complete refactory of all dependencies

app.py

@@ -291,9 +291,5 @@ app.include_router(googleRouter)
 from modules.routes.routeVoiceGoogle import router as voiceGoogleRouter
 app.include_router(voiceGoogleRouter)
 
-from modules.routes.routeVoiceStreaming import router as voiceStreamingRouter
-app.include_router(voiceStreamingRouter)
-
-# Admin security routes (token listing and revocation, logs, db tools)
 from modules.routes.routeSecurityAdmin import router as adminSecurityRouter
 app.include_router(adminSecurityRouter)

env_dev.env

@@ -64,6 +64,13 @@ Connector_AiAnthropic_MODEL_NAME = claude-3-5-sonnet-20241022
 Connector_AiAnthropic_TEMPERATURE = 0.2
 Connector_AiAnthropic_MAX_TOKENS = 2000
 
+# LangDoc configuration
+Connector_AiLangdoc_API_URL = https://api.langdock.com/v1/chat/completions
+Connector_AiLangdoc_API_SECRET = sk-9KaNH1FfEx7SkTijsFpXeTIc9_xOmoo7e0hW6SqrYavFq_bgjcULa7PXp3kWQpp4gfk8-U0B4L91CP6YpAJxZg
+Connector_AiLangdoc_MODEL_NAME = gpt-4o
+Connector_AiLangdoc_TEMPERATURE = 0.2
+Connector_AiLangdoc_MAX_TOKENS = 2000
+
 # Agent Mail configuration
 Service_MSFT_CLIENT_ID = c7e7112d-61dc-4f3a-8cd3-08cc4cd7504c
 Service_MSFT_CLIENT_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpEQk4xYnpmbnItUEU3dHU4eHB5dzVYay1WT012RTRLUWJDTlBILVY5dC1FX3VMNjZmLThrbDRFNWFSNGprY3RRTlpYNGlubVBpNnY3MjNJcGtzVk9PMzRacl9LUlM2RU5vTVVZWHJvaUhWSHVfc1pNR0pfQmI5SEprOG5KdlB1QnQ=

env_int.env

@@ -64,6 +64,13 @@ Connector_AiAnthropic_MODEL_NAME = claude-3-5-sonnet-20241022
 Connector_AiAnthropic_TEMPERATURE = 0.2
 Connector_AiAnthropic_MAX_TOKENS = 2000
 
+# LangDoc configuration
+Connector_AiLangdoc_API_URL = https://api.langdock.com/v1/chat/completions
+Connector_AiLangdoc_API_SECRET = sk-9KaNH1FfEx7SkTijsFpXeTIc9_xOmoo7e0hW6SqrYavFq_bgjcULa7PXp3kWQpp4gfk8-U0B4L91CP6YpAJxZg
+Connector_AiLangdoc_MODEL_NAME = gpt-4o
+Connector_AiLangdoc_TEMPERATURE = 0.2
+Connector_AiLangdoc_MAX_TOKENS = 2000
+
 # Agent Mail configuration
 Service_MSFT_CLIENT_ID = c7e7112d-61dc-4f3a-8cd3-08cc4cd7504c
 Service_MSFT_CLIENT_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjNzB2M3ZjaE1SVE9ON2FKam9yVURxcHl1Ym5VNVUtS0MyWUpNVXVlaWpWS2U3VVd3em9vQl9lcnVYay03bS04YjNBbDZZNTB4eUtjT3ppQjJjY3dOT0FNLW9LeDhIUU5iaTNqNURUWE5La3kzaHNGcU9yNVI0YjhWZTZRRFktcTk=

env_prod.env

@@ -64,6 +64,13 @@ Connector_AiAnthropic_MODEL_NAME = claude-3-5-sonnet-20241022
 Connector_AiAnthropic_TEMPERATURE = 0.2
 Connector_AiAnthropic_MAX_TOKENS = 2000
 
+# LangDoc configuration
+Connector_AiLangdoc_API_URL = https://api.langdock.com/v1/chat/completions
+Connector_AiLangdoc_API_SECRET = sk-9KaNH1FfEx7SkTijsFpXeTIc9_xOmoo7e0hW6SqrYavFq_bgjcULa7PXp3kWQpp4gfk8-U0B4L91CP6YpAJxZg
+Connector_AiLangdoc_MODEL_NAME = gpt-4o
+Connector_AiLangdoc_TEMPERATURE = 0.2
+Connector_AiLangdoc_MAX_TOKENS = 2000
+
 # Agent Mail configuration
 Service_MSFT_CLIENT_ID = c7e7112d-61dc-4f3a-8cd3-08cc4cd7504c
 Service_MSFT_CLIENT_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pVEhHdlZHU3FNMmhuRGVwaGc3YzIxSjlZNzBCQjlOV2pSYVNXb0t1ZnVwQzZsQzY4cHMtVlZtNF85OEVaV1BMTzdXMmpzaGZpaG1DalJ0bkNPMHA5ZUcwZjNDdGk1TFdxYTJSZnVrVmhhZ2VRUEZxbjJOOGFhWk9EYlY3dmRVTnI=

modules/connectors/connectorAiLangdoc.py

@@ -0,0 +1,406 @@
+import logging
+import httpx
+import asyncio
+import re
+from typing import Dict, Any, List, Union, Optional
+from fastapi import HTTPException
+from modules.shared.configuration import APP_CONFIG
+
+# Configure logger
+logger = logging.getLogger(__name__)
+
+def loadConfigData():
+    """Load configuration data for LangDoc connector"""
+    return {
+        "apiKey": APP_CONFIG.get('Connector_AiLangdoc_API_SECRET'),
+        "apiUrl": APP_CONFIG.get('Connector_AiLangdoc_API_URL'),
+        "modelName": APP_CONFIG.get('Connector_AiLangdoc_MODEL_NAME'),
+        "temperature": float(APP_CONFIG.get('Connector_AiLangdoc_TEMPERATURE')),
+        "maxTokens": int(APP_CONFIG.get('Connector_AiLangdoc_MAX_TOKENS'))
+    }
+
+class AiLangdoc:
+    """Connector for communication with the LangDoc API (OpenAI-compatible)."""
+    
+    def __init__(self):
+        # Load configuration
+        self.config = loadConfigData()
+        self.apiKey = self.config["apiKey"]
+        self.apiUrl = self.config["apiUrl"]
+        self.modelName = self.config["modelName"]
+        
+        # HttpClient for API calls
+        self.httpClient = httpx.AsyncClient(
+            timeout=120.0,  # Longer timeout for complex requests
+            headers={
+                "Authorization": f"Bearer {self.apiKey}",
+                "Content-Type": "application/json"
+            }
+        )
+        
+        logger.info(f"LangDoc Connector initialized with model: {self.modelName}")
+    
+    async def callAiBasic(self, messages: List[Dict[str, Any]], temperature: float = None, maxTokens: int = None) -> str:
+        """
+        Calls the LangDoc API with the given messages.
+        
+        Args:
+            messages: List of messages in OpenAI format (role, content)
+            temperature: Temperature for response generation (0.0-1.0)
+            maxTokens: Maximum number of tokens in the response
+            
+        Returns:
+            The response from the LangDoc API
+            
+        Raises:
+            HTTPException: For errors in API communication
+        """
+        try:
+            # Use parameters from configuration if none were overridden
+            if temperature is None:
+                temperature = self.config.get("temperature", 0.2)
+                
+            if maxTokens is None:
+                maxTokens = self.config.get("maxTokens", 2000)
+            
+            payload = {
+                "model": self.modelName,
+                "messages": messages,
+                "temperature": temperature,
+                "max_tokens": maxTokens
+            }
+            
+            response = await self.httpClient.post(
+                self.apiUrl,
+                json=payload
+            )
+            
+            if response.status_code != 200:
+                error_detail = f"LangDoc API error: {response.status_code} - {response.text}"
+                logger.error(error_detail)
+                
+                # Provide more specific error messages based on status code
+                if response.status_code == 429:
+                    error_message = "Rate limit exceeded. Please wait before making another request."
+                elif response.status_code == 401:
+                    error_message = "Invalid API key. Please check your LangDoc API configuration."
+                elif response.status_code == 400:
+                    error_message = f"Invalid request to LangDoc API: {response.text}"
+                else:
+                    error_message = f"LangDoc API error ({response.status_code}): {response.text}"
+                
+                raise HTTPException(status_code=500, detail=error_message)
+            
+            responseJson = response.json()
+            content = responseJson["choices"][0]["message"]["content"]
+            return content
+            
+        except Exception as e:
+            logger.error(f"Error calling LangDoc API: {str(e)}")
+            raise HTTPException(status_code=500, detail=f"Error calling LangDoc API: {str(e)}")
+    
+    async def callAiImage(self, prompt: str, imageData: Union[str, bytes], mimeType: str = None) -> str:
+        """
+        Analyzes an image using LangDoc's vision capabilities.
+        
+        Args:
+            imageData: Either a file path (str) or image data (bytes)
+            mimeType: The MIME type of the image (optional, only for binary data)
+            prompt: The prompt for analysis
+            
+        Returns:
+            The analysis response as text
+        """
+        try:
+            # Distinguish between file path and binary data
+            if isinstance(imageData, str):
+                # It's a file path - import filehandling only when needed
+                from modules import agentserviceFilemanager as fileHandler
+                base64Data, autoMimeType = fileHandler.encodeFileToBase64(imageData)
+                mimeType = mimeType or autoMimeType
+            else:
+                # It's binary data
+                import base64
+                base64Data = base64.b64encode(imageData).decode('utf-8')
+                # MIME type must be specified for binary data
+                if not mimeType:
+                    # Fallback to generic image type
+                    mimeType = "image/png"
+            
+            # Prepare the payload for the Vision API
+            messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": prompt},
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:{mimeType};base64,{base64Data}"
+                            }
+                        }
+                    ]
+                }
+            ]
+            
+            # Use the existing callAiBasic function
+            response = await self.callAiBasic(messages)
+            
+            return response
+        
+        except Exception as e:
+            logger.error(f"Error during image analysis: {str(e)}", exc_info=True)
+            return f"[Error during image analysis: {str(e)}]"
+    
+    async def listModels(self) -> List[Dict[str, Any]]:
+        """
+        Lists available models from the LangDoc API.
+        
+        Returns:
+            List of available models with their details
+        """
+        try:
+            # LangDoc uses OpenAI-compatible endpoints
+            modelsUrl = self.apiUrl.replace("/chat/completions", "/models")
+            
+            response = await self.httpClient.get(modelsUrl)
+            
+            if response.status_code != 200:
+                error_detail = f"LangDoc API error listing models: {response.status_code} - {response.text}"
+                logger.error(error_detail)
+                raise HTTPException(status_code=500, detail=error_detail)
+            
+            responseJson = response.json()
+            return responseJson.get("data", [])
+            
+        except Exception as e:
+            logger.error(f"Error listing LangDoc models: {str(e)}")
+            raise HTTPException(status_code=500, detail=f"Error listing LangDoc models: {str(e)}")
+    
+    async def getModelInfo(self, modelName: str = None) -> Dict[str, Any]:
+        """
+        Gets information about a specific model.
+        
+        Args:
+            modelName: Name of the model to get info for (uses default if None)
+            
+        Returns:
+            Model information dictionary
+        """
+        try:
+            if modelName is None:
+                modelName = self.modelName
+            
+            models = await self.listModels()
+            
+            for model in models:
+                if model.get("id") == modelName:
+                    return model
+            
+            raise HTTPException(status_code=404, detail=f"Model {modelName} not found")
+            
+        except Exception as e:
+            logger.error(f"Error getting LangDoc model info: {str(e)}")
+            raise HTTPException(status_code=500, detail=f"Error getting LangDoc model info: {str(e)}")
+    
+    async def generateImage(self, prompt: str, size: str = "1024x1024", quality: str = "standard", style: str = "vivid") -> Dict[str, Any]:
+        """
+        Generates an image using LangDoc's DALL-E 3 integration.
+        
+        Args:
+            prompt: Text description of the image to generate
+            size: Image size - "1024x1024", "1792x1024", or "1024x1792"
+            quality: Image quality - "standard" or "hd"
+            style: Image style - "vivid" or "natural"
+            
+        Returns:
+            Dictionary containing the generated image data and metadata
+            
+        Raises:
+            HTTPException: For errors in API communication
+        """
+        try:
+            # Use OpenAI-compatible images endpoint
+            imagesUrl = self.apiUrl.replace("/chat/completions", "/images/generations")
+            
+            payload = {
+                "model": "dall-e-3",
+                "prompt": prompt,
+                "size": size,
+                "quality": quality,
+                "style": style,
+                "n": 1
+            }
+            
+            response = await self.httpClient.post(
+                imagesUrl,
+                json=payload
+            )
+            
+            if response.status_code != 200:
+                error_detail = f"LangDoc Image Generation API error: {response.status_code} - {response.text}"
+                logger.error(error_detail)
+                
+                # Provide more specific error messages
+                if response.status_code == 429:
+                    error_message = "Rate limit exceeded for image generation. Please wait before making another request."
+                elif response.status_code == 401:
+                    error_message = "Invalid API key for image generation. Please check your LangDoc API configuration."
+                elif response.status_code == 400:
+                    error_message = f"Invalid request to LangDoc Image API: {response.text}"
+                else:
+                    error_message = f"LangDoc Image API error ({response.status_code}): {response.text}"
+                
+                raise HTTPException(status_code=500, detail=error_message)
+            
+            responseJson = response.json()
+            
+            # Extract image data
+            imageData = responseJson.get("data", [])
+            if not imageData:
+                raise HTTPException(status_code=500, detail="No image data returned from LangDoc API")
+            
+            imageInfo = imageData[0]
+            
+            return {
+                "success": True,
+                "image_url": imageInfo.get("url"),
+                "revised_prompt": imageInfo.get("revised_prompt"),
+                "size": size,
+                "quality": quality,
+                "style": style,
+                "model": "dall-e-3",
+                "created": responseJson.get("created"),
+                "raw_response": responseJson
+            }
+            
+        except Exception as e:
+            logger.error(f"Error generating image with LangDoc: {str(e)}")
+            raise HTTPException(status_code=500, detail=f"Error generating image with LangDoc: {str(e)}")
+    
+    async def generateImageWithVariations(self, prompt: str, variations: int = 1, size: str = "1024x1024", quality: str = "standard", style: str = "vivid") -> List[Dict[str, Any]]:
+        """
+        Generates multiple image variations using LangDoc's DALL-E 3 integration.
+        
+        Args:
+            prompt: Text description of the image to generate
+            variations: Number of variations to generate (1-4)
+            size: Image size - "1024x1024", "1792x1024", or "1024x1792"
+            quality: Image quality - "standard" or "hd"
+            style: Image style - "vivid" or "natural"
+            
+        Returns:
+            List of dictionaries containing generated image data and metadata
+            
+        Raises:
+            HTTPException: For errors in API communication
+        """
+        try:
+            # Limit variations to reasonable number
+            variations = min(max(variations, 1), 4)
+            
+            # Use OpenAI-compatible images endpoint
+            imagesUrl = self.apiUrl.replace("/chat/completions", "/images/generations")
+            
+            results = []
+            
+            # Generate multiple variations by making multiple API calls
+            for i in range(variations):
+                # Add variation to prompt to get different results
+                variationPrompt = f"{prompt} (variation {i+1})"
+                
+                payload = {
+                    "model": "dall-e-3",
+                    "prompt": variationPrompt,
+                    "size": size,
+                    "quality": quality,
+                    "style": style,
+                    "n": 1
+                }
+                
+                response = await self.httpClient.post(
+                    imagesUrl,
+                    json=payload
+                )
+                
+                if response.status_code != 200:
+                    logger.warning(f"Failed to generate variation {i+1}: {response.status_code} - {response.text}")
+                    continue
+                
+                responseJson = response.json()
+                imageData = responseJson.get("data", [])
+                
+                if imageData:
+                    imageInfo = imageData[0]
+                    results.append({
+                        "variation": i + 1,
+                        "image_url": imageInfo.get("url"),
+                        "revised_prompt": imageInfo.get("revised_prompt"),
+                        "size": size,
+                        "quality": quality,
+                        "style": style,
+                        "model": "dall-e-3",
+                        "created": responseJson.get("created")
+                    })
+                
+                # Add small delay between requests to avoid rate limiting
+                if i < variations - 1:
+                    await asyncio.sleep(1)
+            
+            return results
+            
+        except Exception as e:
+            logger.error(f"Error generating image variations with LangDoc: {str(e)}")
+            raise HTTPException(status_code=500, detail=f"Error generating image variations with LangDoc: {str(e)}")
+    
+    async def generateImageWithChat(self, prompt: str, size: str = "1024x1024", quality: str = "standard", style: str = "vivid") -> str:
+        """
+        Generates an image using LangDoc's chat interface with image generation tools.
+        This method uses the chat completions endpoint with image generation capabilities.
+        
+        Args:
+            prompt: Text description of the image to generate
+            size: Image size - "1024x1024", "1792x1024", or "1024x1792"
+            quality: Image quality - "standard" or "hd"
+            style: Image style - "vivid" or "natural"
+            
+        Returns:
+            Response text from the chat model (may include image references)
+            
+        Raises:
+            HTTPException: For errors in API communication
+        """
+        try:
+            # Create a prompt that requests image generation
+            imagePrompt = f"Please generate an image with the following description: {prompt}. Size: {size}, Quality: {quality}, Style: {style}"
+            
+            messages = [
+                {
+                    "role": "user", 
+                    "content": imagePrompt
+                }
+            ]
+            
+            # Use the chat completions endpoint
+            response = await self.callAiBasic(messages)
+            
+            return response
+            
+        except Exception as e:
+            logger.error(f"Error generating image with chat: {str(e)}")
+            raise HTTPException(status_code=500, detail=f"Error generating image with chat: {str(e)}")
+    
+    async def _testConnection(self) -> bool:
+        """
+        Tests the connection to the LangDoc API.
+        
+        Returns:
+            True if connection is successful, False otherwise
+        """
+        try:
+            # Try to list models as a simple connection test
+            await self.listModels()
+            return True
+        except Exception as e:
+            logger.error(f"LangDoc connection test failed: {str(e)}")
+            return False

modules/connectors/connectorVoiceGoogle.py

@@ -665,6 +665,13 @@ class ConnectorGoogleSpeech:
             
             # Build the voice request
             selected_voice = voice_name or self._get_default_voice(language_code)
+            
+            if not selected_voice:
+                return {
+                    "success": False,
+                    "error": f"No voice specified for language {language_code}. Please select a voice."
+                }
+            
             logger.info(f"Using TTS voice: {selected_voice} for language: {language_code}")
             
             voice = texttospeech.VoiceSelectionParams(
@@ -704,121 +711,108 @@ class ConnectorGoogleSpeech:
     def _get_default_voice(self, language_code: str) -> str:
         """
         Get default voice name for a language code.
-        Uses female voices as default for better user experience.
+        Returns None - no defaults, let the frontend handle voice selection.
         """
-        voice_mapping = {
-            # European Languages
-            'de-DE': 'de-DE-Wavenet-B',  # German, female
-            'en-US': 'en-US-Wavenet-B',  # English US, female
-            'en-GB': 'en-GB-Wavenet-B',  # English UK, female
-            'en-AU': 'en-AU-Wavenet-B',  # English Australia, female
-            'en-CA': 'en-CA-Wavenet-B',  # English Canada, female
-            'en-IN': 'en-IN-Wavenet-B',  # English India, female
-            'fr-FR': 'fr-FR-Wavenet-B',  # French, female
-            'fr-CA': 'fr-CA-Wavenet-B',  # French Canada, female
-            'es-ES': 'es-ES-Wavenet-B',  # Spanish Spain, female
-            'es-MX': 'es-MX-Wavenet-B',  # Spanish Mexico, female
-            'es-AR': 'es-AR-Wavenet-B',  # Spanish Argentina, female
-            'es-CO': 'es-CO-Wavenet-B',  # Spanish Colombia, female
-            'es-PE': 'es-PE-Wavenet-B',  # Spanish Peru, female
-            'es-VE': 'es-VE-Wavenet-B',  # Spanish Venezuela, female
-            'es-CL': 'es-CL-Wavenet-B',  # Spanish Chile, female
-            'es-UY': 'es-UY-Wavenet-B',  # Spanish Uruguay, female
-            'es-BO': 'es-BO-Wavenet-B',  # Spanish Bolivia, female
-            'es-CR': 'es-CR-Wavenet-B',  # Spanish Costa Rica, female
-            'es-EC': 'es-EC-Wavenet-B',  # Spanish Ecuador, female
-            'es-GT': 'es-GT-Wavenet-B',  # Spanish Guatemala, female
-            'es-HN': 'es-HN-Wavenet-B',  # Spanish Honduras, female
-            'es-NI': 'es-NI-Wavenet-B',  # Spanish Nicaragua, female
-            'es-PA': 'es-PA-Wavenet-B',  # Spanish Panama, female
-            'es-PY': 'es-PY-Wavenet-B',  # Spanish Paraguay, female
-            'es-PR': 'es-PR-Wavenet-B',  # Spanish Puerto Rico, female
-            'es-DO': 'es-DO-Wavenet-B',  # Spanish Dominican Republic, female
-            'es-SV': 'es-SV-Wavenet-B',  # Spanish El Salvador, female
-            'it-IT': 'it-IT-Wavenet-B',  # Italian, female
-            'pt-PT': 'pt-PT-Wavenet-B',  # Portuguese Portugal, female
-            'pt-BR': 'pt-BR-Wavenet-B',  # Portuguese Brazil, female
-            'nl-NL': 'nl-NL-Wavenet-B',  # Dutch, female
-            'pl-PL': 'pl-PL-Wavenet-B',  # Polish, female
-            'ru-RU': 'ru-RU-Wavenet-B',  # Russian, female
-            'uk-UA': 'uk-UA-Wavenet-B',  # Ukrainian, female
-            'cs-CZ': 'cs-CZ-Wavenet-B',  # Czech, female
-            'sk-SK': 'sk-SK-Wavenet-B',  # Slovak, female
-            'hu-HU': 'hu-HU-Wavenet-B',  # Hungarian, female
-            'ro-RO': 'ro-RO-Wavenet-B',  # Romanian, female
-            'bg-BG': 'bg-BG-Wavenet-B',  # Bulgarian, female
-            'hr-HR': 'hr-HR-Wavenet-B',  # Croatian, female
-            'sr-RS': 'sr-RS-Wavenet-B',  # Serbian, female
-            'sl-SI': 'sl-SI-Wavenet-B',  # Slovenian, female
-            'et-EE': 'et-EE-Wavenet-B',  # Estonian, female
-            'lv-LV': 'lv-LV-Wavenet-B',  # Latvian, female
-            'lt-LT': 'lt-LT-Wavenet-B',  # Lithuanian, female
-            'fi-FI': 'fi-FI-Wavenet-B',  # Finnish, female
-            'sv-SE': 'sv-SE-Wavenet-B',  # Swedish, female
-            'no-NO': 'no-NO-Wavenet-B',  # Norwegian, female
-            'da-DK': 'da-DK-Wavenet-B',  # Danish, female
-            'is-IS': 'is-IS-Wavenet-B',  # Icelandic, female
-            'el-GR': 'el-GR-Wavenet-B',  # Greek, female
-            'ca-ES': 'ca-ES-Wavenet-B',  # Catalan, female
-            'eu-ES': 'eu-ES-Wavenet-B',  # Basque, female
-            'gl-ES': 'gl-ES-Wavenet-B',  # Galician, female
-            'cy-GB': 'cy-GB-Wavenet-B',  # Welsh, female
-            'ga-IE': 'ga-IE-Wavenet-B',  # Irish, female
-            'mt-MT': 'mt-MT-Wavenet-B',  # Maltese, female
+        return None
+    
+    async def get_available_languages(self) -> Dict[str, Any]:
+        """
+        Get available languages from Google Cloud Text-to-Speech.
+        
+        Returns:
+            Dict containing success status and list of available languages
+        """
+        try:
+            logger.info("🌐 Getting available languages from Google Cloud TTS")
             
-            # Asian Languages
-            'ja-JP': 'ja-JP-Wavenet-B',  # Japanese, female
-            'ko-KR': 'ko-KR-Wavenet-B',  # Korean, female
-            'zh-CN': 'cmn-CN-Wavenet-B', # Chinese Mandarin, female
-            'zh-TW': 'cmn-TW-Wavenet-B', # Chinese Traditional, female
-            'zh-HK': 'cmn-HK-Wavenet-B', # Chinese Hong Kong, female
-            'hi-IN': 'hi-IN-Wavenet-B',  # Hindi, female
-            'bn-IN': 'bn-IN-Wavenet-B',  # Bengali, female
-            'te-IN': 'te-IN-Wavenet-B',  # Telugu, female
-            'ta-IN': 'ta-IN-Wavenet-B',  # Tamil, female
-            'gu-IN': 'gu-IN-Wavenet-B',  # Gujarati, female
-            'kn-IN': 'kn-IN-Wavenet-B',  # Kannada, female
-            'ml-IN': 'ml-IN-Wavenet-B',  # Malayalam, female
-            'pa-IN': 'pa-IN-Wavenet-B',  # Punjabi, female
-            'or-IN': 'or-IN-Wavenet-B',  # Odia, female
-            'as-IN': 'as-IN-Wavenet-B',  # Assamese, female
-            'ne-NP': 'ne-NP-Wavenet-B',  # Nepali, female
-            'si-LK': 'si-LK-Wavenet-B',  # Sinhala, female
-            'th-TH': 'th-TH-Wavenet-B',  # Thai, female
-            'vi-VN': 'vi-VN-Wavenet-B',  # Vietnamese, female
-            'id-ID': 'id-ID-Wavenet-B',  # Indonesian, female
-            'ms-MY': 'ms-MY-Wavenet-B',  # Malay, female
-            'tl-PH': 'fil-PH-Wavenet-B', # Filipino, female
-            'tr-TR': 'tr-TR-Wavenet-B',  # Turkish, female
+            # List voices from Google Cloud TTS
+            voices = self.tts_client.list_voices()
             
-            # Middle Eastern & African Languages
-            'ar-SA': 'ar-SA-Wavenet-B',  # Arabic Saudi Arabia, female
-            'ar-EG': 'ar-EG-Wavenet-B',  # Arabic Egypt, female
-            'ar-AE': 'ar-AE-Wavenet-B',  # Arabic UAE, female
-            'ar-JO': 'ar-JO-Wavenet-B',  # Arabic Jordan, female
-            'ar-KW': 'ar-KW-Wavenet-B',  # Arabic Kuwait, female
-            'ar-LB': 'ar-LB-Wavenet-B',  # Arabic Lebanon, female
-            'ar-QA': 'ar-QA-Wavenet-B',  # Arabic Qatar, female
-            'ar-BH': 'ar-BH-Wavenet-B',  # Arabic Bahrain, female
-            'ar-OM': 'ar-OM-Wavenet-B',  # Arabic Oman, female
-            'ar-IQ': 'ar-IQ-Wavenet-B',  # Arabic Iraq, female
-            'ar-PS': 'ar-PS-Wavenet-B',  # Arabic Palestine, female
-            'ar-SY': 'ar-SY-Wavenet-B',  # Arabic Syria, female
-            'ar-YE': 'ar-YE-Wavenet-B',  # Arabic Yemen, female
-            'ar-MA': 'ar-MA-Wavenet-B',  # Arabic Morocco, female
-            'ar-DZ': 'ar-DZ-Wavenet-B',  # Arabic Algeria, female
-            'ar-TN': 'ar-TN-Wavenet-B',  # Arabic Tunisia, female
-            'ar-LY': 'ar-LY-Wavenet-B',  # Arabic Libya, female
-            'ar-SD': 'ar-SD-Wavenet-B',  # Arabic Sudan, female
-            'he-IL': 'he-IL-Wavenet-B',  # Hebrew, female
-            'fa-IR': 'fa-IR-Wavenet-B',  # Persian, female
-            'ur-PK': 'ur-PK-Wavenet-B',  # Urdu, female
-            'af-ZA': 'af-ZA-Wavenet-B',  # Afrikaans, female
-            'sw-KE': 'sw-KE-Wavenet-B',  # Swahili Kenya, female
-            'am-ET': 'am-ET-Wavenet-B',  # Amharic, female
-            'sw-TZ': 'sw-TZ-Wavenet-B',  # Swahili Tanzania, female
-            'zu-ZA': 'zu-ZA-Wavenet-B',  # Zulu, female
-            'xh-ZA': 'xh-ZA-Wavenet-B',  # Xhosa, female
-        }
-        return voice_mapping.get(language_code, 'en-US-Wavenet-B')
+            # Extract unique language codes
+            language_codes = set()
+            for voice in voices:
+                if voice.language_codes:
+                    language_codes.update(voice.language_codes)
+            
+            # Convert to sorted list
+            available_languages = sorted(list(language_codes))
+            
+            logger.info(f"✅ Found {len(available_languages)} available languages")
+            
+            return {
+                "success": True,
+                "languages": available_languages
+            }
+            
+        except Exception as e:
+            logger.error(f"❌ Failed to get available languages: {e}")
+            return {
+                "success": False,
+                "error": str(e),
+                "languages": []
+            }
+    
+    async def get_available_voices(self, language_code: Optional[str] = None) -> Dict[str, Any]:
+        """
+        Get available voices from Google Cloud Text-to-Speech.
+        
+        Args:
+            language_code: Optional language code to filter voices (e.g., 'de-DE', 'en-US')
+            
+        Returns:
+            Dict containing success status and list of available voices
+        """
+        try:
+            logger.info(f"🎤 Getting available voices from Google Cloud TTS, language filter: {language_code}")
+            
+            # List voices from Google Cloud TTS
+            voices = self.tts_client.list_voices()
+            
+            available_voices = []
+            
+            for voice in voices:
+                # Extract language code from voice name (e.g., 'de-DE-Wavenet-A' -> 'de-DE')
+                voice_language = voice.language_codes[0] if voice.language_codes else None
+                
+                # Filter by language if specified
+                if language_code and voice_language != language_code:
+                    continue
+                
+                # Determine gender from voice name (A/C = male, B/D = female)
+                gender = "Unknown"
+                if voice.name:
+                    if voice.name.endswith(('-A', '-C')):
+                        gender = "Male"
+                    elif voice.name.endswith(('-B', '-D')):
+                        gender = "Female"
+                
+                # Create voice info
+                voice_info = {
+                    "name": voice.name,
+                    "language_code": voice_language,
+                    "gender": gender,
+                    "ssml_gender": voice.ssml_gender.name if voice.ssml_gender else "NEUTRAL",
+                    "natural_sample_rate_hertz": voice.natural_sample_rate_hertz
+                }
+                
+                available_voices.append(voice_info)
+            
+            # Sort by language code, then by gender, then by name
+            available_voices.sort(key=lambda x: (x["language_code"], x["gender"], x["name"]))
+            
+            logger.info(f"✅ Found {len(available_voices)} voices for language filter: {language_code}")
+            
+            return {
+                "success": True,
+                "voices": available_voices,
+                "total_count": len(available_voices)
+            }
+            
+        except Exception as e:
+            logger.error(f"❌ Failed to get available voices: {e}")
+            return {
+                "success": False,
+                "error": str(e),
+                "voices": []
+            }
 

modules/datamodels/datamodelAi.py

@@ -1,16 +1,98 @@
-from typing import Optional
+from typing import Optional, List
 from pydantic import BaseModel, Field
 
 
-class AiCallOptions(BaseModel):
-    """Options for centralized AI processing (no document extraction here)."""
+# Operation Types
+class OperationType:
+    GENERAL = "general"
+    GENERATE_PLAN = "generate_plan"
+    ANALYSE_CONTENT = "analyse_content"
+    GENERATE_CONTENT = "generate_content"
+    WEB_RESEARCH = "web_research"
+    IMAGE_ANALYSIS = "image_analysis"
+    IMAGE_GENERATION = "image_generation"
 
-    operationType: str = Field(default="general", description="Type of operation")
+
+# Processing Modes
+class ProcessingMode:
+    BASIC = "basic"
+    ADVANCED = "advanced"
+    DETAILED = "detailed"
+
+
+# Priority Levels
+class Priority:
+    SPEED = "speed"
+    QUALITY = "quality"
+    COST = "cost"
+    BALANCED = "balanced"
+
+
+# Model Tags
+class ModelTags:
+    # Core capabilities
+    TEXT = "text"
+    CHAT = "chat"
+    REASONING = "reasoning"
+    ANALYSIS = "analysis"
+    IMAGE = "image"
+    VISION = "vision"
+    MULTIMODAL = "multimodal"
+    WEB = "web"
+    SEARCH = "search"
+    CRAWL = "crawl"
+    EXTRACT = "extract"
+    CONTENT = "content"
+    INFORMATION = "information"
+    
+    # Quality indicators
+    HIGH_QUALITY = "high_quality"
+    FAST = "fast"
+    COST_EFFECTIVE = "cost_effective"
+    GENERAL = "general"
+    
+    # Specialized capabilities
+    IMAGE_GENERATION = "image_generation"
+    ART = "art"
+    VISUAL = "visual"
+    VARIATIONS = "variations"
+    API = "api"
+    INFO = "info"
+    MODELS = "models"
+
+
+# Operation Type to Required Tags Mapping
+OPERATION_TAG_MAPPING = {
+    OperationType.GENERAL: [ModelTags.TEXT, ModelTags.CHAT, ModelTags.REASONING],
+    OperationType.GENERATE_PLAN: [ModelTags.TEXT, ModelTags.REASONING, ModelTags.ANALYSIS],
+    OperationType.ANALYSE_CONTENT: [ModelTags.TEXT, ModelTags.ANALYSIS, ModelTags.REASONING],
+    OperationType.GENERATE_CONTENT: [ModelTags.TEXT, ModelTags.CHAT, ModelTags.REASONING],
+    OperationType.WEB_RESEARCH: [ModelTags.TEXT, ModelTags.ANALYSIS, ModelTags.REASONING],
+    OperationType.IMAGE_ANALYSIS: [ModelTags.IMAGE, ModelTags.VISION, ModelTags.MULTIMODAL],
+    OperationType.IMAGE_GENERATION: [ModelTags.IMAGE_GENERATION, ModelTags.ART, ModelTags.VISUAL],
+}
+
+
+# Processing Mode to Priority Mapping
+PROCESSING_MODE_PRIORITY_MAPPING = {
+    ProcessingMode.BASIC: Priority.SPEED,
+    ProcessingMode.ADVANCED: Priority.BALANCED,
+    ProcessingMode.DETAILED: Priority.QUALITY,
+}
+
+
+class AiCallOptions(BaseModel):
+    """Options for centralized AI processing with clear operation types and tags."""
+
+    operationType: str = Field(default="general", description="Type of operation: general, generate_plan, analyse_content, generate_content, web_research")
     priority: str = Field(default="balanced", description="speed|quality|cost|balanced")
     compressPrompt: bool = Field(default=True, description="Whether to compress the prompt")
     compressContext: bool = Field(default=True, description="Whether to compress optional context")
     maxCost: Optional[float] = Field(default=None, description="Max cost budget")
     maxProcessingTime: Optional[int] = Field(default=None, description="Max processing time in seconds")
+    requiredTags: Optional[List[str]] = Field(default=None, description="Required model tags for selection")
+    processingMode: str = Field(default="basic", description="Processing mode: basic, advanced, detailed")
+    resultFormat: Optional[str] = Field(default=None, description="Expected result format: txt, json, csv, xml, etc.")
 
 
 class AiCallRequest(BaseModel):

modules/datamodels/datamodelChat.py

@@ -260,3 +260,45 @@ register_model_labels(
 )
 
 
+class WorkflowResult(BaseModel, ModelMixin):
+    status: str
+    completed_tasks: int
+    total_tasks: int
+    execution_time: float
+    final_results_count: int
+    error: Optional[str] = None
+    phase: Optional[str] = None
+
+
+register_model_labels(
+    "WorkflowResult",
+    {"en": "Workflow Result", "fr": "Résultat du workflow"},
+    {
+        "status": {"en": "Status", "fr": "Statut"},
+        "completed_tasks": {"en": "Completed Tasks", "fr": "Tâches terminées"},
+        "total_tasks": {"en": "Total Tasks", "fr": "Total des tâches"},
+        "execution_time": {"en": "Execution Time", "fr": "Temps d'exécution"},
+        "final_results_count": {"en": "Final Results Count", "fr": "Nombre de résultats finaux"},
+        "error": {"en": "Error", "fr": "Erreur"},
+        "phase": {"en": "Phase", "fr": "Phase"},
+    },
+)
+
+
+class UserInputRequest(BaseModel, ModelMixin):
+    prompt: str = Field(description="Prompt for the user")
+    listFileId: List[str] = Field(default_factory=list, description="List of file IDs")
+    userLanguage: str = Field(default="en", description="User's preferred language")
+
+
+register_model_labels(
+    "UserInputRequest",
+    {"en": "User Input Request", "fr": "Demande de saisie utilisateur"},
+    {
+        "prompt": {"en": "Prompt", "fr": "Invite"},
+        "listFileId": {"en": "File IDs", "fr": "IDs des fichiers"},
+        "userLanguage": {"en": "User Language", "fr": "Langue de l'utilisateur"},
+    },
+)
+
+

modules/datamodels/datamodelWeb.py

@@ -1,4 +1,4 @@
-"""Web-related modules.datamodels (search, crawl, scrape)."""
+"""Web-related modules"""
 
 from abc import ABC, abstractmethod
 from pydantic import BaseModel, Field, HttpUrl

modules/datamodels/datamodelWorkflow.py

@@ -10,8 +10,6 @@ class ActionDocument(BaseModel, ModelMixin):
     documentName: str = Field(description="Name of the document")
     documentData: Any = Field(description="Content/data of the document")
     mimeType: str = Field(description="MIME type of the document")
-
-
 register_model_labels(
     "ActionDocument",
     {"en": "Action Document", "fr": "Document d'action"},
@@ -43,8 +41,6 @@ class ActionResult(BaseModel, ModelMixin):
     @classmethod
     def isFailure(cls, error: str, documents: List[ActionDocument] = None) -> "ActionResult":
         return cls(success=False, documents=documents or [], error=error)
-
-
 register_model_labels(
     "ActionResult",
     {"en": "Action Result", "fr": "Résultat de l'action"},
@@ -57,9 +53,6 @@ register_model_labels(
 )
 
 
-# ===== Additional workflow models migrated from interfaceChatModel =====
-
-
 class ActionSelection(BaseModel, ModelMixin):
     method: str = Field(description="Method to execute (e.g., web, document, ai)")
     name: str = Field(description="Action name within the method (e.g., search, extract)")
@@ -402,45 +395,5 @@ register_model_labels(
 )
 
 
-class WorkflowResult(BaseModel, ModelMixin):
-    status: str
-    completed_tasks: int
-    total_tasks: int
-    execution_time: float
-    final_results_count: int
-    error: Optional[str] = None
-    phase: Optional[str] = None
-
-
-register_model_labels(
-    "WorkflowResult",
-    {"en": "Workflow Result", "fr": "Résultat du workflow"},
-    {
-        "status": {"en": "Status", "fr": "Statut"},
-        "completed_tasks": {"en": "Completed Tasks", "fr": "Tâches terminées"},
-        "total_tasks": {"en": "Total Tasks", "fr": "Total des tâches"},
-        "execution_time": {"en": "Execution Time", "fr": "Temps d'exécution"},
-        "final_results_count": {"en": "Final Results Count", "fr": "Nombre de résultats finaux"},
-        "error": {"en": "Error", "fr": "Erreur"},
-        "phase": {"en": "Phase", "fr": "Phase"},
-    },
-)
-
-
-class UserInputRequest(BaseModel, ModelMixin):
-    prompt: str = Field(description="Prompt for the user")
-    listFileId: List[str] = Field(default_factory=list, description="List of file IDs")
-    userLanguage: str = Field(default="en", description="User's preferred language")
-
-
-register_model_labels(
-    "UserInputRequest",
-    {"en": "User Input Request", "fr": "Demande de saisie utilisateur"},
-    {
-        "prompt": {"en": "Prompt", "fr": "Invite"},
-        "listFileId": {"en": "File IDs", "fr": "IDs des fichiers"},
-        "userLanguage": {"en": "User Language", "fr": "Langue de l'utilisateur"},
-    },
-)
 
 

modules/features/chatPlayground/mainChatPlayground.py

@@ -3,29 +3,27 @@ import asyncio
 from typing import Optional
 
 from modules.datamodels.datamodelUam import User
-from modules.datamodels.datamodelChat import ChatWorkflow
-from modules.datamodels.datamodelWorkflow import UserInputRequest
-from modules.shared.timezoneUtils import get_utc_timestamp
+from modules.datamodels.datamodelChat import ChatWorkflow, UserInputRequest
+from modules.workflows.workflowManager import WorkflowManager
+from modules.services import getInterface as getServices
 
 logger = logging.getLogger(__name__)
 
-async def chatStart(interfaceChat, currentUser: User, userInput: UserInputRequest, workflowId: Optional[str] = None, workflowMode: str = "Actionplan") -> ChatWorkflow:
+async def chatStart(interfaceDbChat, currentUser: User, userInput: UserInputRequest, workflowId: Optional[str] = None, workflowMode: str = "Actionplan") -> ChatWorkflow:
     """
     Starts a new chat or continues an existing one, then launches processing asynchronously.
     
     Args:
-        interfaceChat: Chat interface instance
+        interfaceDbChat: Chat interface instance
         currentUser: Current user
         userInput: User input request
         workflowId: Optional workflow ID to continue existing workflow
         workflowMode: "Actionplan" for traditional task planning, "React" for iterative react-style processing
     
     Example usage for React mode:
-        workflow = await chatStart(interfaceChat, currentUser, userInput, workflowMode="React")
+        workflow = await chatStart(interfaceDbChat, currentUser, userInput, workflowMode="React")
     """
     try:
-        from modules.workflows.workflowManager import WorkflowManager
-        from modules.services import getInterface as getServices
         services = getServices(currentUser, None)
         workflowManager = WorkflowManager(services)
         workflow = await workflowManager.workflowStart(userInput, workflowId, workflowMode)
@@ -34,11 +32,9 @@ async def chatStart(interfaceChat, currentUser: User, userInput: UserInputReques
         logger.error(f"Error starting chat: {str(e)}")
         raise
 
-async def chatStop(interfaceChat, currentUser: User, workflowId: str) -> ChatWorkflow:
+async def chatStop(interfaceDbChat, currentUser: User, workflowId: str) -> ChatWorkflow:
     """Stops a running chat."""
     try:
-        from modules.workflows.workflowManager import WorkflowManager
-        from modules.services import getInterface as getServices
         services = getServices(currentUser, None)
         workflowManager = WorkflowManager(services)
         return await workflowManager.workflowStop(workflowId)

modules/features/init.py

@@ -2,8 +2,13 @@
 
 import asyncio
 import logging
+from modules.interfaces.interfaceDbAppObjects import getRootInterface
 
 logger = logging.getLogger(__name__)
+rootInterface = getRootInterface()
+eventUser = rootInterface.getUserByUsername("event")
+
+# Custom features launch
 
-# Import the syncDelta module to initialize it (this will register the scheduler)
 from modules.features.syncDelta import mainSyncDelta
+mainSyncDelta.startSyncManager(eventUser)

modules/features/neutralizePlayground/mainNeutralizePlayground.py

@@ -1,9 +1,11 @@
 import logging
+import asyncio
 from typing import Any, Dict, List, Optional
+from urllib.parse import urlparse, unquote
 
 from modules.datamodels.datamodelUam import User
 from modules.datamodels.datamodelNeutralizer import DataNeutralizerAttributes, DataNeutraliserConfig
-from modules.services.serviceNeutralization.mainServiceNeutralization import NeutralizationService
+from modules.services import getInterface as getServices
 
 logger = logging.getLogger(__name__)
 
@@ -13,17 +15,17 @@ class NeutralizationPlayground:
 
     def __init__(self, currentUser: User):
         self.currentUser = currentUser
-        self.service = NeutralizationService(currentUser)
+        self.services = getServices(currentUser, None)
 
     def processText(self, text: str) -> Dict[str, Any]:
-        return self.service.processText(text)
+        return self.services.neutralization.processText(text)
 
     def processFiles(self, fileIds: List[str]) -> Dict[str, Any]:
         results: List[Dict[str, Any]] = []
         errors: List[str] = []
         for fileId in fileIds:
             try:
-                res = self.service.processFile(fileId)
+                res = self.services.neutralization.processFile(fileId)
                 results.append({
                     'file_id': fileId,
                     'neutralized_file_name': res.get('neutralized_file_name'),
@@ -42,20 +44,18 @@ class NeutralizationPlayground:
         }
 
     async def processSharepointFiles(self, sourcePath: str, targetPath: str) -> Dict[str, Any]:
-        from modules.features.neutralizePlayground.sharepoint import SharepointProcessor
-        processor = SharepointProcessor(self.currentUser, self.service)
+        from modules.services.serviceSharepoint.mainServiceSharepoint import SharepointService
+        processor = SharepointProcessor(self.currentUser, self.services)
         return await processor.processSharepointFiles(sourcePath, targetPath)
 
     # Cleanup attributes
     def cleanAttributes(self, fileId: str) -> bool:
-        if not self.service.app_interface:
-            return False
-        return self.service.app_interface.deleteNeutralizationAttributes(fileId)
+        return self.services.neutralization.deleteNeutralizationAttributes(fileId)
 
     # Stats
     def getStats(self) -> Dict[str, Any]:
         try:
-            allAttributes = self.service._getAttributes()
+            allAttributes = self.services.neutralization.getAttributes()
             patternCounts: Dict[str, int] = {}
             for attr in allAttributes:
                 patternType = attr.patternType
@@ -79,26 +79,24 @@ class NeutralizationPlayground:
     # Additional methods needed by the route
     def get_config(self) -> Optional[DataNeutraliserConfig]:
         """Get neutralization configuration"""
-        return self.service.getConfig()
+        return self.services.neutralization.getConfig()
 
     def save_config(self, config_data: Dict[str, Any]) -> DataNeutraliserConfig:
         """Save neutralization configuration"""
-        return self.service.saveConfig(config_data)
+        return self.services.neutralization.saveConfig(config_data)
 
     def neutralize_text(self, text: str, file_id: str = None) -> Dict[str, Any]:
         """Neutralize text content"""
-        return self.service.processText(text)
+        return self.services.neutralization.processText(text)
 
     def resolve_text(self, text: str) -> str:
         """Resolve UIDs in neutralized text back to original text"""
-        return self.service.resolveText(text)
+        return self.services.neutralization.resolveText(text)
 
     def get_attributes(self, file_id: str = None) -> List[DataNeutralizerAttributes]:
         """Get neutralization attributes, optionally filtered by file ID"""
-        if not self.service.app_interface:
-            return []
         try:
-            all_attributes = self.service._getAttributes()
+            all_attributes = self.services.neutralization.getAttributes()
             if file_id:
                 return [attr for attr in all_attributes if attr.fileId == file_id]
             return all_attributes
@@ -126,13 +124,15 @@ class NeutralizationPlayground:
 
 # Internal SharePoint helper module separated to keep feature logic tidy
 class SharepointProcessor:
-    def __init__(self, currentUser: User, service: NeutralizationService):
+    def __init__(self, currentUser: User, services):
         self.currentUser = currentUser
-        self.service = service
+        self.services = services
 
     async def processSharepointFiles(self, sourcePath: str, targetPath: str) -> Dict[str, Any]:
         try:
             logger.info(f"Processing SharePoint files from {sourcePath} to {targetPath}")
+            
+            # Get SharePoint connection
             connection = await self._getSharepointConnection(sourcePath)
             if not connection:
                 return {
@@ -141,16 +141,17 @@ class SharepointProcessor:
                     'processed_files': 0,
                     'errors': ['No SharePoint connection found'],
                 }
-            from modules.security.tokenManager import TokenManager
-            token = TokenManager().getFreshToken(self.service.app_interface, connection['id'])
-            if not token:
+            
+            # Set access token for SharePoint service
+            if not self.services.sharepoint.setAccessTokenFromConnection(connection):
                 return {
                     'success': False,
-                    'message': 'No SharePoint access token found',
+                    'message': 'Failed to set SharePoint access token',
                     'processed_files': 0,
-                    'errors': ['No SharePoint access token found'],
+                    'errors': ['Failed to set SharePoint access token'],
                 }
-            return await self._processSharepointFilesAsync(sourcePath, targetPath, token.tokenAccess)
+            
+            return await self._processSharepointFilesAsync(sourcePath, targetPath)
         except Exception as e:
             logger.error(f"Error processing SharePoint files: {str(e)}")
             return {
@@ -163,9 +164,9 @@ class SharepointProcessor:
     async def _getSharepointConnection(self, sharepointPath: str = None):
         try:
             from modules.datamodels.datamodelUam import UserConnection
-            connections = self.service.app_interface.db.getRecordset(
+            connections = self.services.interfaceDbApp.db.getRecordset(
                 UserConnection,
-                recordFilter={"userId": self.service.app_interface.userId}
+                recordFilter={"userId": self.services.interfaceDbApp.userId}
             )
             msftConnections = [c for c in connections if c.get('authority') == 'msft']
             if not msftConnections:
@@ -184,16 +185,14 @@ class SharepointProcessor:
 
     async def _matchConnectionToPath(self, connections: list, sharepointPath: str):
         try:
-            from urllib.parse import urlparse
             targetDomain = urlparse(sharepointPath).netloc.lower()
             logger.info(f"Looking for connection matching domain: {targetDomain}")
-            from modules.security.tokenManager import TokenManager
+            
             for connection in connections:
                 try:
-                    token = TokenManager().getFreshToken(self.service.app_interface, connection['id'])
-                    if not token:
+                    if not self.services.sharepoint.setAccessTokenFromConnection(connection):
                         continue
-                    if await self._testSharepointAccess(token.tokenAccess, sharepointPath):
+                    if await self._testSharepointAccess(sharepointPath):
                         logger.info(f"Found matching connection for domain {targetDomain}: {connection.get('id')}")
                         return connection
                 except Exception:
@@ -204,44 +203,33 @@ class SharepointProcessor:
             logger.error('Error matching connection to path')
             return connections[0] if connections else None
 
-    async def _testSharepointAccess(self, accessToken: str, sharepointPath: str) -> bool:
+    async def _testSharepointAccess(self, sharepointPath: str) -> bool:
         try:
-            return await self._testSharepointAccessAsync(accessToken, sharepointPath)
-        except Exception:
-            return False
-
-    async def _testSharepointAccessAsync(self, accessToken: str, sharepointPath: str) -> bool:
-        try:
-            from modules.services.serviceSharepoint.mainSharepoint import SharepointService
-            connector = SharepointService(access_token=accessToken)
             siteUrl, _ = self._parseSharepointPath(sharepointPath)
             if not siteUrl:
                 return False
-            siteInfo = await connector.find_site_by_web_url(siteUrl)
+            siteInfo = await self.services.sharepoint.find_site_by_web_url(siteUrl)
             return siteInfo is not None
         except Exception:
             return False
 
-    async def _processSharepointFilesAsync(self, sourcePath: str, targetPath: str, accessToken: str) -> Dict[str, Any]:
+    async def _processSharepointFilesAsync(self, sourcePath: str, targetPath: str) -> Dict[str, Any]:
         try:
-            import asyncio
-            from modules.services.serviceSharepoint.mainSharepoint import SharepointService
-            connector = SharepointService(access_token=accessToken)
             sourceSite, sourceFolder = self._parseSharepointPath(sourcePath)
             targetSite, targetFolder = self._parseSharepointPath(targetPath)
             if not sourceSite or not targetSite:
                 return {'success': False, 'message': 'Invalid SharePoint path format', 'processed_files': 0, 'errors': ['Invalid SharePoint path format']}
-            sourceSiteInfo = await connector.find_site_by_web_url(sourceSite)
+            sourceSiteInfo = await self.services.sharepoint.find_site_by_web_url(sourceSite)
             if not sourceSiteInfo:
                 return {'success': False, 'message': f'Source site not found: {sourceSite}', 'processed_files': 0, 'errors': [f'Source site not found: {sourceSite}']}
-            targetSiteInfo = await connector.find_site_by_web_url(targetSite)
+            targetSiteInfo = await self.services.sharepoint.find_site_by_web_url(targetSite)
             if not targetSiteInfo:
                 return {'success': False, 'message': f'Target site not found: {targetSite}', 'processed_files': 0, 'errors': [f'Target site not found: {targetSite}']}
             logger.info(f"Listing files in folder: {sourceFolder} for site: {sourceSiteInfo['id']}")
-            files = await connector.list_folder_contents(sourceSiteInfo['id'], sourceFolder)
+            files = await self.services.sharepoint.list_folder_contents(sourceSiteInfo['id'], sourceFolder)
             if not files:
                 logger.warning(f"No files found in folder '{sourceFolder}', trying root folder")
-                files = await connector.list_folder_contents(sourceSiteInfo['id'], '')
+                files = await self.services.sharepoint.list_folder_contents(sourceSiteInfo['id'], '')
                 if files:
                     folders = [f for f in files if f.get('type') == 'folder']
                     folderNames = [f.get('name') for f in folders]
@@ -263,16 +251,16 @@ class SharepointProcessor:
 
             async def _processSingle(fileInfo: Dict[str, Any]):
                 try:
-                    fileContent = await connector.download_file(sourceSiteInfo['id'], fileInfo['id'])
+                    fileContent = await self.services.sharepoint.download_file(sourceSiteInfo['id'], fileInfo['id'])
                     if not fileContent:
                         return {'error': f"Failed to download file: {fileInfo['name']}"}
                     try:
                         textContent = fileContent.decode('utf-8')
                     except UnicodeDecodeError:
                         textContent = fileContent.decode('latin-1')
-                    result = self.service._neutralizeText(textContent, 'text')
+                    result = self.services.neutralization.processText(textContent)
                     neutralizedFilename = f"neutralized_{fileInfo['name']}"
-                    uploadResult = await connector.upload_file(targetSiteInfo['id'], targetFolder, neutralizedFilename, result['neutralized_text'].encode('utf-8'))
+                    uploadResult = await self.services.sharepoint.upload_file(targetSiteInfo['id'], targetFolder, neutralizedFilename, result['neutralized_text'].encode('utf-8'))
                     if 'error' in uploadResult:
                         return {'error': f"Failed to upload neutralized file: {neutralizedFilename} - {uploadResult['error']}"}
                     return {
@@ -325,7 +313,6 @@ class SharepointProcessor:
             siteName = parts[1].split('/')[0]
             siteUrl = f"https://{domain}/sites/{siteName}"
             folderParts = parts[1].split('/')[1:]
-            from urllib.parse import unquote
             folderPath = unquote('/'.join(folderParts) if folderParts else '')
             return siteUrl, folderPath
         except Exception:

modules/features/syncDelta/mainSyncDelta.py

@@ -9,18 +9,15 @@ import asyncio
 import logging
 import os
 import io
+import pandas as pd
+import csv as csv_module
+from io import StringIO, BytesIO
 from datetime import datetime, UTC
 from typing import Dict, Any, List, Optional
 from modules.services import getInterface as getServices
-# Removed direct import - now using services.ticket
-from modules.shared.configuration import APP_CONFIG
 
 logger = logging.getLogger(__name__)
 
-# Get environment type from configuration
-APP_ENV_TYPE = APP_CONFIG.get("APP_ENV_TYPE", "dev")
-
-
 class ManagerSyncDelta:
     """Manages Tickets to SharePoint synchronization for Delta Group.
     
@@ -46,7 +43,7 @@ class ManagerSyncDelta:
     
     # Tickets connection parameters
     JIRA_USERNAME = "p.motsch@valueon.ch"
-    JIRA_API_TOKEN = APP_CONFIG.get("Feature_SyncDelta_JIRA_DELTA_TOKEN_SECRET", "")
+    JIRA_API_TOKEN = ""  # Will be set in __init__
     JIRA_URL = "https://deltasecurity.atlassian.net"
     JIRA_PROJECT_CODE = "DCS"
     JIRA_ISSUE_TYPE = "Task"
@@ -84,6 +81,9 @@ class ManagerSyncDelta:
                 self._log_audit_event("SYNC_INIT", "FAILED", "Event user not found")
             else:
                 self.services = getServices(eventUser, None)
+                # Read config values using services
+                self.APP_ENV_TYPE = self.services.utils.configGet("APP_ENV_TYPE", "dev")
+                self.JIRA_API_TOKEN = self.services.utils.configGet("Feature_SyncDelta_JIRA_DELTA_TOKEN_SECRET", "")
                 # Resolve SharePoint connection for the configured user id
                 self.sharepointConnection = self.services.workflow.getUserConnectionByExternalUsername("msft", self.SHAREPOINT_USER_ID)
                 if not self.sharepointConnection:
@@ -93,8 +93,8 @@ class ManagerSyncDelta:
                     self._log_audit_event("SYNC_INIT", "FAILED", f"No SharePoint connection for user: {self.SHAREPOINT_USER_ID}")
                 else:
                     # Configure SharePoint service token and set connector reference
-                    if not self.services.sharepoint.setAccessToken(
-                        self.sharepointConnection, self.services.interfaceApp
+                    if not self.services.sharepoint.setAccessTokenFromConnection(
+                        self.sharepointConnection
                     ):
                         logger.error("Failed to set SharePoint token from UserConnection")
                         self._log_audit_event("SYNC_INIT", "FAILED", "Failed to set SharePoint token")
@@ -110,7 +110,7 @@ class ManagerSyncDelta:
     def _log_audit_event(self, action: str, status: str, details: str):
         """Log audit events for sync operations to memory."""
         try:
-            timestamp = datetime.now(UTC).strftime("%Y-%m-%d %H:%M:%S UTC")
+            timestamp = datetime.fromtimestamp(self.services.utils.getUtcTimestamp(), UTC).strftime("%Y-%m-%d %H:%M:%S UTC")
             user_id = str(self.eventUser.id) if self.eventUser else "system"
             log_entry = f"{timestamp} | {user_id} | {action} | {status} | {details}"
             self.sync_audit_log.append(log_entry)
@@ -146,7 +146,7 @@ class ManagerSyncDelta:
                 return False
             
             # Generate log filename with current timestamp
-            timestamp = datetime.now(UTC).strftime("%Y%m%d_%H%M%S")
+            timestamp = datetime.fromtimestamp(self.services.utils.getUtcTimestamp(), UTC).strftime("%Y%m%d_%H%M%S")
             log_filename = f"log_{timestamp}.log"
             
             # Create log content
@@ -273,7 +273,7 @@ class ManagerSyncDelta:
             logger.info(f"Using sync file: {sync_file_name}")
             
             # Create list-based ticket interface (initialize connector by type)
-            sync_interface = await self.services.ticket.createTicketInterfaceByType(
+            sync_interface = await self.services.ticket._createTicketInterfaceByType(
                 taskSyncDefinition=self.TASK_SYNC_DEFINITION,
                 connectorType="Jira",
                 connectorParams={
@@ -343,7 +343,6 @@ class ManagerSyncDelta:
                         existing_headers["header1"] = csv_lines[0].rstrip('\r\n')
                         existing_headers["header2"] = csv_lines[1].rstrip('\r\n')
                     # Parse existing CSV rows after the two header lines
-                    import pandas as pd
                     df_existing = pd.read_csv(io.BytesIO(csv_content), skiprows=2, quoting=1, escapechar='\\', on_bad_lines='skip', engine='python')
                     existing_data = df_existing.to_dict('records')
                 except Exception:
@@ -362,7 +361,6 @@ class ManagerSyncDelta:
                     csv_content = await self.services.sharepoint.download_file_by_path(
                         site_id=self.targetSite['id'], file_path=file_path
                     )
-                    import pandas as pd
                     df = pd.read_csv(io.BytesIO(csv_content), skiprows=2, quoting=1, escapechar='\\', on_bad_lines='skip', engine='python')
                     csv_rows = df.to_dict('records')
                     self._log_audit_event("SYNC_IMPORT", "INFO", f"Importing {len(csv_rows)} CSV rows back to tickets")
@@ -390,7 +388,7 @@ class ManagerSyncDelta:
 
     async def backupSharepointFile(self, *, filename: str) -> bool:
         try:
-            timestamp = datetime.now(UTC).strftime("%Y%m%d_%H%M%S")
+            timestamp = datetime.fromtimestamp(self.services.utils.getUtcTimestamp(), UTC).strftime("%Y%m%d_%H%M%S")
             backup_filename = f"backup_{timestamp}_{filename}"
             await self.services.sharepoint.copy_file_async(
                 site_id=self.targetSite['id'],
@@ -454,9 +452,7 @@ class ManagerSyncDelta:
         return merged_data, details
 
     def createCsvContent(self, data: list[dict], existing_headers: dict | None = None) -> bytes:
-        import pandas as pd
-        from io import StringIO
-        timestamp = datetime.now(UTC).strftime("%Y-%m-%d %H:%M:%S UTC")
+        timestamp = datetime.fromtimestamp(self.services.utils.getUtcTimestamp(), UTC).strftime("%Y-%m-%d %H:%M:%S UTC")
         if existing_headers is None:
             existing_headers = {"header1": "Header 1", "header2": "Header 2"}
         if not data:
@@ -467,7 +463,6 @@ class ManagerSyncDelta:
         for column in df.columns:
             df[column] = df[column].astype("object").fillna("")
             df[column] = df[column].astype(str).str.replace('\n', '\\n', regex=False).str.replace('"', '""', regex=False)
-        import csv as csv_module
         header1_row = next(csv_module.reader([existing_headers.get("header1", "Header 1")]), [])
         header2_row = next(csv_module.reader([existing_headers.get("header2", "Header 2")]), [])
         if len(header2_row) > 1:
@@ -481,9 +476,7 @@ class ManagerSyncDelta:
         return out.getvalue().encode('utf-8')
 
     def createExcelContent(self, data: list[dict], existing_headers: dict | None = None) -> bytes:
-        import pandas as pd
-        from io import BytesIO
-        timestamp = datetime.now(UTC).strftime("%Y-%m-%d %H:%M:%S UTC")
+        timestamp = datetime.fromtimestamp(self.services.utils.getUtcTimestamp(), UTC).strftime("%Y-%m-%d %H:%M:%S UTC")
         if existing_headers is None:
             existing_headers = {"header1": "Header 1", "header2": "Header 2"}
         if not data:
@@ -494,7 +487,6 @@ class ManagerSyncDelta:
         for column in df.columns:
             df[column] = df[column].astype("object").fillna("")
             df[column] = df[column].astype(str).str.replace('\n', '\\n', regex=False).str.replace('"', '""', regex=False)
-        import csv as csv_module
         header1_row = next(csv_module.reader([existing_headers.get("header1", "Header 1")]), [])
         header2_row = next(csv_module.reader([existing_headers.get("header2", "Header 2")]), [])
         if len(header2_row) > 1:
@@ -508,8 +500,6 @@ class ManagerSyncDelta:
         return buf.getvalue()
 
     def parseExcelContent(self, excel_content: bytes) -> tuple[list[dict], dict]:
-        import pandas as pd
-        from io import BytesIO
         df = pd.read_excel(BytesIO(excel_content), engine='openpyxl', header=None)
         header_row1 = df.iloc[0:1].copy()
         header_row2 = df.iloc[1:2].copy()
@@ -674,96 +664,97 @@ class ManagerSyncDelta:
         result = extract_text_from_content(content)
         return result.strip()
 
-# Utility: dump all ticket fields (name -> field id) to a text file (generic)
-async def dumpTicketFieldsToFile(
-    *,
-    filepath: str = "ticket_sync_fields.txt",
-    connectorType: str = "Jira",
-    connectorParams: dict | None = None,
-    taskSyncDefinition: dict | None = None,
-    ) -> bool:
-    """Write available ticket fields (name -> field id) to a text file (generic)."""
-    try:
-        connectorParams = connectorParams or {}
-        taskSyncDefinition = taskSyncDefinition or ManagerSyncDelta.TASK_SYNC_DEFINITION
-        ticket_interface = await createTicketInterfaceByType(
-            taskSyncDefinition=taskSyncDefinition,
-            connectorType=connectorType,
-            connectorParams=connectorParams,
-        )
-        attributes = await ticket_interface.connector_ticket.read_attributes()
-        if not attributes:
-            logger.warning("No ticket attributes returned; nothing to write.")
+    # Utility: dump all ticket fields (name -> field id) to a text file (generic)
+    async def dumpTicketFieldsToFile(self,
+        *,
+        filepath: str = "ticket_sync_fields.txt",
+        connectorType: str = "Jira",
+        connectorParams: dict | None = None,
+        taskSyncDefinition: dict | None = None,
+        ) -> bool:
+        """Write available ticket fields (name -> field id) to a text file (generic)."""
+        try:
+            connectorParams = connectorParams or {}
+            taskSyncDefinition = taskSyncDefinition or self.TASK_SYNC_DEFINITION
+            ticket_interface = await self.services.ticket._createTicketInterfaceByType(
+                taskSyncDefinition=taskSyncDefinition,
+                connectorType=connectorType,
+                connectorParams=connectorParams,
+            )
+            attributes = await ticket_interface.connector_ticket.read_attributes()
+            if not attributes:
+                logger.warning("No ticket attributes returned; nothing to write.")
+                return False
+            dir_name = os.path.dirname(filepath)
+            if dir_name:
+                os.makedirs(dir_name, exist_ok=True)
+            with open(filepath, "w", encoding="utf-8") as f:
+                for attr in attributes:
+                    f.write(f"'{attr.field_name}': ['get', ['fields', '{attr.field}']]\n")
+            logger.info(f"Wrote {len(attributes)} ticket fields to {filepath}")
+            return True
+        except Exception as e:
+            logger.error(f"Failed to dump ticket fields: {str(e)}")
             return False
-        dir_name = os.path.dirname(filepath)
-        if dir_name:
-            os.makedirs(dir_name, exist_ok=True)
-        with open(filepath, "w", encoding="utf-8") as f:
-            for attr in attributes:
-                f.write(f"'{attr.field_name}': ['get', ['fields', '{attr.field}']]\n")
-        logger.info(f"Wrote {len(attributes)} ticket fields to {filepath}")
-        return True
-    except Exception as e:
-        logger.error(f"Failed to dump ticket fields: {str(e)}")
-        return False
 
-# Utility: dump actual ticket data for debugging (generic)
-async def dumpTicketDataToFile(
-    *,
-    filepath: str = "ticket_sync_data.txt",
-    connectorType: str = "Jira",
-    connectorParams: dict | None = None,
-    taskSyncDefinition: dict | None = None,
-    sampleLimit: int = 5,
-    ) -> bool:
-    """Write actual ticket data to a text file for debugging field mapping (generic)."""
-    try:
-        connectorParams = connectorParams or {}
-        taskSyncDefinition = taskSyncDefinition or ManagerSyncDelta.TASK_SYNC_DEFINITION
-        ticket_interface = await createTicketInterfaceByType(
-            taskSyncDefinition=taskSyncDefinition,
-            connectorType=connectorType,
-            connectorParams=connectorParams,
-        )
-        tickets = await ticket_interface.connector_ticket.read_tasks(limit=sampleLimit)
-        if not tickets:
-            logger.warning("No tickets returned; nothing to write.")
+    # Utility: dump actual ticket data for debugging (generic)
+    async def dumpTicketDataToFile(self,
+        *,
+        filepath: str = "ticket_sync_data.txt",
+        connectorType: str = "Jira",
+        connectorParams: dict | None = None,
+        taskSyncDefinition: dict | None = None,
+        sampleLimit: int = 5,
+        ) -> bool:
+        """Write actual ticket data to a text file for debugging field mapping (generic)."""
+        try:
+            connectorParams = connectorParams or {}
+            taskSyncDefinition = taskSyncDefinition or self.TASK_SYNC_DEFINITION
+            ticket_interface = await self.services.ticket._createTicketInterfaceByType(
+                taskSyncDefinition=taskSyncDefinition,
+                connectorType=connectorType,
+                connectorParams=connectorParams,
+            )
+            tickets = await ticket_interface.connector_ticket.read_tasks(limit=sampleLimit)
+            if not tickets:
+                logger.warning("No tickets returned; nothing to write.")
+                return False
+            dir_name = os.path.dirname(filepath)
+            if dir_name:
+                os.makedirs(dir_name, exist_ok=True)
+            with open(filepath, "w", encoding="utf-8") as f:
+                f.write("=== TICKET DATA DEBUG ===\n\n")
+                for i, ticket in enumerate(tickets):
+                    f.write(f"--- TICKET {i+1} ---\n")
+                    f.write("Raw ticket data:\n")
+                    f.write(f"{ticket.data}\n\n")
+                    f.write("Field mapping analysis:\n")
+                    for fieldName, fieldPath in taskSyncDefinition.items():
+                        if fieldPath[0] == 'get':
+                            try:
+                                value = ticket.data
+                                for key in fieldPath[1]:
+                                    if isinstance(value, dict) and key in value:
+                                        value = value[key]
+                                    else:
+                                        value = f"KEY_NOT_FOUND: {key}"
+                                        break
+                                if isinstance(value, dict) and value.get("type") == "doc":
+                                    pass # value = self.convertAdfToText(value)
+                                elif value is None:
+                                    value = ""
+                                f.write(f"  {fieldName}: {value}\n")
+                            except Exception as e:
+                                f.write(f"  {fieldName}: ERROR - {str(e)}\n")
+                    f.write("\n" + "="*50 + "\n\n")
+            logger.info(f"Wrote ticket data for {len(tickets)} tickets to {filepath}")
+            return True
+        except Exception as e:
+            logger.error(f"Failed to dump ticket data: {str(e)}")
             return False
-        dir_name = os.path.dirname(filepath)
-        if dir_name:
-            os.makedirs(dir_name, exist_ok=True)
-        with open(filepath, "w", encoding="utf-8") as f:
-            f.write("=== TICKET DATA DEBUG ===\n\n")
-            for i, ticket in enumerate(tickets):
-                f.write(f"--- TICKET {i+1} ---\n")
-                f.write("Raw ticket data:\n")
-                f.write(f"{ticket.data}\n\n")
-                f.write("Field mapping analysis:\n")
-                for fieldName, fieldPath in taskSyncDefinition.items():
-                    if fieldPath[0] == 'get':
-                        try:
-                            value = ticket.data
-                            for key in fieldPath[1]:
-                                if isinstance(value, dict) and key in value:
-                                    value = value[key]
-                                else:
-                                    value = f"KEY_NOT_FOUND: {key}"
-                                    break
-                            if isinstance(value, dict) and value.get("type") == "doc":
-                                pass # value = self.convertAdfToText(value)
-                            elif value is None:
-                                value = ""
-                            f.write(f"  {fieldName}: {value}\n")
-                        except Exception as e:
-                            f.write(f"  {fieldName}: ERROR - {str(e)}\n")
-                f.write("\n" + "="*50 + "\n\n")
-        logger.info(f"Wrote ticket data for {len(tickets)} tickets to {filepath}")
-        return True
-    except Exception as e:
-        logger.error(f"Failed to dump ticket data: {str(e)}")
-        return False
 
 # Main part of the module
+
 async def performSync(eventUser) -> bool:
     """Perform tickets to SharePoint synchronization
     
@@ -800,12 +791,29 @@ async def performSync(eventUser) -> bool:
 # Create a global instance of ManagerSyncDelta to use for scheduled runs
 _sync_manager = None
 
-def initialize_sync_manager(eventUser):
+def startSyncManager(eventUser):
     """Initialize the global sync manager with the eventUser."""
     global _sync_manager
     if _sync_manager is None:
         _sync_manager = ManagerSyncDelta(eventUser)
         logger.info("Global sync manager initialized with eventUser")
+        try:
+            # Register scheduled job based on environment using the manager's services
+            if _sync_manager.APP_ENV_TYPE == "prod":
+                _sync_manager.services.utils.eventRegisterCron(
+                    job_id="syncDelta.syncTicket",
+                    func=scheduled_sync,
+                    cron_kwargs={"minute": "0,20,40"},
+                    replace_existing=True,
+                    coalesce=True,
+                    max_instances=1,
+                    misfire_grace_time=1800,
+                )
+                logger.info("Registered DG scheduler (every 20 minutes)")
+            else:
+                logger.info(f"Skipping DG scheduler registration for ticket sync in env: {_sync_manager.APP_ENV_TYPE}")
+        except Exception as e:
+            logger.error(f"Failed to register scheduler for DG sync: {str(e)}")
     return _sync_manager
 
 async def scheduled_sync():
@@ -821,42 +829,4 @@ async def scheduled_sync():
         logger.error(f"Error in scheduled sync: {str(e)}")
         return False
 
-# Initialize sync manager with eventUser
-try:
-    from modules.interfaces.interfaceAppObjects import getRootInterface
-    eventUser = getRootInterface().getUserByUsername("event")
-    if eventUser:
-        initialize_sync_manager(eventUser)
-        logger.info("Sync manager initialized with eventUser")
-    else:
-        logger.error("Event user not found - cannot initialize sync manager")
-except Exception as e:
-    logger.error(f"Failed to initialize sync manager: {str(e)}")
-
-# Register scheduled job on import using the shared event manager
-try:
-    from modules.shared.eventManagement import eventManager
-
-    # Register scheduler only in production
-    if APP_ENV_TYPE == "prod":
-        # Schedule sync every 20 minutes (at minutes 00, 20, 40)
-        eventManager.register_cron(
-            job_id="dgsync",
-            func=scheduled_sync,
-            cron_kwargs={"minute": "0,20,40"},
-            replace_existing=True,
-            coalesce=True,
-            max_instances=1,
-            misfire_grace_time=1800,
-        )
-        logger.info("Registered DG ticket sync via EventManagement (every 20 minutes)")
-    else:
-        logger.info(f"Skipping DG scheduler registration for ticket sync in env: {APP_ENV_TYPE}")
-    
-    # Run initial sync
-    #import asyncio
-    #asyncio.create_task(scheduled_sync())
-    #logger.info("Initial sync scheduled")
-            
-except Exception as e:
-    logger.error(f"Failed to register DG ticket sync: {str(e)}")
+# Scheduler registration and initialization are triggered by startSyncManager(eventUser)

modules/interfaces/interfaceAiObjects.py

@@ -1,49 +1,282 @@
 import logging
-from typing import Dict, Any, List
+from typing import Dict, Any, List, Union
+from dataclasses import dataclass
 
 from modules.connectors.connectorAiOpenai import AiOpenai
 from modules.connectors.connectorAiAnthropic import AiAnthropic
-from modules.datamodels.datamodelAi import AiCallOptions, AiCallRequest, AiCallResponse
+from modules.connectors.connectorAiLangdoc import AiLangdoc
+from modules.connectors.connectorAiTavily import ConnectorWeb
+from modules.datamodels.datamodelAi import (
+    AiCallOptions, 
+    AiCallRequest, 
+    AiCallResponse,
+    OperationType, 
+    ProcessingMode, 
+    Priority, 
+    ModelTags, 
+    OPERATION_TAG_MAPPING, 
+    PROCESSING_MODE_PRIORITY_MAPPING
+)
+from modules.datamodels.datamodelWeb import (
+    WebCrawlActionResult,
+    WebCrawlActionDocument,
+    WebCrawlDocumentData,
+    WebCrawlRequest,
+    WebCrawlResultItem,
+    WebScrapeActionResult,
+    WebScrapeActionDocument,
+    WebSearchDocumentData as WebScrapeDocumentData,
+    WebScrapeRequest,
+    WebScrapeResultItem,
+    WebSearchActionResult,
+    WebSearchActionDocument,
+    WebSearchDocumentData,
+    WebSearchRequest,
+    WebSearchResultItem,
+)
+from modules.datamodels.datamodelWorkflow import ActionDocument
 
 
 logger = logging.getLogger(__name__)
 
 
-# Local model registry (connectors specification) belongs in interface layer, not service
+# Comprehensive model registry with capability tags and function mapping
 aiModels: Dict[str, Dict[str, Any]] = {
-    "openai_gpt4o": {
+    # OpenAI Models
+    "openai_callAiBasic": {
         "connector": "openai",
+        "function": "callAiBasic",
+        "llmName": "gpt-4o",
         "contextLength": 128000,
         "costPer1kTokens": 0.03,
         "costPer1kTokensOutput": 0.06,
         "speedRating": 8,
         "qualityRating": 9,
+        "capabilities": ["text_generation", "chat", "reasoning"],
+        "tags": ["text", "chat", "reasoning", "general"]
     },
-    "openai_gpt35": {
+    "openai_callAiBasic_gpt35": {
         "connector": "openai",
+        "function": "callAiBasic",
+        "llmName": "gpt-3.5-turbo",
         "contextLength": 16000,
         "costPer1kTokens": 0.0015,
         "costPer1kTokensOutput": 0.002,
         "speedRating": 9,
         "qualityRating": 7,
+        "capabilities": ["text_generation", "chat", "reasoning"],
+        "tags": ["text", "chat", "reasoning", "general", "fast"]
     },
-    "anthropic_claude": {
+    "openai_callAiImage": {
+        "connector": "openai",
+        "function": "callAiImage",
+        "llmName": "gpt-4o",
+        "contextLength": 128000,
+        "costPer1kTokens": 0.03,
+        "costPer1kTokensOutput": 0.06,
+        "speedRating": 7,
+        "qualityRating": 9,
+        "capabilities": ["image_analysis", "vision", "multimodal"],
+        "tags": ["image", "vision", "multimodal"]
+    },
+    "openai_generateImage": {
+        "connector": "openai",
+        "function": "generateImage",
+        "llmName": "dall-e-3",
+        "contextLength": 0,
+        "costPer1kTokens": 0.04,
+        "costPer1kTokensOutput": 0.0,
+        "speedRating": 6,
+        "qualityRating": 9,
+        "capabilities": ["image_generation", "art", "visual_creation"],
+        "tags": ["image_generation", "art", "visual"]
+    },
+    
+    # Anthropic Models
+    "anthropic_callAiBasic": {
         "connector": "anthropic",
+        "function": "callAiBasic",
+        "llmName": "claude-3-5-sonnet-20241022",
         "contextLength": 200000,
         "costPer1kTokens": 0.015,
         "costPer1kTokensOutput": 0.075,
         "speedRating": 7,
         "qualityRating": 10,
+        "capabilities": ["text_generation", "chat", "reasoning", "analysis"],
+        "tags": ["text", "chat", "reasoning", "analysis", "high_quality"]
     },
+    "anthropic_callAiImage": {
+        "connector": "anthropic",
+        "function": "callAiImage",
+        "llmName": "claude-3-5-sonnet-20241022",
+        "contextLength": 200000,
+        "costPer1kTokens": 0.015,
+        "costPer1kTokensOutput": 0.075,
+        "speedRating": 7,
+        "qualityRating": 10,
+        "capabilities": ["image_analysis", "vision", "multimodal"],
+        "tags": ["image", "vision", "multimodal", "high_quality"]
+    },
+    
+    # LangDoc Models
+    "langdoc_callAiBasic": {
+        "connector": "langdoc",
+        "function": "callAiBasic",
+        "llmName": "gpt-4o",
+        "contextLength": 128000,
+        "costPer1kTokens": 0.02,
+        "costPer1kTokensOutput": 0.04,
+        "speedRating": 8,
+        "qualityRating": 9,
+        "capabilities": ["text_generation", "chat", "reasoning"],
+        "tags": ["text", "chat", "reasoning", "general", "cost_effective"]
+    },
+    "langdoc_callAiImage": {
+        "connector": "langdoc",
+        "function": "callAiImage",
+        "llmName": "gpt-4o",
+        "contextLength": 128000,
+        "costPer1kTokens": 0.02,
+        "costPer1kTokensOutput": 0.04,
+        "speedRating": 7,
+        "qualityRating": 9,
+        "capabilities": ["image_analysis", "vision", "multimodal"],
+        "tags": ["image", "vision", "multimodal", "cost_effective"]
+    },
+    "langdoc_generateImage": {
+        "connector": "langdoc",
+        "function": "generateImage",
+        "llmName": "dall-e-3",
+        "contextLength": 0,
+        "costPer1kTokens": 0.04,
+        "costPer1kTokensOutput": 0.0,
+        "speedRating": 6,
+        "qualityRating": 9,
+        "capabilities": ["image_generation", "art", "visual_creation"],
+        "tags": ["image_generation", "art", "visual", "cost_effective"]
+    },
+    "langdoc_generateImageWithVariations": {
+        "connector": "langdoc",
+        "function": "generateImageWithVariations",
+        "llmName": "dall-e-3",
+        "contextLength": 0,
+        "costPer1kTokens": 0.04,
+        "costPer1kTokensOutput": 0.0,
+        "speedRating": 5,
+        "qualityRating": 9,
+        "capabilities": ["image_generation", "art", "visual_creation", "variations"],
+        "tags": ["image_generation", "art", "visual", "variations", "cost_effective"]
+    },
+    "langdoc_generateImageWithChat": {
+        "connector": "langdoc",
+        "function": "generateImageWithChat",
+        "llmName": "gpt-4o",
+        "contextLength": 128000,
+        "costPer1kTokens": 0.02,
+        "costPer1kTokensOutput": 0.04,
+        "speedRating": 6,
+        "qualityRating": 8,
+        "capabilities": ["image_generation", "chat", "visual_creation"],
+        "tags": ["image_generation", "chat", "visual", "cost_effective"]
+    },
+    "langdoc_listModels": {
+        "connector": "langdoc",
+        "function": "listModels",
+        "llmName": "api",
+        "contextLength": 0,
+        "costPer1kTokens": 0.0,
+        "costPer1kTokensOutput": 0.0,
+        "speedRating": 9,
+        "qualityRating": 5,
+        "capabilities": ["model_listing", "api_info"],
+        "tags": ["api", "info", "models"]
+    },
+    "langdoc_getModelInfo": {
+        "connector": "langdoc",
+        "function": "getModelInfo",
+        "llmName": "api",
+        "contextLength": 0,
+        "costPer1kTokens": 0.0,
+        "costPer1kTokensOutput": 0.0,
+        "speedRating": 9,
+        "qualityRating": 5,
+        "capabilities": ["model_info", "api_info"],
+        "tags": ["api", "info", "models"]
+    },
+    
+    # Tavily Web Models
+    "tavily_search": {
+        "connector": "tavily",
+        "function": "search",
+        "llmName": "tavily-search",
+        "contextLength": 0,
+        "costPer1kTokens": 0.0,
+        "costPer1kTokensOutput": 0.0,
+        "speedRating": 8,
+        "qualityRating": 8,
+        "capabilities": ["web_search", "information_retrieval", "url_discovery"],
+        "tags": ["web", "search", "urls", "information"]
+    },
+    "tavily_crawl": {
+        "connector": "tavily",
+        "function": "crawl",
+        "llmName": "tavily-extract",
+        "contextLength": 0,
+        "costPer1kTokens": 0.0,
+        "costPer1kTokensOutput": 0.0,
+        "speedRating": 6,
+        "qualityRating": 8,
+        "capabilities": ["web_crawling", "content_extraction", "text_extraction"],
+        "tags": ["web", "crawl", "extract", "content"]
+    },
+    "tavily_scrape": {
+        "connector": "tavily",
+        "function": "scrape",
+        "llmName": "tavily-search-extract",
+        "contextLength": 0,
+        "costPer1kTokens": 0.0,
+        "costPer1kTokensOutput": 0.0,
+        "speedRating": 6,
+        "qualityRating": 8,
+        "capabilities": ["web_search", "web_crawling", "content_extraction", "information_retrieval"],
+        "tags": ["web", "search", "crawl", "extract", "content", "information"]
+    }
 }
 
 
+@dataclass(slots=True)
 class AiObjects:
-    """Centralized AI interface: selects model and calls connector. No document handling."""
+    """Centralized AI interface: selects model and calls connector. Includes web functionality."""
 
-    def __init__(self):
-        self.openaiService = AiOpenai()
-        self.anthropicService = AiAnthropic()
+    openaiService: AiOpenai
+    anthropicService: AiAnthropic
+    langdocService: AiLangdoc
+    tavilyService: ConnectorWeb
+
+    def __post_init__(self) -> None:
+        if self.openaiService is None:
+            raise TypeError("openaiService must be provided")
+        if self.anthropicService is None:
+            raise TypeError("anthropicService must be provided")
+        if self.langdocService is None:
+            raise TypeError("langdocService must be provided")
+        if self.tavilyService is None:
+            raise TypeError("tavilyService must be provided")
+
+    @classmethod
+    async def create(cls) -> "AiObjects":
+        """Create AiObjects instance with all connectors initialized."""
+        openaiService = AiOpenai()
+        anthropicService = AiAnthropic()
+        langdocService = AiLangdoc()
+        tavilyService = await ConnectorWeb.create()
+        
+        return cls(
+            openaiService=openaiService,
+            anthropicService=anthropicService,
+            langdocService=langdocService,
+            tavilyService=tavilyService
+        )
 
     def _estimateCost(self, modelInfo: Dict[str, Any], contentSize: int) -> float:
         estimatedTokens = contentSize / 4
@@ -52,32 +285,85 @@ class AiObjects:
         return inputCost + outputCost
 
     def _selectModel(self, prompt: str, context: str, options: AiCallOptions) -> str:
+        """Select the best model based on operation type, tags, and requirements."""
         totalSize = len(prompt.encode("utf-8")) + len(context.encode("utf-8"))
         candidates: Dict[str, Dict[str, Any]] = {}
+        
+        # Determine required tags from operation type
+        requiredTags = options.requiredTags
+        if not requiredTags:
+            requiredTags = OPERATION_TAG_MAPPING.get(options.operationType, [ModelTags.TEXT, ModelTags.CHAT])
+        
+        # Override priority based on processing mode if not explicitly set
+        effectivePriority = options.priority
+        if options.priority == Priority.BALANCED:
+            effectivePriority = PROCESSING_MODE_PRIORITY_MAPPING.get(options.processingMode, Priority.BALANCED)
+        
+        logger.info(f"Model selection - Operation: {options.operationType}, Required tags: {requiredTags}, Priority: {effectivePriority}")
+        
         for name, info in aiModels.items():
-            if totalSize > info["contextLength"] * 0.8:
+            # Check context length
+            if info["contextLength"] > 0 and totalSize > info["contextLength"] * 0.8:
                 continue
+            
+            # Check cost constraints
             if options.maxCost is not None:
                 if self._estimateCost(info, totalSize) > options.maxCost:
                     continue
+            
+            # Check required tags/capabilities
+            modelTags = info.get("tags", [])
+            if requiredTags and not any(tag in modelTags for tag in requiredTags):
+                continue
+            
+            # Check processing mode requirements
+            if options.processingMode == ProcessingMode.DETAILED and ModelTags.FAST in modelTags:
+                # Skip fast models for detailed processing
+                continue
+            
             candidates[name] = info
+        
         if not candidates:
-            return "openai_gpt35"
-        if options.priority == "speed":
+            # Fallback based on operation type
+            if options.operationType == OperationType.IMAGE_ANALYSIS:
+                return "openai_callAiImage"
+            elif options.operationType == OperationType.IMAGE_GENERATION:
+                return "openai_generateImage"
+            elif options.operationType == OperationType.WEB_RESEARCH:
+                return "langdoc_callAiBasic"
+            else:
+                return "openai_callAiBasic_gpt35"
+        
+        # Select based on priority
+        if effectivePriority == Priority.SPEED:
             return max(candidates, key=lambda k: candidates[k]["speedRating"])
-        if options.priority == "quality":
+        elif effectivePriority == Priority.QUALITY:
             return max(candidates, key=lambda k: candidates[k]["qualityRating"])
-        if options.priority == "cost":
-            return min(candidates, key=lambda k: candidates[k]["costPer1kTokens"]) 
-        def balancedScore(name: str) -> float:
-            info = candidates[name]
-            return info["qualityRating"] * 0.4 + info["speedRating"] * 0.3 + (10 - info["costPer1kTokens"] * 1000) * 0.3
-        return max(candidates, key=balancedScore)
+        elif effectivePriority == Priority.COST:
+            return min(candidates, key=lambda k: candidates[k]["costPer1kTokens"])
+        else:  # BALANCED
+            def balancedScore(name: str) -> float:
+                info = candidates[name]
+                return info["qualityRating"] * 0.4 + info["speedRating"] * 0.3 + (10 - info["costPer1kTokens"] * 1000) * 0.3
+            
+            return max(candidates, key=balancedScore)
 
     def _connectorFor(self, modelName: str):
-        return self.openaiService if aiModels[modelName]["connector"] == "openai" else self.anthropicService
+        """Get the appropriate connector for the model."""
+        connectorType = aiModels[modelName]["connector"]
+        if connectorType == "openai":
+            return self.openaiService
+        elif connectorType == "anthropic":
+            return self.anthropicService
+        elif connectorType == "langdoc":
+            return self.langdocService
+        elif connectorType == "tavily":
+            return self.tavilyService
+        else:
+            raise ValueError(f"Unknown connector type: {connectorType}")
 
     async def call(self, request: AiCallRequest) -> AiCallResponse:
+        """Call AI model for text generation."""
         prompt = request.prompt
         context = request.context or ""
         options = request.options
@@ -94,6 +380,7 @@ class AiObjects:
         if options.compressContext and len(context.encode("utf-8")) > 70000:
             context = maybeTruncate(context, 70000)
 
+        # Select model for text generation
         modelName = self._selectModel(prompt, context, options)
 
         messages: List[Dict[str, Any]] = []
@@ -102,11 +389,17 @@ class AiObjects:
         messages.append({"role": "user", "content": prompt})
 
         connector = self._connectorFor(modelName)
-        if aiModels[modelName]["connector"] == "openai":
-            content = await connector.callAiBasic(messages)
+        functionName = aiModels[modelName]["function"]
+        
+        # Call the appropriate function
+        if functionName == "callAiBasic":
+            if aiModels[modelName]["connector"] == "openai":
+                content = await connector.callAiBasic(messages)
+            else:
+                response = await connector.callAiBasic(messages)
+                content = response["choices"][0]["message"]["content"]
         else:
-            response = await connector.callAiBasic(messages)
-            content = response["choices"][0]["message"]["content"]
+            raise ValueError(f"Function {functionName} not supported for text generation")
 
         # Estimate cost/tokens
         totalSize = len((prompt + context).encode("utf-8"))
@@ -115,3 +408,106 @@ class AiObjects:
 
         return AiCallResponse(content=content, modelName=modelName, usedTokens=usedTokens, costEstimate=cost)
 
+    async def callImage(self, prompt: str, imageData: Union[str, bytes], mimeType: str = None, options: AiCallOptions = None) -> str:
+        """Call AI model for image analysis."""
+        if options is None:
+            options = AiCallOptions(operationType=OperationType.IMAGE_ANALYSIS)
+        
+        # Select model for image analysis
+        modelName = self._selectModel(prompt, "", options)
+        
+        connector = self._connectorFor(modelName)
+        functionName = aiModels[modelName]["function"]
+        
+        if functionName == "callAiImage":
+            return await connector.callAiImage(prompt, imageData, mimeType)
+        else:
+            raise ValueError(f"Function {functionName} not supported for image analysis")
+
+    async def generateImage(self, prompt: str, size: str = "1024x1024", quality: str = "standard", style: str = "vivid", options: AiCallOptions = None) -> Dict[str, Any]:
+        """Generate an image using AI."""
+        if options is None:
+            options = AiCallOptions(operationType=OperationType.IMAGE_GENERATION)
+        
+        # Select model for image generation
+        modelName = self._selectModel(prompt, "", options)
+        
+        connector = self._connectorFor(modelName)
+        functionName = aiModels[modelName]["function"]
+        
+        if functionName == "generateImage":
+            return await connector.generateImage(prompt, size, quality, style)
+        elif functionName == "generateImageWithVariations":
+            results = await connector.generateImageWithVariations(prompt, 1, size, quality, style)
+            return results[0] if results else {}
+        elif functionName == "generateImageWithChat":
+            content = await connector.generateImageWithChat(prompt, size, quality, style)
+            return {"content": content, "success": True}
+        else:
+            raise ValueError(f"Function {functionName} not supported for image generation")
+
+    # Web functionality methods
+    async def webSearch(self, web_search_request: WebSearchRequest) -> WebSearchActionResult:
+        """Perform web search using Tavily."""
+        return await self.tavilyService.search(web_search_request)
+
+    async def webCrawl(self, web_crawl_request: WebCrawlRequest) -> WebCrawlActionResult:
+        """Crawl web pages using Tavily."""
+        return await self.tavilyService.crawl(web_crawl_request)
+
+    async def webScrape(self, web_scrape_request: WebScrapeRequest) -> WebScrapeActionResult:
+        """Scrape web content using Tavily."""
+        return await self.tavilyService.scrape(web_scrape_request)
+
+    async def webQuery(self, query: str, context: str = "", options: AiCallOptions = None) -> str:
+        """Use LangDoc AI to provide the best answers for web-related queries."""
+        if options is None:
+            options = AiCallOptions(operationType=OperationType.WEB_RESEARCH)
+        
+        # Create a comprehensive prompt for web queries
+        webPrompt = f"""You are an expert web researcher and information analyst. Please provide a comprehensive and accurate answer to the following web-related query.
+
+Query: {query}
+
+{f"Additional Context: {context}" if context else ""}
+
+Please provide:
+1. A clear, well-structured answer to the query
+2. Key points and important details
+3. Relevant insights and analysis
+4. Any important considerations or caveats
+5. Suggestions for further research if applicable
+
+Format your response in a clear, professional manner that would be helpful for someone researching this topic."""
+
+        messages = [{"role": "user", "content": webPrompt}]
+        
+        try:
+            # Use LangDoc for the best answers
+            response = await self.langdocService.callAiBasic(messages)
+            return response
+        except Exception as e:
+            logger.error(f"LangDoc web query failed: {str(e)}")
+            raise Exception(f"Failed to process web query: {str(e)}")
+
+    # Utility methods
+    async def listAvailableModels(self, connectorType: str = None) -> List[Dict[str, Any]]:
+        """List available models, optionally filtered by connector type."""
+        if connectorType:
+            return [info for name, info in aiModels.items() if info["connector"] == connectorType]
+        return list(aiModels.values())
+
+    async def getModelInfo(self, modelName: str) -> Dict[str, Any]:
+        """Get information about a specific model."""
+        if modelName not in aiModels:
+            raise ValueError(f"Model {modelName} not found")
+        return aiModels[modelName]
+
+    async def getModelsByCapability(self, capability: str) -> List[str]:
+        """Get model names that support a specific capability."""
+        return [name for name, info in aiModels.items() if capability in info.get("capabilities", [])]
+
+    async def getModelsByTag(self, tag: str) -> List[str]:
+        """Get model names that have a specific tag."""
+        return [name for name, info in aiModels.items() if tag in info.get("tags", [])]
+

modules/interfaces/interfaceChatModel.py

@@ -1,981 +0,0 @@
-"""
-Chat model classes for the chat system.
-"""
-
-from pydantic import BaseModel, Field
-from typing import List, Dict, Any, Optional
-from datetime import datetime, UTC
-import uuid
-from enum import Enum
-
-from modules.shared.attributeUtils import register_model_labels, ModelMixin
-from modules.shared.timezoneUtils import get_utc_timestamp
-
-# ===== Method Models =====
-
-class ActionDocument(BaseModel, ModelMixin):
-    """Clear document structure for action results"""
-    documentName: str = Field(description="Name of the document")
-    documentData: Any = Field(description="Content/data of the document")
-    mimeType: str = Field(description="MIME type of the document")
-
-# Register labels for ActionDocument
-register_model_labels(
-    "ActionDocument",
-    {"en": "Action Document", "fr": "Document d'action"},
-    {
-        "documentName": {"en": "Document Name", "fr": "Nom du document"},
-        "documentData": {"en": "Document Data", "fr": "Données du document"},
-        "mimeType": {"en": "MIME Type", "fr": "Type MIME"}
-    }
-)
-
-class ActionResult(BaseModel, ModelMixin):
-    """Clean action result with documents as primary output
-    
-    IMPORTANT: Action methods should NOT set resultLabel in their return value.
-    The resultLabel is managed by the action handler using the action's execResultLabel
-    from the action plan. This ensures consistent document routing throughout the workflow.
-    """
-    # Core result
-    success: bool = Field(description="Whether execution succeeded")
-    error: Optional[str] = Field(None, description="Error message if failed")
-    
-    # Primary output - documents
-    documents: List[ActionDocument] = Field(default_factory=list, description="Document outputs")
-    resultLabel: Optional[str] = Field(None, description="Label for document routing (set by action handler, not by action methods)")
-    
-    @classmethod
-    def isSuccess(cls, documents: List[ActionDocument] = None) -> 'ActionResult':
-        """Create a successful action result
-        
-        Note: Do not set resultLabel - this is managed by the action handler
-        """
-        return cls(
-            success=True,
-            documents=documents or []
-        )
-    
-    @classmethod
-    def isFailure(cls, error: str, documents: List[ActionDocument] = None) -> 'ActionResult':
-        """Create a failed action result
-        
-        Note: Do not set resultLabel - this is managed by the action handler
-        """
-        return cls(
-            success=False,
-            documents=documents or [],
-            error=error
-        )
-
-# Register labels for ActionResult
-register_model_labels(
-    "ActionResult",
-    {"en": "Action Result", "fr": "Résultat de l'action"},
-    {
-        "success": {"en": "Success", "fr": "Succès"},
-        "error": {"en": "Error", "fr": "Erreur"},
-        "documents": {"en": "Documents", "fr": "Documents"},
-        "resultLabel": {"en": "Result Label", "fr": "Étiquette du résultat"}
-    }
-)
-
-# ===== Minimal ReAct-style Workflow Models =====
-
-class ActionSelection(BaseModel, ModelMixin):
-    """Model for selecting exactly one action in a step"""
-    method: str = Field(description="Method to execute (e.g., web, document, ai)")
-    name: str = Field(description="Action name within the method (e.g., search, extract)")
-
-register_model_labels(
-    "ActionSelection",
-    {"en": "Action Selection", "fr": "Sélection d'action"},
-    {
-        "method": {"en": "Method", "fr": "Méthode"},
-        "name": {"en": "Action Name", "fr": "Nom de l'action"}
-    }
-)
-
-class ActionParameters(BaseModel, ModelMixin):
-    """Model for specifying only the parameters for the selected action"""
-    parameters: Dict[str, Any] = Field(default_factory=dict, description="Parameters to execute the selected action")
-
-register_model_labels(
-    "ActionParameters",
-    {"en": "Action Parameters", "fr": "Paramètres d'action"},
-    {
-        "parameters": {"en": "Parameters", "fr": "Paramètres"}
-    }
-)
-
-class ObservationPreview(BaseModel, ModelMixin):
-    """Compact preview item for observations"""
-    name: str = Field(description="Document name or URL label")
-    mime: str = Field(description="MIME type or kind")
-    snippet: str = Field(description="Short snippet or summary")
-
-register_model_labels(
-    "ObservationPreview",
-    {"en": "Observation Preview", "fr": "Aperçu d'observation"},
-    {
-        "name": {"en": "Name", "fr": "Nom"},
-        "mime": {"en": "MIME", "fr": "MIME"},
-        "snippet": {"en": "Snippet", "fr": "Extrait"}
-    }
-)
-
-class Observation(BaseModel, ModelMixin):
-    """Compact observation returned to the model after each action"""
-    success: bool = Field(description="Action execution success flag")
-    resultLabel: str = Field(description="Deterministic label for produced documents")
-    documentsCount: int = Field(description="Number of produced documents")
-    previews: List[ObservationPreview] = Field(default_factory=list, description="Compact previews of outputs")
-    notes: List[str] = Field(default_factory=list, description="Short notes or key facts")
-
-register_model_labels(
-    "Observation",
-    {"en": "Observation", "fr": "Observation"},
-    {
-        "success": {"en": "Success", "fr": "Succès"},
-        "resultLabel": {"en": "Result Label", "fr": "Étiquette du résultat"},
-        "documentsCount": {"en": "Documents Count", "fr": "Nombre de documents"},
-        "previews": {"en": "Previews", "fr": "Aperçus"},
-        "notes": {"en": "Notes", "fr": "Notes"}
-    }
-)
-
-# ===== Base Enums and Simple Models =====
-
-class TaskStatus(str, Enum):
-    """Task status enumeration"""
-    PENDING = "pending"
-    RUNNING = "running"
-    COMPLETED = "completed"
-    FAILED = "failed"
-    CANCELLED = "cancelled"
-
-# Register labels for TaskStatus
-register_model_labels(
-    "TaskStatus",
-    {"en": "Task Status", "fr": "Statut de la tâche"},
-    {
-        "PENDING": {"en": "Pending", "fr": "En attente"},
-        "RUNNING": {"en": "Running", "fr": "En cours"},
-        "COMPLETED": {"en": "Completed", "fr": "Terminé"},
-        "FAILED": {"en": "Failed", "fr": "Échec"},
-        "CANCELLED": {"en": "Cancelled", "fr": "Annulé"},
-        "ROLLED_BACK": {"en": "Rolled Back", "fr": "Annulé"}
-    }
-)
-
-class UserInputRequest(BaseModel, ModelMixin):
-    """Data model for a user input request"""
-    prompt: str = Field(description="Prompt for the user")
-    listFileId: List[str] = Field(default_factory=list, description="List of file IDs")
-    userLanguage: str = Field(default="en", description="User's preferred language")
-
-# Register labels for UserInputRequest
-register_model_labels(
-    "UserInputRequest",
-    {"en": "User Input Request", "fr": "Demande de saisie utilisateur"},
-    {
-        "prompt": {"en": "Prompt", "fr": "Invite"},
-        "listFileId": {"en": "File IDs", "fr": "IDs des fichiers"},
-        "userLanguage": {"en": "User Language", "fr": "Langue de l'utilisateur"}
-    }
-)
-
-# ===== Content Models =====
-
-class ContentMetadata(BaseModel, ModelMixin):
-    """Metadata for content items"""
-    size: int = Field(description="Content size in bytes")
-    pages: Optional[int] = Field(None, description="Number of pages for multi-page content")
-    error: Optional[str] = Field(None, description="Processing error if any")
-    width: Optional[int] = Field(None, description="Width in pixels for images/videos")
-    height: Optional[int] = Field(None, description="Height in pixels for images/videos")
-    colorMode: Optional[str] = Field(None, description="Color mode (e.g., RGB, CMYK, grayscale)")
-    fps: Optional[float] = Field(None, description="Frames per second for videos")
-    durationSec: Optional[float] = Field(None, description="Duration in seconds for videos/audio")
-    mimeType: str = Field(description="MIME type of the content")
-    base64Encoded: bool = Field(description="Whether the data is base64 encoded")
-
-# Register labels for ContentMetadata
-register_model_labels(
-    "ContentMetadata",
-    {"en": "Content Metadata", "fr": "Métadonnées du contenu"},
-    {
-        "size": {"en": "Size", "fr": "Taille"},
-        "pages": {"en": "Pages", "fr": "Pages"},
-        "error": {"en": "Error", "fr": "Erreur"},
-        "width": {"en": "Width", "fr": "Largeur"},
-        "height": {"en": "Height", "fr": "Hauteur"},
-        "colorMode": {"en": "Color Mode", "fr": "Mode de couleur"},
-        "fps": {"en": "FPS", "fr": "IPS"},
-        "durationSec": {"en": "Duration", "fr": "Durée"},
-        "mimeType": {"en": "MIME Type", "fr": "Type MIME"},
-        "base64Encoded": {"en": "Base64 Encoded", "fr": "Encodé en Base64"}
-    }
-)
-
-class ContentItem(BaseModel, ModelMixin):
-    """Individual content item from a document"""
-    label: str = Field(description="Content label (e.g., tab name, tag name)")
-    data: str = Field(description="Extracted text content")
-    metadata: ContentMetadata = Field(description="Content metadata")
-
-# Register labels for ContentItem
-register_model_labels(
-    "ContentItem",
-    {"en": "Content Item", "fr": "Élément de contenu"},
-    {
-        "label": {"en": "Label", "fr": "Étiquette"},
-        "data": {"en": "Data", "fr": "Données"},
-        "metadata": {"en": "Metadata", "fr": "Métadonnées"}
-    }
-)
-
-class ChatDocument(BaseModel, ModelMixin):
-    """Data model for a chat document"""
-    id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Primary key")
-    messageId: str = Field(description="Foreign key to message")
-    fileId: str = Field(description="Foreign key to file")
-    
-    # Direct file attributes (copied from file object)
-    fileName: str = Field(description="Name of the file")
-    fileSize: int = Field(description="Size of the file")
-    mimeType: str = Field(description="MIME type of the file")
-    
-    # Workflow context fields
-    roundNumber: Optional[int] = Field(None, description="Round number in workflow")
-    taskNumber: Optional[int] = Field(None, description="Task number within round")
-    actionNumber: Optional[int] = Field(None, description="Action number within task")
-    
-    # Reference to action that created this document
-    actionId: Optional[str] = Field(None, description="ID of the action that created this document")
-
-
-
-# Register labels for ChatDocument
-register_model_labels(
-    "ChatDocument",
-    {"en": "Chat Document", "fr": "Document de chat"},
-    {
-        "id": {"en": "ID", "fr": "ID"},
-        "messageId": {"en": "Message ID", "fr": "ID du message"},
-        "fileId": {"en": "File ID", "fr": "ID du fichier"},
-        "fileName": {"en": "File Name", "fr": "Nom du fichier"},
-        "fileSize": {"en": "File Size", "fr": "Taille du fichier"},
-        "mimeType": {"en": "MIME Type", "fr": "Type MIME"},
-        "roundNumber": {"en": "Round Number", "fr": "Numéro de tour"},
-        "taskNumber": {"en": "Task Number", "fr": "Numéro de tâche"},
-        "actionNumber": {"en": "Action Number", "fr": "Numéro d'action"},
-        "actionId": {"en": "Action ID", "fr": "ID de l'action"}
-    }
-)
-
-class DocumentExchange(BaseModel, ModelMixin):
-    """Data model for document exchange between AI actions"""
-    documentsLabel: str = Field(description="Label for the set of documents")
-    documents: List[str] = Field(default_factory=list, description="List of document references")
-
-# Register labels for DocumentExchange
-register_model_labels(
-    "DocumentExchange",
-    {"en": "Document Exchange", "fr": "Échange de documents"},
-    {
-        "documentsLabel": {"en": "Documents Label", "fr": "Label des documents"},
-        "documents": {"en": "Documents", "fr": "Documents"}
-    }
-)
-
-class ExtractedContent(BaseModel, ModelMixin):
-    """Data model for extracted content"""
-    id: str = Field(description="Reference to source ChatDocument")
-    contents: List[ContentItem] = Field(default_factory=list, description="List of content items")
-
-# Register labels for ExtractedContent
-register_model_labels(
-    "ExtractedContent",
-    {"en": "Extracted Content", "fr": "Contenu extrait"},
-    {
-        "objectId": {"en": "Object ID", "fr": "ID de l'objet"},
-        "objectType": {"en": "Object Type", "fr": "Type d'objet"},
-        "contents": {"en": "Contents", "fr": "Contenus"}
-    }
-)
-
-# ===== Task Models =====
-
-class TaskAction(BaseModel, ModelMixin):
-    """Model for task actions"""
-    id: str = Field(..., description="Action ID")
-    execMethod: str = Field(..., description="Method to execute")
-    execAction: str = Field(..., description="Action to perform")
-    execParameters: Dict[str, Any] = Field(default_factory=dict, description="Action parameters")
-    execResultLabel: Optional[str] = Field(None, description="Label for the set of result documents")
-    # NEW: Optional document format specification
-    expectedDocumentFormats: Optional[List[Dict[str, str]]] = Field(None, description="Expected document formats (optional)")
-    
-    # User message in user's language
-    userMessage: Optional[str] = Field(None, description="User-friendly message in user's language")
-
-    status: TaskStatus = Field(default=TaskStatus.PENDING, description="Action status")
-    error: Optional[str] = Field(None, description="Error message if action failed")
-    retryCount: int = Field(default=0, description="Number of retries attempted")
-    retryMax: int = Field(default=3, description="Maximum number of retries")
-    processingTime: Optional[float] = Field(None, description="Processing time in seconds")
-    timestamp: float = Field(default_factory=get_utc_timestamp, description="When the action was executed (UTC timestamp in seconds)")
-    result: Optional[str] = Field(None, description="Result of the action")
-    resultDocuments: Optional[List[ChatDocument]] = Field(None, description="Result documents from the action")
-
-    def isSuccessful(self) -> bool:
-        """Check if action was successful"""
-        return self.status == TaskStatus.COMPLETED
-
-    def hasError(self) -> bool:
-        """Check if action has an error"""
-        return self.status == TaskStatus.FAILED
-
-    def getErrorMessage(self) -> Optional[str]:
-        """Get error message if any"""
-        return self.error if self.hasError() else None
-
-    def setError(self, error: str) -> None:
-        """Set action error"""
-        self.error = error
-        self.status = TaskStatus.FAILED
-
-    def setSuccess(self) -> None:
-        """Set action as successful"""
-        self.status = TaskStatus.COMPLETED
-        self.error = None
-
-# Register labels for TaskAction
-register_model_labels(
-    "TaskAction",
-    {"en": "Task Action", "fr": "Action de tâche"},
-    {
-        "id": {"en": "Action ID", "fr": "ID de l'action"},
-        "execMethod": {"en": "Method", "fr": "Méthode"},
-        "execAction": {"en": "Action", "fr": "Action"},
-        "execParameters": {"en": "Parameters", "fr": "Paramètres"},
-        "execResultLabel": {"en": "Result Label", "fr": "Label du résultat"},
-        "expectedDocumentFormats": {"en": "Expected Document Formats", "fr": "Formats de documents attendus"},
-        "userMessage": {"en": "User Message", "fr": "Message utilisateur"},
-        "status": {"en": "Status", "fr": "Statut"},
-        "error": {"en": "Error", "fr": "Erreur"},
-        "retryCount": {"en": "Retry Count", "fr": "Nombre de tentatives"},
-        "retryMax": {"en": "Max Retries", "fr": "Tentatives max"},
-        "processingTime": {"en": "Processing Time", "fr": "Temps de traitement"},
-        "timestamp": {"en": "Timestamp", "fr": "Horodatage"},
-        "result": {"en": "Result", "fr": "Résultat"},
-        "resultDocuments": {"en": "Result Documents", "fr": "Documents de résultat"}
-    }
-)
-
-class TaskResult(BaseModel, ModelMixin):
-    """Model for task results"""
-    taskId: str = Field(..., description="Task ID")
-    status: TaskStatus = Field(default=TaskStatus.PENDING, description="Task status")
-    success: bool = Field(..., description="Whether the task was successful")
-    feedback: Optional[str] = Field(None, description="Task feedback message")
-    error: Optional[str] = Field(None, description="Error message if task failed")
-
-# Register labels for TaskResult
-register_model_labels(
-    "TaskResult",
-    {"en": "Task Result", "fr": "Résultat de tâche"},
-    {
-        "taskId": {"en": "Task ID", "fr": "ID de la tâche"},
-        "status": {"en": "Status", "fr": "Statut"},
-        "success": {"en": "Success", "fr": "Succès"},
-        "feedback": {"en": "Feedback", "fr": "Retour"},
-        "error": {"en": "Error", "fr": "Erreur"}
-    }
-)
-
-class TaskItem(BaseModel, ModelMixin):
-    """Model for workflow tasks"""
-    id: str = Field(..., description="Task ID")
-    workflowId: str = Field(..., description="Workflow ID")
-    userInput: str = Field(..., description="User input that triggered the task")
-    status: TaskStatus = Field(default=TaskStatus.PENDING, description="Task status")
-    error: Optional[str] = Field(None, description="Error message if task failed")
-    startedAt: Optional[float] = Field(None, description="When the task started (UTC timestamp in seconds)")
-    finishedAt: Optional[float] = Field(None, description="When the task finished (UTC timestamp in seconds)")
-    actionList: List[TaskAction] = Field(default_factory=list, description="List of actions to execute")
-    retryCount: int = Field(default=0, description="Number of retries attempted")
-    retryMax: int = Field(default=3, description="Maximum number of retries")
-    rollbackOnFailure: bool = Field(default=True, description="Whether to rollback on failure")
-    dependencies: List[str] = Field(default_factory=list, description="List of task IDs this task depends on")
-    feedback: Optional[str] = Field(None, description="Task feedback message")
-    processingTime: Optional[float] = Field(None, description="Total processing time in seconds")
-    resultLabels: Optional[Dict[str, Any]] = Field(default_factory=dict, description="Map of result labels to their values")
-
-    def isSuccessful(self) -> bool:
-        """Check if task was successful"""
-        return self.status == TaskStatus.COMPLETED
-
-    def hasError(self) -> bool:
-        """Check if task has an error"""
-        return self.status == TaskStatus.FAILED
-
-    def getErrorMessage(self) -> Optional[str]:
-        """Get error message if any"""
-        return self.error if self.hasError() else None
-
-    def getResultDocuments(self) -> List[ChatDocument]:
-        """Get all documents from all successful actions"""
-        documents = []
-        for action in self.actionList:
-            if action.isSuccessful() and action.resultDocuments:
-                documents.extend(action.resultDocuments)
-        return documents
-
-    def getResultDocumentLabel(self) -> Optional[str]:
-        """Get the label for the result documents"""
-        for action in self.actionList:
-            if action.isSuccessful() and action.execResultLabel:
-                return action.execResultLabel
-        return None
-
-    def getResultLabel(self, label: str) -> Optional[Any]:
-        """Get value for a specific result label"""
-        return self.resultLabels.get(label) if self.resultLabels else None
-
-# Register labels for TaskItem
-register_model_labels(
-    "TaskItem",
-    {"en": "Task", "fr": "Tâche"},
-    {
-        "id": {"en": "Task ID", "fr": "ID de la tâche"},
-        "workflowId": {"en": "Workflow ID", "fr": "ID du workflow"},
-        "userInput": {"en": "User Input", "fr": "Entrée utilisateur"},
-        "status": {"en": "Status", "fr": "Statut"},
-        "error": {"en": "Error", "fr": "Erreur"},
-        "startedAt": {"en": "Started At", "fr": "Démarré à"},
-        "finishedAt": {"en": "Finished At", "fr": "Terminé à"},
-        "actionList": {"en": "Actions", "fr": "Actions"},
-        "retryCount": {"en": "Retry Count", "fr": "Nombre de tentatives"},
-        "retryMax": {"en": "Max Retries", "fr": "Tentatives max"},
-        "rollbackOnFailure": {"en": "Rollback On Failure", "fr": "Annuler en cas d'échec"},
-        "dependencies": {"en": "Dependencies", "fr": "Dépendances"},
-        "feedback": {"en": "Feedback", "fr": "Retour"},
-        "processingTime": {"en": "Processing Time", "fr": "Temps de traitement"}
-    }
-)
-
-class ChatStat(BaseModel, ModelMixin):
-    """Data model for chat statistics - ONLY statistics, not workflow progress"""
-    id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Primary key")
-    workflowId: Optional[str] = Field(None, description="Foreign key to workflow (for workflow stats)")
-    messageId: Optional[str] = Field(None, description="Foreign key to message (for message stats)")
-    processingTime: Optional[float] = Field(None, description="Processing time in seconds")
-    tokenCount: Optional[int] = Field(None, description="Number of tokens processed")
-    bytesSent: Optional[int] = Field(None, description="Number of bytes sent")
-    bytesReceived: Optional[int] = Field(None, description="Number of bytes received")
-    successRate: Optional[float] = Field(None, description="Success rate of operations")
-    errorCount: Optional[int] = Field(None, description="Number of errors encountered")
-
-# Register labels for ChatStat
-register_model_labels(
-    "ChatStat",
-    {"en": "Chat Statistics", "fr": "Statistiques de chat"},
-    {
-        "id": {"en": "ID", "fr": "ID"},
-        "workflowId": {"en": "Workflow ID", "fr": "ID du workflow"},
-        "messageId": {"en": "Message ID", "fr": "ID du message"},
-        "processingTime": {"en": "Processing Time", "fr": "Temps de traitement"},
-        "tokenCount": {"en": "Token Count", "fr": "Nombre de tokens"},
-        "bytesSent": {"en": "Bytes Sent", "fr": "Octets envoyés"},
-        "bytesReceived": {"en": "Bytes Received", "fr": "Octets reçus"},
-        "successRate": {"en": "Success Rate", "fr": "Taux de succès"},
-        "errorCount": {"en": "Error Count", "fr": "Nombre d'erreurs"}
-    }
-)
-
-class ChatLog(BaseModel, ModelMixin):
-    """Data model for chat logs"""
-    id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Primary key")
-    workflowId: str = Field(description="Foreign key to workflow")
-    message: str = Field(description="Log message")
-    type: str = Field(description="Log type (info, warning, error, etc.)")
-    timestamp: float = Field(default_factory=get_utc_timestamp, description="When the log entry was created (UTC timestamp in seconds)")
-    status: Optional[str] = Field(None, description="Status of the log entry")
-    progress: Optional[float] = Field(None, description="Progress indicator (0.0 to 1.0)")
-    performance: Optional[Dict[str, Any]] = Field(None, description="Performance metrics")
-
-# Register labels for ChatLog
-register_model_labels(
-    "ChatLog",
-    {"en": "Chat Log", "fr": "Journal de chat"},
-    {
-        "id": {"en": "ID", "fr": "ID"},
-        "workflowId": {"en": "Workflow ID", "fr": "ID du flux de travail"},
-        "message": {"en": "Message", "fr": "Message"},
-        "type": {"en": "Type", "fr": "Type"},
-        "timestamp": {"en": "Timestamp", "fr": "Horodatage"},
-        "status": {"en": "Status", "fr": "Statut"},
-        "progress": {"en": "Progress", "fr": "Progression"},
-        "performance": {"en": "Performance", "fr": "Performance"}
-    }
-)
-
-class ChatMessage(BaseModel, ModelMixin):
-    """Data model for a chat message"""
-    id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Primary key")
-    workflowId: str = Field(description="Foreign key to workflow")
-    parentMessageId: Optional[str] = Field(None, description="Parent message ID for threading")
-    documents: List[ChatDocument] = Field(default_factory=list, description="Associated documents")
-    documentsLabel: Optional[str] = Field(None, description="Label for the set of documents")
-    message: Optional[str] = Field(None, description="Message content")
-    role: str = Field(description="Role of the message sender")
-    status: str = Field(description="Status of the message (first, step, last)")
-    sequenceNr: int = Field(description="Sequence number of the message (set automatically)")
-    publishedAt: float = Field(default_factory=get_utc_timestamp, description="When the message was published (UTC timestamp in seconds)")
-    stats: Optional[ChatStat] = Field(None, description="Statistics for this message")
-    success: Optional[bool] = Field(None, description="Whether the message processing was successful")
-    actionId: Optional[str] = Field(None, description="ID of the action that produced this message")
-    actionMethod: Optional[str] = Field(None, description="Method of the action that produced this message")
-    actionName: Optional[str] = Field(None, description="Name of the action that produced this message")
-    
-    # New workflow context fields:
-    roundNumber: Optional[int] = Field(None, description="Round number in workflow")
-    taskNumber: Optional[int] = Field(None, description="Task number within round")
-    actionNumber: Optional[int] = Field(None, description="Action number within task")
-    
-    # New workflow progress fields:
-    taskProgress: Optional[str] = Field(
-        None, 
-        description="Task progress status: pending, running, success, fail, retry"
-    )
-    
-    actionProgress: Optional[str] = Field(
-        None, 
-        description="Action progress status: pending, running, success, fail"
-    )
-
-# Register labels for ChatMessage
-register_model_labels(
-    "ChatMessage",
-    {"en": "Chat Message", "fr": "Message de chat"},
-    {
-        "id": {"en": "ID", "fr": "ID"},
-        "workflowId": {"en": "Workflow ID", "fr": "ID du flux de travail"},
-        "parentMessageId": {"en": "Parent Message ID", "fr": "ID du message parent"},
-        "documents": {"en": "Documents", "fr": "Documents"},
-        "documentsLabel": {"en": "Documents Label", "fr": "Label des documents"},
-        "message": {"en": "Message", "fr": "Message"},
-        "role": {"en": "Role", "fr": "Rôle"},
-        "status": {"en": "Status", "fr": "Statut"},
-        "sequenceNr": {"en": "Sequence Number", "fr": "Numéro de séquence"},
-        "publishedAt": {"en": "Published At", "fr": "Publié le"},
-        "stats": {"en": "Statistics", "fr": "Statistiques"},
-        "success": {"en": "Success", "fr": "Succès"},
-        "actionId": {"en": "Action ID", "fr": "ID de l'action"},
-        "actionMethod": {"en": "Action Method", "fr": "Méthode de l'action"},
-        "actionName": {"en": "Action Name", "fr": "Nom de l'action"},
-        "roundNumber": {"en": "Round Number", "fr": "Numéro de tour"},
-        "taskNumber": {"en": "Task Number", "fr": "Numéro de tâche"},
-        "actionNumber": {"en": "Action Number", "fr": "Numéro d'action"},
-        "taskProgress": {"en": "Task Progress", "fr": "Progression de la tâche"},
-        "actionProgress": {"en": "Action Progress", "fr": "Progression de l'action"}
-    }
-)
-
-class ChatWorkflow(BaseModel, ModelMixin):
-    """Data model for a chat workflow"""
-    id: str = Field(
-        default_factory=lambda: str(uuid.uuid4()), 
-        description="Primary key",
-        frontend_type="text",
-        frontend_readonly=True,
-        frontend_required=False
-    )
-    mandateId: str = Field(
-        description="ID of the mandate this workflow belongs to",
-        frontend_type="text",
-        frontend_readonly=True,
-        frontend_required=False
-    )
-    status: str = Field(
-        description="Current status of the workflow",
-        frontend_type="select",
-        frontend_readonly=False,
-        frontend_required=False,
-        frontend_options=[
-            {"value": "running", "label": {"en": "Running", "fr": "En cours"}},
-            {"value": "completed", "label": {"en": "Completed", "fr": "Terminé"}},
-            {"value": "stopped", "label": {"en": "Stopped", "fr": "Arrêté"}},
-            {"value": "error", "label": {"en": "Error", "fr": "Erreur"}}
-        ]
-    )
-    name: Optional[str] = Field(
-        None, 
-        description="Name of the workflow",
-        frontend_type="text",
-        frontend_readonly=False,
-        frontend_required=True
-    )
-    currentRound: int = Field(
-        description="Current round number",
-        frontend_type="integer",
-        frontend_readonly=True,
-        frontend_required=False
-    )
-    currentTask: int = Field(
-        default=0,
-        description="Current task number",
-        frontend_type="integer",
-        frontend_readonly=True,
-        frontend_required=False
-    )
-    currentAction: int = Field(
-        default=0,
-        description="Current action number",
-        frontend_type="integer",
-        frontend_readonly=True,
-        frontend_required=False
-    )
-    totalTasks: int = Field(
-        default=0,
-        description="Total number of tasks in the workflow",
-        frontend_type="integer",
-        frontend_readonly=True,
-        frontend_required=False
-    )
-    totalActions: int = Field(
-        default=0,
-        description="Total number of actions in the workflow",
-        frontend_type="integer",
-        frontend_readonly=True,
-        frontend_required=False
-    )
-    lastActivity: float = Field(
-        default_factory=get_utc_timestamp, 
-        description="Timestamp of last activity (UTC timestamp in seconds)",
-        frontend_type="timestamp",
-        frontend_readonly=True,
-        frontend_required=False
-    )
-    startedAt: float = Field(
-        default_factory=get_utc_timestamp, 
-        description="When the workflow started (UTC timestamp in seconds)",
-        frontend_type="timestamp",
-        frontend_readonly=True,
-        frontend_required=False
-    )
-    logs: List[ChatLog] = Field(
-        default_factory=list, 
-        description="Workflow logs",
-        frontend_type="text",
-        frontend_readonly=True,
-        frontend_required=False
-    )
-    messages: List[ChatMessage] = Field(
-        default_factory=list, 
-        description="Messages in the workflow",
-        frontend_type="text",
-        frontend_readonly=True,
-        frontend_required=False
-    )
-    stats: Optional[ChatStat] = Field(
-        None, 
-        description="Workflow statistics",
-        frontend_type="text",
-        frontend_readonly=True,
-        frontend_required=False
-    )
-    tasks: List[TaskItem] = Field(
-        default_factory=list, 
-        description="List of tasks in the workflow",
-        frontend_type="text",
-        frontend_readonly=True,
-        frontend_required=False
-    )
-    # Workflow mode selection (e.g., Actionplan, React)
-    workflowMode: str = Field(
-        default="Actionplan",
-        description="Workflow mode selector",
-        frontend_type="select",
-        frontend_readonly=False,
-        frontend_required=False,
-        frontend_options=[
-            {"value": "Actionplan", "label": {"en": "Action Plan", "fr": "Plan d'actions"}},
-            {"value": "React", "label": {"en": "React", "fr": "Réactif"}}
-        ]
-    )
-    maxSteps: int = Field(
-        default=5,
-        description="Maximum number of iterations in react mode",
-        frontend_type="integer",
-        frontend_readonly=False,
-        frontend_required=False
-    )
-
-# Register labels for ChatWorkflow
-register_model_labels(
-    "ChatWorkflow",
-    {"en": "Chat Workflow", "fr": "Flux de travail de chat"},
-    {
-        "id": {"en": "ID", "fr": "ID"},
-        "mandateId": {"en": "Mandate ID", "fr": "ID du mandat"},
-        "status": {"en": "Status", "fr": "Statut"},
-        "name": {"en": "Name", "fr": "Nom"},
-        "currentRound": {"en": "Current Round", "fr": "Tour actuel"},
-        "currentTask": {"en": "Current Task", "fr": "Tâche actuelle"},
-        "currentAction": {"en": "Current Action", "fr": "Action actuelle"},
-        "totalTasks": {"en": "Total Tasks", "fr": "Total des tâches"},
-        "totalActions": {"en": "Total Actions", "fr": "Total des actions"},
-        "lastActivity": {"en": "Last Activity", "fr": "Dernière activité"},
-        "startedAt": {"en": "Started At", "fr": "Démarré le"},
-        "logs": {"en": "Logs", "fr": "Journaux"},
-        "messages": {"en": "Messages", "fr": "Messages"},
-        "stats": {"en": "Statistics", "fr": "Statistiques"},
-        "tasks": {"en": "Tasks", "fr": "Tâches"},
-        "workflowMode": {"en": "Workflow Mode", "fr": "Mode de workflow"},
-        "maxSteps": {"en": "Max Steps", "fr": "Étapes max"}
-    }
-)
-
-# ====== WORKFLOW SUPPORT MODELS ======
-
-class TaskStep(BaseModel, ModelMixin):
-    id: str
-    objective: str
-    dependencies: Optional[list[str]] = Field(default_factory=list)
-    success_criteria: Optional[list[str]] = Field(default_factory=list)
-    estimated_complexity: Optional[str] = None
-    userMessage: Optional[str] = Field(None, description="User-friendly message in user's language")
-
-# Register labels for TaskStep
-register_model_labels(
-    "TaskStep",
-    {"en": "Task Step", "fr": "Étape de tâche"},
-    {
-        "id": {"en": "ID", "fr": "ID"},
-        "objective": {"en": "Objective", "fr": "Objectif"},
-        "dependencies": {"en": "Dependencies", "fr": "Dépendances"},
-        "success_criteria": {"en": "Success Criteria", "fr": "Critères de succès"},
-        "estimated_complexity": {"en": "Estimated Complexity", "fr": "Complexité estimée"},
-        "userMessage": {"en": "User Message", "fr": "Message utilisateur"}
-    }
-)
-
-class TaskHandover(BaseModel, ModelMixin):
-    """Structured handover between workflow phases and tasks"""
-    taskId: str = Field(description="Target task ID")
-    sourceTask: Optional[str] = Field(None, description="Source task ID")
-    
-    # Document handovers
-    inputDocuments: List[DocumentExchange] = Field(default_factory=list, description="Available input documents")
-    outputDocuments: List[DocumentExchange] = Field(default_factory=list, description="Produced output documents")
-    
-    # Context and state
-    context: Dict[str, Any] = Field(default_factory=dict, description="Task context")
-    previousResults: List[str] = Field(default_factory=list, description="Previous result summaries")
-    improvements: List[str] = Field(default_factory=list, description="Improvement suggestions")
-    
-    # Workflow context
-    workflowSummary: Optional[str] = Field(None, description="Summarized workflow context")
-    messageHistory: List[str] = Field(default_factory=list, description="Key message summaries")
-    
-    # Metadata
-    timestamp: float = Field(default_factory=get_utc_timestamp, description="When the handover was created (UTC timestamp in seconds)")
-    handoverType: str = Field(default="task", description="Type of handover: task, phase, or workflow")
-    
-    def addInputDocument(self, documentExchange: DocumentExchange) -> None:
-        """Add an input document exchange"""
-        self.inputDocuments.append(documentExchange)
-    
-    def addOutputDocument(self, documentExchange: DocumentExchange) -> None:
-        """Add an output document exchange"""
-        self.outputDocuments.append(documentExchange)
-    
-    def getDocumentsForAction(self, actionId: str) -> List[DocumentExchange]:
-        """Get all document exchanges relevant for a specific action"""
-        relevant = []
-        for doc_exchange in self.inputDocuments + self.outputDocuments:
-            if doc_exchange.isForAction(actionId):
-                relevant.append(doc_exchange)
-        return relevant
-
-# Register labels for TaskHandover
-register_model_labels(
-    "TaskHandover",
-    {"en": "Task Handover", "fr": "Transfert de tâche"},
-    {
-        "taskId": {"en": "Task ID", "fr": "ID de la tâche"},
-        "sourceTask": {"en": "Source Task", "fr": "Tâche source"},
-        "inputDocuments": {"en": "Input Documents", "fr": "Documents d'entrée"},
-        "outputDocuments": {"en": "Output Documents", "fr": "Documents de sortie"},
-        "context": {"en": "Context", "fr": "Contexte"},
-        "previousResults": {"en": "Previous Results", "fr": "Résultats précédents"},
-        "improvements": {"en": "Improvements", "fr": "Améliorations"},
-        "workflowSummary": {"en": "Workflow Summary", "fr": "Résumé du workflow"},
-        "messageHistory": {"en": "Message History", "fr": "Historique des messages"},
-        "timestamp": {"en": "Timestamp", "fr": "Horodatage"},
-        "handoverType": {"en": "Handover Type", "fr": "Type de transfert"}
-    }
-)
-
-class TaskContext(BaseModel, ModelMixin):
-    task_step: TaskStep
-    workflow: Optional['ChatWorkflow'] = None
-    workflow_id: Optional[str] = None
-    
-    # Available resources
-    available_documents: Optional[str] = "No documents available"
-    available_connections: Optional[list[str]] = Field(default_factory=list)
-    
-    # Previous execution state
-    previous_results: Optional[list[str]] = Field(default_factory=list)
-    previous_handover: Optional[TaskHandover] = None
-    
-    # Current execution state
-    improvements: Optional[list[str]] = Field(default_factory=list)
-    retry_count: Optional[int] = 0
-    previous_action_results: Optional[list] = Field(default_factory=list)
-    previous_review_result: Optional[dict] = None
-    is_regeneration: Optional[bool] = False
-    
-    # Failure analysis
-    failure_patterns: Optional[list[str]] = Field(default_factory=list)
-    failed_actions: Optional[list] = Field(default_factory=list)
-    successful_actions: Optional[list] = Field(default_factory=list)
-    
-    # Criteria progress tracking for retries
-    criteria_progress: Optional[dict] = None
-    
-    # Iterative loop controls (moved to ChatWorkflow.workflowMode and ChatWorkflow.maxSteps)
-    # reactMode and maxSteps are now controlled at the workflow level
-    
-    def getDocumentReferences(self) -> List[str]:
-        """Get all available document references from previous handover"""
-        docs = []
-        if self.previous_handover:
-            for doc_exchange in self.previous_handover.inputDocuments:
-                docs.extend(doc_exchange.documents)
-        return list(set(docs))  # Remove duplicates
-    
-    def addImprovement(self, improvement: str) -> None:
-        """Add an improvement suggestion"""
-        if improvement not in (self.improvements or []):
-            if self.improvements is None:
-                self.improvements = []
-            self.improvements.append(improvement)
-
-class ReviewContext(BaseModel, ModelMixin):
-    task_step: TaskStep
-    task_actions: Optional[list] = Field(default_factory=list)
-    action_results: Optional[list] = Field(default_factory=list)
-    step_result: Optional[dict] = Field(default_factory=dict)
-    workflow_id: Optional[str] = None
-    previous_results: Optional[list[str]] = Field(default_factory=list)
-
-class ReviewResult(BaseModel, ModelMixin):
-    status: str
-    reason: Optional[str] = None
-    improvements: Optional[list[str]] = Field(default_factory=list)
-    quality_score: Optional[int] = 5
-    missing_outputs: Optional[list[str]] = Field(default_factory=list)
-    met_criteria: Optional[list[str]] = Field(default_factory=list)
-    unmet_criteria: Optional[list[str]] = Field(default_factory=list)
-    confidence: Optional[float] = 0.5
-    userMessage: Optional[str] = Field(None, description="User-friendly message in user's language")
-
-# Register labels for ReviewResult
-register_model_labels(
-    "ReviewResult",
-    {"en": "Review Result", "fr": "Résultat de l'évaluation"},
-    {
-        "status": {"en": "Status", "fr": "Statut"},
-        "reason": {"en": "Reason", "fr": "Raison"},
-        "improvements": {"en": "Improvements", "fr": "Améliorations"},
-        "quality_score": {"en": "Quality Score", "fr": "Score de qualité"},
-        "missing_outputs": {"en": "Missing Outputs", "fr": "Sorties manquantes"},
-        "met_criteria": {"en": "Met Criteria", "fr": "Critères respectés"},
-        "unmet_criteria": {"en": "Unmet Criteria", "fr": "Critères non respectés"},
-        "confidence": {"en": "Confidence", "fr": "Confiance"},
-        "userMessage": {"en": "User Message", "fr": "Message utilisateur"}
-    }
-)
-
-class TaskPlan(BaseModel, ModelMixin):
-    overview: str
-    tasks: list[TaskStep]
-    userMessage: Optional[str] = Field(None, description="Overall user-friendly message for the task plan")
-
-# Register labels for TaskPlan
-register_model_labels(
-    "TaskPlan",
-    {"en": "Task Plan", "fr": "Plan de tâches"},
-    {
-        "overview": {"en": "Overview", "fr": "Aperçu"},
-        "tasks": {"en": "Tasks", "fr": "Tâches"},
-        "userMessage": {"en": "User Message", "fr": "Message utilisateur"}
-    }
-)
-
-class WorkflowResult(BaseModel, ModelMixin):
-    status: str
-    completed_tasks: int
-    total_tasks: int
-    execution_time: float
-    final_results_count: int
-    error: Optional[str] = None
-    phase: Optional[str] = None
-
-# Register labels for WorkflowResult
-register_model_labels(
-    "WorkflowResult",
-    {"en": "Workflow Result", "fr": "Résultat du workflow"},
-    {
-        "status": {"en": "Status", "fr": "Statut"},
-        "completed_tasks": {"en": "Completed Tasks", "fr": "Tâches terminées"},
-        "total_tasks": {"en": "Total Tasks", "fr": "Total des tâches"},
-        "execution_time": {"en": "Execution Time", "fr": "Temps d'exécution"},
-        "final_results_count": {"en": "Final Results Count", "fr": "Nombre de résultats finaux"},
-        "error": {"en": "Error", "fr": "Erreur"},
-        "phase": {"en": "Phase", "fr": "Phase"}
-    }
-)
-
-# ===== Centralized AI Call Response Models =====
-
-class AiResult(BaseModel, ModelMixin):
-    """Document result from centralized AI call"""
-    filename: str = Field(description="Name of the result document")
-    mimetype: str = Field(description="MIME type of the result document")
-    content: str = Field(description="Content of the result document")
-
-# Register labels for AiResult
-register_model_labels(
-    "AiResult",
-    {"en": "Result Document", "fr": "Document de résultat"},
-    {
-        "filename": {"en": "Filename", "fr": "Nom de fichier"},
-        "mimetype": {"en": "MIME Type", "fr": "Type MIME"},
-        "content": {"en": "Content", "fr": "Contenu"}
-    }
-)
-
-class CentralizedAiResponse(BaseModel, ModelMixin):
-    """Standardized response format from centralized AI calls"""
-    aiResults: List[AiResult] = Field(default_factory=list, description="List of result documents")
-    success: bool = Field(description="Whether the AI call was successful")
-    error: Optional[str] = Field(None, description="Error message if the call failed")
-
-# Register labels for CentralizedAiResponse
-register_model_labels(
-    "CentralizedAiResponse",
-    {"en": "Centralized AI Response", "fr": "Réponse IA centralisée"},
-    {
-        "aiResults": {"en": "Result Documents", "fr": "Documents de résultat"},
-        "success": {"en": "Success", "fr": "Succès"},
-        "error": {"en": "Error", "fr": "Erreur"}
-    }
-)
-
-

modules/interfaces/interfaceDbAppObjects.py

@@ -16,7 +16,7 @@ import re
 from modules.connectors.connectorDbPostgre import DatabaseConnector
 from modules.shared.configuration import APP_CONFIG
 from modules.shared.timezoneUtils import get_utc_now, get_utc_timestamp
-from modules.interfaces.interfaceAppAccess import AppAccess
+from modules.interfaces.interfaceDbAppAccess import AppAccess
 from modules.datamodels.datamodelUam import (
     User, Mandate, UserInDB, UserConnection,
     AuthAuthority, UserPrivilege, ConnectionStatus,
@@ -951,8 +951,6 @@ class AppObjects:
     def cleanupExpiredTokens(self) -> int:
         """Clean up expired tokens for all connections, returns count of cleaned tokens"""
         try:
-            from modules.shared.timezoneUtils import get_utc_timestamp
-            
             current_time = get_utc_timestamp()
             cleaned_count = 0
             

modules/interfaces/interfaceDbChatObjects.py

@@ -11,10 +11,22 @@ from typing import Dict, Any, List, Optional, Union, get_origin, get_args
 
 import asyncio
 
-from modules.interfaces.interfaceChatAccess import ChatAccess
-from modules.datamodels.datamodelWorkflow import UserInputRequest, TaskAction, TaskResult
-from modules.datamodels.datamodelWorkflow import TaskItem, TaskStatus, ActionResult
-from modules.datamodels.datamodelChat import ChatDocument, ChatStat, ChatLog, ChatMessage, ChatWorkflow
+from modules.interfaces.interfaceDbChatAccess import ChatAccess
+from modules.datamodels.datamodelWorkflow import (
+    TaskAction, 
+    TaskResult,
+    TaskItem, 
+    TaskStatus, 
+    ActionResult
+)
+from modules.datamodels.datamodelChat import (
+    UserInputRequest,
+    ChatDocument, 
+    ChatStat, 
+    ChatLog, 
+    ChatMessage, 
+    ChatWorkflow
+)
 from modules.datamodels.datamodelUam import User
 
 # DYNAMIC PART: Connectors to the Interface

modules/interfaces/interfaceDbComponentObjects.py

@@ -5,23 +5,20 @@ Uses the JSON connector for data access with added language support.
 
 import os
 import logging
+import base64
+import hashlib
 from datetime import datetime, UTC
 from typing import Dict, Any, List, Optional, Union
 
-import hashlib
-
-from modules.interfaces.interfaceComponentAccess import ComponentAccess
+from modules.connectors.connectorDbPostgre import DatabaseConnector
+from modules.interfaces.interfaceDbComponentAccess import ComponentAccess
 from modules.datamodels.datamodelFiles import FilePreview, FileItem, FileData
 from modules.datamodels.datamodelUtils import Prompt
 from modules.datamodels.datamodelVoice import VoiceSettings
 from modules.datamodels.datamodelUam import User, Mandate
-
-# DYNAMIC PART: Connectors to the Interface
-from modules.connectors.connectorDbPostgre import DatabaseConnector
-
-# Basic Configurations
 from modules.shared.configuration import APP_CONFIG
 from modules.shared.timezoneUtils import get_utc_timestamp
+
 logger = logging.getLogger(__name__)
 
 # Singleton factory for Management instances with AI service per context
@@ -150,7 +147,7 @@ class ComponentObjects:
                 return
 
             # Get the root interface to access the initial mandate ID
-            from modules.interfaces.interfaceAppObjects import getRootInterface
+            from modules.interfaces.interfaceDbAppObjects import getRootInterface
             rootInterface = getRootInterface()
             
             # Get initial mandate ID through the root interface
@@ -388,7 +385,6 @@ class ComponentObjects:
 
     def getMimeType(self, fileName: str) -> str:
         """Determines the MIME type based on the file extension."""
-        import os
         ext = os.path.splitext(fileName)[1].lower()[1:]
         extensionToMime = {
             "pdf": "application/pdf",
@@ -558,7 +554,6 @@ class ComponentObjects:
 
     def createFile(self, name: str, mimeType: str, content: bytes) -> FileItem:
         """Creates a new file entry if user has permission. Computes fileHash and fileSize from content."""
-        import hashlib
         if not self._canModify(FileItem):
             raise PermissionError("No permission to create files")
         
@@ -655,8 +650,6 @@ class ComponentObjects:
     def createFileData(self, fileId: str, data: bytes) -> bool:
         """Stores the binary data of a file in the database."""
         try:
-            import base64
-            
             # Check file access
             file = self.getFile(fileId)
             if not file:
@@ -715,8 +708,6 @@ class ComponentObjects:
             logger.warning(f"No access to file ID {fileId}")
             return None
             
-        import base64
-        
         fileDataEntries = self.db.getRecordset(FileData, recordFilter={"id": fileId})
         if not fileDataEntries:
             logger.warning(f"No data found for file ID {fileId}")
@@ -791,12 +782,10 @@ class ComponentObjects:
                     encoding = 'latin-1'
             elif file.mimeType.startswith("image/"):
                 # For images, return base64
-                import base64
                 content = base64.b64encode(fileContent).decode('utf-8')
                 isText = False
             else:
                 # For other files, return as base64
-                import base64
                 content = base64.b64encode(fileContent).decode('utf-8')
                 isText = False
 
@@ -827,7 +816,6 @@ class ComponentObjects:
                 raise ValueError(f"fileContent must be bytes, got {type(fileContent)}")
             
             # Compute file hash first to check for duplicates
-            import hashlib
             fileHash = hashlib.sha256(fileContent).hexdigest()
             
             # Check for exact name+hash match first (same name + same content)
@@ -894,10 +882,8 @@ class ComponentObjects:
             # Ensure timestamps are set for validation
             settings_data = filteredSettings[0]
             if not settings_data.get("creationDate"):
-                from modules.shared.timezoneUtils import get_utc_timestamp
                 settings_data["creationDate"] = get_utc_timestamp()
             if not settings_data.get("lastModified"):
-                from modules.shared.timezoneUtils import get_utc_timestamp
                 settings_data["lastModified"] = get_utc_timestamp()
                 
             return VoiceSettings.from_dict(settings_data)
@@ -946,7 +932,6 @@ class ComponentObjects:
                 raise ValueError(f"Voice settings not found for user {userId}")
             
             # Update lastModified timestamp
-            from modules.shared.timezoneUtils import get_utc_timestamp
             updateData["lastModified"] = get_utc_timestamp()
             
             # Update voice settings record

modules/interfaces/interfaceTicketObjects.py

@@ -2,7 +2,7 @@ from typing import Any, Optional
 from datetime import datetime, timezone
 
 # Module-level factory to create TicketInterface by connector type
-async def createTicketInterfaceByType(
+async def _createTicketInterfaceByType(
     *,
     taskSyncDefinition: dict,
     connectorType: str,

modules/interfaces/interfaceVoiceObjects.py

@@ -0,0 +1,498 @@
+"""
+Interface for Voice Services
+Provides a generic interface layer between routes and voice connectors.
+Handles voice operations including speech-to-text, text-to-speech, and translation.
+"""
+
+import logging
+from typing import Dict, Any, Optional, List
+from datetime import datetime, UTC
+
+from modules.connectors.connectorVoiceGoogle import ConnectorGoogleSpeech
+from modules.datamodels.datamodelVoice import VoiceSettings
+from modules.datamodels.datamodelUam import User
+from modules.shared.timezoneUtils import get_utc_timestamp
+
+logger = logging.getLogger(__name__)
+
+# Singleton factory for Voice instances
+_instancesVoice = {}
+
+class VoiceObjects:
+    """
+    Interface for Voice Services.
+    Provides a generic interface layer between routes and voice connectors.
+    """
+    
+    def __init__(self):
+        """Initialize the Voice Interface."""
+        self.currentUser: Optional[User] = None
+        self.userId: Optional[str] = None
+        self._google_speech_connector: Optional[ConnectorGoogleSpeech] = None
+        
+    def setUserContext(self, currentUser: User):
+        """Set the user context for the interface."""
+        if not currentUser:
+            logger.info("Initializing voice interface without user context")
+            return
+            
+        self.currentUser = currentUser
+        self.userId = currentUser.id
+        
+        if not self.userId:
+            raise ValueError("Invalid user context: id is required")
+            
+        logger.debug(f"Voice interface user context set: userId={self.userId}")
+    
+    def _getGoogleSpeechConnector(self) -> ConnectorGoogleSpeech:
+        """Get or create Google Cloud Speech connector instance."""
+        if self._google_speech_connector is None:
+            try:
+                self._google_speech_connector = ConnectorGoogleSpeech()
+                logger.info("✅ Google Cloud Speech connector initialized")
+            except Exception as e:
+                logger.error(f"❌ Failed to initialize Google Cloud Speech connector: {e}")
+                raise
+        return self._google_speech_connector
+    
+    # Speech-to-Text Operations
+    
+    async def speechToText(self, audioContent: bytes, language: str = "de-DE", 
+                          sampleRate: int = None, channels: int = None) -> Dict[str, Any]:
+        """
+        Convert speech to text using Google Cloud Speech-to-Text API.
+        
+        Args:
+            audioContent: Raw audio data
+            language: Language code (e.g., 'de-DE', 'en-US')
+            sampleRate: Audio sample rate (auto-detected if None)
+            channels: Number of audio channels (auto-detected if None)
+            
+        Returns:
+            Dict containing transcribed text, confidence, and metadata
+        """
+        try:
+            logger.info(f"🎤 Speech-to-text request: {len(audioContent)} bytes, language: {language}")
+            
+            connector = self._getGoogleSpeechConnector()
+            result = await connector.speech_to_text(
+                audio_content=audioContent,
+                language=language,
+                sample_rate=sampleRate,
+                channels=channels
+            )
+            
+            if result["success"]:
+                logger.info(f"✅ Speech-to-text successful: '{result['text']}' (confidence: {result['confidence']:.2f})")
+            else:
+                logger.warning(f"⚠️ Speech-to-text failed: {result.get('error', 'Unknown error')}")
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"❌ Speech-to-text error: {e}")
+            return {
+                "success": False,
+                "text": "",
+                "confidence": 0.0,
+                "error": str(e)
+            }
+    
+    # Translation Operations
+    
+    async def translateText(self, text: str, sourceLanguage: str = "de", 
+                          targetLanguage: str = "en") -> Dict[str, Any]:
+        """
+        Translate text using Google Cloud Translation API.
+        
+        Args:
+            text: Text to translate
+            sourceLanguage: Source language code (e.g., 'de', 'en')
+            targetLanguage: Target language code (e.g., 'en', 'de')
+            
+        Returns:
+            Dict containing translated text and metadata
+        """
+        try:
+            logger.info(f"🌐 Translation request: '{text}' ({sourceLanguage} -> {targetLanguage})")
+            
+            if not text.strip():
+                return {
+                    "success": False,
+                    "translated_text": "",
+                    "error": "Empty text provided"
+                }
+            
+            connector = self._getGoogleSpeechConnector()
+            result = await connector.translate_text(
+                text=text,
+                source_language=sourceLanguage,
+                target_language=targetLanguage
+            )
+            
+            if result["success"]:
+                logger.info(f"✅ Translation successful: '{result['translated_text']}'")
+            else:
+                logger.warning(f"⚠️ Translation failed: {result.get('error', 'Unknown error')}")
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"❌ Translation error: {e}")
+            return {
+                "success": False,
+                "translated_text": "",
+                "error": str(e)
+            }
+    
+    # Combined Operations
+    
+    async def speechToTranslatedText(self, audioContent: bytes, 
+                                   fromLanguage: str = "de-DE", 
+                                   toLanguage: str = "en") -> Dict[str, Any]:
+        """
+        Complete pipeline: Speech-to-Text + Translation.
+        
+        Args:
+            audioContent: Raw audio data
+            fromLanguage: Source language for speech recognition
+            toLanguage: Target language for translation
+            
+        Returns:
+            Dict containing original text, translated text, and metadata
+        """
+        try:
+            logger.info(f"🔄 Speech-to-translation pipeline: {fromLanguage} -> {toLanguage}")
+            
+            connector = self._getGoogleSpeechConnector()
+            result = await connector.speech_to_translated_text(
+                audio_content=audioContent,
+                from_language=fromLanguage,
+                to_language=toLanguage
+            )
+            
+            if result["success"]:
+                logger.info(f"✅ Complete pipeline successful:")
+                logger.info(f"   Original: '{result['original_text']}'")
+                logger.info(f"   Translated: '{result['translated_text']}'")
+            else:
+                logger.warning(f"⚠️ Speech-to-translation pipeline failed: {result.get('error', 'Unknown error')}")
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"❌ Speech-to-translation pipeline error: {e}")
+            return {
+                "success": False,
+                "original_text": "",
+                "translated_text": "",
+                "error": str(e)
+            }
+    
+    # Text-to-Speech Operations
+    
+    async def textToSpeech(self, text: str, languageCode: str = "de-DE", 
+                          voiceName: str = None) -> Dict[str, Any]:
+        """
+        Convert text to speech using Google Cloud Text-to-Speech.
+        
+        Args:
+            text: Text to convert to speech
+            languageCode: Language code (e.g., 'de-DE', 'en-US')
+            voiceName: Specific voice name (optional)
+        
+        Returns:
+            Dict with success status and audio data
+        """
+        try:
+            logger.info(f"🔊 Text-to-Speech request: '{text[:50]}...' in {languageCode}")
+            
+            if not text.strip():
+                return {
+                    "success": False,
+                    "error": "Empty text provided for text-to-speech"
+                }
+            
+            connector = self._getGoogleSpeechConnector()
+            result = await connector.text_to_speech(
+                text=text,
+                language_code=languageCode,
+                voice_name=voiceName
+            )
+            
+            if result["success"]:
+                logger.info(f"✅ Text-to-Speech successful: {len(result['audio_content'])} bytes")
+            else:
+                logger.warning(f"⚠️ Text-to-Speech failed: {result.get('error', 'Unknown error')}")
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"❌ Text-to-Speech error: {e}")
+            return {
+                "success": False,
+                "error": str(e)
+            }
+    
+    # Voice Settings Management
+    
+    def getVoiceSettings(self, userId: str) -> Optional[VoiceSettings]:
+        """
+        Get voice settings for a user.
+        
+        Args:
+            userId: User ID to get settings for
+            
+        Returns:
+            VoiceSettings object or None if not found
+        """
+        try:
+            # This would typically query the database
+            # For now, return None as this is handled by the database interface
+            logger.debug(f"Getting voice settings for user: {userId}")
+            return None
+            
+        except Exception as e:
+            logger.error(f"❌ Error getting voice settings: {e}")
+            return None
+    
+    def createVoiceSettings(self, settingsData: Dict[str, Any]) -> Optional[VoiceSettings]:
+        """
+        Create new voice settings.
+        
+        Args:
+            settingsData: Dictionary containing voice settings data
+            
+        Returns:
+            Created VoiceSettings object or None if failed
+        """
+        try:
+            logger.info(f"Creating voice settings: {settingsData}")
+            
+            # Add timestamps
+            currentTime = get_utc_timestamp()
+            settingsData["creationDate"] = currentTime
+            settingsData["lastModified"] = currentTime
+            
+            # Create VoiceSettings object
+            voiceSettings = VoiceSettings(**settingsData)
+            
+            logger.info(f"✅ Voice settings created: {voiceSettings.id}")
+            return voiceSettings
+            
+        except Exception as e:
+            logger.error(f"❌ Error creating voice settings: {e}")
+            return None
+    
+    def updateVoiceSettings(self, userId: str, settingsData: Dict[str, Any]) -> Optional[VoiceSettings]:
+        """
+        Update existing voice settings.
+        
+        Args:
+            userId: User ID to update settings for
+            settingsData: Dictionary containing updated voice settings data
+            
+        Returns:
+            Updated VoiceSettings object or None if failed
+        """
+        try:
+            logger.info(f"Updating voice settings for user {userId}: {settingsData}")
+            
+            # Add last modified timestamp
+            settingsData["lastModified"] = get_utc_timestamp()
+            
+            # Create updated VoiceSettings object
+            voiceSettings = VoiceSettings(**settingsData)
+            
+            logger.info(f"✅ Voice settings updated: {voiceSettings.id}")
+            return voiceSettings
+            
+        except Exception as e:
+            logger.error(f"❌ Error updating voice settings: {e}")
+            return None
+    
+    def getOrCreateVoiceSettings(self, userId: str) -> Optional[VoiceSettings]:
+        """
+        Get existing voice settings or create default ones.
+        
+        Args:
+            userId: User ID to get/create settings for
+            
+        Returns:
+            VoiceSettings object
+        """
+        try:
+            # Try to get existing settings
+            existingSettings = self.getVoiceSettings(userId)
+            
+            if existingSettings:
+                return existingSettings
+            
+            # Create default settings if none exist
+            defaultSettings = {
+                "userId": userId,
+                "sttLanguage": "de-DE",
+                "ttsLanguage": "de-DE",
+                "ttsVoice": "de-DE-Wavenet-A",
+                "translationEnabled": True,
+                "targetLanguage": "en-US"
+            }
+            
+            return self.createVoiceSettings(defaultSettings)
+            
+        except Exception as e:
+            logger.error(f"❌ Error getting or creating voice settings: {e}")
+            return None
+    
+    # Language and Voice Information
+    
+    async def getAvailableLanguages(self) -> Dict[str, Any]:
+        """
+        Get available languages from Google Cloud Text-to-Speech.
+        
+        Returns:
+            Dict containing success status and list of available languages
+        """
+        try:
+            logger.info("🌐 Getting available languages from Google Cloud TTS")
+            
+            connector = self._getGoogleSpeechConnector()
+            result = await connector.get_available_languages()
+            
+            if result["success"]:
+                logger.info(f"✅ Found {len(result['languages'])} available languages")
+            else:
+                logger.warning(f"⚠️ Failed to get languages: {result.get('error', 'Unknown error')}")
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"❌ Error getting available languages: {e}")
+            return {
+                "success": False,
+                "error": str(e),
+                "languages": []
+            }
+    
+    async def getAvailableVoices(self, languageCode: Optional[str] = None) -> Dict[str, Any]:
+        """
+        Get available voices from Google Cloud Text-to-Speech.
+        
+        Args:
+            languageCode: Optional language code to filter voices
+            
+        Returns:
+            Dict containing success status and list of available voices
+        """
+        try:
+            logger.info(f"🎤 Getting available voices, language filter: {languageCode}")
+            
+            connector = self._getGoogleSpeechConnector()
+            result = await connector.get_available_voices(language_code=languageCode)
+            
+            if result["success"]:
+                logger.info(f"✅ Found {len(result['voices'])} voices for language filter: {languageCode}")
+            else:
+                logger.warning(f"⚠️ Failed to get voices: {result.get('error', 'Unknown error')}")
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"❌ Error getting available voices: {e}")
+            return {
+                "success": False,
+                "error": str(e),
+                "voices": []
+            }
+    
+    # Audio Validation
+    
+    def validateAudioFormat(self, audioContent: bytes) -> Dict[str, Any]:
+        """
+        Validate audio format for Google Cloud Speech-to-Text.
+        
+        Args:
+            audioContent: Raw audio data
+            
+        Returns:
+            Dict containing validation results
+        """
+        try:
+            logger.debug(f"Validating audio format: {len(audioContent)} bytes")
+            
+            connector = self._getGoogleSpeechConnector()
+            result = connector.validate_audio_format(audioContent)
+            
+            if result["valid"]:
+                logger.debug(f"✅ Audio validation successful: {result['format']}, {result['sample_rate']}Hz, {result['channels']}ch")
+            else:
+                logger.warning(f"⚠️ Audio validation failed: {result.get('error', 'Unknown error')}")
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"❌ Audio validation error: {e}")
+            return {
+                "valid": False,
+                "error": str(e)
+            }
+    
+    # Health Check
+    
+    async def healthCheck(self) -> Dict[str, Any]:
+        """
+        Perform health check for voice services.
+        
+        Returns:
+            Dict containing health status and test results
+        """
+        try:
+            logger.info("🏥 Performing voice services health check")
+            
+            connector = self._getGoogleSpeechConnector()
+            
+            # Test with a simple translation
+            testResult = await connector.translate_text(
+                text="Hello",
+                source_language="en",
+                target_language="de"
+            )
+            
+            if testResult["success"]:
+                return {
+                    "status": "healthy",
+                    "service": "Google Cloud Speech-to-Text & Translation",
+                    "test_translation": testResult["translated_text"]
+                }
+            else:
+                return {
+                    "status": "unhealthy",
+                    "error": testResult.get("error", "Unknown error")
+                }
+                
+        except Exception as e:
+            logger.error(f"❌ Health check failed: {e}")
+            return {
+                "status": "unhealthy",
+                "error": str(e)
+            }
+
+
+def getVoiceInterface(currentUser: User = None) -> VoiceObjects:
+    """
+    Factory function to get or create Voice interface instance.
+    
+    Args:
+        currentUser: User object for context (optional)
+        
+    Returns:
+        VoiceObjects instance
+    """
+    # For now, create a new instance each time
+    # In the future, this could be enhanced with singleton pattern per user
+    voiceInterface = VoiceObjects()
+    
+    if currentUser:
+        voiceInterface.setUserContext(currentUser)
+    
+    return voiceInterface

modules/interfaces/interfaceWebObjects.py

@@ -1,51 +0,0 @@
-from dataclasses import dataclass
-
-from modules.datamodels.datamodelWeb import (
-    WebCrawlActionResult,
-    WebCrawlActionDocument,
-    WebCrawlDocumentData,
-    WebCrawlRequest,
-    WebCrawlResultItem,
-    WebScrapeActionResult,
-    WebScrapeActionDocument,
-    WebSearchDocumentData as WebScrapeDocumentData,
-    WebScrapeRequest,
-    WebScrapeResultItem,
-    WebSearchActionResult,
-    WebSearchActionDocument,
-    WebSearchDocumentData,
-    WebSearchRequest,
-    WebSearchResultItem,
-)
-from modules.connectors.connectorWebTavily import ConnectorWeb
-from modules.datamodels.datamodelWorkflow import ActionDocument
- 
-
-@dataclass(slots=True)
-class WebInterface:
-    connectorWebTavily: ConnectorWeb
-
-    def __post_init__(self) -> None:
-        if self.connectorWebTavily is None:
-            raise TypeError(
-                "connectorWebTavily must be provided. "
-                "Use `await WebInterface.create()` or pass a ConnectorWeb."
-            )
-
-    @classmethod
-    async def create(cls) -> "WebInterface":
-        connectorWebTavily = await ConnectorWeb.create()
-        return WebInterface(connectorWebTavily=connectorWebTavily)
-
-    # Methods
-
-    async def search(self, web_search_request: WebSearchRequest) -> WebSearchActionResult:
-        return await self.connectorWebTavily.search(web_search_request)
-
-    async def crawl(self, web_crawl_request: WebCrawlRequest) -> WebCrawlActionResult:
-        return await self.connectorWebTavily.crawl(web_crawl_request)
-
-    async def scrape(self, web_scrape_request: WebScrapeRequest) -> WebScrapeActionResult:
-        return await self.connectorWebTavily.scrape(web_scrape_request)
-
-    # Helpers moved to MethodWeb

modules/routes/routeAdmin.py

@@ -11,7 +11,7 @@ from datetime import datetime
 from modules.shared.configuration import APP_CONFIG
 from modules.security.auth import limiter, getCurrentUser
 from modules.datamodels.datamodelUam import User
-from modules.interfaces.interfaceAppObjects import getRootInterface
+from modules.interfaces.interfaceDbAppObjects import getRootInterface
 
 # Static folder setup - using absolute path from app root
 baseDir = FilePath(__file__).parent.parent.parent  # Go up to gateway root

modules/routes/routeChatPlayground.py

@@ -12,12 +12,11 @@ from datetime import datetime
 from modules.security.auth import limiter, getCurrentUser
 
 # Import interfaces
-import modules.interfaces.interfaceChatObjects as interfaceChatObjects
-from modules.interfaces.interfaceChatObjects import getInterface
+import modules.interfaces.interfaceDbChatObjects as interfaceDbChatObjects
+from modules.interfaces.interfaceDbChatObjects import getInterface
 
 # Import models
-from modules.datamodels.datamodelChat import ChatWorkflow
-from modules.datamodels.datamodelWorkflow import UserInputRequest
+from modules.datamodels.datamodelChat import ChatWorkflow, UserInputRequest
 from modules.datamodels.datamodelUam import User
 
 # Import workflow control functions
@@ -34,7 +33,7 @@ router = APIRouter(
 )
 
 def getServiceChat(currentUser: User):
-    return interfaceChatObjects.getInterface(currentUser)
+    return interfaceDbChatObjects.getInterface(currentUser)
 
 # Workflow start endpoint
 @router.post("/start", response_model=ChatWorkflow)
@@ -55,10 +54,10 @@ async def start_workflow(
     """
     try:
         # Get service center
-        interfaceChat = getServiceChat(currentUser)
+        interfaceDbChat = getServiceChat(currentUser)
         
         # Start or continue workflow using playground controller
-        workflow = await chatStart(interfaceChat, currentUser, userInput, workflowId, workflowMode)
+        workflow = await chatStart(interfaceDbChat, currentUser, userInput, workflowId, workflowMode)
         
         return workflow
         
@@ -80,10 +79,10 @@ async def stop_workflow(
     """Stops a running workflow."""
     try:
         # Get service center
-        interfaceChat = getServiceChat(currentUser)
+        interfaceDbChat = getServiceChat(currentUser)
         
         # Stop workflow using playground controller
-        workflow = await chatStop(interfaceChat, currentUser, workflowId)
+        workflow = await chatStop(interfaceDbChat, currentUser, workflowId)
         
         return workflow
         
@@ -109,10 +108,10 @@ async def get_workflow_chat_data(
     """
     try:
         # Get service center
-        interfaceChat = getServiceChat(currentUser)
+        interfaceDbChat = getServiceChat(currentUser)
         
         # Verify workflow exists
-        workflow = interfaceChat.getWorkflow(workflowId)
+        workflow = interfaceDbChat.getWorkflow(workflowId)
         if not workflow:
             raise HTTPException(
                 status_code=404,
@@ -120,7 +119,7 @@ async def get_workflow_chat_data(
             )
         
         # Get unified chat data using the new method
-        chatData = interfaceChat.getUnifiedChatData(workflowId, afterTimestamp)
+        chatData = interfaceDbChat.getUnifiedChatData(workflowId, afterTimestamp)
         
         return chatData
         

modules/routes/routeDataConnections.py

@@ -18,7 +18,7 @@ import json
 from modules.datamodels.datamodelUam import User, UserConnection, AuthAuthority, ConnectionStatus
 from modules.datamodels.datamodelSecurity import Token
 from modules.security.auth import getCurrentUser, limiter
-from modules.interfaces.interfaceAppObjects import getInterface, getRootInterface
+from modules.interfaces.interfaceDbAppObjects import getInterface, getRootInterface
 from modules.shared.timezoneUtils import get_utc_timestamp
 
 # Configure logger

modules/routes/routeDataFiles.py

@@ -14,7 +14,7 @@ from pydantic import BaseModel
 from modules.security.auth import limiter, getCurrentUser
 
 # Import interfaces
-import modules.interfaces.interfaceComponentObjects as interfaceComponentObjects
+import modules.interfaces.interfaceDbComponentObjects as interfaceDbComponentObjects
 from modules.datamodels.datamodelFiles import FileItem, FilePreview
 from modules.shared.attributeUtils import getModelAttributeDefinitions, AttributeResponse, AttributeDefinition
 from modules.datamodels.datamodelUam import User
@@ -46,7 +46,7 @@ async def get_files(
 ) -> List[FileItem]:
     """Get all files"""
     try:
-        managementInterface = interfaceComponentObjects.getInterface(currentUser)
+        managementInterface = interfaceDbComponentObjects.getInterface(currentUser)
         
         # Get all files generically - only metadata, no binary data
         files = managementInterface.getAllFiles()
@@ -72,17 +72,17 @@ async def upload_file(
     file.fileName = file.filename
     """Upload a file"""
     try:
-        managementInterface = interfaceComponentObjects.getInterface(currentUser)
+        managementInterface = interfaceDbComponentObjects.getInterface(currentUser)
         
         # Read file
         fileContent = await file.read()
         
         # Check size limits
-        maxSize = int(interfaceComponentObjects.APP_CONFIG.get("File_Management_MAX_UPLOAD_SIZE_MB")) * 1024 * 1024  # in bytes
+        maxSize = int(interfaceDbComponentObjects.APP_CONFIG.get("File_Management_MAX_UPLOAD_SIZE_MB")) * 1024 * 1024  # in bytes
         if len(fileContent) > maxSize:
             raise HTTPException(
                 status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE,
-                detail=f"File too large. Maximum size: {interfaceComponentObjects.APP_CONFIG.get('File_Management_MAX_UPLOAD_SIZE_MB')}MB"
+                detail=f"File too large. Maximum size: {interfaceDbComponentObjects.APP_CONFIG.get('File_Management_MAX_UPLOAD_SIZE_MB')}MB"
             )
         
         # Save file via LucyDOM interface in the database
@@ -115,7 +115,7 @@ async def upload_file(
             "isDuplicate": duplicateType != "new_file"
         })
         
-    except interfaceComponentObjects.FileStorageError as e:
+    except interfaceDbComponentObjects.FileStorageError as e:
         logger.error(f"Error during file upload (storage): {str(e)}")
         raise HTTPException(
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, 
@@ -137,7 +137,7 @@ async def get_file(
 ) -> FileItem:
     """Get a file"""
     try:
-        managementInterface = interfaceComponentObjects.getInterface(currentUser)
+        managementInterface = interfaceDbComponentObjects.getInterface(currentUser)
         
         # Get file via LucyDOM interface from the database
         fileData = managementInterface.getFile(fileId)
@@ -149,19 +149,19 @@ async def get_file(
             
         return fileData
             
-    except interfaceComponentObjects.FileNotFoundError as e:
+    except interfaceDbComponentObjects.FileNotFoundError as e:
         logger.warning(f"File not found: {str(e)}")
         raise HTTPException(
             status_code=status.HTTP_404_NOT_FOUND,
             detail=str(e)
         )
-    except interfaceComponentObjects.FilePermissionError as e:
+    except interfaceDbComponentObjects.FilePermissionError as e:
         logger.warning(f"No permission for file: {str(e)}")
         raise HTTPException(
             status_code=status.HTTP_403_FORBIDDEN,
             detail=str(e)
         )
-    except interfaceComponentObjects.FileError as e:
+    except interfaceDbComponentObjects.FileError as e:
         logger.error(f"Error retrieving file: {str(e)}")
         raise HTTPException(
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
@@ -184,7 +184,7 @@ async def update_file(
 ) -> FileItem:
     """Update file info"""
     try:
-        managementInterface = interfaceComponentObjects.getInterface(currentUser)
+        managementInterface = interfaceDbComponentObjects.getInterface(currentUser)
         
         # Get the file from the database
         file = managementInterface.getFile(fileId)
@@ -230,7 +230,7 @@ async def delete_file(
     currentUser: User = Depends(getCurrentUser)
 ) -> Dict[str, Any]:
     """Delete a file"""
-    managementInterface = interfaceComponentObjects.getInterface(currentUser)
+    managementInterface = interfaceDbComponentObjects.getInterface(currentUser)
     
     # Check if the file exists
     existingFile = managementInterface.getFile(fileId)
@@ -257,7 +257,7 @@ async def get_file_stats(
 ) -> Dict[str, Any]:
     """Returns statistics about the stored files"""
     try:
-        managementInterface = interfaceComponentObjects.getInterface(currentUser)
+        managementInterface = interfaceDbComponentObjects.getInterface(currentUser)
         
         # Get all files - metadata only
         allFiles = managementInterface.getAllFiles()
@@ -296,7 +296,7 @@ async def download_file(
 ) -> Response:
     """Download a file"""
     try:
-        managementInterface = interfaceComponentObjects.getInterface(currentUser)
+        managementInterface = interfaceDbComponentObjects.getInterface(currentUser)
         
         # Get file data
         fileData = managementInterface.getFile(fileId)
@@ -344,7 +344,7 @@ async def preview_file(
 ) -> FilePreview:
     """Preview a file's content"""
     try:
-        managementInterface = interfaceComponentObjects.getInterface(currentUser)
+        managementInterface = interfaceDbComponentObjects.getInterface(currentUser)
         
         # Get file preview using the correct method
         preview = managementInterface.getFileContent(fileId)

modules/routes/routeDataMandates.py

@@ -17,7 +17,7 @@ from pydantic import BaseModel
 from modules.security.auth import limiter, getCurrentUser
 
 # Import interfaces
-import modules.interfaces.interfaceAppObjects as interfaceAppObjects
+import modules.interfaces.interfaceDbAppObjects as interfaceDbAppObjects
 from modules.shared.attributeUtils import getModelAttributeDefinitions, AttributeResponse, AttributeDefinition
 
 # Import the model classes
@@ -44,7 +44,7 @@ async def get_mandates(
 ) -> List[Mandate]:
     """Get all mandates"""
     try:
-        appInterface = interfaceAppObjects.getInterface(currentUser)
+        appInterface = interfaceDbAppObjects.getInterface(currentUser)
         mandates = appInterface.getAllMandates()
         return mandates
     except Exception as e:
@@ -63,7 +63,7 @@ async def get_mandate(
 ) -> Mandate:
     """Get a specific mandate by ID"""
     try:
-        appInterface = interfaceAppObjects.getInterface(currentUser)
+        appInterface = interfaceDbAppObjects.getInterface(currentUser)
         mandate = appInterface.getMandate(mandateId)
         
         if not mandate:
@@ -91,7 +91,7 @@ async def create_mandate(
 ) -> Mandate:
     """Create a new mandate"""
     try:
-        appInterface = interfaceAppObjects.getInterface(currentUser)
+        appInterface = interfaceDbAppObjects.getInterface(currentUser)
         
         # Create mandate
         newMandate = appInterface.createMandate(
@@ -125,7 +125,7 @@ async def update_mandate(
 ) -> Mandate:
     """Update an existing mandate"""
     try:
-        appInterface = interfaceAppObjects.getInterface(currentUser)
+        appInterface = interfaceDbAppObjects.getInterface(currentUser)
         
         # Check if mandate exists
         existingMandate = appInterface.getMandate(mandateId)
@@ -163,7 +163,7 @@ async def delete_mandate(
 ) -> Dict[str, Any]:
     """Delete a mandate"""
     try:
-        appInterface = interfaceAppObjects.getInterface(currentUser)
+        appInterface = interfaceDbAppObjects.getInterface(currentUser)
         
         # Check if mandate exists
         existingMandate = appInterface.getMandate(mandateId)

modules/routes/routeDataPrompts.py

@@ -12,7 +12,7 @@ from pydantic import BaseModel
 from modules.security.auth import limiter, getCurrentUser
 
 # Import interfaces
-import modules.interfaces.interfaceComponentObjects as interfaceComponentObjects
+import modules.interfaces.interfaceDbComponentObjects as interfaceDbComponentObjects
 from modules.datamodels.datamodelUtils import Prompt
 from modules.shared.attributeUtils import getModelAttributeDefinitions, AttributeResponse, AttributeDefinition
 from modules.datamodels.datamodelUam import User
@@ -34,7 +34,7 @@ async def get_prompts(
     currentUser: User = Depends(getCurrentUser)
 ) -> List[Prompt]:
     """Get all prompts"""
-    managementInterface = interfaceComponentObjects.getInterface(currentUser)
+    managementInterface = interfaceDbComponentObjects.getInterface(currentUser)
     prompts = managementInterface.getAllPrompts()
     return prompts
 
@@ -46,7 +46,7 @@ async def create_prompt(
     currentUser: User = Depends(getCurrentUser)
 ) -> Prompt:
     """Create a new prompt"""
-    managementInterface = interfaceComponentObjects.getInterface(currentUser)
+    managementInterface = interfaceDbComponentObjects.getInterface(currentUser)
     
     # Convert Prompt to dict for interface
     prompt_data = prompt.dict()
@@ -64,7 +64,7 @@ async def get_prompt(
     currentUser: User = Depends(getCurrentUser)
 ) -> Prompt:
     """Get a specific prompt"""
-    managementInterface = interfaceComponentObjects.getInterface(currentUser)
+    managementInterface = interfaceDbComponentObjects.getInterface(currentUser)
     
     # Get prompt
     prompt = managementInterface.getPrompt(promptId)
@@ -85,7 +85,7 @@ async def update_prompt(
     currentUser: User = Depends(getCurrentUser)
 ) -> Prompt:
     """Update an existing prompt"""
-    managementInterface = interfaceComponentObjects.getInterface(currentUser)
+    managementInterface = interfaceDbComponentObjects.getInterface(currentUser)
     
     # Check if the prompt exists
     existingPrompt = managementInterface.getPrompt(promptId)
@@ -117,7 +117,7 @@ async def delete_prompt(
     currentUser: User = Depends(getCurrentUser)
 ) -> Dict[str, Any]:
     """Delete a prompt"""
-    managementInterface = interfaceComponentObjects.getInterface(currentUser)
+    managementInterface = interfaceDbComponentObjects.getInterface(currentUser)
     
     # Check if the prompt exists
     existingPrompt = managementInterface.getPrompt(promptId)

modules/routes/routeDataUsers.py

@@ -14,7 +14,7 @@ import os
 from pydantic import BaseModel
 
 # Import interfaces and models
-import modules.interfaces.interfaceAppObjects as interfaceAppObjects
+import modules.interfaces.interfaceDbAppObjects as interfaceDbAppObjects
 from modules.security.auth import getCurrentUser, limiter, getCurrentUser
 
 # Import the attribute definition and helper functions
@@ -40,7 +40,7 @@ async def get_users(
 ) -> List[User]:
     """Get all users in the current mandate"""
     try:
-        appInterface = interfaceAppObjects.getInterface(currentUser)
+        appInterface = interfaceDbAppObjects.getInterface(currentUser)
         # If mandateId is provided, use it, otherwise use the current user's mandate
         targetMandateId = mandateId or currentUser.mandateId
         # Get all users without filtering by enabled status
@@ -62,7 +62,7 @@ async def get_user(
 ) -> User:
     """Get a specific user by ID"""
     try:
-        appInterface = interfaceAppObjects.getInterface(currentUser)
+        appInterface = interfaceDbAppObjects.getInterface(currentUser)
         # Get user without filtering by enabled status
         user = appInterface.getUser(userId)
         
@@ -90,7 +90,7 @@ async def create_user(
     currentUser: User = Depends(getCurrentUser)
 ) -> User:
     """Create a new user"""
-    appInterface = interfaceAppObjects.getInterface(currentUser)
+    appInterface = interfaceDbAppObjects.getInterface(currentUser)
     
     # Convert User to dict for interface
     user_dict = user_data.dict()
@@ -109,7 +109,7 @@ async def update_user(
     currentUser: User = Depends(getCurrentUser)
 ) -> User:
     """Update an existing user"""
-    appInterface = interfaceAppObjects.getInterface(currentUser)
+    appInterface = interfaceDbAppObjects.getInterface(currentUser)
     
     # Check if the user exists
     existingUser = appInterface.getUser(userId)
@@ -300,7 +300,7 @@ async def delete_user(
     currentUser: User = Depends(getCurrentUser)
 ) -> Dict[str, Any]:
     """Delete a user"""
-    appInterface = interfaceAppObjects.getInterface(currentUser)
+    appInterface = interfaceDbAppObjects.getInterface(currentUser)
     
     # Check if the user exists
     existingUser = appInterface.getUser(userId)

modules/routes/routeSecurityAdmin.py

@@ -5,7 +5,7 @@ import os
 import logging
 
 from modules.security.auth import getCurrentUser, limiter
-from modules.interfaces.interfaceAppObjects import getInterface, getRootInterface
+from modules.interfaces.interfaceDbAppObjects import getInterface, getRootInterface
 from modules.datamodels.datamodelUam import User, UserInDB, AuthAuthority
 from modules.datamodels.datamodelSecurity import Token
 from modules.shared.configuration import APP_CONFIG
@@ -253,17 +253,17 @@ async def list_databases(
     # Get database names from configuration for each interface
     databases = []
     
-    # App database (interfaceAppObjects.py)
+    # App database (interfaceDbAppObjects.py)
     app_db = APP_CONFIG.get("DB_APP_DATABASE")
     if app_db:
         databases.append(app_db)
     
-    # Chat database (interfaceChatObjects.py)
+    # Chat database (interfaceDbChatObjects.py)
     chat_db = APP_CONFIG.get("DB_CHAT_DATABASE")
     if chat_db:
         databases.append(chat_db)
     
-    # Management database (interfaceComponentObjects.py)
+    # Management database (interfaceDbComponentObjects.py)
     management_db = APP_CONFIG.get("DB_MANAGEMENT_DATABASE")
     if management_db:
         databases.append(management_db)
@@ -308,11 +308,11 @@ async def get_database_tables(
             appInterface = getRootInterface()
             tables = appInterface.db.getTables()
         elif database_name == chat_db:
-            from modules.interfaces.interfaceChatObjects import getInterface as getChatInterface
+            from modules.interfaces.interfaceDbChatObjects import getInterface as getChatInterface
             chatInterface = getChatInterface(currentUser)
             tables = chatInterface.db.getTables()
         elif database_name == management_db:
-            from modules.interfaces.interfaceComponentObjects import getInterface as getComponentInterface
+            from modules.interfaces.interfaceDbComponentObjects import getInterface as getComponentInterface
             componentInterface = getComponentInterface(currentUser)
             tables = componentInterface.db.getTables()
         else:
@@ -358,10 +358,10 @@ async def drop_table(
         if database_name == app_db:
             interface = getRootInterface()
         elif database_name == chat_db:
-            from modules.interfaces.interfaceChatObjects import getInterface as getChatInterface
+            from modules.interfaces.interfaceDbChatObjects import getInterface as getChatInterface
             interface = getChatInterface(currentUser)
         elif database_name == management_db:
-            from modules.interfaces.interfaceComponentObjects import getInterface as getComponentInterface
+            from modules.interfaces.interfaceDbComponentObjects import getInterface as getComponentInterface
             interface = getComponentInterface(currentUser)
         else:
             raise HTTPException(status_code=400, detail="Database not found")
@@ -423,10 +423,10 @@ async def drop_database(
         if db_name == app_db:
             interface = getRootInterface()
         elif db_name == chat_db:
-            from modules.interfaces.interfaceChatObjects import getInterface as getChatInterface
+            from modules.interfaces.interfaceDbChatObjects import getInterface as getChatInterface
             interface = getChatInterface(currentUser)
         elif db_name == management_db:
-            from modules.interfaces.interfaceComponentObjects import getInterface as getComponentInterface
+            from modules.interfaces.interfaceDbComponentObjects import getInterface as getComponentInterface
             interface = getComponentInterface(currentUser)
         else:
             raise HTTPException(status_code=400, detail="Database not found")

modules/routes/routeSecurityGoogle.py

@@ -12,7 +12,7 @@ from requests_oauthlib import OAuth2Session
 import httpx
 
 from modules.shared.configuration import APP_CONFIG
-from modules.interfaces.interfaceAppObjects import getInterface, getRootInterface
+from modules.interfaces.interfaceDbAppObjects import getInterface, getRootInterface
 from modules.datamodels.datamodelUam import AuthAuthority, User, ConnectionStatus, UserConnection
 from modules.datamodels.datamodelSecurity import Token
 from modules.security.auth import getCurrentUser, limiter
@@ -640,7 +640,7 @@ async def verify_token(
         
         # Get a fresh token via TokenManager convenience method
         from modules.security.tokenManager import TokenManager
-        current_token = TokenManager().getFreshToken(appInterface, google_connection.id)
+        current_token = TokenManager().getFreshToken(google_connection.id)
 
         if not current_token:
             raise HTTPException(
@@ -714,7 +714,7 @@ async def refresh_token(
         
         # Get the token for this specific connection (fresh if expiring soon)
         from modules.security.tokenManager import TokenManager
-        current_token = TokenManager().getFreshToken(appInterface, google_connection.id)
+        current_token = TokenManager().getFreshToken(google_connection.id)
         
         if not current_token:
             raise HTTPException(

modules/routes/routeSecurityLocal.py

@@ -15,7 +15,7 @@ from pydantic import BaseModel
 # Import auth modules
 from modules.security.auth import getCurrentUser, limiter, SECRET_KEY, ALGORITHM
 from modules.security.jwtService import createAccessToken, createRefreshToken, setAccessTokenCookie, setRefreshTokenCookie
-from modules.interfaces.interfaceAppObjects import getInterface, getRootInterface
+from modules.interfaces.interfaceDbAppObjects import getInterface, getRootInterface
 from modules.datamodels.datamodelUam import User, UserInDB, AuthAuthority, UserPrivilege
 from modules.datamodels.datamodelSecurity import Token
 from modules.shared.attributeUtils import ModelMixin

modules/routes/routeSecurityMsft.py

@@ -12,7 +12,7 @@ import msal
 import httpx
 
 from modules.shared.configuration import APP_CONFIG
-from modules.interfaces.interfaceAppObjects import getInterface, getRootInterface
+from modules.interfaces.interfaceDbAppObjects import getInterface, getRootInterface
 from modules.datamodels.datamodelUam import AuthAuthority, User, ConnectionStatus, UserConnection
 from modules.datamodels.datamodelSecurity import Token
 from modules.security.auth import getCurrentUser, limiter
@@ -563,7 +563,7 @@ async def refresh_token(
         
         # Get a fresh token via TokenManager convenience method
         from modules.security.tokenManager import TokenManager
-        current_token = TokenManager().getFreshToken(appInterface, msft_connection.id)
+        current_token = TokenManager().getFreshToken(msft_connection.id)
         
         if not current_token:
             raise HTTPException(

modules/routes/routeVoiceGoogle.py

@@ -1,41 +1,63 @@
 """
 Google Cloud Voice Services Routes
 Replaces Azure voice services with Google Cloud Speech-to-Text and Translation
+Includes WebSocket support for real-time voice streaming
 """
 
 import os
 import logging
-from fastapi import APIRouter, File, Form, UploadFile, Depends, HTTPException, Body
+import json
+import base64
+import asyncio
+from fastapi import APIRouter, File, Form, UploadFile, Depends, HTTPException, Body, WebSocket, WebSocketDisconnect
 from fastapi.responses import Response
-from typing import Optional, Dict, Any
-from modules.connectors.connectorGoogleSpeech import ConnectorGoogleSpeech
+from typing import Optional, Dict, Any, List
 from modules.security.auth import getCurrentUser
 from modules.datamodels.datamodelUam import User
-from modules.interfaces.interfaceComponentObjects import getInterface
+from modules.interfaces.interfaceDbComponentObjects import getInterface
+from modules.interfaces.interfaceVoiceObjects import getVoiceInterface, VoiceObjects
 
 logger = logging.getLogger(__name__)
 router = APIRouter(prefix="/voice-google", tags=["Voice Google"])
 
-# Global connector instance
-_google_speech_connector = None
+# Store active WebSocket connections
+active_connections: Dict[str, WebSocket] = {}
 
-def get_google_speech_connector() -> ConnectorGoogleSpeech:
-    """Get or create Google Cloud Speech connector instance."""
-    global _google_speech_connector
-    
-    if _google_speech_connector is None:
+class ConnectionManager:
+    def __init__(self):
+        self.active_connections: List[WebSocket] = []
+
+    async def connect(self, websocket: WebSocket, connection_id: str):
+        await websocket.accept()
+        self.active_connections.append(websocket)
+        active_connections[connection_id] = websocket
+        logger.info(f"WebSocket connected: {connection_id}")
+
+    def disconnect(self, websocket: WebSocket, connection_id: str):
+        if websocket in self.active_connections:
+            self.active_connections.remove(websocket)
+        if connection_id in active_connections:
+            del active_connections[connection_id]
+        logger.info(f"WebSocket disconnected: {connection_id}")
+
+    async def send_personal_message(self, message: dict, websocket: WebSocket):
         try:
-            _google_speech_connector = ConnectorGoogleSpeech()
-            logger.info("✅ Google Cloud Speech connector initialized")
-            
+            await websocket.send_text(json.dumps(message))
         except Exception as e:
-            logger.error(f"❌ Failed to initialize Google Cloud Speech connector: {e}")
-            raise HTTPException(
-                status_code=500,
-                detail=f"Failed to initialize Google Cloud Speech connector: {str(e)}"
-            )
-    
-    return _google_speech_connector
+            logger.error(f"Error sending message: {e}")
+
+manager = ConnectionManager()
+
+def get_voice_interface(current_user: User) -> VoiceObjects:
+    """Get voice interface instance with user context."""
+    try:
+        return getVoiceInterface(current_user)
+    except Exception as e:
+        logger.error(f"Failed to initialize voice interface: {e}")
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to initialize voice interface: {str(e)}"
+        )
 
 @router.post("/speech-to-text")
 async def speech_to_text(
@@ -51,9 +73,11 @@ async def speech_to_text(
         audio_content = await audio_file.read()
         logger.info(f"📊 Audio file size: {len(audio_content)} bytes")
         
+        # Get voice interface
+        voice_interface = get_voice_interface(current_user)
+        
         # Validate audio format
-        connector = get_google_speech_connector()
-        validation = connector.validate_audio_format(audio_content)
+        validation = voice_interface.validateAudioFormat(audio_content)
         
         if not validation["valid"]:
             raise HTTPException(
@@ -62,8 +86,8 @@ async def speech_to_text(
             )
         
         # Perform speech recognition
-        result = await connector.speech_to_text(
-            audio_content=audio_content,
+        result = await voice_interface.speechToText(
+            audioContent=audio_content,
             language=language
         )
         
@@ -111,12 +135,14 @@ async def translate_text(
                 detail="Empty text provided for translation"
             )
         
+        # Get voice interface
+        voice_interface = get_voice_interface(current_user)
+        
         # Perform translation
-        connector = get_google_speech_connector()
-        result = await connector.translate_text(
+        result = await voice_interface.translateText(
             text=text,
-            source_language=source_language,
-            target_language=target_language
+            sourceLanguage=source_language,
+            targetLanguage=target_language
         )
         
         if result["success"]:
@@ -168,9 +194,11 @@ async def realtime_interpreter(
         #     f.write(audio_content)
         # logger.info(f"💾 Saved audio file for debugging: {debug_filename}")
         
+        # Get voice interface
+        voice_interface = get_voice_interface(current_user)
+        
         # Validate audio format
-        connector = get_google_speech_connector()
-        validation = connector.validate_audio_format(audio_content)
+        validation = voice_interface.validateAudioFormat(audio_content)
         
         if not validation["valid"]:
             raise HTTPException(
@@ -179,10 +207,10 @@ async def realtime_interpreter(
             )
         
         # Perform complete pipeline: Speech-to-Text + Translation
-        result = await connector.speech_to_translated_text(
-            audio_content=audio_content,
-            from_language=from_language,
-            to_language=to_language
+        result = await voice_interface.speechToTranslatedText(
+            audioContent=audio_content,
+            fromLanguage=from_language,
+            toLanguage=to_language
         )
         
         if result["success"]:
@@ -236,11 +264,11 @@ async def text_to_speech(
                 detail="Empty text provided for text-to-speech"
             )
         
-        connector = get_google_speech_connector()
-        result = await connector.text_to_speech(
+        voice_interface = get_voice_interface(current_user)
+        result = await voice_interface.textToSpeech(
             text=text,
-            language_code=language,
-            voice_name=voice
+            languageCode=language,
+            voiceName=voice
         )
         
         if result["success"]:
@@ -268,30 +296,76 @@ async def text_to_speech(
             detail=f"Text-to-Speech processing failed: {str(e)}"
         )
 
+@router.get("/languages")
+async def get_available_languages(current_user: User = Depends(getCurrentUser)):
+    """Get available languages from Google Cloud Text-to-Speech."""
+    try:
+        logger.info("🌐 Getting available languages from Google Cloud TTS")
+        
+        voice_interface = get_voice_interface(current_user)
+        result = await voice_interface.getAvailableLanguages()
+        
+        if result["success"]:
+            return {
+                "success": True,
+                "languages": result["languages"]
+            }
+        else:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Failed to get languages: {result.get('error', 'Unknown error')}"
+            )
+            
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"❌ Get languages error: {e}")
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to get available languages: {str(e)}"
+        )
+
+@router.get("/voices")
+async def get_available_voices(
+    language_code: Optional[str] = None,
+    current_user: User = Depends(getCurrentUser)
+):
+    """Get available voices from Google Cloud Text-to-Speech."""
+    try:
+        logger.info(f"🎤 Getting available voices, language filter: {language_code}")
+        
+        voice_interface = get_voice_interface(current_user)
+        result = await voice_interface.getAvailableVoices(languageCode=language_code)
+        
+        if result["success"]:
+            return {
+                "success": True,
+                "voices": result["voices"],
+                "language_filter": language_code
+            }
+        else:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Failed to get voices: {result.get('error', 'Unknown error')}"
+            )
+            
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"❌ Get voices error: {e}")
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to get available voices: {str(e)}"
+        )
+
 @router.get("/health")
 async def health_check(current_user: User = Depends(getCurrentUser)):
     """Health check for Google Cloud voice services."""
     try:
-        connector = get_google_speech_connector()
+        voice_interface = get_voice_interface(current_user)
+        test_result = await voice_interface.healthCheck()
         
-        # Test with a simple translation
-        test_result = await connector.translate_text(
-            text="Hello",
-            source_language="en",
-            target_language="de"
-        )
-        
-        if test_result["success"]:
-            return {
-                "status": "healthy",
-                "service": "Google Cloud Speech-to-Text & Translation",
-                "test_translation": test_result["translated_text"]
-            }
-        else:
-            return {
-                "status": "unhealthy",
-                "error": test_result.get("error", "Unknown error")
-            }
+        return test_result
             
     except Exception as e:
         logger.error(f"❌ Health check failed: {e}")
@@ -306,11 +380,11 @@ async def get_voice_settings(current_user: User = Depends(getCurrentUser)):
     try:
         logger.info(f"Getting voice settings for user: {current_user.id}")
         
-        # Get database interface with user context
-        interface = getInterface(current_user)
+        # Get voice interface
+        voice_interface = get_voice_interface(current_user)
         
         # Get or create voice settings for the user
-        voice_settings = interface.getOrCreateVoiceSettings(current_user.id)
+        voice_settings = voice_interface.getOrCreateVoiceSettings(current_user.id)
         
         if voice_settings:
             # Return user settings
@@ -376,23 +450,23 @@ async def save_voice_settings(
         if "targetLanguage" not in settings:
             settings["targetLanguage"] = "en-US"
         
-        # Get database interface with user context
-        interface = getInterface(current_user)
+        # Get voice interface
+        voice_interface = get_voice_interface(current_user)
         
         # Check if settings already exist for this user
-        existing_settings = interface.getVoiceSettings(current_user.id)
+        existing_settings = voice_interface.getVoiceSettings(current_user.id)
         
         if existing_settings:
             # Update existing settings
             logger.info(f"Updating existing voice settings for user {current_user.id}")
-            updated_settings = interface.updateVoiceSettings(current_user.id, settings)
+            updated_settings = voice_interface.updateVoiceSettings(current_user.id, settings)
             logger.info(f"Voice settings updated for user {current_user.id}: {updated_settings}")
         else:
             # Create new settings
             logger.info(f"Creating new voice settings for user {current_user.id}")
             # Add userId to settings
             settings["userId"] = current_user.id
-            created_settings = interface.createVoiceSettings(settings)
+            created_settings = voice_interface.createVoiceSettings(settings)
             logger.info(f"Voice settings created for user {current_user.id}: {created_settings}")
         
         return {
@@ -409,3 +483,190 @@ async def save_voice_settings(
             status_code=500,
             detail=f"Failed to save voice settings: {str(e)}"
         )
+
+# WebSocket endpoints for real-time voice streaming
+
+@router.websocket("/ws/realtime-interpreter")
+async def websocket_realtime_interpreter(
+    websocket: WebSocket,
+    user_id: str = "default",
+    from_language: str = "de-DE",
+    to_language: str = "en-US"
+):
+    """WebSocket endpoint for real-time voice interpretation"""
+    connection_id = f"realtime_{user_id}_{from_language}_{to_language}"
+    
+    try:
+        await manager.connect(websocket, connection_id)
+        
+        # Send connection confirmation
+        await manager.send_personal_message({
+            "type": "connected",
+            "connection_id": connection_id,
+            "message": "Connected to real-time interpreter"
+        }, websocket)
+        
+        # Initialize voice interface
+        voice_interface = get_voice_interface(User(id=user_id))
+        
+        while True:
+            # Receive message from client
+            data = await websocket.receive_text()
+            message = json.loads(data)
+            
+            if message["type"] == "audio_chunk":
+                # Process audio chunk
+                try:
+                    # Decode base64 audio data
+                    audio_data = base64.b64decode(message["data"])
+                    
+                    # For now, just acknowledge receipt
+                    # In a full implementation, this would:
+                    # 1. Buffer audio chunks
+                    # 2. Process with Google Cloud Speech-to-Text streaming
+                    # 3. Send partial results back
+                    # 4. Handle translation
+                    
+                    await manager.send_personal_message({
+                        "type": "audio_received",
+                        "chunk_size": len(audio_data),
+                        "timestamp": message.get("timestamp")
+                    }, websocket)
+                    
+                except Exception as e:
+                    logger.error(f"Error processing audio chunk: {e}")
+                    await manager.send_personal_message({
+                        "type": "error",
+                        "error": f"Failed to process audio: {str(e)}"
+                    }, websocket)
+            
+            elif message["type"] == "ping":
+                # Respond to ping
+                await manager.send_personal_message({
+                    "type": "pong",
+                    "timestamp": message.get("timestamp")
+                }, websocket)
+            
+            else:
+                logger.warning(f"Unknown message type: {message['type']}")
+                
+    except WebSocketDisconnect:
+        manager.disconnect(websocket, connection_id)
+        logger.info(f"Client disconnected: {connection_id}")
+    except Exception as e:
+        logger.error(f"WebSocket error: {e}")
+        manager.disconnect(websocket, connection_id)
+
+@router.websocket("/ws/speech-to-text")
+async def websocket_speech_to_text(
+    websocket: WebSocket,
+    user_id: str = "default",
+    language: str = "de-DE"
+):
+    """WebSocket endpoint for real-time speech-to-text"""
+    connection_id = f"stt_{user_id}_{language}"
+    
+    try:
+        await manager.connect(websocket, connection_id)
+        
+        await manager.send_personal_message({
+            "type": "connected",
+            "connection_id": connection_id,
+            "message": "Connected to speech-to-text"
+        }, websocket)
+        
+        # Initialize voice interface
+        voice_interface = get_voice_interface(User(id=user_id))
+        
+        while True:
+            data = await websocket.receive_text()
+            message = json.loads(data)
+            
+            if message["type"] == "audio_chunk":
+                try:
+                    audio_data = base64.b64decode(message["data"])
+                    
+                    # Process audio chunk
+                    # This would integrate with Google Cloud Speech-to-Text streaming API
+                    
+                    await manager.send_personal_message({
+                        "type": "transcription_result",
+                        "text": "Audio chunk received",  # Placeholder
+                        "confidence": 0.95,
+                        "is_final": False
+                    }, websocket)
+                    
+                except Exception as e:
+                    logger.error(f"Error processing audio: {e}")
+                    await manager.send_personal_message({
+                        "type": "error",
+                        "error": f"Failed to process audio: {str(e)}"
+                    }, websocket)
+            
+            elif message["type"] == "ping":
+                await manager.send_personal_message({
+                    "type": "pong",
+                    "timestamp": message.get("timestamp")
+                }, websocket)
+                
+    except WebSocketDisconnect:
+        manager.disconnect(websocket, connection_id)
+    except Exception as e:
+        logger.error(f"WebSocket error: {e}")
+        manager.disconnect(websocket, connection_id)
+
+@router.websocket("/ws/text-to-speech")
+async def websocket_text_to_speech(
+    websocket: WebSocket,
+    user_id: str = "default",
+    language: str = "de-DE",
+    voice: str = "de-DE-Wavenet-A"
+):
+    """WebSocket endpoint for real-time text-to-speech"""
+    connection_id = f"tts_{user_id}_{language}_{voice}"
+    
+    try:
+        await manager.connect(websocket, connection_id)
+        
+        await manager.send_personal_message({
+            "type": "connected",
+            "connection_id": connection_id,
+            "message": "Connected to text-to-speech"
+        }, websocket)
+        
+        while True:
+            data = await websocket.receive_text()
+            message = json.loads(data)
+            
+            if message["type"] == "text_to_speak":
+                try:
+                    text = message["text"]
+                    
+                    # Process text-to-speech
+                    # This would integrate with Google Cloud Text-to-Speech API
+                    
+                    # For now, send a placeholder response
+                    await manager.send_personal_message({
+                        "type": "audio_data",
+                        "audio": "base64_encoded_audio_here",  # Placeholder
+                        "format": "mp3"
+                    }, websocket)
+                    
+                except Exception as e:
+                    logger.error(f"Error processing text-to-speech: {e}")
+                    await manager.send_personal_message({
+                        "type": "error",
+                        "error": f"Failed to process text: {str(e)}"
+                    }, websocket)
+            
+            elif message["type"] == "ping":
+                await manager.send_personal_message({
+                    "type": "pong",
+                    "timestamp": message.get("timestamp")
+                }, websocket)
+                
+    except WebSocketDisconnect:
+        manager.disconnect(websocket, connection_id)
+    except Exception as e:
+        logger.error(f"WebSocket error: {e}")
+        manager.disconnect(websocket, connection_id)

modules/routes/routeVoiceStreaming.py

@@ -1,231 +0,0 @@
-"""
-Voice Streaming WebSocket Routes
-Provides real-time audio streaming for voice services
-"""
-
-from fastapi import APIRouter, WebSocket, WebSocketDisconnect, Depends
-from fastapi.responses import JSONResponse
-import logging
-import json
-import base64
-import asyncio
-from typing import Dict, List
-
-from modules.shared.configuration import APP_CONFIG
-from modules.connectors.connectorGoogleSpeech import ConnectorGoogleSpeech
-
-logger = logging.getLogger(__name__)
-router = APIRouter(prefix="/api/voice/ws", tags=["Voice Streaming"])
-
-# Store active connections
-active_connections: Dict[str, WebSocket] = {}
-
-class ConnectionManager:
-    def __init__(self):
-        self.active_connections: List[WebSocket] = []
-
-    async def connect(self, websocket: WebSocket, connection_id: str):
-        await websocket.accept()
-        self.active_connections.append(websocket)
-        active_connections[connection_id] = websocket
-        logger.info(f"WebSocket connected: {connection_id}")
-
-    def disconnect(self, websocket: WebSocket, connection_id: str):
-        if websocket in self.active_connections:
-            self.active_connections.remove(websocket)
-        if connection_id in active_connections:
-            del active_connections[connection_id]
-        logger.info(f"WebSocket disconnected: {connection_id}")
-
-    async def send_personal_message(self, message: dict, websocket: WebSocket):
-        try:
-            await websocket.send_text(json.dumps(message))
-        except Exception as e:
-            logger.error(f"Error sending message: {e}")
-
-manager = ConnectionManager()
-
-@router.websocket("/realtime-interpreter")
-async def websocket_realtime_interpreter(
-    websocket: WebSocket,
-    user_id: str = "default",
-    from_language: str = "de-DE",
-    to_language: str = "en-US"
-):
-    """WebSocket endpoint for real-time voice interpretation"""
-    connection_id = f"realtime_{user_id}_{from_language}_{to_language}"
-    
-    try:
-        await manager.connect(websocket, connection_id)
-        
-        # Send connection confirmation
-        await manager.send_personal_message({
-            "type": "connected",
-            "connection_id": connection_id,
-            "message": "Connected to real-time interpreter"
-        }, websocket)
-        
-        # Initialize Google Speech connector
-        google_speech = ConnectorGoogleSpeech()
-        
-        while True:
-            # Receive message from client
-            data = await websocket.receive_text()
-            message = json.loads(data)
-            
-            if message["type"] == "audio_chunk":
-                # Process audio chunk
-                try:
-                    # Decode base64 audio data
-                    audio_data = base64.b64decode(message["data"])
-                    
-                    # For now, just acknowledge receipt
-                    # In a full implementation, this would:
-                    # 1. Buffer audio chunks
-                    # 2. Process with Google Cloud Speech-to-Text streaming
-                    # 3. Send partial results back
-                    # 4. Handle translation
-                    
-                    await manager.send_personal_message({
-                        "type": "audio_received",
-                        "chunk_size": len(audio_data),
-                        "timestamp": message.get("timestamp")
-                    }, websocket)
-                    
-                except Exception as e:
-                    logger.error(f"Error processing audio chunk: {e}")
-                    await manager.send_personal_message({
-                        "type": "error",
-                        "error": f"Failed to process audio: {str(e)}"
-                    }, websocket)
-            
-            elif message["type"] == "ping":
-                # Respond to ping
-                await manager.send_personal_message({
-                    "type": "pong",
-                    "timestamp": message.get("timestamp")
-                }, websocket)
-            
-            else:
-                logger.warning(f"Unknown message type: {message['type']}")
-                
-    except WebSocketDisconnect:
-        manager.disconnect(websocket, connection_id)
-        logger.info(f"Client disconnected: {connection_id}")
-    except Exception as e:
-        logger.error(f"WebSocket error: {e}")
-        manager.disconnect(websocket, connection_id)
-
-@router.websocket("/speech-to-text")
-async def websocket_speech_to_text(
-    websocket: WebSocket,
-    user_id: str = "default",
-    language: str = "de-DE"
-):
-    """WebSocket endpoint for real-time speech-to-text"""
-    connection_id = f"stt_{user_id}_{language}"
-    
-    try:
-        await manager.connect(websocket, connection_id)
-        
-        await manager.send_personal_message({
-            "type": "connected",
-            "connection_id": connection_id,
-            "message": "Connected to speech-to-text"
-        }, websocket)
-        
-        # Initialize Google Speech connector
-        google_speech = ConnectorGoogleSpeech()
-        
-        while True:
-            data = await websocket.receive_text()
-            message = json.loads(data)
-            
-            if message["type"] == "audio_chunk":
-                try:
-                    audio_data = base64.b64decode(message["data"])
-                    
-                    # Process audio chunk
-                    # This would integrate with Google Cloud Speech-to-Text streaming API
-                    
-                    await manager.send_personal_message({
-                        "type": "transcription_result",
-                        "text": "Audio chunk received",  # Placeholder
-                        "confidence": 0.95,
-                        "is_final": False
-                    }, websocket)
-                    
-                except Exception as e:
-                    logger.error(f"Error processing audio: {e}")
-                    await manager.send_personal_message({
-                        "type": "error",
-                        "error": f"Failed to process audio: {str(e)}"
-                    }, websocket)
-            
-            elif message["type"] == "ping":
-                await manager.send_personal_message({
-                    "type": "pong",
-                    "timestamp": message.get("timestamp")
-                }, websocket)
-                
-    except WebSocketDisconnect:
-        manager.disconnect(websocket, connection_id)
-    except Exception as e:
-        logger.error(f"WebSocket error: {e}")
-        manager.disconnect(websocket, connection_id)
-
-@router.websocket("/text-to-speech")
-async def websocket_text_to_speech(
-    websocket: WebSocket,
-    user_id: str = "default",
-    language: str = "de-DE",
-    voice: str = "de-DE-Wavenet-A"
-):
-    """WebSocket endpoint for real-time text-to-speech"""
-    connection_id = f"tts_{user_id}_{language}_{voice}"
-    
-    try:
-        await manager.connect(websocket, connection_id)
-        
-        await manager.send_personal_message({
-            "type": "connected",
-            "connection_id": connection_id,
-            "message": "Connected to text-to-speech"
-        }, websocket)
-        
-        while True:
-            data = await websocket.receive_text()
-            message = json.loads(data)
-            
-            if message["type"] == "text_to_speak":
-                try:
-                    text = message["text"]
-                    
-                    # Process text-to-speech
-                    # This would integrate with Google Cloud Text-to-Speech API
-                    
-                    # For now, send a placeholder response
-                    await manager.send_personal_message({
-                        "type": "audio_data",
-                        "audio": "base64_encoded_audio_here",  # Placeholder
-                        "format": "mp3"
-                    }, websocket)
-                    
-                except Exception as e:
-                    logger.error(f"Error processing text-to-speech: {e}")
-                    await manager.send_personal_message({
-                        "type": "error",
-                        "error": f"Failed to process text: {str(e)}"
-                    }, websocket)
-            
-            elif message["type"] == "ping":
-                await manager.send_personal_message({
-                    "type": "pong",
-                    "timestamp": message.get("timestamp")
-                }, websocket)
-                
-    except WebSocketDisconnect:
-        manager.disconnect(websocket, connection_id)
-    except Exception as e:
-        logger.error(f"WebSocket error: {e}")
-        manager.disconnect(websocket, connection_id)

modules/routes/routeWorkflows.py

@@ -15,8 +15,8 @@ from datetime import datetime, timedelta
 from modules.security.auth import limiter, getCurrentUser
 
 # Import interfaces
-import modules.interfaces.interfaceChatObjects as interfaceChatObjects
-from modules.interfaces.interfaceChatObjects import getInterface
+import modules.interfaces.interfaceDbChatObjects as interfaceDbChatObjects
+from modules.interfaces.interfaceDbChatObjects import getInterface
 
 # Import models
 from modules.datamodels.datamodelChat import (
@@ -24,7 +24,7 @@ from modules.datamodels.datamodelChat import (
     ChatMessage,
     ChatLog,
     ChatStat,
-    ChatDocument,
+    ChatDocument
 )
 from modules.shared.attributeUtils import getModelAttributeDefinitions, AttributeResponse
 from modules.datamodels.datamodelUam import User
@@ -45,7 +45,7 @@ router = APIRouter(
 )
 
 def getServiceChat(currentUser: User):
-    return interfaceChatObjects.getInterface(currentUser)
+    return interfaceDbChatObjects.getInterface(currentUser)
 
 # Consolidated endpoint for getting all workflows
 @router.get("/", response_model=List[ChatWorkflow])
@@ -169,10 +169,10 @@ async def get_workflow_status(
     """Get the current status of a workflow."""
     try:
         # Get service center
-        interfaceChat = getServiceChat(currentUser)
+        interfaceDbChat = getServiceChat(currentUser)
         
         # Retrieve workflow
-        workflow = interfaceChat.getWorkflow(workflowId)
+        workflow = interfaceDbChat.getWorkflow(workflowId)
         if not workflow:
             raise HTTPException(
                 status_code=status.HTTP_404_NOT_FOUND,
@@ -201,10 +201,10 @@ async def get_workflow_logs(
     """Get logs for a workflow with support for selective data transfer."""
     try:
         # Get service center
-        interfaceChat = getServiceChat(currentUser)
+        interfaceDbChat = getServiceChat(currentUser)
         
         # Verify workflow exists
-        workflow = interfaceChat.getWorkflow(workflowId)
+        workflow = interfaceDbChat.getWorkflow(workflowId)
         if not workflow:
             raise HTTPException(
                 status_code=status.HTTP_404_NOT_FOUND,
@@ -212,7 +212,7 @@ async def get_workflow_logs(
             )
         
         # Get all logs
-        allLogs = interfaceChat.getLogs(workflowId)
+        allLogs = interfaceDbChat.getLogs(workflowId)
         
         # Apply selective data transfer if logId is provided
         if logId:
@@ -244,10 +244,10 @@ async def get_workflow_messages(
     """Get messages for a workflow with support for selective data transfer."""
     try:
         # Get service center
-        interfaceChat = getServiceChat(currentUser)
+        interfaceDbChat = getServiceChat(currentUser)
         
         # Verify workflow exists
-        workflow = interfaceChat.getWorkflow(workflowId)
+        workflow = interfaceDbChat.getWorkflow(workflowId)
         if not workflow:
             raise HTTPException(
                 status_code=status.HTTP_404_NOT_FOUND,
@@ -255,7 +255,7 @@ async def get_workflow_messages(
             )
         
         # Get all messages
-        allMessages = interfaceChat.getMessages(workflowId)
+        allMessages = interfaceDbChat.getMessages(workflowId)
         
         # Apply selective data transfer if messageId is provided
         if messageId:
@@ -288,10 +288,10 @@ async def delete_workflow(
     """Deletes a workflow and its associated data."""
     try:
         # Get service center
-        interfaceChat = getServiceChat(currentUser)
+        interfaceDbChat = getServiceChat(currentUser)
         
         # Get raw workflow data from database to check permissions
-        workflows = interfaceChat.db.getRecordset(ChatWorkflow, recordFilter={"id": workflowId})
+        workflows = interfaceDbChat.db.getRecordset(ChatWorkflow, recordFilter={"id": workflowId})
         if not workflows:
             raise HTTPException(
                 status_code=status.HTTP_404_NOT_FOUND,
@@ -301,14 +301,14 @@ async def delete_workflow(
         workflow_data = workflows[0]
         
         # Check if user has permission to delete using the interface's permission system
-        if not interfaceChat._canModify("workflows", workflowId):
+        if not interfaceDbChat._canModify("workflows", workflowId):
             raise HTTPException(
                 status_code=status.HTTP_403_FORBIDDEN,
                 detail="You don't have permission to delete this workflow"
             )
         
         # Delete workflow
-        success = interfaceChat.deleteWorkflow(workflowId)
+        success = interfaceDbChat.deleteWorkflow(workflowId)
         
         if not success:
             raise HTTPException(
@@ -343,10 +343,10 @@ async def delete_workflow_message(
     """Delete a message from a workflow."""
     try:
         # Get service center
-        interfaceChat = getServiceChat(currentUser)
+        interfaceDbChat = getServiceChat(currentUser)
         
         # Verify workflow exists
-        workflow = interfaceChat.getWorkflow(workflowId)
+        workflow = interfaceDbChat.getWorkflow(workflowId)
         if not workflow:
             raise HTTPException(
                 status_code=status.HTTP_404_NOT_FOUND,
@@ -354,7 +354,7 @@ async def delete_workflow_message(
             )
                 
         # Delete the message
-        success = interfaceChat.deleteMessage(workflowId, messageId)
+        success = interfaceDbChat.deleteMessage(workflowId, messageId)
         
         if not success:
             raise HTTPException(
@@ -366,7 +366,7 @@ async def delete_workflow_message(
         messageIds = workflow.get("messageIds", [])
         if messageId in messageIds:
             messageIds.remove(messageId)
-            interfaceChat.updateWorkflow(workflowId, {"messageIds": messageIds})
+            interfaceDbChat.updateWorkflow(workflowId, {"messageIds": messageIds})
         
         return {
             "workflowId": workflowId,
@@ -394,10 +394,10 @@ async def delete_file_from_message(
     """Delete a file reference from a message in a workflow."""
     try:
         # Get service center
-        interfaceChat = getServiceChat(currentUser)
+        interfaceDbChat = getServiceChat(currentUser)
         
         # Verify workflow exists
-        workflow = interfaceChat.getWorkflow(workflowId)
+        workflow = interfaceDbChat.getWorkflow(workflowId)
         if not workflow:
             raise HTTPException(
                 status_code=status.HTTP_404_NOT_FOUND,
@@ -405,7 +405,7 @@ async def delete_file_from_message(
             )
                 
         # Delete file reference from message
-        success = interfaceChat.deleteFileFromMessage(workflowId, messageId, fileId)
+        success = interfaceDbChat.deleteFileFromMessage(workflowId, messageId, fileId)
         
         if not success:
             raise HTTPException(

modules/security/auth.py

@@ -15,7 +15,7 @@ from slowapi.util import get_remote_address
 
 from modules.shared.configuration import APP_CONFIG
 from modules.shared.timezoneUtils import get_utc_now, get_utc_timestamp
-from modules.interfaces.interfaceAppObjects import getRootInterface
+from modules.interfaces.interfaceDbAppObjects import getRootInterface
 from modules.datamodels.datamodelUam import User, AuthAuthority
 from modules.datamodels.datamodelSecurity import Token
 

modules/security/tokenManager.py

@@ -243,21 +243,24 @@ class TokenManager:
             return None
 
     # Convenience wrapper to fetch and ensure fresh token for a connection via interface layer
-    def getFreshToken(self, interfaceApp, connectionId: str, secondsBeforeExpiry: int = 30 * 60) -> Optional[Token]:
+    def getFreshToken(self, connectionId: str, secondsBeforeExpiry: int = 30 * 60) -> Optional[Token]:
         """Return a fresh token for a connection, refreshing when expiring soon.
 
-        Reads the latest stored token via interfaceApp.getConnectionToken, then
+        Reads the latest stored token via interface layer, then
         uses ensure_fresh_token to refresh if needed and persists the refreshed
-        token via interfaceApp.saveConnectionToken.
+        token via interface layer.
         """
         try:
-            token = interfaceApp.getConnectionToken(connectionId)
+            from modules.interfaces.interfaceDbAppObjects import getRootInterface
+            interfaceDbApp = getRootInterface()
+            
+            token = interfaceDbApp.getConnectionToken(connectionId)
             if not token:
                 return None
             return self.ensure_fresh_token(
                 token,
                 seconds_before_expiry=secondsBeforeExpiry,
-                save_callback=lambda t: interfaceApp.saveConnectionToken(t)
+                save_callback=lambda t: interfaceDbApp.saveConnectionToken(t)
             )
         except Exception as e:
             logger.error(f"getFreshToken: Error fetching or refreshing token for connection {connectionId}: {e}")

modules/security/tokenRefreshMiddleware.py

@@ -12,6 +12,7 @@ from starlette.responses import Response as StarletteResponse
 from typing import Callable
 import asyncio
 from modules.security.tokenRefreshService import token_refresh_service
+from modules.shared.timezoneUtils import get_utc_timestamp
 
 logger = logging.getLogger(__name__)
 
@@ -160,7 +161,6 @@ class ProactiveTokenRefreshMiddleware(BaseHTTPMiddleware):
         Check if we should perform proactive refresh for this user
         """
         try:
-            from modules.shared.timezoneUtils import get_utc_timestamp
             current_time = get_utc_timestamp()
             last_check = self.last_check.get(user_id, 0)
             
@@ -184,8 +184,3 @@ class ProactiveTokenRefreshMiddleware(BaseHTTPMiddleware):
             
         except Exception as e:
             logger.error(f"Error in proactive token refresh for user {user_id}: {str(e)}")
-
-def get_utc_timestamp():
-    """Get current UTC timestamp"""
-    from modules.shared.timezoneUtils import get_utc_timestamp as _get_utc_timestamp
-    return _get_utc_timestamp()

modules/security/tokenRefreshService.py

@@ -9,7 +9,7 @@ to ensure users don't experience token expiration issues.
 import logging
 from typing import Optional, Dict, Any, List
 from datetime import datetime, timedelta
-from modules.interfaces.interfaceAppObjects import getInterface
+from modules.interfaces.interfaceDbAppObjects import getInterface
 from modules.datamodels.datamodelUam import User, UserConnection, AuthAuthority
 from modules.datamodels.datamodelSecurity import Token
 from modules.shared.timezoneUtils import get_utc_timestamp
@@ -159,7 +159,7 @@ class TokenRefreshService:
             logger.debug(f"Starting silent token refresh for user {user_id}")
             
             # Get user interface
-            from modules.interfaces.interfaceAppObjects import getRootInterface
+            from modules.interfaces.interfaceDbAppObjects import getRootInterface
             root_interface = getRootInterface()
             
             # Get user connections
@@ -226,7 +226,7 @@ class TokenRefreshService:
             logger.debug(f"Starting proactive token refresh for user {user_id}")
             
             # Get user interface
-            from modules.interfaces.interfaceAppObjects import getRootInterface
+            from modules.interfaces.interfaceDbAppObjects import getRootInterface
             root_interface = getRootInterface()
             
             # Get user connections

modules/services/__init__.py

@@ -44,14 +44,14 @@ class Services:
 
         # Initialize interfaces
 
-        from modules.interfaces.interfaceChatObjects import getInterface as getChatInterface
-        self.interfaceChat = getChatInterface(user)
+        from modules.interfaces.interfaceDbChatObjects import getInterface as getChatInterface
+        self.interfaceDbChat = getChatInterface(user)
         
-        from modules.interfaces.interfaceAppObjects import getInterface as getAppInterface
-        self.interfaceApp = getAppInterface(user)
+        from modules.interfaces.interfaceDbAppObjects import getInterface as getAppInterface
+        self.interfaceDbApp = getAppInterface(user)
         
-        from modules.interfaces.interfaceComponentObjects import getInterface as getComponentInterface
-        self.interfaceComponent = getComponentInterface(user)
+        from modules.interfaces.interfaceDbComponentObjects import getInterface as getComponentInterface
+        self.interfaceDbComponent = getComponentInterface(user)
         
         # Initialize service packages
 
@@ -78,7 +78,12 @@ class Services:
         
         from .serviceWeb.mainServiceWeb import WebService
         self.web = PublicService(WebService(self))
-    
+        
+        from .serviceUtils.mainServiceUtils import UtilsService
+        self.utils = PublicService(UtilsService(self))
+        
+    async def extractContentFromDocument(self, prompt, document):
+        return await self.services.documentExtraction.extractContentFromDocument(prompt, document)
 
 def getInterface(user: User, workflow: ChatWorkflow) -> Services:
     return Services(user, workflow)

modules/services/serviceAi/mainServiceAi.py

@@ -1,9 +1,17 @@
 import logging
-from typing import Dict, Any, List, Optional, Tuple
+from typing import Dict, Any, List, Optional, Tuple, Union
 
 from modules.datamodels.datamodelChat import ChatDocument
 from modules.services.serviceDocument.mainServiceDocumentExtraction import DocumentExtractionService
 from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions
+from modules.datamodels.datamodelWeb import (
+    WebSearchRequest,
+    WebCrawlRequest,
+    WebScrapeRequest,
+    WebSearchActionResult,
+    WebCrawlActionResult,
+    WebScrapeActionResult,
+)
 from modules.interfaces.interfaceAiObjects import AiObjects
 
 
@@ -14,7 +22,7 @@ logger = logging.getLogger(__name__)
 
 
 class AiService:
-    """Centralized AI service orchestrating documents, model selection and failover.
+    """Centralized AI service orchestrating documents, model selection, failover, and web operations.
     """
 
     def __init__(self, serviceCenter=None) -> None:
@@ -25,16 +33,25 @@ class AiService:
         """
         self.serviceCenter = serviceCenter
         # Only depend on interfaces
-        self.aiObjects = AiObjects()
+        self.aiObjects = None  # Will be initialized in create()
         self.documentExtractor = DocumentExtractionService()
 
-    async def callAi(
+    @classmethod
+    async def create(cls, serviceCenter=None) -> "AiService":
+        """Create AiService instance with all connectors initialized."""
+        instance = cls(serviceCenter)
+        instance.aiObjects = await AiObjects.create()
+        return instance
+
+    # AI Text Generation
+    async def callAiText(
         self,
         prompt: str,
         documents: Optional[List[ChatDocument]] = None,
         processDocumentsIndividually: bool = False,
         options: Optional[AiCallOptions] = None,
         ) -> str:
+        """Call AI for text generation using interface.call()."""
         try:
             documentContent = ""
             if documents:
@@ -55,9 +72,91 @@ class AiService:
             response = await self.aiObjects.call(request)
             return response.content
         except Exception as e:
-            logger.error(f"Error in centralized AI call: {str(e)}")
+            logger.error(f"Error in AI text generation: {str(e)}")
             return f"Error: {str(e)}"
 
+
+    # AI Image Analysis
+    async def callAiImage(
+        self,
+        prompt: str,
+        imageData: Union[str, bytes],
+        mimeType: str = None,
+        options: Optional[AiCallOptions] = None,
+        ) -> str:
+        """Call AI for image analysis using interface.callImage()."""
+        try:
+            return await self.aiObjects.callImage(prompt, imageData, mimeType, options)
+        except Exception as e:
+            logger.error(f"Error in AI image analysis: {str(e)}")
+            return f"Error: {str(e)}"
+
+    # AI Image Generation
+    async def generateImage(
+        self,
+        prompt: str,
+        size: str = "1024x1024",
+        quality: str = "standard",
+        style: str = "vivid",
+        options: Optional[AiCallOptions] = None,
+        ) -> Dict[str, Any]:
+        """Generate an image using AI using interface.generateImage()."""
+        try:
+            return await self.aiObjects.generateImage(prompt, size, quality, style, options)
+        except Exception as e:
+            logger.error(f"Error in AI image generation: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    # Web Research (using LangDoc AI)
+    async def webResearch(
+        self,
+        query: str,
+        context: str = "",
+        options: Optional[AiCallOptions] = None,
+        ) -> str:
+        """Perform web research using LangDoc AI via interface.webQuery()."""
+        try:
+            return await self.aiObjects.webQuery(query, context, options)
+        except Exception as e:
+            logger.error(f"Error in web research: {str(e)}")
+            return f"Error: {str(e)}"
+
+    # Web Search (using Tavily)
+    async def webSearch(
+        self,
+        request: WebSearchRequest,
+        ) -> WebSearchActionResult:
+        """Perform web search using Tavily via interface.webSearch()."""
+        try:
+            return await self.aiObjects.webSearch(request)
+        except Exception as e:
+            logger.error(f"Error in web search: {str(e)}")
+            return WebSearchActionResult(success=False, error=str(e))
+
+    # Web Crawl (using Tavily)
+    async def webCrawl(
+        self,
+        request: WebCrawlRequest,
+        ) -> WebCrawlActionResult:
+        """Crawl web pages using Tavily via interface.webCrawl()."""
+        try:
+            return await self.aiObjects.webCrawl(request)
+        except Exception as e:
+            logger.error(f"Error in web crawl: {str(e)}")
+            return WebCrawlActionResult(success=False, error=str(e))
+
+    # Web Scrape (using Tavily)
+    async def webScrape(
+        self,
+        request: WebScrapeRequest,
+        ) -> WebScrapeActionResult:
+        """Scrape web content using Tavily via interface.webScrape()."""
+        try:
+            return await self.aiObjects.webScrape(request)
+        except Exception as e:
+            logger.error(f"Error in web scrape: {str(e)}")
+            return WebScrapeActionResult(success=False, error=str(e))
+
     async def _processDocumentsForAi(
         self,
         documents: List[ChatDocument],

modules/services/serviceDocument/mainServiceDocumentExtraction.py

@@ -9,7 +9,7 @@ from pathlib import Path
 import xml.etree.ElementTree as ET
 from bs4 import BeautifulSoup
 import uuid
-from modules.services.serviceDocument.documentUtility import (
+from modules.services.serviceDocument.subDocumentUtility import (
     getFileExtension,
     getMimeTypeFromExtension,
     detectMimeTypeFromContent,
@@ -18,9 +18,11 @@ from modules.services.serviceDocument.documentUtility import (
 )
 
 from modules.datamodels.datamodelWorkflow import ExtractedContent
-from modules.datamodels.datamodelChat import ContentItem, ContentMetadata
+from modules.datamodels.datamodelChat import ContentItem, ContentMetadata, ChatDocument
 from modules.services.serviceNeutralization.mainServiceNeutralization import NeutralizationService
 from modules.shared.configuration import APP_CONFIG
+from modules.services.serviceAi.mainServiceAi import AiService
+from modules.interfaces.interfaceAiObjects import AiObjects
 
 logger = logging.getLogger(__name__)
 
@@ -280,7 +282,7 @@ class DocumentExtractionService:
             # Decode base64 if needed
             if base64Encoded:
                 fileData = base64.b64decode(fileData)
-            # Use documentUtility for mime type detection
+            # Use subDocumentUtility for mime type detection
             if mimeType == "application/octet-stream":
                 mimeType = detectMimeTypeFromData(fileData, fileName, self._serviceCenter)
             # Process document based on type
@@ -335,7 +337,7 @@ class DocumentExtractionService:
             content_size = len(content.encode('utf-8'))
 
             
-            # Use documentUtility for mime type
+            # Use subDocumentUtility for mime type
             mime_type = getMimeTypeFromExtension(getFileExtension(fileName))
             return [ContentItem(
                 label="main",
@@ -1421,11 +1423,8 @@ class DocumentExtractionService:
                             
                             Original prompt: {prompt}
                             """
-                            from modules.datamodels.datamodelChat import ChatDocument
                             image_doc = ChatDocument(fileData=chunk, fileName="image", mimeType=mimeType)
                             # Use direct import to avoid circular dependency
-                            from modules.services.serviceAi.mainServiceAi import AiService
-                            from modules.interfaces.interfaceAiObjects import AiObjects
                             aiService = AiService(AiObjects())
                             processedContent = await aiService.callAi(
                                     prompt=imagePrompt,
@@ -1463,8 +1462,6 @@ class DocumentExtractionService:
                                 processedContent = contentToProcess
                             else:
                                 # Use direct import to avoid circular dependency
-                                from modules.services.serviceAi.mainServiceAi import AiService
-                                from modules.interfaces.interfaceAiObjects import AiObjects
                                 aiService = AiService(AiObjects())
                                 processedContent = await aiService.callAi(
                                     prompt=aiPrompt,

modules/services/serviceDocument/mainServiceDocumentGeneration.py

@@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional
 from datetime import datetime, UTC
 import re
 from modules.shared.timezoneUtils import get_utc_timestamp
-from modules.services.serviceDocument.documentUtility import (
+from modules.services.serviceDocument.subDocumentUtility import (
     getFileExtension,
     getMimeTypeFromExtension,
     detectMimeTypeFromContent,

modules/services/serviceNeutralization/mainServiceNeutralization.py

@@ -33,7 +33,7 @@ class NeutralizationService:
             NamesToParse: List of names to parse and replace (case-insensitive)
         """
         self.serviceCenter = serviceCenter
-        self.interfaceApp = serviceCenter.interfaceApp
+        self.interfaceDbApp = serviceCenter.interfaceDbApp
         
         # Initialize anonymization processors
         self.NamesToParse = NamesToParse or []
@@ -44,15 +44,15 @@ class NeutralizationService:
         
     def getConfig(self) -> Optional[DataNeutraliserConfig]:
         """Get the neutralization configuration for the current user's mandate"""
-        if not self.interfaceApp:
+        if not self.interfaceDbApp:
             return None
-        return self.interfaceApp.getNeutralizationConfig()
+        return self.interfaceDbApp.getNeutralizationConfig()
     
     def saveConfig(self, config_data: Dict[str, Any]) -> DataNeutraliserConfig:
         """Save or update the neutralization configuration"""
-        if not self.interfaceApp:
+        if not self.interfaceDbApp:
             raise ValueError("User context required for saving configuration")
-        return self.interfaceApp.createOrUpdateNeutralizationConfig(config_data)
+        return self.interfaceDbApp.createOrUpdateNeutralizationConfig(config_data)
     
     # Public API: process text or file
 
@@ -62,18 +62,18 @@ class NeutralizationService:
 
     def processFile(self, fileId: str) -> Dict[str, Any]:
         """Neutralize a file referenced by its fileId using app interface."""
-        if not self.interfaceApp:
+        if not self.interfaceDbApp:
             raise ValueError("User context is required to process a file by fileId")
         # Fetch file data and metadata
         fileInfo = None
         try:
             # getFile returns an object; fallback to dict-like
-            fileInfo = self.interfaceApp.getFile(fileId)
+            fileInfo = self.interfaceDbApp.getFile(fileId)
         except Exception:
             fileInfo = None
         fileName = getattr(fileInfo, 'fileName', None) if fileInfo else None
         mimeType = getattr(fileInfo, 'mimeType', None) if fileInfo else None
-        fileData = self.interfaceApp.getFileData(fileId)
+        fileData = self.interfaceDbApp.getFileData(fileId)
         if not fileData:
             raise ValueError(f"No file data found for fileId: {fileId}")
 
@@ -103,17 +103,17 @@ class NeutralizationService:
         return result
 
     def resolveText(self, text: str) -> str:
-        if not self.interfaceApp:
+        if not self.interfaceDbApp:
             return text
         try:
             placeholder_pattern = r'\[([a-z]+)\.([a-f0-9-]{36})\]'
             matches = re.findall(placeholder_pattern, text)
             resolved_text = text
             for placeholder_type, uid in matches:
-                attributes = self.interfaceApp.db.getRecordset(
+                attributes = self.interfaceDbApp.db.getRecordset(
                     DataNeutralizerAttributes,
                     recordFilter={
-                        "mandateId": self.interfaceApp.mandateId,
+                        "mandateId": self.interfaceDbApp.mandateId,
                         "id": uid
                     }
                 )
@@ -124,7 +124,27 @@ class NeutralizationService:
             return resolved_text
         except Exception:
             return text
-   
+
+    def getAttributes(self) -> List[DataNeutralizerAttributes]:
+        """Get all neutralization attributes for the current user's mandate"""
+        if not self.interfaceDbApp:
+            return []
+        try:
+            return self.interfaceDbApp.db.getRecordset(
+                DataNeutralizerAttributes,
+                recordFilter={"mandateId": self.interfaceDbApp.mandateId}
+            )
+        except Exception as e:
+            logger.error(f"Error getting neutralization attributes: {str(e)}")
+            return []
+
+    def deleteNeutralizationAttributes(self, fileId: str) -> bool:
+        """Delete neutralization attributes for a specific file"""
+        if not self.interfaceDbApp:
+            return False
+        return self.interfaceDbApp.deleteNeutralizationAttributes(fileId)
+
+
     # Helper functions
 
     def _neutralizeText(self, text: str, textType: str = None) -> Dict[str, Any]:
@@ -186,19 +206,6 @@ class NeutralizationService:
                 processed_info={'type': 'error', 'error': str(e)}
             ).model_dump()
         
-    def _getAttributes(self) -> List[DataNeutralizerAttributes]:
-        """Get all neutralization attributes for the current user's mandate"""
-        if not self.interfaceApp:
-            return []
-        try:
-            return self.interfaceApp.db.getRecordset(
-                DataNeutralizerAttributes,
-                recordFilter={"mandateId": self.interfaceApp.mandateId}
-            )
-        except Exception as e:
-            logger.error(f"Error getting neutralization attributes: {str(e)}")
-            return []
-
     def _getContentTypeFromMime(self, mime_type: str) -> str:
         """Determine content type from MIME type for neutralization processing"""
         if mime_type.startswith('text/'):

modules/services/serviceSharepoint/mainServiceSharepoint.py

@@ -19,18 +19,17 @@ class SharepointService:
         Args:
             serviceCenter: Service center instance for accessing other services
         
-        Use setAccessToken() method to configure the access token before making API calls.
+        Use setAccessTokenFromConnection() method to configure the access token before making API calls.
         """
         self.serviceCenter = serviceCenter
         self.access_token = None
         self.base_url = "https://graph.microsoft.com/v1.0"
     
-    def setAccessToken(self, userConnection, interfaceApp) -> bool:
+    def setAccessTokenFromConnection(self, userConnection) -> bool:
         """Set access token from UserConnection.
         
         Args:
             userConnection: UserConnection object containing token information
-            interfaceApp: InterfaceApp instance used by TokenManager to resolve the token
             
         Returns:
             bool: True if token was set successfully, False otherwise
@@ -42,7 +41,7 @@ class SharepointService:
             
             # Get a fresh token for this specific connection
             from modules.security.tokenManager import TokenManager
-            token = TokenManager().getFreshToken(interfaceApp, userConnection.id)
+            token = TokenManager().getFreshToken(userConnection.id)
             if not token:
                 logger.error(f"No token found for connection {userConnection.id}")
                 return False
@@ -59,8 +58,8 @@ class SharepointService:
         """Make a Microsoft Graph API call with proper error handling."""
         try:
             if self.access_token is None:
-                logger.error("Access token is not set. Please call setAccessToken() before using the SharePoint service.")
-                return {"error": "Access token is not set. Please call setAccessToken() before using the SharePoint service."}
+                logger.error("Access token is not set. Please call setAccessTokenFromConnection() before using the SharePoint service.")
+                return {"error": "Access token is not set. Please call setAccessTokenFromConnection() before using the SharePoint service."}
             
             headers = {
                 "Authorization": f"Bearer {self.access_token}",
@@ -318,7 +317,7 @@ class SharepointService:
         """Download a file from SharePoint."""
         try:
             if self.access_token is None:
-                logger.error("Access token is not set. Please call setAccessToken() before using the SharePoint service.")
+                logger.error("Access token is not set. Please call setAccessTokenFromConnection() before using the SharePoint service.")
                 return None
             
             endpoint = f"sites/{site_id}/drive/items/{file_id}/content"
@@ -458,7 +457,7 @@ class SharepointService:
         """Download a file by its path within a site."""
         try:
             if self.access_token is None:
-                logger.error("Access token is not set. Please call setAccessToken() before using the SharePoint service.")
+                logger.error("Access token is not set. Please call setAccessTokenFromConnection() before using the SharePoint service.")
                 return None
             
             # Clean the path

modules/services/serviceTicket/mainServiceTicket.py

@@ -2,7 +2,7 @@
 
 import logging
 from typing import Dict, Any, Optional
-from modules.interfaces.interfaceTicketObjects import createTicketInterfaceByType
+from modules.interfaces.interfaceTicketObjects import _createTicketInterfaceByType
 
 logger = logging.getLogger(__name__)
 
@@ -18,7 +18,7 @@ class TicketService:
         """
         self.serviceCenter = serviceCenter
 
-    async def createTicketInterfaceByType(
+    async def _createTicketInterfaceByType(
         self,
         taskSyncDefinition: Dict[str, Any],
         connectorType: str,
@@ -34,7 +34,7 @@ class TicketService:
         Returns:
             Ticket interface instance
         """
-        return await createTicketInterfaceByType(
+        return await _createTicketInterfaceByType(
             taskSyncDefinition=taskSyncDefinition,
             connectorType=connectorType,
             connectorParams=connectorParams

modules/services/serviceUtils/mainServiceUtils.py

@@ -0,0 +1,142 @@
+"""
+Utility service for common operations across the gateway.
+Provides centralized access to configuration, events, and other utilities.
+"""
+
+import logging
+from typing import Any, Optional, Dict, Callable
+from modules.shared.configuration import APP_CONFIG
+from modules.shared.eventManagement import eventManager
+from modules.shared.timezoneUtils import get_utc_timestamp
+from modules.security.tokenManager import TokenManager
+
+logger = logging.getLogger(__name__)
+
+class UtilsService:
+    """Utility service providing common operations."""
+
+    def __init__(self, services):
+        self.services = services
+
+    def eventRegisterCron(self, job_id: str, func: Callable, cron_kwargs: Dict[str, Any], 
+                         replace_existing: bool = True, coalesce: bool = True, 
+                         max_instances: int = 1, misfire_grace_time: int = 1800):
+        """
+        Register a cron job with the event manager.
+        
+        Args:
+            job_id: Unique identifier for the job
+            func: Function to execute
+            cron_kwargs: Cron schedule parameters
+            replace_existing: Whether to replace existing job with same ID
+            coalesce: Whether to coalesce multiple pending executions
+            max_instances: Maximum number of concurrent instances
+            misfire_grace_time: Grace time for misfired jobs in seconds
+        """
+        try:
+            eventManager.register_cron(
+                job_id=job_id,
+                func=func,
+                cron_kwargs=cron_kwargs,
+                replace_existing=replace_existing,
+                coalesce=coalesce,
+                max_instances=max_instances,
+                misfire_grace_time=misfire_grace_time
+            )
+            logger.info(f"Registered cron job '{job_id}' with schedule: {cron_kwargs}")
+        except Exception as e:
+            logger.error(f"Error registering cron job '{job_id}': {str(e)}")
+
+    def eventRegisterInterval(self, job_id: str, func: Callable, seconds: Optional[int] = None,
+                            minutes: Optional[int] = None, hours: Optional[int] = None,
+                            replace_existing: bool = True, coalesce: bool = True,
+                            max_instances: int = 1, misfire_grace_time: int = 1800):
+        """
+        Register an interval job with the event manager.
+        
+        Args:
+            job_id: Unique identifier for the job
+            func: Function to execute
+            seconds: Interval in seconds
+            minutes: Interval in minutes
+            hours: Interval in hours
+            replace_existing: Whether to replace existing job with same ID
+            coalesce: Whether to coalesce multiple pending executions
+            max_instances: Maximum number of concurrent instances
+            misfire_grace_time: Grace time for misfired jobs in seconds
+        """
+        try:
+            eventManager.register_interval(
+                job_id=job_id,
+                func=func,
+                seconds=seconds,
+                minutes=minutes,
+                hours=hours,
+                replace_existing=replace_existing,
+                coalesce=coalesce,
+                max_instances=max_instances,
+                misfire_grace_time=misfire_grace_time
+            )
+            logger.info(f"Registered interval job '{job_id}' (h={hours}, m={minutes}, s={seconds})")
+        except Exception as e:
+            logger.error(f"Error registering interval job '{job_id}': {str(e)}")
+
+    def eventRemove(self, job_id: str):
+        """
+        Remove a scheduled job from the event manager.
+        
+        Args:
+            job_id: ID of the job to remove
+        """
+        try:
+            eventManager.remove(job_id)
+            logger.info(f"Removed job '{job_id}'")
+        except Exception as e:
+            logger.error(f"Error removing job '{job_id}': {str(e)}")
+
+    def configGet(self, key: str, default: Any = None, user_id: str = "system") -> Any:
+        """
+        Get a configuration value with optional default.
+        
+        Args:
+            key: Configuration key to retrieve
+            default: Default value if key not found
+            user_id: User ID for audit logging (default: "system")
+            
+        Returns:
+            Configuration value or default
+        """
+        try:
+            return APP_CONFIG.get(key, default, user_id)
+        except Exception as e:
+            logger.error(f"Error getting config '{key}': {str(e)}")
+            return default
+
+    def getUtcTimestamp(self) -> float:
+        """
+        Get current UTC timestamp.
+        
+        Returns:
+            float: Current UTC timestamp in seconds
+        """
+        try:
+            return get_utc_timestamp()
+        except Exception as e:
+            logger.error(f"Error getting UTC timestamp: {str(e)}")
+            return 0.0
+
+    def getFreshConnectionToken(self, connectionId: str):
+        """
+        Get a fresh token for a specific connection.
+        
+        Args:
+            connectionId: ID of the connection to get token for
+            
+        Returns:
+            Token object or None if not found/expired
+        """
+        try:
+            return TokenManager().getFreshToken(connectionId)
+        except Exception as e:
+            logger.error(f"Error getting fresh token for connection {connectionId}: {str(e)}")
+            return None

modules/services/serviceWeb/mainServiceWeb.py

@@ -1,49 +0,0 @@
-import logging
-from typing import Optional, List
-
-from modules.datamodels.datamodelWeb import (
-    WebSearchRequest,
-    WebCrawlRequest,
-    WebScrapeRequest,
-    WebSearchActionResult,
-    WebCrawlActionResult,
-    WebScrapeActionResult,
-)
-from modules.interfaces.interfaceWebObjects import WebInterface
-
-
-logger = logging.getLogger(__name__)
-
-
-class WebService:
-    """Centralized Web service providing wrappers around web interface actions.
-    """
-
-    def __init__(self, serviceCenter=None) -> None:
-        self.serviceCenter = serviceCenter
-
-    async def webSearch(self, request: WebSearchRequest) -> WebSearchActionResult:
-        try:
-            web_interface = await WebInterface.create()
-            return await web_interface.search(request)
-        except Exception as e:
-            logger.error(f"Error in webSearch: {str(e)}")
-            raise
-
-    async def webCrawl(self, request: WebCrawlRequest) -> WebCrawlActionResult:
-        try:
-            web_interface = await WebInterface.create()
-            return await web_interface.crawl(request)
-        except Exception as e:
-            logger.error(f"Error in webCrawl: {str(e)}")
-            raise
-
-    async def webScrape(self, request: WebScrapeRequest) -> WebScrapeActionResult:
-        try:
-            web_interface = await WebInterface.create()
-            return await web_interface.scrape(request)
-        except Exception as e:
-            logger.error(f"Error in webScrape: {str(e)}")
-            raise
-
-

modules/services/serviceWorkflow/mainServiceWorkflow.py

@@ -5,8 +5,10 @@ from modules.datamodels.datamodelUam import User, UserConnection
 from modules.datamodels.datamodelChat import ChatDocument, ChatMessage
 from modules.datamodels.datamodelChat import ExtractedContent
 from modules.services.serviceDocument.mainServiceDocumentExtraction import DocumentExtractionService
-from modules.services.serviceDocument.documentUtility import getFileExtension, getMimeTypeFromExtension, detectContentTypeFromData
+from modules.services.serviceDocument.subDocumentUtility import getFileExtension, getMimeTypeFromExtension, detectContentTypeFromData
 from modules.shared.timezoneUtils import get_utc_timestamp
+from modules.services.serviceAi.mainServiceAi import AiService
+from modules.security.tokenManager import TokenManager
 
 logger = logging.getLogger(__name__)
 
@@ -17,9 +19,9 @@ class WorkflowService:
         self.serviceCenter = serviceCenter
         self.user = serviceCenter.user
         self.workflow = serviceCenter.workflow
-        self.interfaceChat = serviceCenter.interfaceChat
-        self.interfaceComponent = serviceCenter.interfaceComponent
-        self.interfaceApp = serviceCenter.interfaceApp
+        self.interfaceDbChat = serviceCenter.interfaceDbChat
+        self.interfaceDbComponent = serviceCenter.interfaceDbComponent
+        self.interfaceDbApp = serviceCenter.interfaceDbApp
 
     async def summarizeChat(self, messages: List[ChatMessage]) -> str:
         """
@@ -55,7 +57,6 @@ class WorkflowService:
             Please provide a comprehensive summary of this conversation."""
             
             # Get summary using AI service directly (avoiding circular dependency)
-            from modules.services.serviceAi.mainServiceAi import AiService
             ai_service = AiService(self)
             return await ai_service.callAi(
                 prompt=prompt,
@@ -221,8 +222,7 @@ class WorkflowService:
         try:
             # Get a fresh token via TokenManager convenience method
             logger.debug(f"Getting fresh token for connection {connection.id}")
-            from modules.security.tokenManager import TokenManager
-            token = TokenManager().getFreshToken(self.interfaceApp, connection.id)
+            token = TokenManager().getFreshToken(connection.id)
             if token:
                 if hasattr(token, 'expiresAt') and token.expiresAt:
                     current_time = get_utc_timestamp()
@@ -256,7 +256,7 @@ class WorkflowService:
         try:
             if not authority or not externalUsername:
                 return None
-            user_connections = self.interfaceApp.getUserConnections(self.user.id)
+            user_connections = self.interfaceDbApp.getUserConnections(self.user.id)
             for connection in user_connections:
                 # Normalize authority for comparison (enum vs string)
                 connection_authority = connection.authority.value if hasattr(connection.authority, 'value') else str(connection.authority)
@@ -283,7 +283,7 @@ class WorkflowService:
             conn_id = parts[3]
             
             # Get user connections through AppObjects interface
-            user_connections = self.interfaceApp.getUserConnections(self.user.id)
+            user_connections = self.interfaceDbApp.getUserConnections(self.user.id)
             
             # Find matching connection
             for conn in user_connections:
@@ -297,7 +297,7 @@ class WorkflowService:
 
     def getFileInfo(self, fileId: str) -> Dict[str, Any]:
         """Get file information"""
-        file_item = self.interfaceComponent.getFile(fileId)
+        file_item = self.interfaceDbComponent.getFile(fileId)
         if file_item:
             return {
                 "id": file_item.id,
@@ -311,7 +311,7 @@ class WorkflowService:
 
     def getFileData(self, fileId: str) -> bytes:
         """Get file data by ID"""
-        return self.interfaceComponent.getFileData(fileId)
+        return self.interfaceDbComponent.getFileData(fileId)
     
     async def extractContentFromDocument(self, prompt: str, document: ChatDocument) -> ExtractedContent:
         """Extract content from ChatDocument using prompt"""
@@ -350,7 +350,6 @@ class WorkflowService:
                     raise RuntimeError(f"Document {document.id} properties are inaccessible and recovery failed. Diagnosis: {diagnosis}")
             
             # Process with DocumentExtractionService directly (no circular dependency)
-            from modules.services.serviceDocument.mainServiceDocumentExtraction import DocumentExtractionService
             docService = DocumentExtractionService(None)  # Pass None to avoid circular dependency
             content_items = await docService.processFileData(
                 fileData=fileData,
@@ -411,7 +410,7 @@ class WorkflowService:
             
             # Try to access the file directly
             try:
-                file_info = self.interfaceComponent.getFile(document.fileId)
+                file_info = self.interfaceDbComponent.getFile(document.fileId)
                 if file_info:
                     diagnosis['file_exists'] = True
                     diagnosis['file_info'] = {
@@ -442,7 +441,7 @@ class WorkflowService:
             logger.info(f"Attempting to recover document access for document {document.id}")
             
             # Re-set the component interface
-            document.setComponentInterface(self.interfaceComponent)
+            document.setComponentInterface(self.interfaceDbComponent)
             
             # Test if we can now access the fileName
             try:
@@ -466,15 +465,15 @@ class WorkflowService:
         else:
             content_bytes = content.encode('utf-8')
         
-        # Create the file (hash and size are computed inside interfaceComponent)
-        file_item = self.interfaceComponent.createFile(
+        # Create the file (hash and size are computed inside interfaceDbComponent)
+        file_item = self.interfaceDbComponent.createFile(
             name=fileName,
             mimeType=mimeType,
             content=content_bytes
         )
         
         # Then store the file data
-        self.interfaceComponent.createFileData(file_item.id, content_bytes)
+        self.interfaceDbComponent.createFileData(file_item.id, content_bytes)
         
         # Get file info to copy attributes
         file_info = self.getFileInfo(file_item.id)
@@ -549,7 +548,7 @@ class WorkflowService:
             
             # Persist changes to database if any updates were made
             if update_data:
-                self.interfaceChat.updateWorkflow(self.workflow.id, update_data)
+                self.interfaceDbChat.updateWorkflow(self.workflow.id, update_data)
             
             logger.debug(f"Updated workflow context: Round {self.workflow.currentRound if hasattr(self.workflow, 'currentRound') else 'N/A'}, Task {self.workflow.currentTask if hasattr(self.workflow, 'currentTask') else 'N/A'}, Action {self.workflow.currentAction if hasattr(self.workflow, 'currentAction') else 'N/A'}")
         except Exception as e:

modules/shared/configuration.py

@@ -15,6 +15,7 @@ from pathlib import Path
 from cryptography.fernet import Fernet
 from cryptography.hazmat.primitives import hashes
 from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
+# audit_logger imported lazily to avoid circular import
 
 # Set up basic logging for configuration loading
 logging.basicConfig(

modules/workflows/methods/methodAi.py

@@ -9,20 +9,16 @@ from datetime import datetime, UTC
 
 from modules.workflows.methods.methodBase import MethodBase, action
 from modules.datamodels.datamodelWorkflow import ActionResult
-from modules.shared.timezoneUtils import get_utc_timestamp
 
 logger = logging.getLogger(__name__)
 
 class MethodAi(MethodBase):
     """AI processing methods."""
     
-    def __init__(self, service):
-        super().__init__(service)
+    def __init__(self, services):
+        super().__init__(services)
         self.name = "ai"
         self.description = "AI processing methods"
-        # Centralized services interface (for AI)
-        from modules.services import getInterface as getServices
-        self.services = getServices(self.service.user, self.service.workflow)
     
     def _format_timestamp_for_filename(self) -> str:
         """Format current timestamp as YYYYMMDD-hhmmss for filenames."""
@@ -37,9 +33,13 @@ class MethodAi(MethodBase):
             aiPrompt (str): The AI prompt for processing
             documentList (list, optional): List of document references to include in context
             expectedDocumentFormats (list, optional): Expected output formats with extension, mimeType, description
-            processingMode (str, optional): Processing mode ('basic', 'advanced', 'detailed') - defaults to 'basic'
+            processingMode (str, optional): Processing mode - use 'basic', 'advanced', or 'detailed' (defaults to 'basic')
             includeMetadata (bool, optional): Whether to include metadata (default: True)
-            customInstructions (str, optional): Additional custom instructions for the AI
+            operationType (str, optional): Operation type - use 'general', 'generate_plan', 'analyse_content', 'generate_content', 'web_research', 'image_analysis', or 'image_generation'
+            priority (str, optional): Priority level - use 'speed', 'quality', 'cost', or 'balanced'
+            maxCost (float, optional): Maximum cost budget for the AI call
+            maxProcessingTime (int, optional): Maximum processing time in seconds
+            requiredTags (list, optional): Required model tags - use 'text', 'chat', 'reasoning', 'analysis', 'image', 'vision', 'web', 'search', etc.
         """
         try:
             aiPrompt = parameters.get("aiPrompt")
@@ -49,7 +49,11 @@ class MethodAi(MethodBase):
             expectedDocumentFormats = parameters.get("expectedDocumentFormats", [])
             processingMode = parameters.get("processingMode", "basic")
             includeMetadata = parameters.get("includeMetadata", True)
-            customInstructions = parameters.get("customInstructions", "")
+            operationType = parameters.get("operationType", "general")
+            priority = parameters.get("priority", "balanced")
+            maxCost = parameters.get("maxCost")
+            maxProcessingTime = parameters.get("maxProcessingTime")
+            requiredTags = parameters.get("requiredTags")
             
             if not aiPrompt:
                 return ActionResult.isFailure(
@@ -69,11 +73,11 @@ class MethodAi(MethodBase):
             # Build context from documents if provided
             context = ""
             if documentList:
-                chatDocuments = self.service.getChatDocumentsFromDocumentList(documentList)
+                chatDocuments = self.services.workflow.getChatDocumentsFromDocumentList(documentList)
                 if chatDocuments:
                     context_parts = []
                     for doc in chatDocuments:
-                        file_info = self.service.getFileInfo(doc.fileId)
+                        file_info = self.services.workflow.getFileInfo(doc.fileId)
                         
                         try:
                             # Use the document content extraction service with the specific AI prompt context
@@ -96,7 +100,7 @@ class MethodAi(MethodBase):
                             
                             logger.debug(f"Extracting content from {doc.fileName} with task-specific prompt: {extraction_prompt[:100]}...")
                             
-                            extracted_content = await self.service.extractContentFromDocument(
+                            extracted_content = await self.services.documentExtraction.extractContentFromDocument(
                                 prompt=extraction_prompt.strip(), 
                                 document=doc
                             )
@@ -186,7 +190,7 @@ class MethodAi(MethodBase):
                 if documentList:
                     for d in (chatDocuments or []):
                         try:
-                            file_data = self.service.getFileData(d.fileId)
+                            file_data = self.services.workflow.getFileData(d.fileId)
                             documents.append(
                                 ChatDocument(
                                     fileData=file_data,
@@ -200,22 +204,32 @@ class MethodAi(MethodBase):
                 documents = None
 
             output_format = output_extension.replace('.', '') or 'txt'
+            
+            # Build options from parameters
+            options = {
+                "process_type": "text",
+                "operation_type": operationType,
+                "priority": priority,
+                "compress_prompt": processingMode != "detailed",
+                "compress_documents": True,
+                "process_documents_individually": True,
+                "processing_mode": processingMode,
+                "result_format_requested": output_format,
+                "include_metadata": includeMetadata
+            }
+            
+            # Add optional parameters if provided
+            if maxCost is not None:
+                options["max_cost"] = maxCost
+            if maxProcessingTime is not None:
+                options["max_processing_time"] = maxProcessingTime
+            if requiredTags is not None:
+                options["required_tags"] = requiredTags
+            
             result = await self.services.ai.callAi(
                 prompt=call_prompt,
                 documents=documents or None,
-                options={
-                    "process_type": "text",
-                    "operation_type": "generate_content",
-                    "priority": "quality" if processingMode in ["advanced", "detailed"] else "speed",
-                    "compress_prompt": processingMode != "detailed",
-                    "compress_documents": True,
-                    "process_documents_individually": True,
-                    "processing_mode": processingMode,
-                    "result_format_requested": output_format,
-                    "include_metadata": includeMetadata,
-                    "max_cost": 0.05 if processingMode in ["advanced", "detailed"] else 0.02,
-                    "max_processing_time": 45 if processingMode in ["advanced", "detailed"] else 20
-                }
+                options=options
             )
 
             # If expected JSON and too short/not JSON, retry with stricter JSON guardrails

modules/workflows/methods/methodBase.py

@@ -30,9 +30,9 @@ def action(func):
 class MethodBase:
     """Base class for all methods"""
     
-    def __init__(self, serviceCenter: Any):
-        """Initialize method with service center"""
-        self.service = serviceCenter
+    def __init__(self, services: Any):
+        """Initialize method with services object"""
+        self.services = services
         self.name: str
         self.description: str
         self.logger = logging.getLogger(f"{__name__}.{self.__class__.__name__}")

modules/workflows/methods/methodDocument.py

@@ -10,21 +10,17 @@ from datetime import datetime, UTC
 
 from modules.workflows.methods.methodBase import MethodBase, action
 from modules.datamodels.datamodelWorkflow import ActionResult, ChatDocument
-from modules.shared.timezoneUtils import get_utc_timestamp
 
 logger = logging.getLogger(__name__)
 
 class MethodDocument(MethodBase):
     """Document method implementation for document operations"""
     
-    def __init__(self, serviceCenter: Any):
+    def __init__(self, services):
         """Initialize the document method"""
-        super().__init__(serviceCenter)
+        super().__init__(services)
         self.name = "document"
         self.description = "Handle document operations like extraction and analysis"
-        # Centralized services interface (for AI)
-        from modules.services import getInterface as getServices
-        self.services = getServices(self.service.user, self.service.workflow)
 
     def _format_timestamp_for_filename(self) -> str:
         """Format current timestamp as YYYYMMDD-hhmmss for filenames."""
@@ -59,7 +55,7 @@ class MethodDocument(MethodBase):
                     error="AI prompt is required"
                 )
             
-            chatDocuments = self.service.getChatDocumentsFromDocumentList(documentList)
+            chatDocuments = self.services.workflow.getChatDocumentsFromDocumentList(documentList)
             if not chatDocuments:
                 return ActionResult.isFailure(
                     error="No documents found for the provided reference"
@@ -70,12 +66,12 @@ class MethodDocument(MethodBase):
             file_infos = []
             
             for chatDocument in chatDocuments:
-                file_info = self.service.getFileInfo(chatDocument.fileId)
+                file_info = self.services.workflow.getFileInfo(chatDocument.fileId)
                 
                 try:
                     # Use the document content extraction service with the specific AI prompt
                     # This handles all document types (text, binary, image, etc.) intelligently
-                    extracted_content = await self.service.extractContentFromDocument(
+                    extracted_content = await self.services.documentExtraction.extractContentFromDocument(
                         prompt=aiPrompt,
                         document=chatDocument
                     )
@@ -158,7 +154,7 @@ class MethodDocument(MethodBase):
                     "content": final_content,
                     "originalfileName": original_fileName,
                     "fileInfos": [file_infos[i]] if includeMetadata and i < len(file_infos) else None,
-                    "timestamp": get_utc_timestamp()
+                    "timestamp": self.services.utils.getUtcTimestamp()
                 }
                 
                 logger.info(f"Created output document: {output_fileName} with {len(final_content)} characters")
@@ -210,7 +206,7 @@ class MethodDocument(MethodBase):
                 )
             
             # Get chat documents for original documents list
-            chat_documents = self.service.getChatDocumentsFromDocumentList(document_list)
+            chat_documents = self.services.workflow.getChatDocumentsFromDocumentList(document_list)
             logger.info(f"Found {len(chat_documents)} chat documents")
             
             if not chat_documents:
@@ -231,7 +227,7 @@ class MethodDocument(MethodBase):
                 if hasattr(chat_document, 'fileId') and chat_document.fileId:
                     try:
                         # Get file data directly without AI processing
-                        file_data = self.service.getFileData(chat_document.fileId)
+                        file_data = self.services.workflow.getFileData(chat_document.fileId)
                         if file_data:
                             # Check if it's text data and convert to string
                             if isinstance(file_data, bytes):
@@ -321,7 +317,7 @@ class MethodDocument(MethodBase):
                         "content": formatted_content,
                         "outputFormat": target_format,
                         "originalDocument": original_name,
-                        "timestamp": get_utc_timestamp()
+                        "timestamp": self.services.utils.getUtcTimestamp()
                     }
                     
                     logger.info(f"Generated document: {output_fileName} with {len(formatted_content)} characters")
@@ -403,7 +399,7 @@ class MethodDocument(MethodBase):
                 "content": formatted_content,
                 "outputFormat": target_format,
                 "originalDocuments": original_file_names,
-                "timestamp": get_utc_timestamp(),
+                "timestamp": self.services.utils.getUtcTimestamp(),
                 "merged": True
             }
             
@@ -591,22 +587,22 @@ class MethodDocument(MethodBase):
             elif extension == ".json":
                 # Simple JSON fallback
                 content_escaped = content.replace('"', '\\"')
-                timestamp = get_utc_timestamp()
+                timestamp = self.services.utils.getUtcTimestamp()
                 return f'{{"content": "{content_escaped}", "format": "json", "timestamp": {timestamp}}}'
                 
             elif extension == ".xml":
                 # Simple XML fallback
-                timestamp = get_utc_timestamp()
+                timestamp = self.services.utils.getUtcTimestamp()
                 return f'<?xml version="1.0" encoding="UTF-8"?>\n<document>\n<content>{content}</content>\n<format>xml</format>\n<timestamp>{timestamp}</timestamp>\n</document>'
                 
             elif extension == ".html":
                 # Simple HTML fallback
-                timestamp = int(get_utc_timestamp())
+                timestamp = int(self.services.utils.getUtcTimestamp())
                 return f'<!DOCTYPE html>\n<html>\n<head><meta charset="UTF-8"><title>Generated Document</title></head>\n<body>\n<pre>{content}</pre>\n<p><em>Generated on {timestamp}</em></p>\n</body>\n</html>'
                 
             elif extension == ".md":
                 # Simple Markdown fallback
-                timestamp = int(get_utc_timestamp())
+                timestamp = int(self.services.utils.getUtcTimestamp())
                 return f"# Generated Document\n\n{content}\n\n---\n*Generated on {timestamp}*"
                 
             else:
@@ -646,7 +642,7 @@ class MethodDocument(MethodBase):
                     error="Prompt is required to specify what kind of report to generate"
                 )
             
-            chatDocuments = self.service.getChatDocumentsFromDocumentList(documentList)
+            chatDocuments = self.services.workflow.getChatDocumentsFromDocumentList(documentList)
             logger.info(f"Retrieved {len(chatDocuments)} chat documents for report generation")
             
             if not chatDocuments:
@@ -658,14 +654,14 @@ class MethodDocument(MethodBase):
             html_content = await self._generateHtmlReport(chatDocuments, title, includeMetadata, prompt)
             
             # Create output fileName
-            timestamp = int(get_utc_timestamp())
+            timestamp = int(self.services.utils.getUtcTimestamp())
             output_fileName = f"report_{self._format_timestamp_for_filename()}.html"
             
             result_data = {
                 "documentCount": len(chatDocuments),
                 "content": html_content,
                 "title": title,
-                "timestamp": get_utc_timestamp()
+                "timestamp": self.services.utils.getUtcTimestamp()
             }
             
             logger.info(f"Generated HTML report: {output_fileName} with {len(html_content)} characters")
@@ -698,7 +694,7 @@ class MethodDocument(MethodBase):
                 
                 # Get actual file content using the document content extraction service
                 try:
-                    extracted_content = await self.service.extractContentFromDocument(
+                    extracted_content = await self.services.documentExtraction.extractContentFromDocument(
                         prompt="Extract readable text content for HTML report generation", 
                         document=doc
                     )
@@ -728,7 +724,7 @@ class MethodDocument(MethodBase):
             
             if not validDocuments:
                 # No readable content; return a minimal valid HTML document
-                timestamp = int(get_utc_timestamp())
+                timestamp = int(self.services.utils.getUtcTimestamp())
                 return f"<!DOCTYPE html><html><head><meta charset=\"UTF-8\"><title>{title}</title></head><body><h1>{title}</h1><p>Keine auswertbaren Inhalte gefunden.</p><p>Generated: {timestamp}</p></body></html>"
             
             # Create AI prompt for comprehensive report generation using user's prompt
@@ -770,7 +766,7 @@ SOURCE DOCUMENT CONTENT:
             try:
                 for d in validDocuments:
                     try:
-                        data = self.service.getFileData(d.fileId) if hasattr(d, 'fileId') else None
+                        data = self.services.workflow.getFileData(d.fileId) if hasattr(d, 'fileId') else None
                         if data:
                             documents.append(ChatDocument(fileData=data, fileName=d.fileName, mimeType=d.mimeType))
                     except Exception:

modules/workflows/methods/methodOutlook.py

@@ -14,22 +14,17 @@ import requests
 from modules.workflows.methods.methodBase import MethodBase, action
 from modules.datamodels.datamodelWorkflow import ActionResult, ChatDocument
 from modules.datamodels.datamodelUam import ConnectionStatus
-from modules.shared.timezoneUtils import get_utc_timestamp
-from modules.services import getInterface as getServices
-from modules.security.tokenManager import TokenManager
 
 logger = logging.getLogger(__name__)
 
 class MethodOutlook(MethodBase):
     """Outlook method implementation for email operations"""
     
-    def __init__(self, serviceCenter: Any):
+    def __init__(self, services):
         """Initialize the Outlook method"""
-        super().__init__(serviceCenter)
+        super().__init__(services)
         self.name = "outlook"
         self.description = "Handle Microsoft Outlook email operations"
-        # Centralized services interface (for AI)
-        self.services = getServices(self.service.user, self.service.workflow)
     
     def _format_timestamp_for_filename(self) -> str:
         """Format current timestamp as YYYYMMDD-hhmmss for filenames."""
@@ -43,28 +38,21 @@ class MethodOutlook(MethodBase):
             logger.debug(f"Getting Microsoft connection for reference: {connectionReference}")
             
             # Get the connection from the service
-            userConnection = self.service.getUserConnectionFromConnectionReference(connectionReference)
+            userConnection = self.services.workflow.getUserConnectionFromConnectionReference(connectionReference)
             if not userConnection:
                 logger.error(f"Connection not found: {connectionReference}")
                 return None
             
             logger.debug(f"Found connection: {userConnection.id}, status: {userConnection.status.value}, authority: {userConnection.authority.value}")
             
-            # Get a fresh token for this specific connection
-            token = TokenManager().getFreshToken(self.service.interfaceApp, userConnection.id)
+            # Get a fresh token for this connection
+            token = self.services.utils.getFreshConnectionToken(userConnection.id)
             if not token:
-                logger.error(f"Token not found for connection: {userConnection.id}")
+                logger.error(f"Fresh token not found for connection: {userConnection.id}")
                 logger.debug(f"Connection details: {userConnection}")
                 return None
             
-            logger.debug(f"Token retrieved for connection {userConnection.id}")
-            
-            # Check if token is expired
-            if hasattr(token, 'expiresAt') and token.expiresAt:
-                current_time = get_utc_timestamp()
-                if current_time > token.expiresAt:
-                    logger.error(f"Token for connection {userConnection.id} is expired (expiresAt: {token.expiresAt}, current: {current_time})")
-                    return None
+            logger.debug(f"Fresh token retrieved for connection {userConnection.id}")
             
             # Check if connection is active
             if userConnection.status.value != "active":
@@ -435,7 +423,7 @@ class MethodOutlook(MethodBase):
                             "authority": "microsoft",
                             "reference": connectionReference
                         },
-                        "timestamp": get_utc_timestamp()
+                        "timestamp": self.services.utils.getUtcTimestamp()
                     },
                     "mimeType": "application/json"
                 }]
@@ -492,7 +480,7 @@ class MethodOutlook(MethodBase):
             
             # Get the composed email document
 
-            composed_email_docs = self.service.getChatDocumentsFromDocumentList([composed_email_ref])
+            composed_email_docs = self.services.workflow.getChatDocumentsFromDocumentList([composed_email_ref])
             if not composed_email_docs or len(composed_email_docs) == 0:
                 logger.error(f"Could not find composed email document: {composed_email_ref}")
                 return ActionResult.isFailure(error=f"Could not find composed email document: {composed_email_ref}")
@@ -517,7 +505,7 @@ class MethodOutlook(MethodBase):
                 # Read the actual file content from the database
                 try:
                     # Use the correct service interface to read file data
-                    file_content = self.service.getFileData(file_id)
+                    file_content = self.services.workflow.getFileData(file_id)
                     if not file_content:
                         logger.error(f"Failed to read file content for fileId: {file_id}")
                         return ActionResult.isFailure(error="Failed to read composed email file content")
@@ -645,7 +633,7 @@ class MethodOutlook(MethodBase):
         
                         
                         # Get attachment document from service center
-                        attachment_docs = self.service.getChatDocumentsFromDocumentList([attachment_ref])
+                        attachment_docs = self.services.workflow.getChatDocumentsFromDocumentList([attachment_ref])
                         if attachment_docs:
                             for doc in attachment_docs:
                                 # Get the actual file content using fileId
@@ -653,7 +641,7 @@ class MethodOutlook(MethodBase):
                                 if file_id:
                                     try:
                                         # Read the actual file content
-                                        file_content = self.service.getFileData(file_id)
+                                        file_content = self.services.workflow.getFileData(file_id)
                                         if file_content:
                                             # Convert to base64 for Graph API
                                             if isinstance(file_content, bytes):
@@ -721,7 +709,7 @@ class MethodOutlook(MethodBase):
                                 "mailbox": connection.get('userEmail', 'Unknown'),
                                 "subject": subject,
                                 "recipients": to,
-                                "timestamp": get_utc_timestamp()
+                                "timestamp": self.services.utils.getUtcTimestamp()
                             },
                             "mimeType": "application/json"
                         }]
@@ -758,7 +746,7 @@ class MethodOutlook(MethodBase):
                             "authority": "microsoft",
                             "reference": connectionReference
                         },
-                        "timestamp": get_utc_timestamp()
+                        "timestamp": self.services.utils.getUtcTimestamp()
                     },
                     "mimeType": "application/json"
                 }]
@@ -964,7 +952,7 @@ class MethodOutlook(MethodBase):
                             "authority": "microsoft",
                             "reference": connectionReference
                         },
-                        "timestamp": get_utc_timestamp()
+                        "timestamp": self.services.utils.getUtcTimestamp()
                     },
                     "mimeType": "application/json"
                 }]
@@ -1085,7 +1073,7 @@ class MethodOutlook(MethodBase):
                             "authority": "microsoft",
                             "reference": connectionReference
                         },
-                        "timestamp": get_utc_timestamp()
+                        "timestamp": self.services.utils.getUtcTimestamp()
                     },
                     "mimeType": "application/json"
                 }]
@@ -1193,7 +1181,7 @@ class MethodOutlook(MethodBase):
                             "authority": "microsoft",
                             "reference": connectionReference
                         },
-                        "timestamp": get_utc_timestamp()
+                        "timestamp": self.services.utils.getUtcTimestamp()
                     },
                     "mimeType": "application/json"
                 }]
@@ -1332,7 +1320,7 @@ class MethodOutlook(MethodBase):
                             "authority": "microsoft",
                             "reference": connectionReference
                         },
-                        "timestamp": get_utc_timestamp()
+                        "timestamp": self.services.utils.getUtcTimestamp()
                     },
                     "mimeType": "application/json"
                 }]
@@ -1405,7 +1393,7 @@ class MethodOutlook(MethodBase):
 
                 try:
                     # Get document content from service center
-                    docs = self.service.getChatDocumentsFromDocumentList(documentList)
+                    docs = self.services.workflow.getChatDocumentsFromDocumentList(documentList)
                     if docs:
                         for doc in docs:
                             composition_documents.append(doc)
@@ -1415,7 +1403,7 @@ class MethodOutlook(MethodBase):
                                 if hasattr(doc, 'fileId') and doc.fileId:
                                     # Use the document content extraction service instead of raw file reading
                                     try:
-                                        extracted_content = await self.service.extractContentFromDocument(
+                                        extracted_content = await self.services.documentExtraction.extractContentFromDocument(
                                             prompt="Extract readable text content for email composition", 
                                             document=doc
                                         )
@@ -1453,7 +1441,7 @@ class MethodOutlook(MethodBase):
 
                 try:
                     # Get attachment documents from service center
-                    attachment_docs = self.service.getChatDocumentsFromDocumentList(attachmentDocumentList)
+                    attachment_docs = self.services.workflow.getChatDocumentsFromDocumentList(attachmentDocumentList)
                     if attachment_docs:
                         for doc in attachment_docs:
                             # Add to attachments list
@@ -1520,7 +1508,7 @@ class MethodOutlook(MethodBase):
                     if composition_documents:
                         for d in composition_documents:
                             try:
-                                data = self.service.getFileData(d.fileId) if hasattr(d, 'fileId') else None
+                                data = self.services.workflow.getFileData(d.fileId) if hasattr(d, 'fileId') else None
                                 if data:
                                     documents.append(ChatDocument(fileData=data, fileName=d.fileName, mimeType=d.mimeType))
                             except Exception:
@@ -1577,7 +1565,7 @@ class MethodOutlook(MethodBase):
                     "context": context,
                     "recipient": recipient,
                     "tone": tone,
-                    "timestamp": get_utc_timestamp(),
+                    "timestamp": self.services.utils.getUtcTimestamp(),
                     "usage": "This document contains a composed email that can be used with the sendEmail action",
                     "compositionDocuments": len(composition_documents),
                     "attachmentDocuments": len(unique_attachments),

modules/workflows/methods/methodSharepoint.py

@@ -15,15 +15,14 @@ import asyncio
 
 from modules.workflows.methods.methodBase import MethodBase, action
 from modules.datamodels.datamodelWorkflow import ActionResult
-from modules.shared.timezoneUtils import get_utc_timestamp
 
 logger = logging.getLogger(__name__)
 
 class MethodSharepoint(MethodBase):
     """SharePoint operations methods."""
     
-    def __init__(self, service):
-        super().__init__(service)
+    def __init__(self, services):
+        super().__init__(services)
         self.name = "sharepoint"
         self.description = "SharePoint operations methods"
     
@@ -49,7 +48,7 @@ class MethodSharepoint(MethodBase):
                 return None
             
             # Configure SharePoint service with the UserConnection
-            if not self.service.sharepoint.setAccessToken(userConnection, self.service.interfaceApp):
+            if not self.service.sharepoint.setAccessTokenFromConnection(userConnection):
                 logger.warning(f"Failed to configure SharePoint service with connection {userConnection.id}")
                 return None
             
@@ -785,7 +784,7 @@ class MethodSharepoint(MethodBase):
                     "totalResults": len(found_documents),
                     "maxResults": maxResults,
                     "foundDocuments": found_documents,
-                    "timestamp": get_utc_timestamp()
+                    "timestamp": self.services.utils.getUtcTimestamp()
                 }
                 
             except Exception as e:
@@ -844,13 +843,13 @@ class MethodSharepoint(MethodBase):
                 try:
                     import json
                     # Resolve the reference label to get the actual document list
-                    document_list = self.service.getChatDocumentsFromDocumentList([pathObject])
+                    document_list = self.services.workflow.getChatDocumentsFromDocumentList([pathObject])
                     if not document_list or len(document_list) == 0:
                         return ActionResult.isFailure(error=f"No document list found for reference: {pathObject}")
                     
                     # Get the first document's content (which should be the JSON)
                     first_document = document_list[0]
-                    file_data = self.service.getFileData(first_document.fileId)
+                    file_data = self.services.workflow.getFileData(first_document.fileId)
                     if not file_data:
                         return ActionResult.isFailure(error=f"No file data found for document: {pathObject}")
                     
@@ -878,7 +877,7 @@ class MethodSharepoint(MethodBase):
             
             # Get documents from reference - ensure documentList is a list, not a string
             # documentList is already normalized above
-            chatDocuments = self.service.getChatDocumentsFromDocumentList(documentList)
+            chatDocuments = self.services.workflow.getChatDocumentsFromDocumentList(documentList)
             
             if not chatDocuments:
                 return ActionResult.isFailure(error="No documents found for the provided reference")
@@ -1063,7 +1062,7 @@ class MethodSharepoint(MethodBase):
                     "authority": "microsoft",
                     "reference": connectionReference
                 },
-                "timestamp": get_utc_timestamp()
+                "timestamp": self.services.utils.getUtcTimestamp()
             }
             
             # Use default JSON format for output
@@ -1120,13 +1119,13 @@ class MethodSharepoint(MethodBase):
                 try:
                     import json
                     # Resolve the reference label to get the actual document list
-                    document_list = self.service.getChatDocumentsFromDocumentList([pathObject])
+                    document_list = self.services.workflow.getChatDocumentsFromDocumentList([pathObject])
                     if not document_list or len(document_list) == 0:
                         return ActionResult.isFailure(error=f"No document list found for reference: {pathObject}")
                     
                     # Get the first document's content (which should be the JSON)
                     first_document = document_list[0]
-                    file_data = self.service.getFileData(first_document.fileId)
+                    file_data = self.services.workflow.getFileData(first_document.fileId)
                     if not file_data:
                         return ActionResult.isFailure(error=f"No file data found for document: {pathObject}")
                     
@@ -1221,7 +1220,7 @@ class MethodSharepoint(MethodBase):
             # Get documents from reference - ensure documentList is a list, not a string
             if isinstance(documentList, str):
                 documentList = [documentList]  # Convert string to list
-            chatDocuments = self.service.getChatDocumentsFromDocumentList(documentList)
+            chatDocuments = self.services.workflow.getChatDocumentsFromDocumentList(documentList)
             if not chatDocuments:
                 return ActionResult.isFailure(error="No documents found for the provided reference")
             
@@ -1311,7 +1310,7 @@ class MethodSharepoint(MethodBase):
             for i, (chatDocument, fileName) in enumerate(zip(chatDocuments, fileNames)):
                 try:
                     fileId = chatDocument.fileId
-                    file_data = self.service.getFileData(fileId)
+                    file_data = self.services.workflow.getFileData(fileId)
                     
                     if not file_data:
                         logger.warning(f"File data not found for fileId: {fileId}")
@@ -1414,7 +1413,7 @@ class MethodSharepoint(MethodBase):
                     "authority": "microsoft",
                     "reference": connectionReference
                 },
-                "timestamp": get_utc_timestamp()
+                "timestamp": self.services.utils.getUtcTimestamp()
             }
             
             # Use default JSON format for output
@@ -1471,14 +1470,14 @@ class MethodSharepoint(MethodBase):
                 try:
                     import json
                     # Resolve the reference label to get the actual document list
-                    document_list = self.service.getChatDocumentsFromDocumentList([pathObject])
+                    document_list = self.services.workflow.getChatDocumentsFromDocumentList([pathObject])
                     if not document_list or len(document_list) == 0:
                         return ActionResult.isFailure(error=f"No document list found for reference: {pathObject}")
                     
                     # Get the first document's content (which should be the JSON)
                     first_document = document_list[0]
                     logger.info(f"Document fileId: {first_document.fileId}, fileName: {first_document.fileName}")
-                    file_data = self.service.getFileData(first_document.fileId)
+                    file_data = self.services.workflow.getFileData(first_document.fileId)
                     if not file_data:
                         return ActionResult.isFailure(error=f"No file data found for document: {pathObject} (fileId: {first_document.fileId})")
                     logger.info(f"File data length: {len(file_data) if file_data else 0}")
@@ -1807,7 +1806,7 @@ class MethodSharepoint(MethodBase):
                 "includeSubfolders": includeSubfolders,
                 "sitesSearched": len(sites),
                 "listResults": list_results,
-                "timestamp": get_utc_timestamp()
+                "timestamp": self.services.utils.getUtcTimestamp()
             }
             
             # Use default JSON format for output

modules/workflows/methods/methodWeb.py

@@ -18,13 +18,10 @@ logger = logging.getLogger(__name__)
 class MethodWeb(MethodBase):
     """Web method implementation for web operations."""
 
-    def __init__(self, serviceCenter: Any):
-        super().__init__(serviceCenter)
+    def __init__(self, services):
+        super().__init__(services)
         self.name = "web"
         self.description = "Web search, crawling, and scraping operations using Tavily"
-        # Centralized services interface (for AI)
-        from modules.services import getInterface as getServices
-        self.services = getServices(self.service.user, self.service.workflow)
 
     @action
     async def search(self, parameters: Dict[str, Any]) -> ActionResult:
@@ -143,7 +140,7 @@ class MethodWeb(MethodBase):
                 )
 
             # Resolve document list reference to ChatDocument objects
-            chat_documents = self.service.getChatDocumentsFromDocumentList(document_list)
+            chat_documents = self.services.workflow.getChatDocumentsFromDocumentList(document_list)
 
             if not chat_documents:
                 return ActionResult(
@@ -160,7 +157,7 @@ class MethodWeb(MethodBase):
                 logger.info(f"Processing document {i+1}/{len(chat_documents)}: {doc.fileName}")
                 
                 # Get file data using the service center
-                file_data = self.service.getFileData(doc.fileId)
+                file_data = self.services.workflow.getFileData(doc.fileId)
                 if not file_data:
                     logger.warning(f"Could not retrieve file data for document: {doc.fileName}")
                     continue

modules/workflows/processing/handlingTasks.py

@@ -1,17 +1,29 @@
 # handlingTasks.py
 # Refactored for clarity and consolidation
 
+import uuid
 import asyncio
 import logging
 import json
 import time
 from typing import Dict, Any, Optional, List, Union
 from datetime import datetime, UTC
-from modules.datamodels.datamodelWorkflow import (TaskStep, TaskContext, ReviewResult, TaskPlan, WorkflowResult, TaskResult, ReviewContext)
-from modules.datamodels.datamodelWorkflow import TaskStatus, ActionResult
-from modules.datamodels.datamodelChat import ChatWorkflow, ChatMessage, ChatDocument
-from modules.interfaces.interfaceAppObjects import getInterface as getAppObjects
-from modules.shared.timezoneUtils import get_utc_timestamp
+from modules.datamodels.datamodelWorkflow import (
+    TaskStep, 
+    TaskContext, 
+    ReviewResult, 
+    TaskPlan, 
+    TaskResult, 
+    ReviewContext,
+    TaskStatus, 
+    ActionResult
+)
+from modules.datamodels.datamodelChat import (
+    WorkflowResult,
+    ChatWorkflow, 
+    ChatMessage, 
+    ChatDocument
+)
 from modules.workflows.processing.executionState import TaskExecutionState
 from modules.workflows.processing.promptFactory import (
     createTaskPlanningPrompt, 
@@ -23,7 +35,8 @@ from modules.workflows.processing.promptFactory import (
 )
 from modules.services.serviceDocument.mainServiceDocumentGeneration import DocumentGenerationService
 from modules.workflows.processing.promptFactory import methods
-import uuid
+from modules.workflows.processing.executionState import should_continue
+from modules.datamodels.datamodelAi import AiCallOptions, OperationType, ProcessingMode, Priority
 
 logger = logging.getLogger(__name__)
 
@@ -73,7 +86,6 @@ class HandlingTasks:
             
             # Create proper context object for task planning
             # For task planning, we need to create a minimal TaskStep since TaskContext requires it
-            from modules.datamodels.datamodelWorkflow import TaskStep
             planning_task_step = TaskStep(
                 id="planning",
                 objective=userInput,
@@ -114,19 +126,21 @@ class HandlingTasks:
             self.writeTraceLog("Task Plan Prompt", task_planning_prompt)
             
             # Centralized AI call: Task planning (quality, detailed)
-            prompt = await self.services.ai.callAi(
+            
+            options = AiCallOptions(
+                operationType=OperationType.GENERATE_PLAN,
+                priority=Priority.QUALITY,
+                compressPrompt=False,
+                compressContext=False,
+                processingMode=ProcessingMode.DETAILED,
+                maxCost=0.10,
+                maxProcessingTime=30
+            )
+            
+            prompt = await self.services.ai.callAiText(
                 prompt=task_planning_prompt,
                 documents=None,
-                options={
-                    "process_type": "text",
-                    "operation_type": "generate_plan",
-                    "priority": "quality",
-                    "compress_prompt": False,
-                    "compress_documents": False,
-                    "processing_mode": "detailed",
-                    "max_cost": 0.10,
-                    "max_processing_time": 30
-                }
+                options=options
             )
             
             # Check if AI response is valid
@@ -252,7 +266,7 @@ class HandlingTasks:
                 "message": task_summary,
                 "status": "step",
                 "sequenceNr": len(workflow.messages) + 1,
-                "publishedAt": get_utc_timestamp(),
+                "publishedAt": self.services.utils.getUtcTimestamp(),
                 "documentsLabel": "task_plan",
                 "documents": [],
                 # Add workflow context fields - use current workflow round instead of hardcoded 1
@@ -374,19 +388,20 @@ class HandlingTasks:
             self.writeTraceLog("Action Plan Prompt", action_prompt)
             
             # Centralized AI call: Action planning (quality, detailed)
-            prompt = await self.services.ai.callAi(
+            options = AiCallOptions(
+                operationType=OperationType.GENERATE_PLAN,
+                priority=Priority.QUALITY,
+                compressPrompt=False,
+                compressContext=False,
+                processingMode=ProcessingMode.DETAILED,
+                maxCost=0.10,
+                maxProcessingTime=30
+            )
+            
+            prompt = await self.services.ai.callAiText(
                 prompt=action_prompt,
                 documents=None,
-                options={
-                    "process_type": "text",
-                    "operation_type": "generate_plan",
-                    "priority": "quality",
-                    "compress_prompt": False,
-                    "compress_documents": False,
-                    "processing_mode": "detailed",
-                    "max_cost": 0.10,
-                    "max_processing_time": 30
-                }
+                options=options
             )
             
             # Check if AI response is valid
@@ -466,19 +481,20 @@ class HandlingTasks:
         prompt = createActionSelectionPrompt(context, self.service)
         self.writeTraceLog("React Plan Selection Prompt", prompt)
         # Centralized AI call for plan selection (use plan generation quality)
-        response = await self.services.ai.callAi(
+        options = AiCallOptions(
+            operationType=OperationType.GENERATE_PLAN,
+            priority=Priority.QUALITY,
+            compressPrompt=False,
+            compressContext=False,
+            processingMode=ProcessingMode.DETAILED,
+            maxCost=0.10,
+            maxProcessingTime=30
+        )
+        
+        response = await self.services.ai.callAiText(
             prompt=prompt,
             documents=None,
-            options={
-                "process_type": "text",
-                "operation_type": "generate_plan",
-                "priority": "quality",
-                "compress_prompt": False,
-                "compress_documents": False,
-                "processing_mode": "detailed",
-                "max_cost": 0.10,
-                "max_processing_time": 30
-            }
+            options=options
         )
         self.writeTraceLog("React Plan Selection Response", response)
         json_start = response.find('{') if response else -1
@@ -496,19 +512,20 @@ class HandlingTasks:
         params_prompt = createActionParameterPrompt(context, action, self.service)
         self.writeTraceLog("React Parameters Prompt", params_prompt)
         # Centralized AI call for parameter suggestion (balanced analysis)
-        params_resp = await self.services.ai.callAi(
+        options = AiCallOptions(
+            operationType=OperationType.ANALYSE_CONTENT,
+            priority=Priority.BALANCED,
+            compressPrompt=True,
+            compressContext=False,
+            processingMode=ProcessingMode.ADVANCED,
+            maxCost=0.05,
+            maxProcessingTime=30
+        )
+        
+        params_resp = await self.services.ai.callAiText(
             prompt=params_prompt,
             documents=None,
-            options={
-                "process_type": "text",
-                "operation_type": "analyse_content",
-                "priority": "balanced",
-                "compress_prompt": True,
-                "compress_documents": False,
-                "processing_mode": "advanced",
-                "max_cost": 0.05,
-                "max_processing_time": 30
-            }
+            options=options
         )
         self.writeTraceLog("React Parameters Response", params_resp)
         js = params_resp[params_resp.find('{'):params_resp.rfind('}')+1] if params_resp else '{}'
@@ -564,19 +581,20 @@ class HandlingTasks:
         prompt = createRefinementPrompt(context, observation)
         self.writeTraceLog("React Refinement Prompt", prompt)
         # Centralized AI call for refinement decision (balanced analysis)
-        resp = await self.services.ai.callAi(
+        options = AiCallOptions(
+            operationType=OperationType.ANALYSE_CONTENT,
+            priority=Priority.BALANCED,
+            compressPrompt=True,
+            compressContext=False,
+            processingMode=ProcessingMode.ADVANCED,
+            maxCost=0.05,
+            maxProcessingTime=30
+        )
+        
+        resp = await self.services.ai.callAiText(
             prompt=prompt,
             documents=None,
-            options={
-                "process_type": "text",
-                "operation_type": "analyse_content",
-                "priority": "balanced",
-                "compress_prompt": True,
-                "compress_documents": False,
-                "processing_mode": "advanced",
-                "max_cost": 0.05,
-                "max_processing_time": 30
-            }
+            options=options
         )
         self.writeTraceLog("React Refinement Response", resp)
         js = resp[resp.find('{'):resp.rfind('}')+1] if resp else '{}'
@@ -613,7 +631,7 @@ class HandlingTasks:
                 "message": f"🚀 **Task {task_progress}**",
                 "status": "step",
                 "sequenceNr": len(workflow.messages) + 1,
-                "publishedAt": get_utc_timestamp(),
+                "publishedAt": self.services.utils.getUtcTimestamp(),
                 "documentsLabel": f"task_{task_index}_start",
                 "documents": [],
                 # Add workflow context fields
@@ -671,7 +689,7 @@ class HandlingTasks:
                         "message": f"🔁 Step {step}/{state.max_steps}: {selection.get('action',{}).get('method','')}.{selection.get('action',{}).get('name','')} → {'✅' if result.success else '❌'}",
                         "status": "step",
                         "sequenceNr": len(workflow.messages) + 1,
-                        "publishedAt": get_utc_timestamp(),
+                        "publishedAt": self.services.utils.getUtcTimestamp(),
                         "documentsLabel": observation.get('resultLabel'),
                         "documents": [],
                         "roundNumber": workflow.currentRound,
@@ -684,7 +702,6 @@ class HandlingTasks:
                     logger.error(f"React step {step} error: {e}")
                     break
 
-                from modules.workflows.processing.executionState import should_continue
                 if not should_continue(observation, last_review_dict, step, state.max_steps):
                     break
                 step += 1
@@ -759,7 +776,7 @@ class HandlingTasks:
                     "message": f"⚡ **Action {action_number}/{total_actions}** (Method {action.execMethod}.{action.execAction})",
                     "status": "step",
                     "sequenceNr": len(workflow.messages) + 1,
-                    "publishedAt": get_utc_timestamp(),
+                    "publishedAt": self.services.utils.getUtcTimestamp(),
                     "documentsLabel": f"action_{action_number}_start",
                     "documents": [],
                     # Add action progress status
@@ -820,7 +837,7 @@ class HandlingTasks:
                     "message": completion_message,
                     "status": "step",
                     "sequenceNr": len(workflow.messages) + 1,
-                    "publishedAt": get_utc_timestamp(),
+                    "publishedAt": self.services.utils.getUtcTimestamp(),
                     "documentsLabel": f"task_{task_index}_completion",
                     "documents": [],
                     # Add workflow context fields
@@ -914,7 +931,7 @@ class HandlingTasks:
                     "message": f"🔄 **Task {task_index}** needs retry: {review_result.improvements}",
                     "status": "step",
                     "sequenceNr": len(workflow.messages) + 1,
-                    "publishedAt": get_utc_timestamp(),
+                    "publishedAt": self.services.utils.getUtcTimestamp(),
                     "documentsLabel": f"task_{task_index}_retry",
                     "documents": [],
                     "roundNumber": workflow.currentRound,
@@ -961,7 +978,7 @@ class HandlingTasks:
                     "message": error_message,
                     "status": "step",
                     "sequenceNr": len(workflow.messages) + 1,
-                    "publishedAt": get_utc_timestamp(),
+                    "publishedAt": self.services.utils.getUtcTimestamp(),
                     "actionId": None,
                     "actionMethod": "task",
                     "actionName": "task_retry",
@@ -1015,7 +1032,7 @@ class HandlingTasks:
             "message": error_message,
             "status": "step",
             "sequenceNr": len(workflow.messages) + 1,
-            "publishedAt": get_utc_timestamp(),
+            "publishedAt": self.services.utils.getUtcTimestamp(),
             "actionId": None,
             "actionMethod": "task",
             "actionName": "task_failure",
@@ -1095,19 +1112,20 @@ class HandlingTasks:
             self.writeTraceLog("Result Review Prompt", prompt)
             
             # Centralized AI call: Result validation (balanced analysis)
-            response = await self.services.ai.callAi(
+            options = AiCallOptions(
+                operationType=OperationType.ANALYSE_CONTENT,
+                priority=Priority.BALANCED,
+                compressPrompt=True,
+                compressContext=False,
+                processingMode=ProcessingMode.ADVANCED,
+                maxCost=0.05,
+                maxProcessingTime=30
+            )
+            
+            response = await self.services.ai.callAiText(
                 prompt=prompt,
                 documents=None,
-                options={
-                    "process_type": "text",
-                    "operation_type": "analyse_content",
-                    "priority": "balanced",
-                    "compress_prompt": True,
-                    "compress_documents": False,
-                    "processing_mode": "advanced",
-                    "max_cost": 0.05,
-                    "max_processing_time": 30
-                }
+                options=options
             )
             
             # Log result review response received
@@ -1220,7 +1238,7 @@ class HandlingTasks:
                 'actions': [action.to_dict() for action in task_actions],
                 'review_result': review_result,
                 'workflow_id': workflow.id,
-                'handover_time': get_utc_timestamp()
+                'handover_time': self.services.utils.getUtcTimestamp()
             }
             logger.info(f"Prepared handover for task {task_step.id} in workflow {workflow.id}")
             return handover_data
@@ -1269,7 +1287,7 @@ class HandlingTasks:
                 retryCount=createdAction.get("retryCount", 0),
                 retryMax=createdAction.get("retryMax", 3),
                 processingTime=createdAction.get("processingTime"),
-                timestamp=float(createdAction.get("timestamp", get_utc_timestamp())),
+                timestamp=float(createdAction.get("timestamp", self.services.utils.getUtcTimestamp())),
                 result=createdAction.get("result"),
                 resultDocuments=createdAction.get("resultDocuments", []),
                 userMessage=createdAction.get("userMessage")
@@ -1462,7 +1480,7 @@ class HandlingTasks:
                 "message": message_text,
                 "status": "step",
                 "sequenceNr": len(workflow.messages) + 1,
-                "publishedAt": get_utc_timestamp(),
+                "publishedAt": self.services.utils.getUtcTimestamp(),
                 "actionId": action.id,
                 "actionMethod": action.execMethod,
                 "actionName": action.execAction,
@@ -1784,14 +1802,13 @@ class HandlingTasks:
             import logging
             import os
             from datetime import datetime, UTC
-            from modules.shared.configuration import APP_CONFIG
             
             # Only write if logger is in debug mode
             if logger.level > logging.DEBUG:
                 return
                 
             # Get log directory from configuration
-            logDir = APP_CONFIG.get("APP_LOGGING_LOG_DIR", "./")
+            logDir = self.services.utils.configGet("APP_LOGGING_LOG_DIR", "./")
             if not os.path.isabs(logDir):
                 # If relative path, make it relative to the gateway directory
                 gatewayDir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
@@ -1804,7 +1821,7 @@ class HandlingTasks:
             trace_file = os.path.join(logDir, "log_trace.log")
             
             # Format the trace entry
-            timestamp = datetime.now(UTC).strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
+            timestamp = datetime.fromtimestamp(self.services.utils.getUtcTimestamp(), UTC).strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
             trace_entry = f"[{timestamp}] {contextText}\n"
             
             # Add data if provided
@@ -1830,10 +1847,9 @@ class HandlingTasks:
         try:
             import logging
             import os
-            from modules.shared.configuration import APP_CONFIG
             
             # Get log directory from configuration
-            logDir = APP_CONFIG.get("APP_LOGGING_LOG_DIR", "./")
+            logDir = self.services.utils.configGet("APP_LOGGING_LOG_DIR", "./")
             if not os.path.isabs(logDir):
                 # If relative path, make it relative to the gateway directory
                 gatewayDir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

modules/workflows/processing/promptFactory.py

@@ -9,7 +9,7 @@ import inspect
 from typing import Any, Dict, List
 from modules.datamodels.datamodelWorkflow import TaskContext, ReviewContext, DocumentExchange
 from modules.datamodels.datamodelChat import ChatDocument
-from modules.services.serviceDocument.documentUtility import getFileExtension
+from modules.services.serviceDocument.subDocumentUtility import getFileExtension
 from modules.workflows.methods.methodBase import MethodBase
 
 # Set up logger
@@ -212,8 +212,8 @@ def _getAvailableDocuments(workflow) -> str:
 def _getConnectionReferenceList(service) -> List[str]:
     """Get list of all UserConnection objects as references with enhanced state information"""
     connections = []
-    # Get user connections through AppObjects interface
-    user_connections = service.interfaceApp.getUserConnections(service.user.id)
+    # Get user connections
+    user_connections = service.interfaceDbApp.getUserConnections(service.user.id)
 
     refreshed_count = 0
     for conn in user_connections:

modules/workflows/workflowManager.py

@@ -4,16 +4,16 @@ from datetime import datetime, UTC
 import uuid
 import asyncio
 
-from modules.interfaces.interfaceAppObjects import User
-
-from modules.datamodels.datamodelWorkflow import UserInputRequest
-from modules.datamodels.datamodelChat import ChatMessage, ChatWorkflow, ChatDocument
-from modules.datamodels.datamodelWorkflow import TaskItem, TaskStatus
-from modules.interfaces.interfaceChatObjects import ChatObjects
+from modules.datamodels.datamodelChat import (
+    UserInputRequest,
+    ChatMessage, 
+    ChatWorkflow, 
+    ChatDocument,
+    WorkflowResult
+)
+from modules.datamodels.datamodelWorkflow import TaskItem, TaskStatus, TaskContext
 from modules.workflows.processing.handlingTasks import HandlingTasks, WorkflowStoppedException
-from modules.datamodels.datamodelWorkflow import WorkflowResult
-from modules.shared.timezoneUtils import get_utc_timestamp
-import uuid
+
 
 logger = logging.getLogger(__name__)
 
@@ -31,7 +31,7 @@ class WorkflowManager:
         try:
             # Debug log to check workflowMode parameter
             logger.info(f"WorkflowManager received workflowMode: {workflowMode}")
-            currentTime = get_utc_timestamp()
+            currentTime = self.services.utils.getUtcTimestamp()
 
             if workflowId:
                 workflow = self.services.workflow.getWorkflow(workflowId)
@@ -127,7 +127,7 @@ class WorkflowManager:
                 raise ValueError(f"Workflow {workflowId} not found")
 
             workflow.status = "stopped"
-            workflow.lastActivity = get_utc_timestamp()
+            workflow.lastActivity = self.services.utils.getUtcTimestamp()
             self.services.workflow.updateWorkflow(workflowId, {
                 "status": "stopped",
                 "lastActivity": workflow.lastActivity
@@ -181,7 +181,7 @@ class WorkflowManager:
                 "message": userInput.prompt,
                 "status": "first",
                 "sequenceNr": 1,
-                "publishedAt": get_utc_timestamp(),
+                "publishedAt": self.services.utils.getUtcTimestamp(),
                 "documentsLabel": context_label,
                 "documents": [],
                 # Add workflow context fields
@@ -241,7 +241,6 @@ class WorkflowManager:
             logger.info(f"Task {current_task_index}/{total_tasks}: {task_step.objective}")
 
             # Build TaskContext (mode-specific behavior is inside HandlingTasks)
-            from modules.datamodels.datamodelWorkflow import TaskContext
             task_context = TaskContext(
                 task_step=task_step,
                 workflow=workflow,
@@ -298,7 +297,7 @@ class WorkflowManager:
                     "message": "🛑 Workflow stopped by user",
                     "status": "last",
                     "sequenceNr": len(workflow.messages) + 1,
-                    "publishedAt": get_utc_timestamp(),
+                    "publishedAt": self.services.utils.getUtcTimestamp(),
                     "documentsLabel": "workflow_stopped",
                     "documents": [],
                     # Add workflow context fields
@@ -315,7 +314,7 @@ class WorkflowManager:
                 
                 # Update workflow status to stopped
                 workflow.status = "stopped"
-                workflow.lastActivity = get_utc_timestamp()
+                workflow.lastActivity = self.services.utils.getUtcTimestamp()
                 self.services.workflow.updateWorkflow(workflow.id, {
                     "status": "stopped",
                     "lastActivity": workflow.lastActivity
@@ -330,7 +329,7 @@ class WorkflowManager:
                     "message": "🛑 Workflow stopped by user",
                     "status": "last",
                     "sequenceNr": len(workflow.messages) + 1,
-                    "publishedAt": get_utc_timestamp(),
+                    "publishedAt": self.services.utils.getUtcTimestamp(),
                     "documentsLabel": "workflow_stopped",
                     "documents": [],
                     # Add workflow context fields
@@ -347,7 +346,7 @@ class WorkflowManager:
                 
                 # Update workflow status to stopped
                 workflow.status = "stopped"
-                workflow.lastActivity = get_utc_timestamp()
+                workflow.lastActivity = self.services.utils.getUtcTimestamp()
                 self.services.workflow.updateWorkflow(workflow.id, {
                     "status": "stopped",
                     "lastActivity": workflow.lastActivity,
@@ -372,7 +371,7 @@ class WorkflowManager:
                     "message": f"Workflow failed: {workflow_result.error or 'Unknown error'}",
                     "status": "last",
                     "sequenceNr": len(workflow.messages) + 1,
-                    "publishedAt": get_utc_timestamp(),
+                    "publishedAt": self.services.utils.getUtcTimestamp(),
                     "documentsLabel": "workflow_failure",
                     "documents": [],
                     # Add workflow context fields
@@ -389,7 +388,7 @@ class WorkflowManager:
                 
                 # Update workflow status to failed
                 workflow.status = "failed"
-                workflow.lastActivity = get_utc_timestamp()
+                workflow.lastActivity = self.services.utils.getUtcTimestamp()
                 self.services.workflow.updateWorkflow(workflow.id, {
                     "status": "failed",
                     "lastActivity": workflow.lastActivity,
@@ -419,7 +418,7 @@ class WorkflowManager:
                 "message": f"Error processing workflow results: {str(e)}",
                 "status": "last",
                 "sequenceNr": len(workflow.messages) + 1,
-                "publishedAt": get_utc_timestamp(),
+                "publishedAt": self.services.utils.getUtcTimestamp(),
                 "documentsLabel": "workflow_error",
                 "documents": [],
                 # Add workflow context fields
@@ -436,7 +435,7 @@ class WorkflowManager:
             
             # Update workflow status to failed
             workflow.status = "failed"
-            workflow.lastActivity = get_utc_timestamp()
+            workflow.lastActivity = self.services.utils.getUtcTimestamp()
             self.services.workflow.updateWorkflow(workflow.id, {
                 "status": "failed",
                 "lastActivity": workflow.lastActivity,
@@ -462,7 +461,7 @@ class WorkflowManager:
                 "message": feedback,
                 "status": "last",
                 "sequenceNr": len(workflow.messages) + 1,
-                "publishedAt": get_utc_timestamp(),
+                "publishedAt": self.services.utils.getUtcTimestamp(),
                 "documentsLabel": "workflow_feedback",
                 "documents": [],
                 # Add workflow context fields
@@ -481,7 +480,7 @@ class WorkflowManager:
             
             # Update workflow status to completed
             workflow.status = "completed"
-            workflow.lastActivity = get_utc_timestamp()
+            workflow.lastActivity = self.services.utils.getUtcTimestamp()
             
             # Update workflow in database
             self.services.workflow.updateWorkflow(workflow.id, {