivity": workflow.lastActivity
|
|
})
|
|
|
|
# Add completion log entry
|
|
self.services.chat.storeLog(workflow, {
|
|
"message": "Workflow completed",
|
|
"type": "success",
|
|
"status": "completed",
|
|
"progress": 100
|
|
})
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error sending last message: {str(e)}")
|
|
raise
|
|
|
|
async def _generateWorkflowFeedback(self) -> str:
|
|
"""Generate feedback message for workflow completion"""
|
|
try:
|
|
workflow = self.services.workflow
|
|
checkWorkflowStopped(self.services)
|
|
|
|
# Count messages by role
|
|
userMessages = [msg for msg in workflow.messages if msg.role == 'user']
|
|
assistantMessages = [msg for msg in workflow.messages if msg.role == 'assistant']
|
|
|
|
# Generate summary feedback
|
|
feedback = f"Workflow completed.\n\n"
|
|
feedback += f"Processed {len(userMessages)} user inputs and generated {len(assistantMessages)} responses.\n"
|
|
|
|
# Add final status
|
|
if workflow.status == "completed":
|
|
feedback += "All tasks completed successfully."
|
|
elif workflow.status == "partial":
|
|
feedback += "Some tasks completed with partial success."
|
|
else:
|
|
feedback += f"Workflow status: {workflow.status}"
|
|
|
|
return feedback
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating workflow feedback: {str(e)}")
|
|
return "Workflow processing completed."
|
|
|
|
def _handleWorkflowStop(self) -> None:
|
|
"""Handle workflow stop exception"""
|
|
workflow = self.services.workflow
|
|
logger.info("Workflow stopped by user")
|
|
|
|
# Update workflow status to stopped
|
|
workflow.status = "stopped"
|
|
workflow.lastActivity = self.services.utils.timestampGetUtc()
|
|
self.services.chat.updateWorkflow(workflow.id, {
|
|
"status": "stopped",
|
|
"lastActivity": workflow.lastActivity,
|
|
"totalTasks": workflow.totalTasks,
|
|
"totalActions": workflow.totalActions
|
|
})
|
|
|
|
# Create final stopped message
|
|
stopped_message = {
|
|
"workflowId": workflow.id,
|
|
"role": "assistant",
|
|
"message": "🛑 Workflow stopped by user",
|
|
"status": "last",
|
|
"sequenceNr": len(workflow.messages) + 1,
|
|
"publishedAt": self.services.utils.timestampGetUtc(),
|
|
"documentsLabel": "workflow_stopped",
|
|
"documents": [],
|
|
# Add workflow context fields
|
|
"roundNumber": workflow.currentRound,
|
|
"taskNumber": 0,
|
|
"actionNumber": 0,
|
|
# Add progress status
|
|
"taskProgress": "pending",
|
|
"actionProgress": "pending"
|
|
}
|
|
self.services.chat.storeMessageWithDocuments(workflow, stopped_message, [])
|
|
|
|
# Add log entry
|
|
self.services.chat.storeLog(workflow, {
|
|
"message": "Workflow stopped by user",
|
|
"type": "warning",
|
|
"status": "stopped",
|
|
"progress": 100
|
|
})
|
|
|
|
def _handleWorkflowError(self, error: Exception) -> None:
|
|
"""Handle workflow error exception"""
|
|
workflow = self.services.workflow
|
|
logger.error(f"Workflow processing error: {str(error)}")
|
|
|
|
# Update workflow status to failed
|
|
workflow.status = "failed"
|
|
workflow.lastActivity = self.services.utils.timestampGetUtc()
|
|
self.services.chat.updateWorkflow(workflow.id, {
|
|
"status": "failed",
|
|
"lastActivity": workflow.lastActivity,
|
|
"totalTasks": workflow.totalTasks,
|
|
"totalActions": workflow.totalActions
|
|
})
|
|
|
|
# Create error message
|
|
error_message = {
|
|
"workflowId": workflow.id,
|
|
"role": "assistant",
|
|
"message": f"Workflow processing failed: {str(error)}",
|
|
"status": "last",
|
|
"sequenceNr": len(workflow.messages) + 1,
|
|
"publishedAt": self.services.utils.timestampGetUtc(),
|
|
"documentsLabel": "workflow_error",
|
|
"documents": [],
|
|
# Add workflow context fields
|
|
"roundNumber": workflow.currentRound,
|
|
"taskNumber": 0,
|
|
"actionNumber": 0,
|
|
# Add progress status
|
|
"taskProgress": "fail",
|
|
"actionProgress": "fail"
|
|
}
|
|
self.services.chat.storeMessageWithDocuments(workflow, error_message, [])
|
|
|
|
# Add error log entry
|
|
self.services.chat.storeLog(workflow, {
|
|
"message": f"Workflow failed: {str(error)}",
|
|
"type": "error",
|
|
"status": "failed",
|
|
"progress": 100
|
|
})
|
|
|
|
raise
|
|
|
|
async def _processFileIds(self, fileIds: List[str], messageId: str = None) -> List[ChatDocument]:
|
|
"""Process file IDs from existing files and return ChatDocument objects"""
|
|
documents = []
|
|
for fileId in fileIds:
|
|
try:
|
|
# Get file info from chat service
|
|
fileInfo = self.services.chat.getFileInfo(fileId)
|
|
if fileInfo:
|
|
# Create document directly with all file attributes
|
|
document = ChatDocument(
|
|
id=str(uuid.uuid4()),
|
|
messageId=messageId or "", # Use provided messageId or empty string as fallback
|
|
fileId=fileId,
|
|
fileName=fileInfo.get("fileName", "unknown"),
|
|
fileSize=fileInfo.get("size", 0),
|
|
mimeType=fileInfo.get("mimeType", "application/octet-stream")
|
|
)
|
|
documents.append(document)
|
|
logger.info(f"Processed file ID {fileId} -> {document.fileName}")
|
|
else:
|
|
logger.warning(f"No file info found for file ID {fileId}")
|
|
except Exception as e:
|
|
logger.error(f"Error processing file ID {fileId}: {str(e)}")
|
|
return documents
|
|
|
|
|
|
def _setUserLanguage(self, language: str) -> None:
|
|
"""Set user language for the service center"""
|
|
self.services.user.language = language
|
|
|
|
async def _neutralizeContentIfEnabled(self, contentBytes: bytes, mimeType: str) -> bytes:
|
|
"""Neutralize content if neutralization is enabled in user settings"""
|
|
try:
|
|
# Check if neutralization is enabled
|
|
config = self.services.neutralization.getConfig()
|
|
if not config or not config.enabled:
|
|
return contentBytes
|
|
|
|
# Decode content to text for neutralization
|
|
try:
|
|
textContent = contentBytes.decode('utf-8')
|
|
except UnicodeDecodeError:
|
|
# Try alternative encodings
|
|
for enc in ['latin-1', 'cp1252', 'iso-8859-1']:
|
|
try:
|
|
textContent = contentBytes.decode(enc)
|
|
break
|
|
except UnicodeDecodeError:
|
|
continue
|
|
else:
|
|
# If unable to decode, return original bytes (binary content)
|
|
logger.debug(f"Unable to decode content for neutralization, skipping: {mimeType}")
|
|
return contentBytes
|
|
|
|
# Neutralize the text content
|
|
# Note: The neutralization service should use names from config when processing
|
|
result = self.services.neutralization.processText(textContent)
|
|
if result and 'neutralized_text' in result:
|
|
neutralizedText = result['neutralized_text']
|
|
# Encode back to bytes using the same encoding
|
|
try:
|
|
return neutralizedText.encode('utf-8')
|
|
except Exception as e:
|
|
logger.warning(f"Error encoding neutralized text: {str(e)}")
|
|
return contentBytes
|
|
else:
|
|
logger.warning("Neutralization did not return neutralized_text")
|
|
return contentBytes
|
|
except Exception as e:
|
|
logger.error(f"Error during content neutralization: {str(e)}")
|
|
# Return original content on error
|
|
return contentBytes
|