1081 lines
44 KiB
Python
1081 lines
44 KiB
Python
"""
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Workflow Manager Module for state machine-based backend chat workflow.
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Implements the state machine as defined in the documentation.
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"""
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import asyncio
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import os
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import logging
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import json
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import uuid
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import base64
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from datetime import datetime, UTC, timedelta
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from typing import Dict, Any, List, Optional, Union, Tuple, Callable, TypedDict, Protocol
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import time
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from modules.shared.mimeUtils import isTextMimeType
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# Required imports
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from modules.workflow.agentManager import getAgentManager
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from modules.interfaces.serviceChatModel import (
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UserInputRequest, ChatWorkflow, ChatMessage, ChatLog,
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ChatDocument, ChatStat, Task, AgentResponse, AgentProfile
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)
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# Configure logger
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logger = logging.getLogger(__name__)
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# Global settings for the workflow management
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GLOBAL_WORKFLOW_LABELS = {
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"systemName": "AI Assistant", # Default system name for logs
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"workflowStatusMessages": {
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"init": "Workflow initialized",
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"running": "Running workflow",
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"waiting": "Waiting for input",
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"completed": "Workflow completed successfully",
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"stopped": "Workflow stopped by user",
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"failed": "Error in workflow"
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}
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}
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class WorkflowStoppedException(Exception):
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"""Exception raised when a workflow is forcibly stopped with function checkExitCriteria() """
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pass
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class ServiceObject:
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"""Service container for agent operations."""
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def __init__(self):
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"""Initialize the service container."""
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# Core service components
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self.logAdd = None
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self.workflow = None # Direct reference to workflow object
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self.user = None
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self.functions = None
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self.model = None
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# Components initialized by agentManager
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self.document = None
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self.task = None
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self.data = None
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class WorkflowManager:
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"""Manages the execution of workflows and their associated agents."""
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def __init__(self, service: ServiceObject):
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"""Initialize the workflow manager with service container."""
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# Store service container
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self.service = service
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# Initialize ALL service components
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self.service.logAdd = self.logAdd
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# Initialize workflow as direct reference
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self.service.workflow = self # Direct reference to workflow manager
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# Initialize user data
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self.service.user = {
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'id': None,
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'name': None,
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'language': 'en'
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}
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# Initialize generic functions
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self.service.functions = {
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'forEach': lambda items, action: [action(item) for item in items],
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'while': lambda condition, action: [action() for _ in iter(lambda: condition(), False)]
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}
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# Initialize AI model operations
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self.service.model = {
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'callAiBasic': self._callAiBasic,
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'callAiComplex': self._callAiComplex,
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'callAiImage': self._callAiImage
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}
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# Initialize document operations (will be populated by agentManager)
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self.service.document = {}
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# Initialize task operations (will be populated by agentManager)
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self.service.task = {}
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# Initialize data operations (will be populated by agentManager)
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self.service.data = {
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'public': {},
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'connections': {}
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}
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# Initialize managers
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self.agentManager = getAgentManager()
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self.agentManager.initialize(self.service)
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# Internal functions
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def _extractFileContent(self, file):
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"""Extract content from a file for agent processing."""
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try:
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fileData = self.service.functions.getFileData(file['id'])
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if fileData is None:
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return None
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# Handle base64 encoded content
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if file.get('base64Encoded', False):
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import base64
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return base64.b64decode(fileData)
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# Handle text content
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if isinstance(fileData, bytes):
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return fileData.decode('utf-8')
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return fileData
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except Exception as e:
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logger.error(f"Error extracting file content: {str(e)}")
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return None
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def _convertFileRefToId(self, ref):
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"""Convert agent file reference to file ID."""
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try:
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# Extract file ID from reference format
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if isinstance(ref, str) and ';' in ref:
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return int(ref.split(';')[1])
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return int(ref)
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except Exception as e:
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logger.error(f"Error converting file reference to ID: {str(e)}")
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return None
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def _convertFileIdToRef(self, fileId):
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"""Convert file ID to agent file reference."""
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try:
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file = self.service.functions.getFile(fileId)
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if not file:
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return None
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return f"{file['name']};{fileId}"
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except Exception as e:
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logger.error(f"Error converting file ID to reference: {str(e)}")
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return None
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def _convertDataFormat(self, data, format):
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"""Convert data between different formats."""
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try:
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if format == 'json':
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if isinstance(data, str):
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return json.loads(data)
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return json.dumps(data)
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elif format == 'base64':
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import base64
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if isinstance(data, str):
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return base64.b64encode(data.encode('utf-8')).decode('utf-8')
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return base64.b64encode(data).decode('utf-8')
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return data
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except Exception as e:
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logger.error(f"Error converting data format: {str(e)}")
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return data
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def _createAgentInputFileList(self, files):
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"""Create a list of input files for agent processing."""
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try:
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inputFiles = []
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for file in files:
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fileId = self._convertFileRefToId(file)
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if fileId:
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fileData = self.service.functions.getFile(fileId)
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if fileData:
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inputFiles.append({
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'id': fileId,
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'name': fileData['name'],
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'mimeType': fileData['mimeType'],
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'content': self._extractFileContent(fileData)
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})
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return inputFiles
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except Exception as e:
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logger.error(f"Error creating agent input file list: {str(e)}")
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return []
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def _saveAgentOutputFiles(self, files):
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"""Save output files from agent processing."""
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try:
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savedFiles = []
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for file in files:
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# Create file metadata
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fileMeta = self.service.functions.createFile(
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name=file['name'],
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mimeType=file.get('mimeType', 'application/octet-stream'),
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size=len(file['content'])
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)
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if fileMeta and 'id' in fileMeta:
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# Save file content
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if self.service.functions.createFileData(fileMeta['id'], file['content']):
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savedFiles.append({
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'id': fileMeta['id'],
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'name': file['name'],
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'mimeType': file.get('mimeType', 'application/octet-stream')
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})
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return savedFiles
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except Exception as e:
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logger.error(f"Error saving agent output files: {str(e)}")
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return []
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# Main function to start workflow process
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async def workflowProcess(self, userInput: UserInputRequest, workflow: ChatWorkflow) -> ChatWorkflow:
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"""
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Main processing function that implements the workflow state machine.
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Handles the complete workflow process from user input to final response.
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Args:
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userInput: User input with prompt and optional file list
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workflow: Current ChatWorkflow object
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Returns:
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Updated ChatWorkflow object with processing results
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"""
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startTime = time.time()
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try:
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# State 3: User Message Processing
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self.checkExitCriteria(workflow)
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messageUser = await self.chatMessageToWorkflow("user", None, userInput, workflow)
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messageUser.status = "first" # For first message
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# State 4: Project Manager Analysis
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self.checkExitCriteria(workflow)
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self.logAdd(workflow, "Analyzing request and planning work", level="info", progress=10)
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projectManagerResponse = await self.projectManagerAnalysis(messageUser, workflow)
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objFinalDocuments = projectManagerResponse.get("objFinalDocuments", [])
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objWorkplan = projectManagerResponse.get("objWorkplan", [])
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objUserResponse = projectManagerResponse.get("objUserResponse", "")
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# Get detected language and set it in the serviceBase interface
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self.checkExitCriteria(workflow)
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userLanguage = projectManagerResponse.get("userLanguage", "en")
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workflow.userLanguage = userLanguage
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self.service.functions.setUserLanguage(userLanguage)
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# Save the response as a message in the workflow and add log entries
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self.checkExitCriteria(workflow)
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responseMessage = ChatMessage(
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id=str(uuid.uuid4()),
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workflowId=workflow.id,
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agentName="Project Manager",
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message=objUserResponse,
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role="assistant",
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status="step",
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sequenceNr=len(workflow.messages) + 1,
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startedAt=datetime.now(UTC).isoformat(),
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finishedAt=datetime.now(UTC).isoformat(),
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success=True
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)
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workflow.messages.append(responseMessage)
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# Add detailed log entry about the task plan
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taskPlanLog = "Input: "
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if objFinalDocuments:
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taskPlanLog += ", ".join(objFinalDocuments) + "<br>"
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else:
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taskPlanLog += "No input files<br>"
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# Work Plan Steps
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for i, task in enumerate(objWorkplan, 1):
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agentName = task.get("agent", "unknown")
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taskPlanLog += f"{i}. Agent {agentName}<br>"
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# Input Documents
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inputDocs = task.get("inputDocuments", [])
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if inputDocs:
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inputLabels = [doc.get("label", "unknown") for doc in inputDocs]
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taskPlanLog += f"- Input: {', '.join(inputLabels)}<br>"
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# Task Prompt
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prompt = task.get('prompt', 'No prompt')
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taskPlanLog += f"- Task: {prompt}<br>"
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# Output Documents
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outputDocs = task.get("outputDocuments", [])
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if outputDocs:
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outputLabels = [doc.get("label", "unknown") for doc in outputDocs]
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taskPlanLog += f"- Output: {', '.join(outputLabels)}<br>"
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# Final Results
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taskPlanLog += "Result: "
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if objFinalDocuments:
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taskPlanLog += ", ".join(objFinalDocuments)
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else:
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taskPlanLog += "No result files"
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self.logAdd(workflow, taskPlanLog, level="info", progress=25)
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# State 5: Agent Execution
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objResults = []
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if objWorkplan:
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totalTasks = len(objWorkplan)
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for taskIndex, task in enumerate(objWorkplan):
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self.checkExitCriteria(workflow)
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agentName = task.get("agent", "unknown")
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progressValue = 30 + int((taskIndex / totalTasks) * 60) # Progress from 30% to 90%
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progressMsg = f"Running task {taskIndex+1}/{totalTasks}: {agentName}"
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self.logAdd(workflow, progressMsg, level="info", progress=progressValue)
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taskResults = await self.agentProcessing(task, workflow)
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objResults.extend(taskResults)
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# Log completion of this task
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self.logAdd(
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workflow,
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f"Completed task {taskIndex+1}/{totalTasks}: {agentName}",
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level="info",
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progress=progressValue + (60/totalTasks)/2
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)
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# State 6: Final Response Generation
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self.checkExitCriteria(workflow)
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self.logAdd(workflow, "Creating final response", level="info", progress=90)
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finalMessage = await self.generateFinalMessage(objUserResponse, objFinalDocuments, objResults)
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finalMessage.status = "last" # As per state machine specification
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workflow.messages.append(finalMessage)
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# State 7: Workflow Completion
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self.checkExitCriteria(workflow)
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self.workflowFinish(workflow)
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# Update processing time
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endTime = time.time()
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workflow.stats.processingTime = endTime - startTime
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# Update workflow in database
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self.service.functions.updateWorkflow(workflow.id, {
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"status": workflow.status,
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"lastActivity": workflow.lastActivity,
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"stats": workflow.stats.to_dict(),
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"messages": [msg.to_dict() for msg in workflow.messages]
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})
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return workflow
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except Exception as e:
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# State 2: Workflow Exception
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logger.error(f"Workflow processing error: {str(e)}", exc_info=True)
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workflow.status = "failed"
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workflow.lastActivity = datetime.now(UTC).isoformat()
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# Update processing time even on error
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endTime = time.time()
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workflow.stats.processingTime = endTime - startTime
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# Update in database
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self.service.functions.updateWorkflow(workflow.id, {
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"status": "failed",
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"lastActivity": workflow.lastActivity,
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"stats": workflow.stats.to_dict()
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})
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self.logAdd(workflow, f"Workflow failed: {str(e)}", level="error", progress=100)
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return workflow
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# Workflow state machine functions
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def checkExitCriteria(self, workflow: ChatWorkflow) -> None:
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"""
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Check if the workflow should exit based on the current state.
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Raises WorkflowStoppedException if workflow should stop.
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Args:
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workflow: ChatWorkflow object to check
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"""
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current_workflow = self.service.functions.loadWorkflowState(workflow.id)
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if current_workflow["status"] in ["stopped", "failed"]:
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self.logAdd(workflow, f"Workflow processing terminated due to status: {current_workflow['status']}", level="info")
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# Raise an exception to stop execution
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raise WorkflowStoppedException(f"Workflow execution stopped due to status: {current_workflow['status']}")
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def workflowFinish(self, workflow: ChatWorkflow) -> ChatWorkflow:
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"""
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Finalizes a workflow and sets the status to 'completed' (State 7: Workflow Completion).
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Args:
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workflow: ChatWorkflow object
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Returns:
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Updated ChatWorkflow object
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"""
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# Prepare workflow update data
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workflowUpdate = {
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"status": "completed",
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"lastActivity": datetime.now().isoformat(),
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}
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# Update the workflow object in memory
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workflow.status = workflowUpdate["status"]
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workflow.lastActivity = workflowUpdate["lastActivity"]
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# Save workflow state to database - only relevant fields
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self.service.functions.updateWorkflow(workflow.id, workflowUpdate)
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self.logAdd(workflow, GLOBAL_WORKFLOW_LABELS["workflowStatusMessages"]["completed"], level="info", progress=100)
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return workflow
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async def projectManagerAnalysis(self, messageUser: ChatMessage, workflow: ChatWorkflow) -> Dict[str, Any]:
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"""
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Creates the prompt for the project manager and processes the response (State 4: Project Manager Analysis).
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Args:
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messageUser: Message object with user request
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workflow: Current workflow object
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Returns:
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Project manager's response with objFinalDocuments, objWorkplan and objUserResponse
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"""
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# Get available agents with their capabilities
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availableAgents = self.agentProfiles()
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# Create a workflow summary
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workflowSummary = await self.workflowSummarize(workflow, messageUser)
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# Create a list of currently available documents from user input or previously generated documents
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availableDocuments = self.getAvailableDocuments(workflow, messageUser)
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availableDocsStr = json.dumps(availableDocuments, indent=2)
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# Create the prompt for the project manager with language detection requirement
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prompt = f"""
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Based on the user request and the provided documents, please analyze the requirements and create a processing plan.
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Also, identify the language of the user's request and include it in your response.
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<userrequest>
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{messageUser.content}
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</userrequest>
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# Previous conversation history:
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{workflowSummary}
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# Available documents (currently in workflow):
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{availableDocsStr}
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# Available agents and their capabilities:
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{self.parseJson2text(availableAgents)}
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Please analyze the request and create:
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1. A list of required result documents (objFinalDocuments)
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2. A plan for executing agents (objWorkplan)
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3. A clear response to the user explaining what you're doing (objUserResponse)
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4. Identified language of the user's request (userLanguage)
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## IMPORTANT RULES FOR THE WORKPLAN:
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1. Each input document must either already exist (provided by the user or previously created by an agent) or be created by an agent before it's used.
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2. Document data is already extracted for the agent based on your prompt to the agent. He does not need to do this again.
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3. Do not define document inputs that don't exist or haven't been generated beforehand.
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4. Create a logical sequence - earlier agents can create documents that are later used as inputs.
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5. If the user has provided documents but hasn't clearly stated what they want, try to act according to the context.
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6. ALL documents provided by the user (where fileSource is "user") MUST be included in the work plan, even if they don't have content summaries or if content extraction failed.
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## AGENT SELECTION GUIDELINES:
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1. Carefully analyze the task requirements and match them with agent capabilities
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2. Consider the type of operation needed (data processing, analysis, documentation, etc.)
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3. Review each agent's capabilities and select the most appropriate one for the task
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4. Ensure the selected agent has the necessary capabilities to handle the input and output formats
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5. If multiple agents could handle the task, choose the one with the most specific capabilities for the task
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Your answer must be strictly in the JSON_OUTPUT format, with no additions before or after the JSON object.
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JSON_OUTPUT = {{
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"objFinalDocuments": ["label",...], # document label in the format 'filename.ext'
|
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"objWorkplan": [
|
|
{{
|
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"agent": "agentName", # Name of an available agent
|
|
"prompt": "Specific instructions to the agent, that he knows what to do with which documents and which output to provide."
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|
"outputDocuments": [
|
|
{{
|
|
"label":"document label in the format 'filename.ext'",
|
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"prompt":"AI prompt to describe the content of the file"
|
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}}
|
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],
|
|
"inputDocuments": [
|
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{{
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"label":"document label in the format 'filename.ext'",
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"fileId":id, # if refering to an existing document, provide fileId to select the correct file
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"contentPart":"", # provide empty string, if all document contents to consider, otherwise the contentPart of the document to focus on
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"prompt":"AI prompt to describe what data to extract from the file."
|
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}}
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], # If no input documents are needed, include "inputDocuments" as an empty list
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}}
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# Multiple agent tasks can be added here and should build logically on each other
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],
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"objUserResponse": "Information to the user about how his request will be solved, in the language of the user's request.",
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"userLanguage": "en" # Language code (e.g., en, de, fr, es) based on the user's request
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}}
|
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|
|
## RULES for inputDocuments:
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|
1. The user request refers to documents where "fileSource" in available documents is "user". Those documents are in the focus for input
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2. In case of redundant label in available documents, use document with highest sequenceNr if not specified differently
|
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3. ALL documents provided by the user MUST be included in the work plan, even if they don't have content summaries or if content extraction failed
|
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|
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## STRICT RULES FOR document "label":
|
|
1. Every document label MUST include a proper file extension that matches the content type.
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|
2. Use standard extensions like:
|
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- ".txt" for text files
|
|
- ".md" for markdown files
|
|
- ".csv" for comma-separated values
|
|
- ".json" for JSON data
|
|
- ".html" for HTML content
|
|
- ".jpg" or ".png" for images
|
|
- ".docx" for Word documents
|
|
- ".xlsx" for Excel files
|
|
- ".pdf" for PDF documents
|
|
3. Use descriptive filenames that indicate the document's purpose (e.g., "analysis_report.txt" rather than just "report.txt")
|
|
4. If you use label for an existing file
|
|
"""
|
|
|
|
# Call the AI service through serviceBase for language support
|
|
logger.debug(f"PROJECT MANAGER Planning prompt: {prompt}")
|
|
projectManagerOutput = await self.service.functions.callAi([
|
|
{
|
|
"role": "system",
|
|
"content": "You are an experienced project manager who analyzes user requests and creates work plans. You pay very careful attention to ensure that all document dependencies are correct and that no non-existent documents are defined as inputs. The output follows strictly the specified format."
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": prompt
|
|
}
|
|
])
|
|
|
|
# Parse the JSON response
|
|
logger.debug(f"PROJECT MANAGER Planning answer: {projectManagerOutput}")
|
|
return self.parseJsonResponse(projectManagerOutput)
|
|
|
|
async def agentProcessing(self, task: Task, workflow: ChatWorkflow) -> List[ChatDocument]:
|
|
"""
|
|
Process a single agent task from the workflow (State 5: Agent Execution).
|
|
Uses the new Task and AgentResponse models.
|
|
|
|
Args:
|
|
task: The task definition containing agent name, prompt, and document specifications
|
|
workflow: The current workflow object
|
|
|
|
Returns:
|
|
List of document objects created by the agent
|
|
"""
|
|
try:
|
|
# Create Task object
|
|
task_obj = Task(
|
|
id=str(uuid.uuid4()),
|
|
workflowId=workflow.id,
|
|
agentName=task.get("agent"),
|
|
status="pending",
|
|
progress=0.0,
|
|
prompt=task.get("prompt", ""),
|
|
filesInput=task.get("inputDocuments", []),
|
|
filesOutput=task.get("outputDocuments", []),
|
|
userLanguage=workflow.userLanguage
|
|
)
|
|
|
|
# Execute agent
|
|
response, updated_task = await self.agentManager.executeAgent(task_obj)
|
|
|
|
# Update workflow stats
|
|
if response.performance:
|
|
workflow.stats.tokensUsed += response.performance.get("tokensUsed", 0)
|
|
workflow.stats.bytesSent += response.performance.get("bytesSent", 0)
|
|
workflow.stats.bytesReceived += response.performance.get("bytesReceived", 0)
|
|
|
|
# Update in database
|
|
self.service.functions.updateWorkflow(workflow.id, {
|
|
"stats": workflow.stats.to_dict()
|
|
})
|
|
|
|
# Log the agent response
|
|
self.logAdd(
|
|
workflow,
|
|
f"Agent {task.get('agent')} completed task. Feedback: {response.message.message if response.message else 'No feedback provided'}",
|
|
level="info"
|
|
)
|
|
|
|
# Create a message in the workflow with the agent's response
|
|
agentMessage = await self.chatMessageToWorkflow("assistant", task.get("agent"), UserInputRequest(
|
|
message=response.message.message if response.message else "",
|
|
listFileId=response.message.documents if response.message else []
|
|
), workflow)
|
|
agentMessage.status = "step" # As per state machine specification
|
|
|
|
return agentMessage.documents
|
|
|
|
except Exception as e:
|
|
errorMsg = f"Error executing agent '{task.get('agent')}': {str(e)}"
|
|
logger.error(errorMsg, exc_info=True)
|
|
self.logAdd(workflow, errorMsg, level="error")
|
|
return []
|
|
|
|
async def generateFinalMessage(self, objUserResponse: str, objFinalDocuments: List[str], objResults: List[Dict[str, Any]]) -> ChatMessage:
|
|
"""
|
|
Creates the final response message with review of promised and delivered documents (State 6: Final Response Generation).
|
|
|
|
Args:
|
|
objUserResponse: Initial text response to the user
|
|
objFinalDocuments: List of expected response documents
|
|
objResults: List of generated result documents
|
|
|
|
Returns:
|
|
Complete message object with content and relevant documents
|
|
"""
|
|
# Find documents that match the objFinalDocuments requirements
|
|
matchingDocuments = []
|
|
|
|
if len(objFinalDocuments) > 0:
|
|
for answerLabel in objFinalDocuments:
|
|
# Find matching document in results
|
|
for doc in objResults:
|
|
docName = self.getFilename(doc)
|
|
# Check if this document matches the answer specification
|
|
if docName == answerLabel:
|
|
contentRef = []
|
|
for c in doc.get("contents", []):
|
|
contentRef.append(c.get("summary", ""))
|
|
docRef = {
|
|
"label": docName,
|
|
"contentSummary": contentRef
|
|
}
|
|
matchingDocuments.append(docRef)
|
|
break
|
|
|
|
# Use the serviceBase for language-aware AI calls
|
|
finalPrompt = await self.service.functions.callAi([
|
|
{"role": "system", "content": "You are a project manager, who delivers results to a user."},
|
|
{"role": "user", "content": f"""
|
|
Give the final short feedback to the user with reference to the initial statement (objUserResponse). Inform him about the list of filesDelivered. You do not need to send the files, this is handled separately. If in the list of filesDelivered some files_promised would be missing, just give a comment on this, otherwise task is now completed successfully.
|
|
|
|
Here the data:
|
|
objUserResponse = {self.parseJson2text(objUserResponse)}
|
|
filesPromised = {self.parseJson2text(objFinalDocuments)}
|
|
filesDelivered = {self.parseJson2text(matchingDocuments)}
|
|
"""
|
|
}
|
|
], produceUserAnswer=True)
|
|
|
|
# Create basic message structure with proper fields
|
|
logger.debug(f"FINAL PROMPT = {self.parseJson2text(finalPrompt)}.")
|
|
finalMessage = {
|
|
"role": "assistant",
|
|
"agentName": "Project Manager",
|
|
"content": finalPrompt,
|
|
"documents": [] # DO NOT include the results documents, already with agents
|
|
}
|
|
|
|
logger.debug(f"FINAL MESSAGE = {self.parseJson2text(finalMessage)}.")
|
|
return finalMessage
|
|
|
|
async def workflowSummarize(self, workflow: ChatWorkflow, messageUser: ChatMessage) -> str:
|
|
"""
|
|
Creates a summary of the workflow without the current user message.
|
|
|
|
Args:
|
|
workflow: Workflow object
|
|
messageUser: Current user message
|
|
|
|
Returns:
|
|
Summary of the workflow
|
|
"""
|
|
if not workflow or "messages" not in workflow or not workflow["messages"]:
|
|
return "" # First message
|
|
|
|
# Go through messages in chronological order
|
|
messages = sorted(workflow["messages"], key=lambda m: m.get("sequenceNo", 0), reverse=False)
|
|
|
|
summaryParts = []
|
|
for message in messages:
|
|
if message["id"] != messageUser["id"]:
|
|
messageSummary = await self.messageSummarize(message)
|
|
summaryParts.append(messageSummary)
|
|
|
|
return "\n\n".join(summaryParts)
|
|
|
|
async def messageSummarize(self, message: ChatMessage) -> str:
|
|
"""
|
|
Creates a summary of a message including its documents.
|
|
|
|
Args:
|
|
message: Message to summarize
|
|
|
|
Returns:
|
|
Summary of the message
|
|
"""
|
|
role = message.role
|
|
agentName = message.agentName
|
|
content = message.content
|
|
|
|
try:
|
|
# Use the serviceBase for language-aware AI calls
|
|
contentSummary = await self.service.functions.callAi([
|
|
{"role": "system", "content": f"You are a chat message summarizer. Create a very concise summary (2-3 sentences, maximum 300 characters)"},
|
|
{"role": "user", "content": content}
|
|
])
|
|
except Exception as e:
|
|
logger.error(f"Error creating summary: {str(e)}")
|
|
contentSummary = content[:200] + "..."
|
|
|
|
# Summarize documents
|
|
docsSummary = ""
|
|
if "documents" in message and message["documents"]:
|
|
docsList = []
|
|
for i, doc in enumerate(message["documents"]):
|
|
docName = self.getFilename(doc)
|
|
docsList.append(docName)
|
|
if docsList:
|
|
docsSummary = "\nDocuments:" + "\n- ".join(docsList)
|
|
|
|
return f"[{role} {agentName}]: {contentSummary}{docsSummary}"
|
|
|
|
async def chatMessageToWorkflow(self, role: str, agent: Union[str, Dict[str, Any]], chatMessage: UserInputRequest, workflow: ChatWorkflow) -> ChatMessage:
|
|
"""
|
|
Integrates chat message input into a Message object including files with complete contents.
|
|
|
|
Args:
|
|
role: Role of the message sender (e.g., 'user', 'assistant')
|
|
agent: Agent name or configuration
|
|
chatMessage: UserInputRequest object containing message data and file references
|
|
workflow: Current workflow state
|
|
|
|
Returns:
|
|
ChatMessage object with complete file contents
|
|
"""
|
|
try:
|
|
# Process additional files with complete contents
|
|
additionalFileIds = chatMessage.listFileId or []
|
|
additionalFiles = await self.service.processFileIds(additionalFileIds)
|
|
|
|
# Create message object
|
|
message = ChatMessage(
|
|
id=str(uuid.uuid4()),
|
|
workflowId=workflow.id,
|
|
role=role,
|
|
agentName=agent if isinstance(agent, str) else agent.get("name", ""),
|
|
message=chatMessage.message,
|
|
documents=additionalFiles,
|
|
status="completed",
|
|
startedAt=datetime.now().isoformat()
|
|
)
|
|
|
|
return message
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating workflow message: {str(e)}")
|
|
raise
|
|
|
|
def logAdd(self, workflow: ChatWorkflow, message: str, level: str = "info",
|
|
progress: Optional[int] = None) -> str:
|
|
"""
|
|
Add a log entry to the workflow.
|
|
|
|
Args:
|
|
workflow: ChatWorkflow object
|
|
message: Log message
|
|
level: Log level (info, warning, error)
|
|
progress: Optional progress percentage
|
|
|
|
Returns:
|
|
str: ID of the created log entry
|
|
"""
|
|
try:
|
|
# Generate log ID
|
|
logId = str(uuid.uuid4())
|
|
|
|
# Create log entry
|
|
logEntry = ChatLog(
|
|
id=logId,
|
|
workflowId=workflow.id,
|
|
message=message,
|
|
level=level,
|
|
progress=progress,
|
|
timestamp=datetime.now().isoformat()
|
|
)
|
|
|
|
# Add to workflow logs
|
|
workflow.logs.append(logEntry)
|
|
|
|
# Also log to Python logger
|
|
logLevel = getattr(logging, level.upper())
|
|
logger.log(logLevel, f"[Workflow {workflow.id}] {message}")
|
|
|
|
# Save to database
|
|
self.service.functions.saveWorkflowLog(workflow.id, logEntry.to_dict())
|
|
|
|
return logId
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error adding log entry: {str(e)}")
|
|
return ""
|
|
|
|
def saveAgentDocuments(self, agentResults: Dict[str, Any]) -> List[int]:
|
|
"""
|
|
Saves all documents from agent results as files and returns a list of file IDs.
|
|
Enhanced to handle the standardized document format from agents with base64Encoded flag.
|
|
|
|
Args:
|
|
agentResults: Dictionary containing agent feedback and documents
|
|
|
|
Returns:
|
|
List of file IDs for the saved documents
|
|
"""
|
|
fileIds = []
|
|
used_names = set() # Track used names to prevent duplicates
|
|
|
|
# Extract documents from agent results
|
|
documents = agentResults.get("documents", [])
|
|
|
|
for doc in documents:
|
|
try:
|
|
# Extract document data according to LucyDOM model
|
|
name = doc.name
|
|
ext = doc.ext
|
|
data = doc.data
|
|
base64Encoded = doc.base64Encoded
|
|
|
|
# Skip if no name or data
|
|
if not name or not data:
|
|
logger.warning(f"Skipping document with missing name or data. Name: {name}, Has data: {bool(data)}")
|
|
continue
|
|
|
|
# Ensure unique filename
|
|
base_name = name
|
|
counter = 1
|
|
while f"{base_name}.{ext}" in used_names:
|
|
base_name = f"{name}_{counter}"
|
|
counter += 1
|
|
used_names.add(f"{base_name}.{ext}")
|
|
|
|
# Convert content to bytes based on base64Encoded flag
|
|
if isinstance(data, str):
|
|
if base64Encoded:
|
|
# Decode base64 to bytes
|
|
try:
|
|
import base64
|
|
fileContent = base64.b64decode(data)
|
|
except Exception as e:
|
|
logger.warning(f"Failed to decode base64 content: {str(e)}")
|
|
fileContent = data.encode('utf-8')
|
|
base64Encoded = False
|
|
else:
|
|
# Convert text to bytes
|
|
fileContent = data.encode('utf-8')
|
|
else:
|
|
# Already bytes
|
|
fileContent = data
|
|
|
|
# Determine MIME type based on extension
|
|
mimeType = self.service.functions.getMimeType(f"{base_name}.{ext}")
|
|
|
|
# Create file metadata
|
|
fileMeta = self.service.functions.createFile(
|
|
name=base_name,
|
|
mimeType=mimeType,
|
|
size=len(fileContent)
|
|
)
|
|
|
|
if fileMeta and "id" in fileMeta:
|
|
# Save file content
|
|
if self.service.functions.createFileData(fileMeta["id"], fileContent):
|
|
fileIds.append(fileMeta["id"])
|
|
logger.info(f"Saved document '{base_name}.{ext}' with file ID: {fileMeta['id']} (base64Encoded: {base64Encoded})")
|
|
else:
|
|
logger.warning(f"Failed to save content for document '{base_name}.{ext}'")
|
|
else:
|
|
logger.warning(f"Failed to create file metadata for '{base_name}.{ext}'")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error saving document from agent results: {str(e)}")
|
|
# Continue with other documents instead of failing
|
|
continue
|
|
|
|
return fileIds
|
|
|
|
def getAvailableDocuments(self, workflow: ChatWorkflow, messageUser: ChatMessage) -> List[ChatDocument]:
|
|
"""
|
|
Determines all currently available documents from user input and already generated documents.
|
|
|
|
Args:
|
|
messageUser: Current message from the user
|
|
workflow: Current workflow object
|
|
|
|
Returns:
|
|
List with information about all available documents, sorted by message sequenceNr in descending order
|
|
"""
|
|
availableDocs = []
|
|
|
|
if "messages" in workflow and workflow["messages"]:
|
|
for message in workflow["messages"]:
|
|
messageId = message.id
|
|
sequenceNr = message.sequenceNo
|
|
|
|
# Determine source
|
|
source = "user" if messageId == messageUser.id else "workflow"
|
|
|
|
# Process documents in this message
|
|
if "documents" in message and message["documents"]:
|
|
for doc in message["documents"]:
|
|
# Get filename using our helper method
|
|
filename = self.getFilename(doc)
|
|
fileId = doc.fileId
|
|
|
|
# Extract summaries from all contents
|
|
contentSummaries = []
|
|
if "contents" in doc and doc["contents"]:
|
|
for content in doc["contents"]:
|
|
contentSummaries.append({
|
|
"contentPart": content.name,
|
|
"metadata": content.metadata,
|
|
"summary": content.summary,
|
|
})
|
|
else:
|
|
# Add a default content summary if no contents exist
|
|
contentSummaries.append({
|
|
"contentPart": "1_undefined",
|
|
"metadata": "",
|
|
"summary": "No content extracted",
|
|
})
|
|
|
|
# Create document info
|
|
docInfo = {
|
|
"sequenceNr": sequenceNr,
|
|
"fileSource": source,
|
|
"fileId": fileId,
|
|
"messageId": messageId,
|
|
"label": filename,
|
|
"contentSummaryList": contentSummaries,
|
|
}
|
|
availableDocs.append(docInfo)
|
|
|
|
# Sort by message sequenceNr in descending order (newest first)
|
|
availableDocs.sort(key=lambda x: x["sequenceNr"], reverse=True)
|
|
|
|
logger.info(f"Available documents: {len(availableDocs)}")
|
|
return availableDocs
|
|
|
|
# Agent functions
|
|
|
|
def agentProfiles(self) -> List[AgentProfile]:
|
|
"""
|
|
Gets information about all available agents.
|
|
|
|
Returns:
|
|
List with information about all available agents
|
|
"""
|
|
return self.agentManager.getAgentInfos()
|
|
|
|
def getFilename(self, document: ChatDocument) -> str:
|
|
"""
|
|
Gets the filename from a document by combining name and extension.
|
|
|
|
Args:
|
|
document: Document object
|
|
|
|
Returns:
|
|
Filename with extension
|
|
"""
|
|
name = document.name
|
|
ext = document.ext
|
|
if ext:
|
|
return f"{name}.{ext}"
|
|
return name
|
|
|
|
def parseJson2text(self, jsonObj: Any) -> str:
|
|
"""
|
|
Converts a JSON object to a readable text representation.
|
|
|
|
Args:
|
|
jsonObj: JSON object to convert
|
|
|
|
Returns:
|
|
Formatted text representation
|
|
"""
|
|
if not jsonObj:
|
|
return "No data available"
|
|
|
|
try:
|
|
# Format with indentation for better readability
|
|
return json.dumps(jsonObj, indent=2, ensure_ascii=False)
|
|
except Exception as e:
|
|
logger.error(f"Error in JSON conversion: {str(e)}")
|
|
return str(jsonObj)
|
|
|
|
def parseJsonResponse(self, responseText: str) -> Dict[str, Any]:
|
|
"""
|
|
Parses the JSON response from a text.
|
|
|
|
Args:
|
|
responseText: Text with JSON content
|
|
|
|
Returns:
|
|
Parsed JSON data
|
|
"""
|
|
try:
|
|
# Extract JSON from the text (if mixed with other content)
|
|
jsonStart = responseText.find('{')
|
|
jsonEnd = responseText.rfind('}') + 1
|
|
|
|
if jsonStart >= 0 and jsonEnd > jsonStart:
|
|
jsonStr = responseText[jsonStart:jsonEnd]
|
|
return json.loads(jsonStr)
|
|
else:
|
|
# Try to parse the entire text
|
|
return json.loads(responseText)
|
|
except json.JSONDecodeError as e:
|
|
logger.error(f"JSON parsing error: {str(e)}")
|
|
# Fallback: Return empty structure
|
|
return {
|
|
"objFinalDocuments": [],
|
|
"objWorkplan": [],
|
|
"objUserResponse": "Sorry, I could not parse your data.",
|
|
"userLanguage": "en"
|
|
}
|
|
|
|
async def _callAiBasic(self, prompt: str, context: Dict[str, Any] = None) -> str:
|
|
"""Call basic AI model."""
|
|
try:
|
|
response = await self.service.base.callAi(prompt, context or {}, model="aiBase")
|
|
return response
|
|
except Exception as e:
|
|
logger.error(f"Error calling basic AI: {str(e)}")
|
|
return ""
|
|
|
|
async def _callAiComplex(self, prompt: str, context: Dict[str, Any] = None) -> str:
|
|
"""Call complex AI model."""
|
|
try:
|
|
response = await self.service.base.callAi(prompt, context or {}, model="aiComplex")
|
|
return response
|
|
except Exception as e:
|
|
logger.error(f"Error calling complex AI: {str(e)}")
|
|
return ""
|
|
|
|
async def _callAiImage(self, prompt: str, context: Dict[str, Any] = None) -> str:
|
|
"""Call image AI model."""
|
|
try:
|
|
response = await self.service.base.callAi(prompt, context or {}, model="aiImage")
|
|
return response
|
|
except Exception as e:
|
|
logger.error(f"Error calling image AI: {str(e)}")
|
|
return ""
|
|
|
|
# Singleton factory for the WorkflowManager
|
|
_workflowManagers = {}
|
|
_workflowManagerLastAccess = {} # Track last access time for cleanup
|
|
|
|
async def getWorkflowManager(service) -> WorkflowManager:
|
|
"""Get or create a workflow manager instance."""
|
|
contextKey = f"{service.functions.mandateId}_{service.functions.userId}"
|
|
|
|
# Check if we have a cached instance
|
|
if contextKey in _workflowManagers:
|
|
_workflowManagerLastAccess[contextKey] = time.time()
|
|
return _workflowManagers[contextKey]
|
|
|
|
# Create new instance
|
|
manager = WorkflowManager(service)
|
|
|
|
# Cache the instance
|
|
_workflowManagers[contextKey] = manager
|
|
_workflowManagerLastAccess[contextKey] = time.time()
|
|
|
|
return manager
|