Changelog update for 2.3.7

- Added Specialist basics for handling phases and automatically transferring data between state and output
- Added QR-code generation for Magic Links

README.md.k8s-logging

@@ -0,0 +1,67 @@
+# Kubernetes Logging Upgrade
+
+## Overzicht
+Deze instructies beschrijven hoe je alle services moet bijwerken om de nieuwe logging configuratie te gebruiken die zowel compatibel is met traditionele bestandsgebaseerde logging (voor ontwikkeling/test) als met Kubernetes (voor productie).
+
+## Stappen voor elke service
+
+Pas de volgende wijzigingen toe in elk van de volgende services:
+
+- eveai_app
+- eveai_workers
+- eveai_api
+- eveai_chat_client
+- eveai_chat_workers
+- eveai_beat
+- eveai_entitlements
+
+### 1. Update de imports
+
+Verander:
+```python
+from config.logging_config import LOGGING
+```
+
+Naar:
+```python
+from config.logging_config import configure_logging
+```
+
+### 2. Update de logging configuratie
+
+Verander:
+```python
+logging.config.dictConfig(LOGGING)
+```
+
+Naar:
+```python
+configure_logging()
+```
+
+## Dockerfile Aanpassingen
+
+Voeg de volgende regels toe aan je Dockerfile voor elke service om de Kubernetes-specifieke logging afhankelijkheden te installeren (alleen voor productie):
+
+```dockerfile
+# Alleen voor productie (Kubernetes) builds
+COPY requirements-k8s.txt /app/
+RUN if [ "$ENVIRONMENT" = "production" ]; then pip install -r requirements-k8s.txt; fi
+```
+
+## Kubernetes Deployment
+
+Zorg ervoor dat je Kubernetes deployment manifests de volgende omgevingsvariabele bevatten:
+
+```yaml
+env:
+  - name: FLASK_ENV
+    value: "production"
+```
+
+## Voordelen
+
+1. De code detecteert automatisch of deze in Kubernetes draait
+2. In ontwikkeling/test omgevingen blijft alles naar bestanden schrijven
+3. In Kubernetes gaan logs naar stdout/stderr in JSON-formaat
+4. Geen wijzigingen nodig in bestaande logger code in de applicatie

common/utils/eveai_exceptions.py

@@ -248,3 +248,14 @@ class EveAIPendingLicensePeriod(EveAIException):
         message = f"Basic Fee Payment has not been received yet. Please ensure payment has been made, and please wait for payment to be processed."
         super().__init__(message, status_code, payload)
 
+
+class EveAISpecialistExecutionError(EveAIException):
+    """Raised when an error occurs during specialist execution"""
+
+    def __init__(self, tenant_id, specialist_id, session_id, details, status_code=400, payload=None):
+        message = (f"Error during specialist {specialist_id} execution \n"
+                   f"with Session ID {session_id} \n"
+                   f"for Tenant {tenant_id}. \n"
+                   f"Details: {details} \n"
+                   f"The System Administrator has been notified. Please try again later.")
+        super().__init__(message, status_code, payload)

config/agents/traicie/TRAICIE_RECRUITER_AGENT/1.0.0.yaml

@@ -1,5 +1,5 @@
 version: "1.0.0"
-name: "Traicie HR BP  "
+name: "Traicie Recruiter"
 role: >
   You are an Expert Recruiter working for {tenant_name}
   {custom_role}
@@ -16,10 +16,10 @@ backstory: >
   AI-driven sourcing. You’re more than a recruiter—you’re a trusted advisor, a brand ambassador, and a connector of 
   people and purpose.
   {custom_backstory}
-full_model_name: "mistral.mistral-medium-latest"
+full_model_name: "mistral.magistral-medium-latest"
 temperature: 0.3
 metadata:
   author: "Josako"
-  date_added: "2025-05-21"
-  description: "HR BP Agent."
+  date_added: "2025-06-18"
+  description: "Traicie Recruiter Agent"
   changes: "Initial version"

config/config.py

@@ -12,10 +12,7 @@ class Config(object):
     DEBUG = False
     DEVELOPMENT = False
     SECRET_KEY = environ.get('SECRET_KEY')
-    SESSION_COOKIE_SECURE = False
-    SESSION_COOKIE_HTTPONLY = True
     COMPONENT_NAME = environ.get('COMPONENT_NAME')
-    SESSION_KEY_PREFIX = f'{COMPONENT_NAME}_'
 
     # Database Settings
     DB_HOST = environ.get('DB_HOST')
@@ -44,8 +41,6 @@ class Config(object):
     # SECURITY_POST_CHANGE_VIEW = '/admin/login'
     # SECURITY_BLUEPRINT_NAME = 'security_bp'
     SECURITY_PASSWORD_SALT = environ.get('SECURITY_PASSWORD_SALT')
-    REMEMBER_COOKIE_SAMESITE = 'strict'
-    SESSION_COOKIE_SAMESITE = 'Lax'
     SECURITY_CONFIRMABLE = True
     SECURITY_TRACKABLE = True
     SECURITY_PASSWORD_COMPLEXITY_CHECKER = 'zxcvbn'
@@ -56,6 +51,10 @@ class Config(object):
     SECURITY_EMAIL_SUBJECT_PASSWORD_NOTICE = 'Your Password Has Been Reset'
     SECURITY_EMAIL_PLAINTEXT = False
     SECURITY_EMAIL_HTML = True
+    SECURITY_SESSION_PROTECTION = 'basic'  # of 'basic' als 'strong' problemen geeft
+    SECURITY_REMEMBER_TOKEN_VALIDITY = timedelta(minutes=60)  # Zelfde als session lifetime
+    SECURITY_AUTO_LOGIN_AFTER_CONFIRM = True
+    SECURITY_AUTO_LOGIN_AFTER_RESET = True
 
     # Ensure Flask-Security-Too is handling CSRF tokens when behind a proxy
     SECURITY_CSRF_PROTECT_MECHANISMS = ['session']
@@ -189,6 +188,15 @@ class Config(object):
     PERMANENT_SESSION_LIFETIME = timedelta(minutes=60)
     SESSION_REFRESH_EACH_REQUEST = True
 
+    SESSION_COOKIE_NAME = f'{COMPONENT_NAME}_session'
+    SESSION_COOKIE_DOMAIN = None  # Laat Flask dit automatisch bepalen
+    SESSION_COOKIE_PATH = '/'
+    SESSION_COOKIE_HTTPONLY = True
+    SESSION_COOKIE_SECURE = False  # True voor production met HTTPS
+    SESSION_COOKIE_SAMESITE = 'Lax'
+    REMEMBER_COOKIE_SAMESITE = 'strict'
+    SESSION_KEY_PREFIX = f'{COMPONENT_NAME}_'
+
     # JWT settings
     JWT_SECRET_KEY = environ.get('JWT_SECRET_KEY')
     JWT_ACCESS_TOKEN_EXPIRES = timedelta(hours=1)  # Set token expiry to 1 hour
@@ -267,6 +275,7 @@ class DevConfig(Config):
     # Define the nginx prefix used for the specific apps
     EVEAI_APP_LOCATION_PREFIX = '/admin'
     EVEAI_CHAT_LOCATION_PREFIX = '/chat'
+    CHAT_CLIENT_PREFIX = 'chat-client/chat/'
 
     # file upload settings
     # UPLOAD_FOLDER = '/app/tenant_files'

config/logging_config.py

@@ -1,15 +1,13 @@
+
 import json
 import os
+import sys
 from datetime import datetime as dt, timezone as tz
 
 from flask import current_app
-from graypy import GELFUDPHandler
 import logging
 import logging.config
 
-# Graylog configuration
-GRAYLOG_HOST = os.environ.get('GRAYLOG_HOST', 'localhost')
-GRAYLOG_PORT = int(os.environ.get('GRAYLOG_PORT', 12201))
 env = os.environ.get('FLASK_ENV', 'development')
 
 
@@ -144,23 +142,6 @@ class TuningFormatter(logging.Formatter):
         return formatted_msg
 
 
-class GraylogFormatter(logging.Formatter):
-    """Maintains existing Graylog formatting while adding tuning fields"""
-
-    def format(self, record):
-        if getattr(record, 'is_tuning_log', False):
-            # Add tuning-specific fields to Graylog
-            record.tuning_fields = {
-                'is_tuning_log': True,
-                'tuning_type': record.tuning_type,
-                'tenant_id': record.tenant_id,
-                'catalog_id': record.catalog_id,
-                'specialist_id': record.specialist_id,
-                'retriever_id': record.retriever_id,
-                'processor_id': record.processor_id,
-                'session_id': record.session_id,
-            }
-        return super().format(record)
 
 class TuningLogger:
     """Helper class to manage tuning logs with consistent structure"""
@@ -177,10 +158,10 @@ class TuningLogger:
             specialist_id: Optional specialist ID for context
             retriever_id: Optional retriever ID for context
             processor_id: Optional processor ID for context
-            session_id: Optional session ID for context and log file naming
-            log_file: Optional custom log file name to use
+            session_id: Optional session ID for context
+            log_file: Optional custom log file name (ignored - all logs go to tuning.log)
         """
-
+        # Always use the standard tuning logger
         self.logger = logging.getLogger(logger_name)
         self.tenant_id = tenant_id
         self.catalog_id = catalog_id
@@ -188,63 +169,8 @@ class TuningLogger:
         self.retriever_id = retriever_id
         self.processor_id = processor_id
         self.session_id = session_id
-        self.log_file = log_file
-        # Determine whether to use a session-specific logger
-        if session_id:
-            # Create a unique logger name for this session
-            session_logger_name = f"{logger_name}_{session_id}"
-            self.logger = logging.getLogger(session_logger_name)
 
-            # If this logger doesn't have handlers yet, configure it
-            if not self.logger.handlers:
-                # Determine log file path
-                if not log_file and session_id:
-                    log_file = f"logs/tuning_{session_id}.log"
-                elif not log_file:
-                    log_file = "logs/tuning.log"
-
-                # Configure the logger
-                self._configure_session_logger(log_file)
-        else:
-            # Use the standard tuning logger
-            self.logger = logging.getLogger(logger_name)
-
-    def _configure_session_logger(self, log_file):
-        """Configure a new session-specific logger with appropriate handlers"""
-        # Create and configure a file handler
-        file_handler = logging.handlers.RotatingFileHandler(
-            filename=log_file,
-            maxBytes=1024 * 1024 * 3,  # 3MB
-            backupCount=3
-        )
-        file_handler.setFormatter(TuningFormatter())
-        file_handler.setLevel(logging.DEBUG)
-
-        # Add the file handler to the logger
-        self.logger.addHandler(file_handler)
-
-        # Add Graylog handler in production
-        env = os.environ.get('FLASK_ENV', 'development')
-        if env == 'production':
-            try:
-                graylog_handler = GELFUDPHandler(
-                    host=GRAYLOG_HOST,
-                    port=GRAYLOG_PORT,
-                    debugging_fields=True
-                )
-                graylog_handler.setFormatter(GraylogFormatter())
-                self.logger.addHandler(graylog_handler)
-            except Exception as e:
-                # Fall back to just file logging if Graylog setup fails
-                fallback_logger = logging.getLogger('eveai_app')
-                fallback_logger.warning(f"Failed to set up Graylog handler: {str(e)}")
-
-        # Set logger level and disable propagation
-        self.logger.setLevel(logging.DEBUG)
-        self.logger.propagate = False
-
-
-def log_tuning(self, tuning_type: str, message: str, data=None, level=logging.DEBUG):
+    def log_tuning(self, tuning_type: str, message: str, data=None, level=logging.DEBUG):
         """Log a tuning event with structured data"""
         try:
             # Create a standard LogRecord for tuning
@@ -275,13 +201,82 @@ def log_tuning(self, tuning_type: str, message: str, data=None, level=logging.DE
             self.logger.handle(record)
 
         except Exception as e:
-            fallback_logger = logging.getLogger('eveai_workers')
-            fallback_logger.exception(f"Failed to log tuning message: {str(e)}")
+            print(f"Failed to log tuning message: {str(e)}")
 
 
 # Set the custom log record factory
 logging.setLogRecordFactory(TuningLogRecord)
 
+def configure_logging():
+    """Configure logging based on environment
+
+    When running in Kubernetes, directs logs to stdout in JSON format
+    Otherwise uses file-based logging for development/testing
+    """
+    try:
+        # Verkrijg het absolute pad naar de logs directory
+        base_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
+        logs_dir = os.path.join(base_dir, 'logs')
+
+        # Zorg ervoor dat de logs directory bestaat met de juiste permissies
+        if not os.path.exists(logs_dir):
+            try:
+                os.makedirs(logs_dir, exist_ok=True)
+                print(f"Logs directory aangemaakt op: {logs_dir}")
+            except (IOError, PermissionError) as e:
+                print(f"WAARSCHUWING: Kan logs directory niet aanmaken: {e}")
+                print(f"Logs worden mogelijk niet correct geschreven!")
+
+        # Check if running in Kubernetes
+        in_kubernetes = os.environ.get('KUBERNETES_SERVICE_HOST') is not None
+
+        # Controleer of de pythonjsonlogger pakket beschikbaar is als we in Kubernetes zijn
+        if in_kubernetes:
+            try:
+                import pythonjsonlogger.jsonlogger
+                has_json_logger = True
+            except ImportError:
+                print("WAARSCHUWING: python-json-logger pakket is niet geïnstalleerd.")
+                print("Voer 'pip install python-json-logger>=2.0.7' uit om JSON logging in te schakelen.")
+                print("Terugvallen op standaard logging formaat.")
+                has_json_logger = False
+                in_kubernetes = False  # Fall back to standard logging
+        else:
+            has_json_logger = False
+
+        # Apply the configuration
+        logging_config = dict(LOGGING)
+
+        # Wijzig de json_console handler om terug te vallen op console als pythonjsonlogger niet beschikbaar is
+        if not has_json_logger and 'json_console' in logging_config['handlers']:
+            # Vervang json_console handler door een console handler met standaard formatter
+            logging_config['handlers']['json_console']['formatter'] = 'standard'
+
+        # In Kubernetes, conditionally modify specific loggers to use JSON console output
+        # This preserves the same logger names but changes where/how they log
+        if in_kubernetes:
+            for logger_name in logging_config['loggers']:
+                if logger_name:  # Skip the root logger
+                    logging_config['loggers'][logger_name]['handlers'] = ['json_console']
+
+        # Controleer of de logs directory schrijfbaar is voordat we de configuratie toepassen
+        logs_dir = os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'logs')
+        if os.path.exists(logs_dir) and not os.access(logs_dir, os.W_OK):
+            print(f"WAARSCHUWING: Logs directory bestaat maar is niet schrijfbaar: {logs_dir}")
+            print("Logs worden mogelijk niet correct geschreven!")
+
+        logging.config.dictConfig(logging_config)
+        logging.info(f"Logging configured. Environment: {'Kubernetes' if in_kubernetes else 'Development/Testing'}")
+        logging.info(f"Logs directory: {logs_dir}")
+    except Exception as e:
+        print(f"Error configuring logging: {str(e)}")
+        print("Gedetailleerde foutinformatie:")
+        import traceback
+        traceback.print_exc()
+        # Fall back to basic configuration
+        logging.basicConfig(level=logging.INFO)
+
+
 
 LOGGING = {
     'version': 1,
@@ -290,7 +285,7 @@ LOGGING = {
         'file_app': {
             'level': 'DEBUG',
             'class': 'logging.handlers.RotatingFileHandler',
-            'filename': 'logs/eveai_app.log',
+            'filename': os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'logs', 'eveai_app.log'),
             'maxBytes': 1024 * 1024 * 1,  # 1MB
             'backupCount': 2,
             'formatter': 'standard',
@@ -298,7 +293,7 @@ LOGGING = {
         'file_workers': {
             'level': 'DEBUG',
             'class': 'logging.handlers.RotatingFileHandler',
-            'filename': 'logs/eveai_workers.log',
+            'filename': os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'logs', 'eveai_workers.log'),
             'maxBytes': 1024 * 1024 * 1,  # 1MB
             'backupCount': 2,
             'formatter': 'standard',
@@ -306,7 +301,7 @@ LOGGING = {
         'file_chat_client': {
             'level': 'DEBUG',
             'class': 'logging.handlers.RotatingFileHandler',
-            'filename': 'logs/eveai_chat_client.log',
+            'filename': os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'logs', 'eveai_chat_client.log'),
             'maxBytes': 1024 * 1024 * 1,  # 1MB
             'backupCount': 2,
             'formatter': 'standard',
@@ -314,7 +309,7 @@ LOGGING = {
         'file_chat_workers': {
             'level': 'DEBUG',
             'class': 'logging.handlers.RotatingFileHandler',
-            'filename': 'logs/eveai_chat_workers.log',
+            'filename': os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'logs', 'eveai_chat_workers.log'),
             'maxBytes': 1024 * 1024 * 1,  # 1MB
             'backupCount': 2,
             'formatter': 'standard',
@@ -322,7 +317,7 @@ LOGGING = {
         'file_api': {
             'level': 'DEBUG',
             'class': 'logging.handlers.RotatingFileHandler',
-            'filename': 'logs/eveai_api.log',
+            'filename': os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'logs', 'eveai_api.log'),
             'maxBytes': 1024 * 1024 * 1,  # 1MB
             'backupCount': 2,
             'formatter': 'standard',
@@ -330,7 +325,7 @@ LOGGING = {
         'file_beat': {
             'level': 'DEBUG',
             'class': 'logging.handlers.RotatingFileHandler',
-            'filename': 'logs/eveai_beat.log',
+            'filename': os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'logs', 'eveai_beat.log'),
             'maxBytes': 1024 * 1024 * 1,  # 1MB
             'backupCount': 2,
             'formatter': 'standard',
@@ -338,7 +333,7 @@ LOGGING = {
         'file_entitlements': {
             'level': 'DEBUG',
             'class': 'logging.handlers.RotatingFileHandler',
-            'filename': 'logs/eveai_entitlements.log',
+            'filename': os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'logs', 'eveai_entitlements.log'),
             'maxBytes': 1024 * 1024 * 1,  # 1MB
             'backupCount': 2,
             'formatter': 'standard',
@@ -346,7 +341,7 @@ LOGGING = {
         'file_sqlalchemy': {
             'level': 'DEBUG',
             'class': 'logging.handlers.RotatingFileHandler',
-            'filename': 'logs/sqlalchemy.log',
+            'filename': os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'logs', 'sqlalchemy.log'),
             'maxBytes': 1024 * 1024 * 1,  # 1MB
             'backupCount': 2,
             'formatter': 'standard',
@@ -354,7 +349,7 @@ LOGGING = {
         'file_security': {
             'level': 'DEBUG',
             'class': 'logging.handlers.RotatingFileHandler',
-            'filename': 'logs/security.log',
+            'filename': os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'logs', 'security.log'),
             'maxBytes': 1024 * 1024 * 1,  # 1MB
             'backupCount': 2,
             'formatter': 'standard',
@@ -362,7 +357,7 @@ LOGGING = {
         'file_rag_tuning': {
             'level': 'DEBUG',
             'class': 'logging.handlers.RotatingFileHandler',
-            'filename': 'logs/rag_tuning.log',
+            'filename': os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'logs', 'rag_tuning.log'),
             'maxBytes': 1024 * 1024 * 1,  # 1MB
             'backupCount': 2,
             'formatter': 'standard',
@@ -370,7 +365,7 @@ LOGGING = {
         'file_embed_tuning': {
             'level': 'DEBUG',
             'class': 'logging.handlers.RotatingFileHandler',
-            'filename': 'logs/embed_tuning.log',
+            'filename': os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'logs', 'embed_tuning.log'),
             'maxBytes': 1024 * 1024 * 1,  # 1MB
             'backupCount': 2,
             'formatter': 'standard',
@@ -378,7 +373,7 @@ LOGGING = {
         'file_business_events': {
             'level': 'INFO',
             'class': 'logging.handlers.RotatingFileHandler',
-            'filename': 'logs/business_events.log',
+            'filename': os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'logs', 'business_events.log'),
             'maxBytes': 1024 * 1024 * 1,  # 1MB
             'backupCount': 2,
             'formatter': 'standard',
@@ -388,100 +383,104 @@ LOGGING = {
             'level': 'DEBUG',
             'formatter': 'standard',
         },
+        'json_console': {
+            'class': 'logging.StreamHandler',
+            'level': 'INFO',
+            'formatter': 'json',
+            'stream': 'ext://sys.stdout',
+        },
         'tuning_file': {
             'level': 'DEBUG',
             'class': 'logging.handlers.RotatingFileHandler',
-            'filename': 'logs/tuning.log',
+            'filename': os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'logs', 'tuning.log'),
             'maxBytes': 1024 * 1024 * 3,  # 3MB
             'backupCount': 3,
             'formatter': 'tuning',
         },
-        'graylog': {
-            'level': 'DEBUG',
-            'class': 'graypy.GELFUDPHandler',
-            'host': GRAYLOG_HOST,
-            'port': GRAYLOG_PORT,
-            'debugging_fields': True,
-            'formatter': 'graylog'
-        },
     },
     'formatters': {
         'standard': {
             'format': '%(asctime)s [%(levelname)s] %(name)s (%(component)s) [%(module)s:%(lineno)d]: %(message)s',
             'datefmt': '%Y-%m-%d %H:%M:%S'
         },
-        'graylog': {
-            'format': '[%(levelname)s] %(name)s (%(component)s) [%(module)s:%(lineno)d in %(funcName)s] '
-                      '[Thread: %(threadName)s]: %(message)s',
-            'datefmt': '%Y-%m-%d %H:%M:%S',
-            '()': GraylogFormatter
-        },
         'tuning': {
             '()': TuningFormatter,
             'datefmt': '%Y-%m-%d %H:%M:%S UTC'
+        },
+        'json': {
+            'format': '%(message)s',
+            'class': 'logging.Formatter' if not 'pythonjsonlogger' in sys.modules else 'pythonjsonlogger.jsonlogger.JsonFormatter',
+            'json_default': lambda obj: str(obj) if isinstance(obj, (dt, Exception)) else None,
+            'json_ensure_ascii': False,
+            'rename_fields': {
+                'asctime': 'timestamp',
+                'levelname': 'severity'
+            },
+            'timestamp': True,
+            'datefmt': '%Y-%m-%dT%H:%M:%S.%fZ'
         }
     },
     'loggers': {
         'eveai_app': {  # logger for the eveai_app
-            'handlers': ['file_app', 'graylog', ] if env == 'production' else ['file_app', ],
+            'handlers': ['file_app'],
             'level': 'DEBUG',
             'propagate': False
         },
         'eveai_workers': {  # logger for the eveai_workers
-            'handlers': ['file_workers', 'graylog', ] if env == 'production' else ['file_workers', ],
+            'handlers': ['file_workers'],
             'level': 'DEBUG',
             'propagate': False
         },
         'eveai_chat_client': {  # logger for the eveai_chat
-            'handlers': ['file_chat_client', 'graylog', ] if env == 'production' else ['file_chat_client', ],
+            'handlers': ['file_chat_client'],
             'level': 'DEBUG',
             'propagate': False
         },
         'eveai_chat_workers': {  # logger for the eveai_chat_workers
-            'handlers': ['file_chat_workers', 'graylog', ] if env == 'production' else ['file_chat_workers', ],
+            'handlers': ['file_chat_workers'],
             'level': 'DEBUG',
             'propagate': False
         },
-        'eveai_api': {  # logger for the eveai_chat_workers
-            'handlers': ['file_api', 'graylog', ] if env == 'production' else ['file_api', ],
+        'eveai_api': {  # logger for the eveai_api
+            'handlers': ['file_api'],
             'level': 'DEBUG',
             'propagate': False
         },
         'eveai_beat': {  # logger for the eveai_beat
-            'handlers': ['file_beat', 'graylog', ] if env == 'production' else ['file_beat', ],
+            'handlers': ['file_beat'],
             'level': 'DEBUG',
             'propagate': False
         },
         'eveai_entitlements': {  # logger for the eveai_entitlements
-            'handlers': ['file_entitlements', 'graylog', ] if env == 'production' else ['file_entitlements', ],
+            'handlers': ['file_entitlements'],
             'level': 'DEBUG',
             'propagate': False
         },
         'sqlalchemy.engine': {  # logger for the sqlalchemy
-            'handlers': ['file_sqlalchemy', 'graylog', ] if env == 'production' else ['file_sqlalchemy', ],
+            'handlers': ['file_sqlalchemy'],
             'level': 'DEBUG',
             'propagate': False
         },
         'security': {  # logger for the security
-            'handlers': ['file_security', 'graylog', ] if env == 'production' else ['file_security', ],
+            'handlers': ['file_security'],
             'level': 'DEBUG',
             'propagate': False
         },
         'business_events': {
-            'handlers': ['file_business_events', 'graylog'],
+            'handlers': ['file_business_events'],
             'level': 'DEBUG',
             'propagate': False
         },
         # Single tuning logger
         'tuning': {
-            'handlers': ['tuning_file', 'graylog'] if env == 'production' else ['tuning_file'],
+            'handlers': ['tuning_file'],
             'level': 'DEBUG',
             'propagate': False,
         },
         '': {  # root logger
-            'handlers': ['console'],
+            'handlers': ['console'] if os.environ.get('KUBERNETES_SERVICE_HOST') is None else ['json_console'],
             'level': 'WARNING',  # Set higher level for root to minimize noise
             'propagate': False
         },
     }
-}
+}

config/specialists/traicie/TRAICIE_SELECTION_SPECIALIST/1.2.0.yaml

@@ -1,4 +1,4 @@
-version: "1.1.0"
+version: "1.3.0"
 name: "Traicie Selection Specialist"
 framework: "crewai"
 partner: "traicie"
@@ -108,13 +108,13 @@ results:
     description: "List of vacancy competencies and their descriptions"
     required: false
 agents:
-  - type: "TRAICIE_HR_BP_AGENT"
+  - type: "TRAICIE_RECRUITER"
     version: "1.0"
 tasks:
-  - type: "TRAICIE_GET_COMPETENCIES_TASK"
-    version: "1.1"
+  - type: "TRAICIE_KO_CRITERIA_INTERVIEW_DEFINITION"
+    version: "1.0"
 metadata:
   author: "Josako"
-  date_added: "2025-05-27"
-  changes: "Add make to the selection specialist"
+  date_added: "2025-06-16"
+  changes: "Realising the actual interaction with the LLM"
   description: "Assistant to create a new Vacancy based on Vacancy Text"

config/specialists/traicie/TRAICIE_SELECTION_SPECIALIST/1.3.0.yaml

@@ -0,0 +1,120 @@
+version: "1.3.0"
+name: "Traicie Selection Specialist"
+framework: "crewai"
+partner: "traicie"
+chat: false
+configuration:
+  name:
+    name: "Name"
+    description: "The name the specialist is called upon."
+    type: "str"
+    required: true
+  role_reference:
+    name: "Role Reference"
+    description: "A customer reference to the role"
+    type: "str"
+    required: false
+  make:
+    name: "Make"
+    description: "The make for which the role is defined and the selection specialist is created"
+    type: "system"
+    system_name: "tenant_make"
+    required: true
+  competencies:
+    name: "Competencies"
+    description: "An ordered list of competencies."
+    type: "ordered_list"
+    list_type: "competency_details"
+    required: true
+  tone_of_voice:
+    name: "Tone of Voice"
+    description: "The tone of voice the specialist uses to communicate"
+    type: "enum"
+    allowed_values: ["Professional & Neutral", "Warm & Empathetic", "Energetic & Enthusiastic", "Accessible & Informal", "Expert & Trustworthy", "No-nonsense & Goal-driven"]
+    default: "Professional & Neutral"
+    required: true
+  language_level:
+    name: "Language Level"
+    description: "Language level to be used when communicating, relating to CEFR levels"
+    type: "enum"
+    allowed_values: ["Basic", "Standard", "Professional"]
+    default: "Standard"
+    required: true
+  welcome_message:
+    name: "Welcome Message"
+    description: "Introductory text given by the specialist - but translated according to Tone of Voice, Language Level and Starting Language"
+    type: "text"
+    required: false
+  closing_message:
+    name: "Closing Message"
+    description: "Closing message given by the specialist - but translated according to Tone of Voice, Language Level and Starting Language"
+    type: "text"
+    required: false
+competency_details:
+  title:
+    name: "Title"
+    description: "Competency Title"
+    type: "str"
+    required: true
+  description:
+    name: "Description"
+    description: "Description (in context of the role) of the competency"
+    type: "text"
+    required: true
+  is_knockout:
+    name: "KO"
+    description: "Defines if the competency is a knock-out criterium"
+    type: "boolean"
+    required: true
+    default: false
+  assess:
+    name: "Assess"
+    description: "Indication if this competency is to be assessed"
+    type: "boolean"
+    required: true
+    default: true
+arguments:
+  region:
+    name: "Region"
+    type: "str"
+    description: "The region of the specific vacancy"
+    required: false
+  working_schedule:
+    name: "Work Schedule"
+    type: "str"
+    description: "The work schedule or employment type of the specific vacancy"
+    required: false
+  start_date:
+    name: "Start Date"
+    type: "date"
+    description: "The start date of the specific vacancy"
+    required: false
+  language:
+    name: "Language"
+    type: "str"
+    description: "The language (2-letter code) used to start the conversation"
+    required: true
+  interaction_mode:
+    name: "Interaction Mode"
+    type: "enum"
+    description: "The interaction mode the specialist will start working in."
+    allowed_values: ["Job Application", "Seduction"]
+    default: "Job Application"
+    required: true
+results:
+  competencies:
+    name: "competencies"
+    type: "List[str, str]"
+    description: "List of vacancy competencies and their descriptions"
+    required: false
+agents:
+  - type: "TRAICIE_RECRUITER_AGENT"
+    version: "1.0"
+tasks:
+  - type: "TRAICIE_KO_CRITERIA_INTERVIEW_DEFINITION_TASK"
+    version: "1.0"
+metadata:
+  author: "Josako"
+  date_added: "2025-06-18"
+  changes: "Add make to the selection specialist"
+  description: "Assistant to create a new Vacancy based on Vacancy Text"

config/tasks/traicie/TRAICIE_KO_CRITERIA_INTERVIEW_DEFINITION_TASK/1.0.0.yaml

@@ -5,17 +5,24 @@ task_description: >
   (both description and title). The criteria are in between triple backquotes.You need to prepare for the interviews, 
   and are to provide for each of these ko criteria:
   
-  - A question to ask the recruitment candidate describing the context of the ko criterium. Use your experience to not 
-  just ask a closed question, but a question from which you can indirectly derive a positive or negative qualification 
-  of the criterium based on the answer of the candidate.
-  - A set of max 5 answers on that question, from the candidates perspective. One of the answers will result in a 
-  positive evaluation of the criterium, the other ones in a negative evaluation. Mark each of the answers as positive
+  - A short question to ask the recruitment candidate describing the context of the ko criterium. Use your experience to
+  ask a question that enables us to verify compliancy to the criterium.
+  - A set of 2 short answers to that question, from the candidates perspective. One of the answers will result in a 
+  positive evaluation of the criterium, the other one in a negative evaluation. Mark each of the answers as positive
   or negative. 
   Describe the answers from the perspective of the candidate. Be sure to include all necessary aspects in you answers. 
   
   Apply the following tone of voice in both questions and answers: {tone_of_voice}
+  Use the following description to understand tone of voice:
+  
+  {tone_of_voice_context}
+  
   Apply the following language level in both questions and answers: {language_level}
+  
   Use {language} as language for both questions and answers.
+  Use the following description to understand language_level:
+  
+  {language_level_context}
   
   ```{ko_criteria}```
   
@@ -25,7 +32,8 @@ expected_output: >
   For each of the ko criteria, you provide:
   - the exact title as specified in the original language
   - the question in {language}
-  - a set of answers, with for each answer an indication if it is the correct answer, or a false response. In {language}.
+  - a positive answer, resulting in a positive evaluation of the criterium. In {language}.
+  - a negative answer, resulting in a negative evaluation of the criterium. In {language}.
   {custom_expected_output}
 metadata:
   author: "Josako"

config/tasks/traicie/TRAICIE_KO_CRITERIA_INTERVIEW_DEFINITION_TASK/1.0.1.yaml

@@ -0,0 +1,37 @@
+version: "1.0.0"
+name: "KO Criteria Interview Definition"
+task_description: >
+  In context of a vacancy in your company {tenant_name}, you are provided with a set of competencies 
+  (both description and title). The competencies are in between triple backquotes. The competencies provided should be
+  handled as knock-out criteria.
+  For each of the knock-out criteria, you need to define
+  
+  - A short (1 sentence), closed-ended question (Yes / No) to ask the recruitment candidate. Use your experience to ask a question that 
+  enables us to verify compliancy to the criterium.
+  - A set of 2 short answers (1 small sentence each) to that question (positive answer / negative answer), from the 
+  candidates perspective. 
+  The positive answer will result in a positive evaluation of the criterium, the negative answer in a negative evaluation
+  of the criterium. Try to avoid just using Yes / No as positive and negative answers.
+  
+  Apply the following tone of voice in both questions and answers: {tone_of_voice}, i.e. {tone_of_voice_context}
+
+  Apply the following language level in both questions and answers: {language_level}, i.e. {language_level_context}
+  
+  Use {language} as language for both questions and answers.
+  
+  ```{ko_criteria}```
+  
+  {custom_description}
+
+expected_output: >
+  For each of the ko criteria, you provide:
+  - the exact title as specified in the original language
+  - the question in {language}
+  - a positive answer, resulting in a positive evaluation of the criterium. In {language}.
+  - a negative answer, resulting in a negative evaluation of the criterium. In {language}.
+  {custom_expected_output}
+metadata:
+  author: "Josako"
+  date_added: "2025-06-20"
+  description: "A Task to define interview Q&A from given KO Criteria"
+  changes: "Improvement to ensure closed-ended questions and short descriptions"

config/type_defs/agent_types.py

@@ -32,5 +32,10 @@ AGENT_TYPES = {
         "name": "Traicie HR BP Agent",
         "description": "An HR Business Partner Agent",
         "partner": "traicie"
-    }
+    },
+    "TRAICIE_RECRUITER_AGENT": {
+        "name": "Traicie Recruiter Agent",
+        "description": "An Senior Recruiter Agent",
+        "partner": "traicie"
+    },
 }

config/type_defs/task_types.py

@@ -41,5 +41,10 @@ TASK_TYPES = {
         "name": "Traicie Get KO Criteria",
         "description": "A Task to get KO Criteria from a Vacancy Text",
         "partner": "traicie"
+    },
+    "TRAICIE_KO_CRITERIA_INTERVIEW_DEFINITION_TASK": {
+        "name": "Traicie KO Criteria Interview Definition",
+        "description": "A Task to define KO Criteria questions to be used during the interview",
+        "partner": "traicie"
     }
 }

content/changelog/1.0/1.0.0.md

@@ -5,10 +5,20 @@ All notable changes to EveAI will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [2.3.7-alfa]
+
+### Added
+- Basic Base Specialist additions for handling phases and transferring data between state and output
+- Introduction of URL and QR-code for MagicLink
+
+### Changed
+- Logging improvement & simplification (remove Graylog)
+- Traicie Selection Specialist v1.3 - full roundtrip & full process
+
 ## [2.3.6-alfa]
 
 ### Added
-- Full Chat Client functionaltiy, including Forms, ESS, theming
+- Full Chat Client functionality, including Forms, ESS, theming
 - First Demo version of Traicie Selection Specialist
 
 ## [2.3.5-alfa]

eveai_api/__init__.py

@@ -12,7 +12,7 @@ import logging.config
 from common.models.user import TenantDomain
 from common.utils.cors_utils import get_allowed_origins
 from common.utils.database import Database
-from config.logging_config import LOGGING
+from config.logging_config import configure_logging
 from .api.document_api import document_ns
 from .api.auth import auth_ns
 from .api.specialist_execution_api import specialist_execution_ns
@@ -40,7 +40,7 @@ def create_app(config_file=None):
     app.celery = make_celery(app.name, app.config)
     init_celery(app.celery, app)
 
-    logging.config.dictConfig(LOGGING)
+    configure_logging()
     logger = logging.getLogger(__name__)
 
     logger.info("eveai_api starting up")

eveai_app/__init__.py

@@ -13,7 +13,7 @@ import common.models.interaction
 import common.models.entitlements
 import common.models.document
 from common.utils.startup_eveai import perform_startup_actions
-from config.logging_config import LOGGING
+from config.logging_config import configure_logging
 from common.utils.security import set_tenant_session_data
 from common.utils.errors import register_error_handlers
 from common.utils.celery_utils import make_celery, init_celery
@@ -47,8 +47,16 @@ def create_app(config_file=None):
     except OSError:
         pass
 
-    logging.config.dictConfig(LOGGING)
-    logger = logging.getLogger(__name__)
+    # Configureer logging op basis van de omgeving (K8s of traditioneel)
+    try:
+        configure_logging()
+        logger = logging.getLogger(__name__)
+        # Test dat logging werkt
+        logger.debug("Logging test in eveai_app")
+    except Exception as e:
+        print(f"Critical Error Initialising Error: {str(e)}")
+        import traceback
+        traceback.print_exc()
 
     logger.info("eveai_app starting up")
 
@@ -92,6 +100,45 @@ def create_app(config_file=None):
     #     app.logger.debug(f"Before request - Session data: {session}")
     #     app.logger.debug(f"Before request - Request headers: {request.headers}")
 
+    @app.before_request
+    def before_request():
+        from flask import session, request
+        from flask_login import current_user
+        import datetime
+
+        app.logger.debug(f"Before request - URL: {request.url}")
+        app.logger.debug(f"Before request - Session permanent: {session.permanent}")
+
+        # Log session expiry tijd als deze bestaat
+        if current_user.is_authenticated:
+            # Controleer of sessie permanent is (nodig voor PERMANENT_SESSION_LIFETIME)
+            if not session.permanent:
+                session.permanent = True
+                app.logger.debug("Session marked as permanent (enables 60min timeout)")
+
+            # Log wanneer sessie zou verlopen
+            if '_permanent' in session:
+                expires_at = datetime.datetime.now() + app.permanent_session_lifetime
+                app.logger.debug(f"Session will expire at: {expires_at} (60 min from now)")
+
+    @app.route('/debug/session')
+    def debug_session():
+        from flask import session
+        from flask_security import current_user
+        import datetime
+
+        if current_user.is_authenticated:
+            info = {
+                'session_permanent': session.permanent,
+                'session_lifetime_minutes': app.permanent_session_lifetime.total_seconds() / 60,
+                'session_refresh_enabled': app.config.get('SESSION_REFRESH_EACH_REQUEST'),
+                'current_time': datetime.datetime.now().isoformat(),
+                'session_data_keys': list(session.keys())
+            }
+            return jsonify(info)
+        else:
+            return jsonify({'error': 'Not authenticated'})
+
     # Register template filters
     register_filters(app)
 

eveai_app/templates/interaction/edit_specialist_magic_link.html

@@ -9,11 +9,30 @@
 {% block content %}
     <form method="post">
         {{ form.hidden_tag() }}
-        {% set disabled_fields = ['magic_link_code'] %}
+        {% set disabled_fields = ['magic_link_code', 'chat_client_url', 'qr_code_url'] %}
         {% set exclude_fields = [] %}
         <!-- Render Static Fields -->
         {% for field in form.get_static_fields() %}
-            {{ render_field(field, disabled_fields, exclude_fields) }}
+            {% if field.name == 'qr_code_url' and field.data %}
+                <div class="form-group">
+                    <label for="{{ field.id }}">{{ field.label.text }}</label>
+                    <div style="max-width: 200px;">
+                        <img src="{{ field.data }}" alt="QR Code" class="img-fluid">
+                    </div>
+                    <input type="hidden" name="{{ field.name }}" value="{{ field.data|e }}">
+                </div>
+            {% elif field.name == 'chat_client_url' %}
+                <div class="form-group">
+                    <label for="{{ field.id }}" class="form-label">{{ field.label.text }}</label>
+                    <div class="input-group">
+                        <input type="text" class="form-control" value="{{ field.data }}" id="{{ field.id }}" readonly>
+                        <a href="{{ field.data }}" class="btn btn-primary" target="_blank">Open link</a>
+                    </div>
+                    <input type="hidden" name="{{ field.name }}" value="{{ field.data|e }}">
+                </div>
+            {% else %}
+                {{ render_field(field, disabled_fields, exclude_fields) }}
+            {% endif %}
         {% endfor %}
         <!-- Render Dynamic Fields -->
         {% for collection_name, fields in form.get_dynamic_fields().items() %}

eveai_app/views/entitlements_views.py

@@ -259,6 +259,10 @@ def view_usages():
     page = request.args.get('page', 1, type=int)
     per_page = request.args.get('per_page', 10, type=int)
 
+    if not session.get('tenant', None):
+        flash('You can only view usage for a Tenant. Select a Tenant to continue!', 'danger')
+        return redirect(prefixed_url_for('user_bp.select_tenant'))
+
     tenant_id = session.get('tenant').get('id')
     query = LicenseUsage.query.filter_by(tenant_id=tenant_id).order_by(desc(LicenseUsage.id))
 

eveai_app/views/interaction_forms.py

@@ -162,6 +162,8 @@ class EditSpecialistMagicLinkForm(DynamicFormBase):
                                   render_kw={'readonly': True})
     specialist_id = IntegerField('Specialist', validators=[DataRequired()], render_kw={'readonly': True})
     specialist_name = StringField('Specialist Name', validators=[DataRequired()], render_kw={'readonly': True})
+    chat_client_url = StringField('Chat Client URL', validators=[Optional()], render_kw={'readonly': True})
+    qr_code_url = StringField('QR Code', validators=[Optional()], render_kw={'readonly': True})
     tenant_make_id = SelectField('Tenant Make', validators=[Optional()], coerce=int)
     valid_from = DateField('Valid From', id='form-control datepicker', validators=[Optional()])
     valid_to = DateField('Valid To', id='form-control datepicker', validators=[Optional()])

eveai_app/views/interaction_views.py

@@ -748,6 +748,56 @@ def edit_specialist_magic_link(specialist_magic_link_id):
         else:
             form.tenant_make_id.data = specialist_ml.tenant_make_id
 
+        # Set the chat client URL
+        tenant_id = session.get('tenant').get('id')
+        chat_client_prefix = current_app.config.get('CHAT_CLIENT_PREFIX', 'chat_client/chat/')
+        base_url = request.url_root
+        magic_link_code = specialist_ml.magic_link_code
+
+        # Parse the URL om poortinformatie te behouden als deze afwijkt van de standaard
+        url_parts = request.url.split('/')
+        host_port = url_parts[2]  # Dit bevat zowel hostname als poort indien aanwezig
+
+        # Generate the full URL for chat client with magic link code
+        chat_client_url = f"{request.scheme}://{host_port}/{chat_client_prefix}{magic_link_code}"
+        form.chat_client_url.data = chat_client_url
+
+        # Generate QR code as data URI for direct embedding in HTML
+        try:
+            import qrcode
+            import io
+            import base64
+
+            # Generate QR code as PNG for better compatibility
+            qr = qrcode.QRCode(
+                version=1,
+                error_correction=qrcode.constants.ERROR_CORRECT_L,
+                box_size=10,
+                border=4
+            )
+            qr.add_data(chat_client_url)
+            qr.make(fit=True)
+
+            # Generate PNG image in memory
+            img = qr.make_image(fill_color="black", back_color="white")
+            buffer = io.BytesIO()
+            img.save(buffer, format='PNG')
+            img_data = buffer.getvalue()
+
+            # Create data URI for direct embedding in HTML
+            img_base64 = base64.b64encode(img_data).decode('utf-8')
+            data_uri = f"data:image/png;base64,{img_base64}"
+
+            # Store the data URI in the form data
+            form.qr_code_url.data = data_uri
+
+            current_app.logger.debug(f"QR code generated successfully for {magic_link_code}")
+            current_app.logger.debug(f"QR code data URI starts with: {data_uri[:50]}...")
+
+        except Exception as e:
+            current_app.logger.error(f"Failed to generate QR code: {str(e)}")
+            form.qr_code_url.data = "Error generating QR code"
+
     if form.validate_on_submit():
         # Update the basic fields
         form.populate_obj(specialist_ml)

eveai_beat/__init__.py

@@ -4,7 +4,7 @@ from flask import Flask
 import os
 
 from common.utils.celery_utils import make_celery, init_celery
-from config.logging_config import LOGGING
+from config.logging_config import configure_logging
 from config.config import get_config
 
 
@@ -21,7 +21,7 @@ def create_app(config_file=None):
         case _:
             app.config.from_object(get_config('dev'))
 
-    logging.config.dictConfig(LOGGING)
+    configure_logging()
 
     register_extensions(app)
 

eveai_chat_client/__init__.py

@@ -9,7 +9,7 @@ from common.extensions import (db, bootstrap, cors, csrf, session,
                               minio_client, simple_encryption, metrics, cache_manager, content_manager)
 from common.models.user import Tenant, SpecialistMagicLinkTenant
 from common.utils.startup_eveai import perform_startup_actions
-from config.logging_config import LOGGING
+from config.logging_config import configure_logging
 from eveai_chat_client.utils.errors import register_error_handlers
 from common.utils.celery_utils import make_celery, init_celery
 from common.utils.template_filters import register_filters
@@ -39,7 +39,7 @@ def create_app(config_file=None):
     except OSError:
         pass
 
-    logging.config.dictConfig(LOGGING)
+    configure_logging()
     logger = logging.getLogger(__name__)
 
     logger.info("eveai_chat_client starting up")

eveai_chat_workers/__init__.py

@@ -5,7 +5,7 @@ import os
 
 from common.utils.celery_utils import make_celery, init_celery
 from common.extensions import db, cache_manager
-from config.logging_config import LOGGING
+from config.logging_config import configure_logging
 from config.config import get_config
 
 
@@ -22,7 +22,7 @@ def create_app(config_file=None):
         case _:
             app.config.from_object(get_config('dev'))
 
-    logging.config.dictConfig(LOGGING)
+    configure_logging()
 
     app.logger.info('Starting up eveai_chat_workers...')
     register_extensions(app)

eveai_chat_workers/definitions/language_level/language_level_v1_0.py

@@ -0,0 +1,20 @@
+LANGUAGE_LEVEL = [
+  {
+    "name": "Basic",
+    "description": "Short, simple sentences. Minimal jargon. Lots of visual and concrete language.",
+    "cefr_level": "A2 - B1",
+    "ideal_audience": "Manual laborers, entry-level roles, newcomers with another native language"
+  },
+  {
+    "name": "Standard",
+    "description": "Clear spoken language. Well-formulated without difficult words.",
+    "cefr_level": "B2",
+    "ideal_audience": "Retail, administration, logistics, early-career professionals"
+  },
+  {
+    "name": "Professional",
+    "description": "Business language with technical terms where needed. More complex sentence structures.",
+    "cefr_level": "C1",
+    "ideal_audience": "Management, HR, technical profiles"
+  }
+]

eveai_chat_workers/definitions/tone_of_voice/tone_of_voice_v1_0.py

@@ -0,0 +1,32 @@
+TONE_OF_VOICE = [
+  {
+    "name": "Professional & Neutral",
+    "description": "Business-like, clear, to the point. Focused on facts.",
+    "when_to_use": "Corporate jobs, legal roles, formal sectors"
+  },
+  {
+    "name": "Warm & Empathetic",
+    "description": "Human, compassionate, reassuring.",
+    "when_to_use": "Healthcare, education, HR, social professions"
+  },
+  {
+    "name": "Energetic & Enthusiastic",
+    "description": "Upbeat, persuasive, motivating.",
+    "when_to_use": "Sales, marketing, hospitality, start-ups"
+  },
+  {
+    "name": "Accessible & Informal",
+    "description": "Casual, approachable, friendly, and human.",
+    "when_to_use": "Youth-focused, entry-level, retail, creative sectors"
+  },
+  {
+    "name": "Expert & Trustworthy",
+    "description": "Calm authority, advisory tone, knowledgeable.",
+    "when_to_use": "IT, engineering, consultancy, medical profiles"
+  },
+  {
+    "name": "No-nonsense & Goal-driven",
+    "description": "Direct, efficient, pragmatic.",
+    "when_to_use": "Technical, logistics, blue-collar jobs, production environments"
+  }
+]

eveai_chat_workers/outputs/traicie/knockout_questions/knockout_questions_v1_0.py

@@ -0,0 +1,15 @@
+from typing import List, Optional
+from pydantic import BaseModel, Field
+from eveai_chat_workers.outputs.globals.basic_types.list_item import ListItem
+
+class KOQuestion(BaseModel):
+    title: str = Field(..., description="The title of the knockout criterium.")
+    question: str = Field(..., description="The corresponding question asked to the candidate.")
+    answer_positive: Optional[str] = Field(None, description="The answer to the question, resulting in a positive outcome.")
+    answer_negative: Optional[str] = Field(None, description="The answer to the question, resulting in a negative outcome.")
+
+class KOQuestions(BaseModel):
+    ko_questions: List[KOQuestion] = Field(
+        default_factory=list,
+        description="KO Questions and answers."
+    )

eveai_chat_workers/specialists/base_specialist.py

@@ -4,7 +4,8 @@ from typing import Dict, Any, List
 from flask import current_app
 
 from common.extensions import cache_manager
-from common.models.interaction import SpecialistRetriever
+from common.models.interaction import SpecialistRetriever, Specialist
+from common.models.user import Tenant
 from common.utils.execution_progress import ExecutionProgressTracker
 from config.logging_config import TuningLogger
 from eveai_chat_workers.retrievers.base import BaseRetriever
@@ -17,7 +18,9 @@ class BaseSpecialistExecutor(ABC):
 
     def __init__(self, tenant_id: int, specialist_id: int, session_id: str, task_id: str):
         self.tenant_id = tenant_id
+        self.tenant = Tenant.query.get_or_404(tenant_id)
         self.specialist_id = specialist_id
+        self.specialist = Specialist.query.get_or_404(specialist_id)
         self.session_id = session_id
         self.task_id = task_id
         self.tuning = False
@@ -96,6 +99,37 @@ class BaseSpecialistExecutor(ABC):
     def update_progress(self, processing_type, data) -> None:
         self.ept.send_update(self.task_id, processing_type, data)
 
+    def _replace_system_variables(self, text: str) -> str:
+        """
+        Replace all system variables in the text with their corresponding values.
+        System variables are in the format 'tenant_<attribute_name>'
+
+        Args:
+            text: The text containing system variables to replace
+
+        Returns:
+            str: The text with all system variables replaced
+        """
+        if not text:
+            return text
+
+        from common.utils.model_utils import replace_variable_in_template
+
+        # Find all tenant_* variables and replace them with tenant attribute values
+        # Format of variables: tenant_name, tenant_code, etc.
+        result = text
+
+        # Get all attributes of the tenant object
+        tenant_attrs = vars(self.tenant)
+
+        # Replace all tenant_* variables
+        for attr_name, attr_value in tenant_attrs.items():
+            variable = f"tenant_{attr_name}"
+            if variable in result:
+                result = replace_variable_in_template(result, variable, str(attr_value))
+
+        return result
+
     @abstractmethod
     def execute_specialist(self, arguments: SpecialistArguments) -> SpecialistResult:
         """Execute the specialist's logic"""

eveai_chat_workers/specialists/crewai_base_specialist.py

@@ -33,10 +33,6 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
     def __init__(self, tenant_id: int, specialist_id: int, session_id: str, task_id):
         super().__init__(tenant_id, specialist_id, session_id, task_id)
 
-        # Check and load the specialist
-        self.specialist = Specialist.query.get_or_404(specialist_id)
-        # Set the specific configuration for the SPIN Specialist
-        # self.specialist_configuration = json.loads(self.specialist.configuration)
         self.tuning = self.specialist.tuning
         # Initialize retrievers
         self.retrievers = self._initialize_retrievers()
@@ -54,15 +50,20 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
         self._task_pydantic_outputs: Dict[str, Type[BaseModel]] = {}
         self._task_state_names: Dict[str, str] = {}
 
-        # Processed configurations
+        # State-Result relations (for adding / restoring information to / from history
+        self._state_result_relations: Dict[str, str] = {}
+
+        # Process configurations
         self._config = cache_manager.crewai_processed_config_cache.get_specialist_config(tenant_id, specialist_id)
         self._config_task_agents()
         self._config_pydantic_outputs()
         self._instantiate_crew_assets()
         self._instantiate_specialist()
+        self._config_state_result_relations()
 
         # Retrieve history
         self._cached_session = cache_manager.chat_session_cache.get_cached_session(self.session_id)
+        self._restore_state_from_history()
         # Format history for the prompt
         self._formatted_history = self._generate_formatted_history()
 
@@ -110,6 +111,19 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
         """Configure the task pydantic outputs by adding task-output combinations. Use _add_pydantic_output()"""
         raise NotImplementedError
 
+    def _add_state_result_relation(self, state_name: str, result_name: str = None):
+        """Add a state-result relation to the specialist. This is used to add information to the history
+           If result_name is None, the state name is used as the result name. (default behavior)
+        """
+        if not result_name:
+            result_name = state_name
+        self._state_result_relations[state_name] = result_name
+
+    @abstractmethod
+    def _config_state_result_relations(self):
+        """Configure the state-result relations by adding state-result combinations. Use _add_state_result_relation()"""
+        raise NotImplementedError
+
     @property
     def task_pydantic_outputs(self):
         return self._task_pydantic_outputs
@@ -127,7 +141,9 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
         for agent in self.specialist.agents:
             agent_config = cache_manager.agents_config_cache.get_config(agent.type, agent.type_version)
             agent_role = agent_config.get('role', '').replace('{custom_role}', agent.role or '')
+            agent_role = self._replace_system_variables(agent_role)
             agent_goal = agent_config.get('goal', '').replace('{custom_goal}', agent.goal or '')
+            agent_goal = self._replace_system_variables(agent_goal)
             agent_backstory = agent_config.get('backstory', '').replace('{custom_backstory}', agent.backstory or '')
             agent_full_model_name = agent_config.get('full_model_name', 'mistral.mistral-large-latest')
             agent_temperature = agent_config.get('temperature', 0.3)
@@ -152,6 +168,7 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
             task_config = cache_manager.tasks_config_cache.get_config(task.type, task.type_version)
             task_description = (task_config.get('task_description', '')
                                 .replace('{custom_description}', task.task_description or ''))
+            task_description = self._replace_system_variables(task_description)
             task_expected_output = (task_config.get('expected_output', '')
                                     .replace('{custom_expected_output}', task.expected_output or ''))
             # dynamically build the arguments
@@ -161,9 +178,12 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
                 "verbose": task.tuning
             }
             task_name = task.type.lower()
+            current_app.logger.debug(f"Task {task_name} is getting processed")
             if task_name in self._task_pydantic_outputs:
                 task_kwargs["output_pydantic"] = self._task_pydantic_outputs[task_name]
+                current_app.logger.debug(f"Task {task_name} has an output pydantic: {self._task_pydantic_outputs[task_name]}")
             if task_name in self._task_agents:
+                current_app.logger.debug(f"Task {task_name} has an agent: {self._task_agents[task_name]}")
                 task_kwargs["agent"] = self._agents[self._task_agents[task_name]]
 
             # Instantiate the task with dynamic arguments
@@ -328,6 +348,27 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
 
         return formatted_context, citations
 
+    def _update_specialist_results(self, specialist_results: SpecialistResult) -> SpecialistResult:
+        """Update the specialist results with the latest state information"""
+        update_data = {}
+        state_dict = self.flow.state.model_dump()
+        for state_name, result_name in self._state_result_relations.items():
+            if state_name in state_dict and state_dict[state_name] is not None:
+                update_data[result_name] = state_dict[state_name]
+
+        return specialist_results.model_copy(update=update_data)
+
+    def _restore_state_from_history(self):
+        """Restore the state from the history"""
+        if not self._cached_session.interactions:
+            return
+        last_interaction = self._cached_session.interactions[-1]
+        if not last_interaction.specialist_results:
+            return
+        for state_name, result_name in self._state_result_relations.items():
+            if result_name in last_interaction.specialist_results:
+                setattr(self.flow.state, state_name, last_interaction.specialist_results[result_name])
+
     @abstractmethod
     def execute(self, arguments: SpecialistArguments, formatted_context: str, citations: List[int]) -> SpecialistResult:
         raise NotImplementedError
@@ -354,8 +395,10 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
                 "detailed_query": detailed_query,
                 "citations": citations,
             }
-            final_result = result.model_copy(update=modified_result)
+            intermediate_result = result.model_copy(update=modified_result)
         else:
-            final_result = self.execute(arguments, "", [])
+            intermediate_result = self.execute(arguments, "", [])
+
+        final_result = self._update_specialist_results(intermediate_result)
 
         return final_result

eveai_chat_workers/specialists/traicie/TRAICIE_ROLE_DEFINITION_SPECIALIST/1_3.py

@@ -18,6 +18,9 @@ from eveai_chat_workers.outputs.traicie.competencies.competencies_v1_1 import Co
 from eveai_chat_workers.specialists.crewai_base_classes import EveAICrewAICrew, EveAICrewAIFlow, EveAIFlowState
 from common.services.interaction.specialist_services import SpecialistServices
 
+NEW_SPECIALIST_TYPE = "TRAICIE_SELECTION_SPECIALIST"
+NEW_SPECIALIST_TYPE_VERSION = "1.3"
+
 
 class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
     """
@@ -117,8 +120,8 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
         new_specialist = Specialist(
             name=name,
             description=f"Specialist for {arguments.role_name} role",
-            type="TRAICIE_SELECTION_SPECIALIST",
-            type_version="1.1",
+            type=NEW_SPECIALIST_TYPE,
+            type_version=NEW_SPECIALIST_TYPE_VERSION,
             tuning=False,
             configuration=selection_config,
         )
@@ -130,7 +133,7 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
             current_app.logger.error(f"Error creating selection specialist: {str(e)}")
             raise e
 
-        SpecialistServices.initialize_specialist(new_specialist.id, "TRAICIE_SELECTION_SPECIALIST", "1.0")
+        SpecialistServices.initialize_specialist(new_specialist.id, NEW_SPECIALIST_TYPE, NEW_SPECIALIST_TYPE_VERSION)
 
 
 

eveai_chat_workers/specialists/traicie/TRAICIE_SELECTION_SPECIALIST/1_3.py

@@ -0,0 +1,350 @@
+import asyncio
+import json
+from os import wait
+from typing import Optional, List, Dict, Any
+from datetime import date
+from time import sleep
+from crewai.flow.flow import start, listen, and_
+from flask import current_app
+from pydantic import BaseModel, Field, EmailStr
+from sqlalchemy.exc import SQLAlchemyError
+
+from common.extensions import db
+from common.models.user import Tenant
+from common.models.interaction import Specialist
+from eveai_chat_workers.outputs.globals.basic_types.list_item import ListItem
+from eveai_chat_workers.outputs.traicie.knockout_questions.knockout_questions_v1_0 import KOQuestions, KOQuestion
+from eveai_chat_workers.specialists.crewai_base_specialist import CrewAIBaseSpecialistExecutor
+from eveai_chat_workers.specialists.specialist_typing import SpecialistResult, SpecialistArguments
+from eveai_chat_workers.outputs.traicie.competencies.competencies_v1_1 import Competencies
+from eveai_chat_workers.specialists.crewai_base_classes import EveAICrewAICrew, EveAICrewAIFlow, EveAIFlowState
+from common.services.interaction.specialist_services import SpecialistServices
+from common.extensions import cache_manager
+from eveai_chat_workers.definitions.language_level.language_level_v1_0 import LANGUAGE_LEVEL
+from eveai_chat_workers.definitions.tone_of_voice.tone_of_voice_v1_0 import TONE_OF_VOICE
+from common.utils.eveai_exceptions import EveAISpecialistExecutionError
+
+
+class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
+    """
+    type: TRAICIE_SELECTION_SPECIALIST
+    type_version: 1.1
+    Traicie Selection Specialist Executor class
+    """
+
+    def __init__(self, tenant_id, specialist_id, session_id, task_id, **kwargs):
+        self.role_definition_crew = None
+
+        super().__init__(tenant_id, specialist_id, session_id, task_id)
+
+        # Load the Tenant & set language
+        self.tenant = Tenant.query.get_or_404(tenant_id)
+
+    @property
+    def type(self) -> str:
+        return "TRAICIE_SELECTION_SPECIALIST"
+
+    @property
+    def type_version(self) -> str:
+        return "1.3"
+
+    def _config_task_agents(self):
+        self._add_task_agent("traicie_ko_criteria_interview_definition_task", "traicie_recruiter_agent")
+
+    def _config_pydantic_outputs(self):
+        self._add_pydantic_output("traicie_ko_criteria_interview_definition_task", KOQuestions, "ko_questions")
+
+    def _config_state_result_relations(self):
+        self._add_state_result_relation("ko_criteria_questions")
+        self._add_state_result_relation("ko_criteria_scores")
+        self._add_state_result_relation("competency_questions")
+        self._add_state_result_relation("competency_scores")
+        self._add_state_result_relation("personal_contact_data")
+
+    def _instantiate_specialist(self):
+        verbose = self.tuning
+
+        ko_def_agents = [self.traicie_recruiter_agent]
+        ko_def_tasks = [self.traicie_ko_criteria_interview_definition_task]
+        self.ko_def_crew = EveAICrewAICrew(
+            self,
+            "KO Criteria Interview Definition Crew",
+            agents=ko_def_agents,
+            tasks=ko_def_tasks,
+            verbose=verbose,
+        )
+
+        self.flow = SelectionFlow(
+            self,
+            self.ko_def_crew
+        )
+
+    def execute(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
+        self.log_tuning("Traicie Selection Specialist execution started", {})
+
+        current_app.logger.debug(f"Arguments: {arguments.model_dump()}")
+        current_app.logger.debug(f"Formatted Context: {formatted_context}")
+        current_app.logger.debug(f"Formatted History: {self._formatted_history}")
+        current_app.logger.debug(f"Cached Chat Session: {self._cached_session}")
+
+        if not self._cached_session.interactions:
+            specialist_phase = "initial"
+        else:
+            specialist_phase = self._cached_session.interactions[-1].specialist_results.get('phase', 'initial')
+
+        results = None
+
+        match specialist_phase:
+            case "initial":
+                results = self.execute_initial_state(arguments, formatted_context, citations)
+            case "ko_question_evaluation":
+                results = self.execute_ko_question_evaluation(arguments, formatted_context, citations)
+            case "personal_contact_data":
+                results = self.execute_personal_contact_data(arguments, formatted_context, citations)
+            case "no_valid_candidate":
+                results = self.execute_no_valid_candidate(arguments, formatted_context, citations)
+            case "candidate_selected":
+                results = self.execute_candidate_selected(arguments, formatted_context, citations)
+
+        self.log_tuning(f"Traicie Selection Specialist execution ended", {"Results": results.model_dump() if results else "No info"})
+
+        return results
+
+
+    def execute_initial_state(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
+        self.log_tuning("Traicie Selection Specialist initial_state_execution started", {})
+
+        current_app.logger.debug(f"Specialist Competencies:\n{self.specialist.configuration.get("competencies", [])}")
+
+        ko_competencies = []
+        for competency in self.specialist.configuration.get("competencies", []):
+            if competency["is_knockout"] is True and competency["assess"] is True:
+                current_app.logger.debug(f"Assessable Knockout competency: {competency}")
+                ko_competencies.append({"title: ": competency["title"], "description": competency["description"]})
+
+        tone_of_voice = self.specialist.configuration.get('tone_of_voice', 'Professional & Neutral')
+        selected_tone_of_voice = next(
+            (item for item in TONE_OF_VOICE if item["name"] == tone_of_voice),
+            None  # fallback indien niet gevonden
+        )
+        current_app.logger.debug(f"Selected tone of voice: {selected_tone_of_voice}")
+        tone_of_voice_context = f"{selected_tone_of_voice["description"]}"
+
+        language_level = self.specialist.configuration.get('language_level', 'Standard')
+        selected_language_level = next(
+            (item for item in LANGUAGE_LEVEL if item["name"] == language_level),
+            None
+        )
+        current_app.logger.debug(f"Selected language level: {selected_language_level}")
+        language_level_context = (f"{selected_language_level['description']}, "
+                                  f"corresponding to CEFR level {selected_language_level['cefr_level']}")
+
+        flow_inputs = {
+            "region": arguments.region,
+            "working_schedule": arguments.working_schedule,
+            "start_date": arguments.start_date,
+            "language": arguments.language,
+            "interaction_mode": arguments.interaction_mode,
+            'tone_of_voice': tone_of_voice,
+            'tone_of_voice_context': tone_of_voice_context,
+            'language_level': language_level,
+            'language_level_context': language_level_context,
+            'ko_criteria': ko_competencies,
+        }
+
+        flow_results = self.flow.kickoff(inputs=flow_inputs)
+
+        current_app.logger.debug(f"Flow results: {flow_results}")
+
+        current_app.logger.debug(f"Flow state: {self.flow.state}")
+
+        fields = {}
+        for ko_question in self.flow.state.ko_criteria_questions:
+            fields[ko_question.title] = {
+                "name": ko_question.title,
+                "description": ko_question.title,
+                "context": ko_question.question,
+                "type": "options",
+                "required": True,
+                "allowed_values": [ko_question.answer_positive, ko_question.answer_negative]
+            }
+
+        ko_form = {
+            "type": "KO_CRITERIA_FORM",
+            "version": "1.0.0",
+            "name": "Starter Questions",
+            "icon": "verified",
+            "fields": fields,
+        }
+
+        results = SpecialistResult.create_for_type(self.type, self.type_version,
+                                                   answer=f"We starten met een aantal KO Criteria vragen",
+                                                   form_request=ko_form,
+                                                   phase="ko_question_evaluation")
+
+        return results
+
+    def execute_ko_question_evaluation(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
+        self.log_tuning("Traicie Selection Specialist ko_question_evaluation started", {})
+
+        # Check if the form has been returned (it should)
+        if not arguments.form_values:
+            raise EveAISpecialistExecutionError(self.tenant_id, self.specialist_id, self.session_id, "No form values returned")
+        current_app.logger.debug(f"Form values: {arguments.form_values}")
+
+        # Load the previous KO Questions
+        previous_ko_questions = self.flow.state.ko_criteria_questions
+        current_app.logger.debug(f"Previous KO Questions: {previous_ko_questions}")
+
+        # Evaluate KO Criteria
+        evaluation = "positive"
+        for criterium, answer in arguments.form_values.items():
+            for qa in previous_ko_questions:
+                if qa.get("title") == criterium:
+                    if qa.get("answer_positive") != answer:
+                        evaluation = "negative"
+                        break
+            if evaluation == "negative":
+                break
+
+        if evaluation == "negative":
+            results = SpecialistResult.create_for_type(self.type, self.type_version,
+                                                       answer=f"We hebben de antwoorden op de KO criteria verwerkt. Je voldoet jammer genoeg niet aan de minimale vereisten voor deze job.",
+                                                       form_request=None,
+                                                       phase="no_valid_candidate")
+        else:
+            # Check if answers to questions are positive
+            contact_form = cache_manager.specialist_forms_config_cache.get_config("PERSONAL_CONTACT_FORM", "1.0")
+            results = SpecialistResult.create_for_type(self.type, self.type_version,
+                                                       answer=f"We hebben de antwoorden op de KO criteria verwerkt. Je bent een geschikte kandidaat. Kan je je contactegevens doorgeven?",
+                                                       form_request=contact_form,
+                                                       phase="personal_contact_data")
+
+        return results
+
+    def execute_personal_contact_data(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
+        self.log_tuning("Traicie Selection Specialist personal_contact_data started", {})
+
+        results = SpecialistResult.create_for_type(self.type, self.type_version,
+                                                   answer=f"We hebben de contactgegevens verwerkt. We nemen zo snel mogelijk contact met je op.",
+                                                   phase="candidate_selected")
+        return results
+
+    def execute_no_valid_candidate(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
+        self.log_tuning("Traicie Selection Specialist no_valid_candidate started", {})
+        results = SpecialistResult.create_for_type(self.type, self.type_version,
+                                                   answer=f"Je voldoet jammer genoeg niet aan de minimale vereisten voor deze job. Maar solliciteer gerust voor één van onze andere jobs.",
+                                                   phase="no_valid_candidate")
+
+    def execute_candidate_selected(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
+        self.log_tuning("Traicie Selection Specialist candidate_selected started", {})
+        results = SpecialistResult.create_for_type(self.type, self.type_version,
+                                                   answer=f"We hebben je contactgegegevens verwerkt. We nemen zo snel mogelijk contact met je op.",
+                                                   phase="candidate_selected")
+        return results
+
+
+class SelectionInput(BaseModel):
+    region: str = Field(..., alias="region")
+    working_schedule: Optional[str] = Field(..., alias="working_schedule")
+    start_date: Optional[date] = Field(None, alias="vacancy_text")
+    language: Optional[str] = Field(None, alias="language")
+    interaction_mode: Optional[str] = Field(None, alias="interaction_mode")
+    tone_of_voice: Optional[str] = Field(None, alias="tone_of_voice")
+    tone_of_voice_context: Optional[str] = Field(None, alias="tone_of_voice_context")
+    language_level: Optional[str] = Field(None, alias="language_level")
+    language_level_context: Optional[str] = Field(None, alias="language_level_context")
+    ko_criteria: Optional[List[Dict[str, str]]] = Field(None, alias="ko_criteria")
+    question: Optional[str] = Field(None, alias="question")
+    field_values: Optional[Dict[str, Any]] = Field(None, alias="field_values")
+
+
+class SelectionKOCriteriumScore(BaseModel):
+    criterium: Optional[str] = Field(None, alias="criterium")
+    answer: Optional[str] = Field(None, alias="answer")
+    score: Optional[int] = Field(None, alias="score")
+
+
+class SelectionCompetencyScore(BaseModel):
+    competency: Optional[str] = Field(None, alias="competency")
+    answer: Optional[str] = Field(None, alias="answer")
+    score: Optional[int] = Field(None, alias="score")
+
+
+class PersonalContactData(BaseModel):
+    name: str = Field(..., description="Your name", alias="name")
+    email: EmailStr = Field(..., description="Your Name", alias="email")
+    phone: str = Field(..., description="Your Phone Number", alias="phone")
+    address: Optional[str] = Field(None, description="Your Address", alias="address")
+    zip: Optional[str] = Field(None, description="Postal Code", alias="zip")
+    city: Optional[str] = Field(None, description="City", alias="city")
+    country: Optional[str] = Field(None, description="Country", alias="country")
+    consent: bool = Field(..., description="Consent", alias="consent")
+
+
+class SelectionResult(SpecialistResult):
+    ko_criteria_questions: Optional[List[ListItem]] = Field(None, alias="ko_criteria_questions")
+    ko_criteria_scores: Optional[List[SelectionKOCriteriumScore]] = Field(None, alias="ko_criteria_scores")
+    competency_questions: Optional[List[ListItem]] = Field(None, alias="competency_questions")
+    competency_scores: Optional[List[SelectionCompetencyScore]] = Field(None, alias="competency_scores")
+    personal_contact_data: Optional[PersonalContactData] = Field(None, alias="personal_contact_data")
+
+
+class SelectionFlowState(EveAIFlowState):
+    """Flow state for Traicie Role Definition specialist that automatically updates from task outputs"""
+    input: Optional[SelectionInput] = None
+    ko_criteria_questions: Optional[List[KOQuestion]] = Field(None, alias="ko_criteria_questions")
+    ko_criteria_scores: Optional[List[SelectionKOCriteriumScore]] = Field(None, alias="ko_criteria_scores")
+    competency_questions: Optional[List[ListItem]] = Field(None, alias="competency_questions")
+    competency_scores: Optional[List[SelectionCompetencyScore]] = Field(None, alias="competency_scores")
+    personal_contact_data: Optional[PersonalContactData] = Field(None, alias="personal_contact_data")
+    phase: Optional[str] = Field(None, alias="phase")
+    interaction_mode: Optional[str] = Field(None, alias="mode")
+
+
+class SelectionFlow(EveAICrewAIFlow[SelectionFlowState]):
+    def __init__(self,
+                 specialist_executor: CrewAIBaseSpecialistExecutor,
+                 ko_def_crew: EveAICrewAICrew,
+                 **kwargs):
+        super().__init__(specialist_executor, "Traicie Role Definition Specialist Flow", **kwargs)
+        self.specialist_executor = specialist_executor
+        self.ko_def_crew = ko_def_crew
+        self.exception_raised = False
+
+    @start()
+    def process_inputs(self):
+        return ""
+
+    @listen(process_inputs)
+    async def execute_ko_def_definition(self):
+        inputs = self.state.input.model_dump()
+        try:
+            current_app.logger.debug("execute_ko_interview_definition")
+            crew_output = await self.ko_def_crew.kickoff_async(inputs=inputs)
+            # Unfortunately, crew_output will only contain the output of the latest task.
+            # As we will only take into account the flow state, we need to ensure both competencies and criteria
+            # are copies to the flow state.
+            update = {}
+            for task in self.ko_def_crew.tasks:
+                current_app.logger.debug(f"Task {task.name} output:\n{task.output}")
+                if task.name == "traicie_ko_criteria_interview_definition_task":
+                    # update["competencies"] = task.output.pydantic.competencies
+                    self.state.ko_criteria_questions = task.output.pydantic.ko_questions
+            # crew_output.pydantic = crew_output.pydantic.model_copy(update=update)
+            self.state.phase = "personal_contact_data"
+            current_app.logger.debug(f"State after execute_ko_def_definition: {self.state}")
+            current_app.logger.debug(f"State dump after execute_ko_def_definition: {self.state.model_dump()}")
+            return crew_output
+        except Exception as e:
+            current_app.logger.error(f"CREW execute_ko_def Kickoff Error: {str(e)}")
+            self.exception_raised = True
+            raise e
+
+    async def kickoff_async(self, inputs=None):
+        current_app.logger.debug(f"Async kickoff {self.name}")
+        current_app.logger.debug(f"Inputs: {inputs}")
+        self.state.input = SelectionInput.model_validate(inputs)
+        current_app.logger.debug(f"State: {self.state}")
+        result = await super().kickoff_async(inputs)
+        return self.state

eveai_entitlements/__init__.py

@@ -5,7 +5,7 @@ import os
 
 from common.utils.celery_utils import make_celery, init_celery
 from common.extensions import db, minio_client, cache_manager
-from config.logging_config import LOGGING
+from config.logging_config import configure_logging
 from config.config import get_config
 
 
@@ -22,7 +22,7 @@ def create_app(config_file=None):
         case _:
             app.config.from_object(get_config('dev'))
 
-    logging.config.dictConfig(LOGGING)
+    configure_logging()
 
     register_extensions(app)
 

eveai_workers/__init__.py

@@ -5,7 +5,7 @@ import os
 
 from common.utils.celery_utils import make_celery, init_celery
 from common.extensions import db, minio_client, cache_manager
-import config.logging_config as logging_config
+from config.logging_config import configure_logging
 from config.config import get_config
 
 
@@ -22,7 +22,7 @@ def create_app(config_file=None):
         case _:
             app.config.from_object(get_config('dev'))
 
-    logging.config.dictConfig(logging_config.LOGGING)
+    configure_logging()
 
     register_extensions(app)
 

logs/.gitkeep

@@ -0,0 +1,2 @@
+# Deze directory bevat logbestanden
+# .gitkeep zorgt ervoor dat de directory wordt meegenomen in Git

requirements-k8s.txt

@@ -0,0 +1,2 @@
+# Extra vereisten voor Kubernetes-omgeving
+python-json-logger>=2.0.7

requirements.txt

@@ -93,3 +93,5 @@ prometheus_client~=0.21.1
 scaleway~=2.9.0
 html2text~=2025.4.15
 markdown~=3.8
+python-json-logger~=2.0.7
+qrcode[pil]==8.2

scripts/check_logs.py

@@ -0,0 +1,102 @@
+#!/usr/bin/env python
+
+"""
+Dit script controleert of de logs directory bestaat en toegankelijk is,
+en test of logging correct werkt.
+"""
+
+import os
+import sys
+import logging
+import traceback
+
+def check_logs_directory():
+    # Verkrijg het absolute pad naar de logs directory
+    base_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
+    logs_dir = os.path.join(base_dir, 'logs')
+
+    print(f"\nControleren van logs directory: {logs_dir}")
+
+    # Controleer of de directory bestaat
+    if not os.path.exists(logs_dir):
+        print("  - Directory bestaat niet. Proberen aan te maken...")
+        try:
+            os.makedirs(logs_dir, exist_ok=True)
+            print("  - Directory succesvol aangemaakt.")
+        except Exception as e:
+            print(f"  - FOUT: Kan directory niet aanmaken: {e}")
+            return False
+    else:
+        print("  - Directory bestaat.")
+
+    # Controleer schrijfrechten
+    if not os.access(logs_dir, os.W_OK):
+        print("  - FOUT: Geen schrijfrechten voor de logs directory.")
+        return False
+    else:
+        print("  - Directory is schrijfbaar.")
+
+    # Probeer een testbestand te schrijven
+    test_file = os.path.join(logs_dir, 'test_write.log')
+    try:
+        with open(test_file, 'w') as f:
+            f.write('Test schrijven naar logs directory.\n')
+        print(f"  - Succesvol testbestand geschreven naar {test_file}")
+        os.remove(test_file)  # Verwijder het testbestand
+        print("  - Testbestand verwijderd.")
+    except Exception as e:
+        print(f"  - FOUT: Kan niet schrijven naar logs directory: {e}")
+        return False
+
+    return True
+
+def check_logging_config():
+    print("\nControleren van logging configuratie...")
+
+    try:
+        from config.logging_config import configure_logging
+        configure_logging()
+        print("  - Logging configuratie geladen.")
+
+        # Test enkele loggers
+        loggers_to_test = ['eveai_app', 'eveai_workers', 'eveai_api', 'tuning']
+        for logger_name in loggers_to_test:
+            logger = logging.getLogger(logger_name)
+            logger.info(f"Test log bericht van {logger_name}")
+            print(f"  - Logger '{logger_name}' getest.")
+
+        print("  - Alle loggers succesvol getest.")
+        return True
+
+    except Exception as e:
+        print(f"  - FOUT bij laden van logging configuratie: {e}")
+        traceback.print_exc()
+        return False
+
+def main():
+    print("\nEveAI Logging Test Utility")
+    print("===========================\n")
+
+    directory_ok = check_logs_directory()
+    if not directory_ok:
+        print("\nPROBLEEM: De logs directory is niet toegankelijk of schrijfbaar.")
+        print("Oplossingen:")
+        print("  1. Zorg ervoor dat de gebruiker die de applicatie uitvoert schrijfrechten heeft voor de logs directory.")
+        print("  2. Voer het commando uit: mkdir -p logs && chmod 777 logs")
+
+    config_ok = check_logging_config()
+    if not config_ok:
+        print("\nPROBLEEM: De logging configuratie kon niet worden geladen.")
+        print("Controleer de config/logging_config.py file.")
+
+    if directory_ok and config_ok:
+        print("\nALLES OK: Logging lijkt correct geconfigureerd.")
+        print("Controleer de logbestanden in de 'logs' directory voor de testberichten.")
+    else:
+        print("\nEr zijn problemen gevonden die opgelost moeten worden.")
+        return 1
+
+    return 0
+
+if __name__ == "__main__":
+    sys.exit(main())