feat: langgraph as state manager

config.ini

@@ -34,4 +34,19 @@ Web_Crawl_RETRY_DELAY = 2
 # Web Research configuration
 Web_Research_MAX_DEPTH = 2
 Web_Research_MAX_LINKS_PER_DOMAIN = 4
-Web_Research_CRAWL_TIMEOUT_MINUTES = 10
+<<<<<<< Updated upstream
+Web_Research_CRAWL_TIMEOUT_MINUTES = 10
+=======
+Web_Research_CRAWL_TIMEOUT_MINUTES = 10
+
+# STAC API Connector configuration (Swiss Topo)
+Connector_StacSwisstopo_BASE_URL = https://data.geo.admin.ch/api/stac/v1
+Connector_StacSwisstopo_TIMEOUT = 30
+Connector_StacSwisstopo_MAX_RETRIES = 3
+Connector_StacSwisstopo_RETRY_DELAY = 1.0
+Connector_StacSwisstopo_ENABLE_CACHE = True
+
+# Tavily AI Connector configuration (Web Search & Research)
+# Get your API key from https://tavily.com
+# Connector_AiTavily_API_SECRET = your_tavily_api_key_here
+>>>>>>> Stashed changes

modules/features/chatbot/__init__.py

@@ -1,7 +1,9 @@
 # Copyright (c) 2025 Patrick Motsch
 # All rights reserved.
+"""
+Chatbot feature - LangGraph-based chatbot implementation.
+"""
 
-from .mainChatbot import chatProcess
+from .service import chatProcess
 
 __all__ = ['chatProcess']
-

modules/features/chatbot/bridges/__init__.py

@@ -0,0 +1,3 @@
+# Copyright (c) 2025 Patrick Motsch
+# All rights reserved.
+"""Bridges to external systems (AI models, database, tools)."""

modules/features/chatbot/bridges/ai.py

@@ -0,0 +1,547 @@
+# Copyright (c) 2025 Patrick Motsch
+# All rights reserved.
+"""
+AI Center to LangChain bridge.
+Implements LangChain BaseChatModel interface using AI center models.
+"""
+
+import logging
+import asyncio
+from typing import Any, AsyncIterator, Dict, List, Optional
+from datetime import datetime
+
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.messages import (
+    BaseMessage,
+    HumanMessage,
+    SystemMessage,
+    AIMessage,
+    ToolMessage,
+    convert_to_openai_messages,
+)
+from langchain_core.outputs import ChatGeneration, ChatResult
+from langchain_core.runnables import RunnableConfig
+
+from modules.aicore.aicoreModelRegistry import modelRegistry
+from modules.aicore.aicoreModelSelector import modelSelector
+from modules.datamodels.datamodelAi import (
+    AiModel,
+    AiModelCall,
+    AiModelResponse,
+    AiCallOptions,
+    OperationTypeEnum,
+    ProcessingModeEnum,
+)
+from modules.datamodels.datamodelUam import User
+
+logger = logging.getLogger(__name__)
+
+
+class AICenterChatModel(BaseChatModel):
+    """
+    LangChain-compatible chat model that uses AI center models.
+    Bridges AI center model selection and calling to LangChain's BaseChatModel interface.
+    """
+    
+    def __init__(
+        self,
+        user: User,
+        operation_type: OperationTypeEnum = OperationTypeEnum.DATA_ANALYSE,
+        processing_mode: ProcessingModeEnum = ProcessingModeEnum.DETAILED,
+        **kwargs
+    ):
+        """
+        Initialize the AI center chat model bridge.
+        
+        Args:
+            user: Current user for RBAC and model selection
+            operation_type: Operation type for model selection
+            processing_mode: Processing mode for model selection
+            **kwargs: Additional arguments passed to BaseChatModel
+        """
+        super().__init__(**kwargs)
+        # Use object.__setattr__ to bypass Pydantic validation for custom attributes
+        object.__setattr__(self, "user", user)
+        object.__setattr__(self, "operation_type", operation_type)
+        object.__setattr__(self, "processing_mode", processing_mode)
+        object.__setattr__(self, "_selected_model", None)
+        
+    @property
+    def _llm_type(self) -> str:
+        """Return type of LLM."""
+        return "aicenter"
+    
+    def _select_model(self, messages: List[BaseMessage]) -> AiModel:
+        """
+        Select the best AI center model for the given messages.
+        
+        Args:
+            messages: List of LangChain messages
+            
+        Returns:
+            Selected AI model
+        """
+        # Convert messages to prompt/context format for model selector
+        prompt_parts = []
+        context_parts = []
+        
+        for msg in messages:
+            if isinstance(msg, SystemMessage):
+                prompt_parts.append(msg.content)
+            elif isinstance(msg, HumanMessage):
+                prompt_parts.append(msg.content)
+            elif isinstance(msg, AIMessage):
+                context_parts.append(msg.content)
+            elif isinstance(msg, ToolMessage):
+                context_parts.append(f"Tool {msg.name}: {msg.content}")
+        
+        prompt = "\n".join(prompt_parts)
+        context = "\n".join(context_parts) if context_parts else ""
+        
+        # Get available models with RBAC filtering
+        from modules.security.rbac import RbacClass
+        from modules.security.rootAccess import getRootDbAppConnector
+        from modules.connectors.connectorDbPostgre import DatabaseConnector
+        from modules.shared.configuration import APP_CONFIG
+        
+        # Get database connectors for RBAC
+        # Create a database connector instance for RBAC with proper configuration
+        dbHost = APP_CONFIG.get("DB_MANAGEMENT_HOST")
+        dbDatabase = APP_CONFIG.get("DB_MANAGEMENT_DATABASE", "management")
+        dbUser = APP_CONFIG.get("DB_MANAGEMENT_USER")
+        dbPassword = APP_CONFIG.get("DB_MANAGEMENT_PASSWORD_SECRET")
+        dbPort = int(APP_CONFIG.get("DB_MANAGEMENT_PORT"))
+        
+        db = DatabaseConnector(
+            dbHost=dbHost,
+            dbDatabase=dbDatabase,
+            dbUser=dbUser,
+            dbPassword=dbPassword,
+            dbPort=dbPort,
+            userId=self.user.id if hasattr(self.user, 'id') else None
+        )
+        dbApp = getRootDbAppConnector()
+        rbac_instance = RbacClass(db, dbApp=dbApp)
+        
+        available_models = modelRegistry.getAvailableModels(
+            currentUser=self.user,
+            rbacInstance=rbac_instance
+        )
+        
+        # Create options for model selector
+        options = AiCallOptions(
+            operationType=self.operation_type,
+            processingMode=self.processing_mode
+        )
+        
+        # Select model
+        selected_model = modelSelector.selectModel(
+            prompt=prompt,
+            context=context,
+            options=options,
+            availableModels=available_models
+        )
+        
+        if not selected_model:
+            raise ValueError(f"No suitable model found for operation type {self.operation_type.value}")
+        
+        logger.info(f"Selected AI center model: {selected_model.displayName} ({selected_model.name})")
+        object.__setattr__(self, "_selected_model", selected_model)
+        return selected_model
+    
+    def _convert_messages_to_ai_format(self, messages: List[BaseMessage]) -> List[Dict[str, Any]]:
+        """
+        Convert LangChain messages to AI center format (OpenAI-style).
+        
+        Args:
+            messages: List of LangChain messages
+            
+        Returns:
+            List of messages in OpenAI format
+        """
+        # Use LangChain's built-in conversion
+        openai_messages = convert_to_openai_messages(messages)
+        return openai_messages
+    
+    def _convert_ai_response_to_langchain(
+        self,
+        response: AiModelResponse,
+        tool_calls: Optional[List[Dict[str, Any]]] = None
+    ) -> AIMessage:
+        """
+        Convert AI center response to LangChain AIMessage.
+        
+        Args:
+            response: AI center response
+            tool_calls: Optional tool calls from the response (format: [{"id": "...", "name": "...", "args": {...}}])
+            
+        Returns:
+            LangChain AIMessage with tool_calls if present
+        """
+        # LangChain expects tool_calls in format: [{"id": "...", "name": "...", "args": {...}}]
+        # The tool_calls parameter should already be in this format
+        
+        kwargs = {}
+        if tool_calls:
+            kwargs["tool_calls"] = tool_calls
+        
+        return AIMessage(content=response.content or "", **kwargs)
+    
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[Any] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        """
+        Synchronous generate method required by BaseChatModel.
+        Wraps the async _agenerate method.
+        
+        Args:
+            messages: List of LangChain messages
+            stop: Optional stop sequences
+            run_manager: Optional callback manager
+            **kwargs: Additional arguments
+            
+        Returns:
+            ChatResult with generations
+        """
+        # Try to get the current event loop
+        try:
+            loop = asyncio.get_event_loop()
+            if loop.is_running():
+                # If we're in an async context, raise an error
+                raise RuntimeError(
+                    "AICenterChatModel._generate() called from async context. "
+                    "Use _agenerate() instead."
+                )
+        except RuntimeError:
+            # No event loop, we can create one
+            pass
+        
+        # Run the async method synchronously
+        return asyncio.run(self._agenerate(messages, stop=stop, run_manager=run_manager, **kwargs))
+    
+    async def _agenerate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[Any] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        """
+        Async generate method required by BaseChatModel.
+        
+        Args:
+            messages: List of LangChain messages
+            stop: Optional stop sequences
+            run_manager: Optional callback manager
+            **kwargs: Additional arguments (may include tools for tool calling)
+            
+        Returns:
+            ChatResult with generations
+        """
+        # Select model if not already selected
+        if not self._selected_model:
+            self._select_model(messages)
+        
+        # Check if tools are bound (for tool calling)
+        tools = getattr(self, "_bound_tools", None)
+        
+        # Convert messages to AI center format
+        ai_messages = self._convert_messages_to_ai_format(messages)
+        
+        # If tools are bound, add tool definitions to the system message
+        # This ensures the model knows about available tools
+        # Some models need explicit tool definitions to enable tool calling
+        if tools:
+            # Find or create system message
+            system_message_idx = None
+            for i, msg in enumerate(ai_messages):
+                if msg.get("role") == "system":
+                    system_message_idx = i
+                    break
+            
+            # Build tool descriptions for the system message
+            tool_descriptions = []
+            for tool in tools:
+                if hasattr(tool, "name") and hasattr(tool, "description"):
+                    # Get tool parameters for better description
+                    args_schema = getattr(tool, "args_schema", None)
+                    params_info = ""
+                    if args_schema:
+                        try:
+                            if hasattr(args_schema, "model_json_schema"):
+                                schema = args_schema.model_json_schema()
+                                if "properties" in schema:
+                                    params = list(schema["properties"].keys())
+                                    params_info = f" (Parameter: {', '.join(params)})"
+                        except:
+                            pass
+                    tool_descriptions.append(f"- {tool.name}: {tool.description}{params_info}")
+            
+            if tool_descriptions:
+                tools_text = "\n".join(tool_descriptions)
+                tools_note = f"\n\n⚠️⚠️⚠️ KRITISCH - TOOL-NUTZUNG ⚠️⚠️⚠️\n\nVERFÜGBARE TOOLS:\n{tools_text}\n\nABSOLUT VERBINDLICH:\n- Du MUSST diese Tools verwenden, um Anfragen zu bearbeiten!\n- Für Status-Updates MUSST du IMMER das Tool 'send_streaming_message' verwenden!\n- VERBOTEN: Normale Text-Nachrichten für Status-Updates!\n- Du MUSST Tools aufrufen, nicht nur darüber sprechen!\n\nBeispiel FALSCH: \"Ich werde die Datenbank durchsuchen...\"\nBeispiel RICHTIG: Rufe das Tool 'send_streaming_message' mit \"Durchsuche Datenbank...\" auf!"
+                
+                if system_message_idx is not None:
+                    # Append to existing system message
+                    ai_messages[system_message_idx]["content"] += tools_note
+                else:
+                    # Add new system message at the beginning
+                    ai_messages.insert(0, {
+                        "role": "system",
+                        "content": tools_note.strip()
+                    })
+        
+        # Convert LangChain tools to OpenAI tool format for potential use
+        # Note: The actual tool calling is handled by the connector if it supports it
+        # This conversion is kept for potential future use or connector support
+        openai_tools = None
+        if tools and self._selected_model.connectorType == "openai":
+            # Convert LangChain tools to OpenAI tool format
+            openai_tools = []
+            for tool in tools:
+                if hasattr(tool, "name") and hasattr(tool, "description"):
+                    # Get tool parameters schema
+                    args_schema = getattr(tool, "args_schema", None)
+                    parameters = {}
+                    if args_schema:
+                        # Check if it's a Pydantic model class or instance
+                        from pydantic import BaseModel
+                        
+                        # Check if it's a class (not an instance)
+                        if isinstance(args_schema, type) and issubclass(args_schema, BaseModel):
+                            # It's a Pydantic model class - get JSON schema
+                            if hasattr(args_schema, "model_json_schema"):
+                                # Pydantic v2
+                                parameters = args_schema.model_json_schema()
+                            elif hasattr(args_schema, "schema"):
+                                # Pydantic v1
+                                parameters = args_schema.schema()
+                        elif isinstance(args_schema, BaseModel):
+                            # It's a Pydantic model instance
+                            if hasattr(args_schema, "model_dump"):
+                                # Pydantic v2
+                                parameters = args_schema.model_dump()
+                            elif hasattr(args_schema, "dict"):
+                                # Pydantic v1
+                                parameters = args_schema.dict()
+                        elif hasattr(args_schema, "schema"):
+                            # Has schema method (might be a class)
+                            try:
+                                parameters = args_schema.schema()
+                            except TypeError:
+                                # If schema() requires instance, try model_json_schema
+                                if hasattr(args_schema, "model_json_schema"):
+                                    parameters = args_schema.model_json_schema()
+                                else:
+                                    parameters = {}
+                        elif isinstance(args_schema, dict):
+                            # Already a dict
+                            parameters = args_schema
+                    
+                    tool_schema = {
+                        "type": "function",
+                        "function": {
+                            "name": tool.name,
+                            "description": tool.description or "",
+                            "parameters": parameters
+                        }
+                    }
+                    openai_tools.append(tool_schema)
+            
+            # Store tools for potential use by connector
+            # Note: The connector may need to access tools from the model_call
+            # This is a workaround since AiModelCall doesn't have a tools field
+            # Tools are added to system message above to ensure model knows about them
+        
+        # Create model call
+        model_call = AiModelCall(
+            messages=ai_messages,
+            model=self._selected_model,
+            options=AiCallOptions(
+                operationType=self.operation_type,
+                processingMode=self.processing_mode,
+                temperature=self._selected_model.temperature
+            )
+        )
+        
+        # If tools are bound and this is an OpenAI model, we need to call the API directly
+        # with tools included, since the connector interface doesn't support tools
+        if openai_tools and self._selected_model.connectorType == "openai":
+            # Call OpenAI API directly with tools (like legacy ChatAnthropic does)
+            import httpx
+            from modules.shared.configuration import APP_CONFIG
+            
+            api_key = APP_CONFIG.get('Connector_AiOpenai_API_SECRET')
+            if not api_key:
+                raise ValueError("OpenAI API key not configured")
+            
+            payload = {
+                "model": self._selected_model.name,
+                "messages": ai_messages,
+                "tools": openai_tools,
+                "tool_choice": "auto",  # Let model decide when to use tools
+                "temperature": self._selected_model.temperature,
+                "max_tokens": self._selected_model.maxTokens
+            }
+            
+            async with httpx.AsyncClient(timeout=600.0) as client:
+                response_obj = await client.post(
+                    self._selected_model.apiUrl,
+                    headers={
+                        "Authorization": f"Bearer {api_key}",
+                        "Content-Type": "application/json"
+                    },
+                    json=payload
+                )
+                
+                if response_obj.status_code != 200:
+                    error_msg = f"OpenAI API error: {response_obj.status_code} - {response_obj.text}"
+                    logger.error(error_msg)
+                    raise ValueError(error_msg)
+                
+                response_json = response_obj.json()
+                choice = response_json["choices"][0]
+                message = choice["message"]
+                
+                # Extract content and tool calls
+                content = message.get("content", "")
+                tool_calls_raw = message.get("tool_calls")
+                
+                # Convert OpenAI tool_calls format to LangChain format
+                # LangChain expects: [{"id": "...", "name": "...", "args": {...}}]
+                tool_calls = None
+                if tool_calls_raw:
+                    tool_calls = []
+                    for tc in tool_calls_raw:
+                        func_data = tc.get("function", {})
+                        func_name = func_data.get("name")
+                        func_args_str = func_data.get("arguments", "{}")
+                        
+                        # Parse JSON arguments string to dict
+                        import json
+                        try:
+                            func_args = json.loads(func_args_str) if isinstance(func_args_str, str) else func_args_str
+                        except:
+                            func_args = {}
+                        
+                        tool_calls.append({
+                            "id": tc.get("id"),
+                            "name": func_name,
+                            "args": func_args
+                        })
+                
+                # Create response object
+                response = AiModelResponse(
+                    content=content or "",
+                    success=True,
+                    modelId=self._selected_model.name,
+                    metadata={
+                        "response_id": response_json.get("id", ""),
+                        "tool_calls": tool_calls
+                    }
+                )
+        else:
+            # No tools or not OpenAI - use connector normally
+            if not self._selected_model.functionCall:
+                raise ValueError(f"Model {self._selected_model.displayName} has no functionCall defined")
+            
+            response: AiModelResponse = await self._selected_model.functionCall(model_call)
+        
+        if not response.success:
+            raise ValueError(f"AI model call failed: {response.error or 'Unknown error'}")
+        
+        # Extract tool calls from response metadata if present
+        tool_calls = None
+        if response.metadata:
+            # Check for tool calls in metadata (format may vary by connector)
+            tool_calls = response.metadata.get("tool_calls") or response.metadata.get("function_calls")
+        
+        # Convert response to LangChain format with tool calls
+        ai_message = self._convert_ai_response_to_langchain(response, tool_calls=tool_calls)
+        
+        # Create generation and result
+        generation = ChatGeneration(message=ai_message)
+        return ChatResult(generations=[generation])
+    
+    def bind_tools(self, tools: List[Any], **kwargs: Any) -> "AICenterChatModel":
+        """
+        Bind tools to the model (required for LangGraph tool calling).
+        
+        Args:
+            tools: List of LangChain tools
+            **kwargs: Additional arguments
+            
+        Returns:
+            New instance with tools bound
+        """
+        # Create a new instance with tools bound
+        # Note: The actual tool binding happens in LangGraph's ToolNode
+        # This method is called by LangGraph to prepare the model
+        bound_model = AICenterChatModel(
+            user=self.user,
+            operation_type=self.operation_type,
+            processing_mode=self.processing_mode
+        )
+        object.__setattr__(bound_model, "_selected_model", self._selected_model)
+        # Store tools for potential use in message conversion
+        object.__setattr__(bound_model, "_bound_tools", tools)
+        return bound_model
+    
+    def invoke(
+        self,
+        input: List[BaseMessage],
+        config: Optional[RunnableConfig] = None,
+        **kwargs: Any,
+    ) -> BaseMessage:
+        """
+        Synchronous invoke method (required by BaseChatModel).
+        Note: This is a wrapper around async _agenerate.
+        
+        Args:
+            input: List of LangChain messages
+            config: Optional runnable config
+            **kwargs: Additional arguments
+            
+        Returns:
+            AIMessage response
+        """
+        import asyncio
+        
+        # Try to get existing event loop
+        try:
+            loop = asyncio.get_event_loop()
+            if loop.is_running():
+                # If loop is running, we need to use a different approach
+                # This shouldn't happen in LangGraph context, but handle it gracefully
+                raise RuntimeError("Cannot use synchronous invoke in async context. Use ainvoke instead.")
+        except RuntimeError:
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+        
+        # Run async generation
+        result = loop.run_until_complete(self._agenerate(input, **kwargs))
+        return result.generations[0].message
+    
+    async def ainvoke(
+        self,
+        input: List[BaseMessage],
+        config: Optional[RunnableConfig] = None,
+        **kwargs: Any,
+    ) -> BaseMessage:
+        """
+        Async invoke method (required by BaseChatModel).
+        
+        Args:
+            input: List of LangChain messages
+            config: Optional runnable config
+            **kwargs: Additional arguments
+            
+        Returns:
+            AIMessage response
+        """
+        result = await self._agenerate(input, **kwargs)
+        return result.generations[0].message

modules/features/chatbot/bridges/memory.py

@@ -0,0 +1,432 @@
+# Copyright (c) 2025 Patrick Motsch
+# All rights reserved.
+"""
+Custom LangGraph checkpointer using existing database interface.
+Maps LangGraph state to existing message storage format.
+"""
+
+import logging
+import uuid
+from typing import Any, Dict, List, Optional, Tuple, NamedTuple
+from datetime import datetime
+
+from langgraph.checkpoint.base import BaseCheckpointSaver, Checkpoint, CheckpointMetadata
+
+# CheckpointTuple might not be directly importable, so we define it as a NamedTuple
+# Based on LangGraph's usage, it needs config, checkpoint, metadata, parent_config, and pending_writes
+class CheckpointTuple(NamedTuple):
+    """Tuple containing config, checkpoint, metadata, parent_config, and pending_writes."""
+    config: Dict[str, Any]
+    checkpoint: Checkpoint
+    metadata: CheckpointMetadata
+    parent_config: Optional[Dict[str, Any]] = None
+    pending_writes: Optional[List[Tuple[str, Any]]] = None
+from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, SystemMessage, ToolMessage
+
+from modules.interfaces.interfaceDbChatObjects import getInterface
+from modules.datamodels.datamodelChat import ChatMessage, ChatWorkflow
+from modules.datamodels.datamodelUam import User
+from modules.shared.timeUtils import getUtcTimestamp
+
+logger = logging.getLogger(__name__)
+
+
+class DatabaseCheckpointer(BaseCheckpointSaver):
+    """
+    Custom LangGraph checkpointer that uses the existing database interface.
+    Maps LangGraph thread_id to workflow.id and stores messages in the existing format.
+    """
+    
+    def __init__(self, user: User, workflow_id: str):
+        """
+        Initialize the database checkpointer.
+        
+        Args:
+            user: Current user for database access
+            workflow_id: Workflow ID (maps to LangGraph thread_id)
+        """
+        self.user = user
+        self.workflow_id = workflow_id
+        self.interface = getInterface(user)
+    
+    def _convert_langchain_to_db_message(
+        self,
+        msg: BaseMessage,
+        sequence_nr: int,
+        round_number: int
+    ) -> Dict[str, Any]:
+        """
+        Convert LangChain message to database message format.
+        
+        Args:
+            msg: LangChain message
+            sequence_nr: Sequence number for ordering
+            round_number: Round number in workflow
+            
+        Returns:
+            Dictionary in database message format
+        """
+        import uuid
+        
+        role = "user"
+        content = ""
+        
+        if isinstance(msg, HumanMessage):
+            role = "user"
+            content = msg.content if isinstance(msg.content, str) else str(msg.content)
+        elif isinstance(msg, AIMessage):
+            role = "assistant"
+            content = msg.content if isinstance(msg.content, str) else str(msg.content)
+        elif isinstance(msg, SystemMessage):
+            # System messages are stored but marked as system
+            role = "system"
+            content = msg.content if isinstance(msg.content, str) else str(msg.content)
+        elif isinstance(msg, ToolMessage):
+            # Tool messages are stored as assistant messages with tool info
+            role = "assistant"
+            content = f"Tool {msg.name}: {msg.content}"
+        
+        return {
+            "id": f"msg_{uuid.uuid4()}",
+            "workflowId": self.workflow_id,
+            "message": content,
+            "role": role,
+            "status": "step" if sequence_nr > 1 else "first",
+            "sequenceNr": sequence_nr,
+            "publishedAt": getUtcTimestamp(),
+            "roundNumber": round_number,
+            "taskNumber": 0,
+            "actionNumber": 0
+        }
+    
+    def _convert_db_to_langchain_messages(
+        self,
+        messages: List[ChatMessage]
+    ) -> List[BaseMessage]:
+        """
+        Convert database messages to LangChain messages.
+        
+        Args:
+            messages: List of database ChatMessage objects
+            
+        Returns:
+            List of LangChain BaseMessage objects
+        """
+        langchain_messages = []
+        
+        for msg in messages:
+            if msg.role == "user":
+                langchain_messages.append(HumanMessage(content=msg.message))
+            elif msg.role == "assistant":
+                langchain_messages.append(AIMessage(content=msg.message))
+            elif msg.role == "system":
+                langchain_messages.append(SystemMessage(content=msg.message))
+            # Skip other roles for now
+        
+        return langchain_messages
+    
+    def put(
+        self,
+        config: Dict[str, Any],
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: Dict[str, int],
+    ) -> None:
+        """
+        Store a checkpoint in the database.
+        
+        Args:
+            config: LangGraph config (contains thread_id)
+            checkpoint: Checkpoint to store
+            metadata: Checkpoint metadata
+            new_versions: New version numbers
+        """
+        try:
+            # Extract thread_id from config (maps to workflow_id)
+            thread_id = config.get("configurable", {}).get("thread_id", self.workflow_id)
+            
+            # Get current workflow to determine round number
+            workflow = self.interface.getWorkflow(thread_id)
+            if not workflow:
+                logger.warning(f"Workflow {thread_id} not found, cannot store checkpoint")
+                return
+            
+            round_number = workflow.currentRound if workflow else 1
+            
+            # Extract messages from checkpoint
+            state = checkpoint.get("channel_values", {})
+            messages = state.get("messages", [])
+            
+            if not messages:
+                logger.debug(f"No messages in checkpoint for workflow {thread_id}")
+                return
+            
+            # Get existing messages to determine what's already stored
+            existing_messages = self.interface.getMessages(thread_id)
+            existing_count = len(existing_messages) if existing_messages else 0
+            
+            # Create a set of existing message content+role for quick lookup
+            existing_content_set = set()
+            if existing_messages:
+                for existing_msg in existing_messages:
+                    # Create a unique key from role and message content
+                    content_key = (existing_msg.role, existing_msg.message)
+                    existing_content_set.add(content_key)
+            
+            # Filter checkpoint messages to only user/assistant (skip system)
+            # Skip intermediate AIMessages with tool_calls (these are tool call requests, not final answers)
+            checkpoint_user_assistant_messages = []
+            for msg in messages:
+                if isinstance(msg, HumanMessage):
+                    # Always store user messages
+                    checkpoint_user_assistant_messages.append(msg)
+                elif isinstance(msg, AIMessage):
+                    # Check if this message has tool_calls
+                    tool_calls = getattr(msg, "tool_calls", None)
+                    
+                    # Skip messages with tool_calls - these are intermediate tool call requests
+                    if tool_calls and len(tool_calls) > 0:
+                        logger.debug(f"Skipping intermediate AIMessage with tool_calls for workflow {thread_id}")
+                        continue
+                    
+                    # Store all other AIMessages (final answers)
+                    checkpoint_user_assistant_messages.append(msg)
+            
+            # Only store new messages that aren't already in the database
+            new_messages_to_store = []
+            for msg in checkpoint_user_assistant_messages:
+                # Determine role
+                role = "user" if isinstance(msg, HumanMessage) else "assistant"
+                content = msg.content if isinstance(msg.content, str) else str(msg.content)
+                
+                # Skip empty messages (they might be status updates)
+                if not content or not content.strip():
+                    continue
+                
+                # Check if this message already exists
+                content_key = (role, content)
+                if content_key not in existing_content_set:
+                    new_messages_to_store.append(msg)
+                    existing_content_set.add(content_key)  # Mark as seen to avoid duplicates in this batch
+            
+            # Store only the new messages
+            if new_messages_to_store:
+                for i, msg in enumerate(new_messages_to_store, 1):
+                    sequence_nr = existing_count + i
+                    
+                    # Convert to database format
+                    db_message_data = self._convert_langchain_to_db_message(
+                        msg,
+                        sequence_nr,
+                        round_number
+                    )
+                    
+                    # Store the message
+                    try:
+                        self.interface.createMessage(db_message_data)
+                        logger.debug(f"Stored message {db_message_data['id']} for workflow {thread_id}")
+                        existing_count += 1  # Update count for next message
+                    except Exception as e:
+                        logger.error(f"Error storing message: {e}", exc_info=True)
+            else:
+                logger.debug(f"No new messages to store for workflow {thread_id} (existing: {existing_count}, checkpoint: {len(checkpoint_user_assistant_messages)})")
+            
+            # Update workflow last activity
+            self.interface.updateWorkflow(thread_id, {
+                "lastActivity": getUtcTimestamp()
+            })
+            
+        except Exception as e:
+            logger.error(f"Error storing checkpoint: {e}", exc_info=True)
+            raise
+    
+    def get(
+        self,
+        config: Dict[str, Any],
+    ) -> Optional[Checkpoint]:
+        """
+        Retrieve a checkpoint from the database.
+        
+        Args:
+            config: LangGraph config (contains thread_id)
+            
+        Returns:
+            Checkpoint if found, None otherwise
+        """
+        try:
+            # Extract thread_id from config (maps to workflow_id)
+            thread_id = config.get("configurable", {}).get("thread_id", self.workflow_id)
+            
+            # Get workflow
+            workflow = self.interface.getWorkflow(thread_id)
+            if not workflow:
+                logger.debug(f"Workflow {thread_id} not found")
+                return None
+            
+            # Get messages
+            messages = self.interface.getMessages(thread_id)
+            
+            checkpoint_id = str(uuid.uuid4())
+            
+            if not messages:
+                # Return empty checkpoint for new workflow
+                return {
+                    "id": checkpoint_id,
+                    "v": 1,
+                    "ts": getUtcTimestamp(),
+                    "channel_values": {
+                        "messages": []
+                    },
+                    "channel_versions": {},
+                    "versions_seen": {}
+                }
+            
+            # Convert to LangChain messages
+            langchain_messages = self._convert_db_to_langchain_messages(messages)
+            
+            # Build checkpoint
+            checkpoint = {
+                "id": checkpoint_id,
+                "v": 1,
+                "ts": getUtcTimestamp(),
+                "channel_values": {
+                    "messages": langchain_messages
+                },
+                "channel_versions": {},
+                "versions_seen": {}
+            }
+            
+            return checkpoint
+            
+        except Exception as e:
+            logger.error(f"Error retrieving checkpoint: {e}", exc_info=True)
+            return None
+    
+    def list(
+        self,
+        config: Dict[str, Any],
+        filter: Optional[Dict[str, Any]] = None,
+        before: Optional[str] = None,
+        limit: Optional[int] = None,
+    ) -> List[Checkpoint]:
+        """
+        List checkpoints (not fully implemented - returns current checkpoint).
+        
+        Args:
+            config: LangGraph config
+            filter: Optional filter
+            before: Optional timestamp before which to list
+            limit: Optional limit on number of results
+            
+        Returns:
+            List of checkpoints
+        """
+        checkpoint = self.get(config)
+        if checkpoint:
+            return [checkpoint]
+        return []
+    
+    def put_writes(
+        self,
+        config: Dict[str, Any],
+        writes: List[Tuple[str, Any]],
+        task_id: str,
+    ) -> None:
+        """
+        Store checkpoint writes (not used in current implementation).
+        
+        Args:
+            config: LangGraph config
+            writes: List of write operations
+            task_id: Task ID
+        """
+        # Not implemented - using put() instead
+        pass
+    
+    async def aget_tuple(
+        self,
+        config: Dict[str, Any],
+    ) -> Optional[CheckpointTuple]:
+        """
+        Async version of get that returns tuple of (config, checkpoint, metadata).
+        
+        Args:
+            config: LangGraph config (contains thread_id)
+            
+        Returns:
+            CheckpointTuple with config, checkpoint and metadata if found, None otherwise
+        """
+        checkpoint = self.get(config)
+        if checkpoint:
+            # Return checkpoint with metadata including step
+            # CheckpointMetadata is typically a TypedDict
+            # LangGraph expects 'step' in metadata
+            metadata: CheckpointMetadata = {
+                "step": 0  # Start at step 0, LangGraph will increment
+            }
+            return CheckpointTuple(
+                config=config,
+                checkpoint=checkpoint,
+                metadata=metadata,
+                parent_config=None,  # No parent checkpoint for our implementation
+                pending_writes=None  # No pending writes in our implementation
+            )
+        return None
+    
+    async def aput(
+        self,
+        config: Dict[str, Any],
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: Dict[str, int],
+    ) -> None:
+        """
+        Async version of put.
+        
+        Args:
+            config: LangGraph config (contains thread_id)
+            checkpoint: Checkpoint to store
+            metadata: Checkpoint metadata
+            new_versions: New version numbers
+        """
+        self.put(config, checkpoint, metadata, new_versions)
+    
+    async def alist(
+        self,
+        config: Dict[str, Any],
+        filter: Optional[Dict[str, Any]] = None,
+        before: Optional[str] = None,
+        limit: Optional[int] = None,
+    ) -> List[Checkpoint]:
+        """
+        Async version of list.
+        
+        Args:
+            config: LangGraph config
+            filter: Optional filter
+            before: Optional timestamp before which to list
+            limit: Optional limit on number of results
+            
+        Returns:
+            List of checkpoints
+        """
+        return self.list(config, filter, before, limit)
+    
+    async def aput_writes(
+        self,
+        config: Dict[str, Any],
+        writes: List[Tuple[str, Any]],
+        task_id: str,
+    ) -> None:
+        """
+        Async version of put_writes.
+        Store checkpoint writes (not used in current implementation).
+        
+        Args:
+            config: LangGraph config
+            writes: List of write operations
+            task_id: Task ID
+        """
+        # Not implemented - using aput() instead
+        # This method is called by LangGraph but we handle writes through aput()
+        pass

modules/features/chatbot/bridges/tools.py

@@ -0,0 +1,313 @@
+# Copyright (c) 2025 Patrick Motsch
+# All rights reserved.
+"""
+Chatbot tools for LangGraph integration.
+Includes SQL query tool, Tavily search tool, and streaming status tool.
+"""
+
+import logging
+from typing import Optional
+from langchain_core.tools import tool
+
+from modules.connectors.connectorPreprocessor import PreprocessorConnector
+from modules.shared.configuration import APP_CONFIG
+
+logger = logging.getLogger(__name__)
+
+
+@tool
+async def sqlite_query(query: str) -> str:
+    """
+    Execute a SQL SELECT query on the Althaus AG database.
+    
+    This tool allows you to query the SQLite database to find articles, prices, 
+    inventory levels, and other product information.
+    
+    Args:
+        query: A valid SQL SELECT query. Must use double quotes for column names 
+               with spaces or special characters (e.g., "Artikelnummer", "S_IST_BESTAND").
+               Only SELECT queries are allowed.
+    
+    Returns:
+        Query results as a formatted string, or an error message if the query fails.
+        
+    Examples:
+        - Find articles by name:
+          SELECT a."Artikelnummer", a."Artikelbezeichnung", a."Lieferant"
+          FROM Artikel a
+          WHERE a."Artikelbezeichnung" LIKE '%Motor%'
+          LIMIT 20
+          
+        - Find articles with price and inventory:
+          SELECT a."Artikelnummer", a."Artikelbezeichnung", e."EP_CHF", 
+                 lp."Lagerplatz" as "Lagerplatzname", l."S_IST_BESTAND", 
+                 l."S_RESERVIERTER__BESTAND",
+                 CASE WHEN l."S_IST_BESTAND" != 'Unbekannt' 
+                      THEN CAST(l."S_IST_BESTAND" AS INTEGER) - COALESCE(l."S_RESERVIERTER__BESTAND", 0) 
+                      ELSE NULL END as "Verfügbarer Bestand"
+          FROM Artikel a
+          LEFT JOIN Einkaufspreis e ON a."I_ID" = e."m_Artikel"
+          LEFT JOIN Lagerplatz_Artikel l ON a."I_ID" = l."R_ARTIKEL"
+          LEFT JOIN Lagerplatz lp ON l."R_LAGERPLATZ" = lp."I_ID"
+          WHERE a."Artikelbezeichnung" LIKE '%Netzgerät%'
+          LIMIT 20
+    """
+    try:
+        connector = PreprocessorConnector()
+        try:
+            result = await connector.executeQuery(query, return_json=True)
+            
+            if result.get("text", "").startswith(("Error:", "Query failed:")):
+                error_msg = result.get("text", "Query failed")
+                logger.error(f"SQL query failed: {error_msg}")
+                return error_msg
+            
+            # Format results
+            data = result.get("data", [])
+            row_count = result.get("row_count", len(data))
+            
+            if not data:
+                return f"Query executed successfully. Returned {row_count} rows (no data)."
+            
+            # Format as readable string
+            lines = [f"Query executed successfully. Returned {row_count} rows:"]
+            
+            # Show column headers from first row
+            if data and isinstance(data[0], dict):
+                headers = list(data[0].keys())
+                lines.append("\nColumns: " + ", ".join(headers))
+                lines.append("\nResults:")
+                
+                # Show first 50 rows
+                for i, row in enumerate(data[:50], 1):
+                    row_str = ", ".join([f"{k}: {v}" for k, v in row.items()])
+                    lines.append(f"{i}. {row_str}")
+                
+                if row_count > 50:
+                    lines.append(f"\n(Showing first 50 of {row_count} rows)")
+            else:
+                # Fallback for non-dict rows
+                for i, row in enumerate(data[:50], 1):
+                    lines.append(f"{i}. {row}")
+            
+            return "\n".join(lines)
+            
+        finally:
+            await connector.close()
+            
+    except Exception as e:
+        error_msg = f"Error executing SQL query: {str(e)}"
+        logger.error(error_msg, exc_info=True)
+        return error_msg
+
+
+@tool
+async def tavily_search(query: str) -> str:
+    """
+    Search the internet for comprehensive information using Tavily search via AI Center.
+    
+    Use this tool when you need to find detailed product information, datasheets, 
+    certifications, technical specifications, market trends, or other comprehensive 
+    information that is not in the database.
+    
+    IMPORTANT: This tool returns FULL content from search results (not truncated).
+    Use all available information to provide comprehensive, detailed answers with 
+    specific facts, numbers, dates, and technical details.
+    
+    Args:
+        query: Search query string. Be specific and include product names, 
+               model numbers, or other relevant keywords. For comprehensive 
+               research, use broad queries like "latest developments in LED technology 2026"
+    
+    Returns:
+        Comprehensive search results with full content, titles, URLs, and sources.
+        Results include up to 15 sources with complete content for detailed analysis.
+        
+    Examples:
+        - Search for comprehensive product information:
+          tavily_search("latest LED technology developments 2026")
+          
+        - Search for product datasheet:
+          tavily_search("Siemens 6AV2 181-8XP00-0AX0 datasheet")
+          
+        - Search for market trends:
+          tavily_search("LED market trends efficiency breakthroughs 2025")
+    """
+    try:
+        # Use AI Center Tavily plugin instead of direct langchain-tavily
+        from modules.aicore.aicoreModelRegistry import modelRegistry
+        from modules.aicore.aicoreModelSelector import modelSelector
+        from modules.datamodels.datamodelAi import (
+            AiModelCall,
+            AiModelResponse,
+            AiCallOptions,
+            OperationTypeEnum,
+            ProcessingModeEnum,
+            AiCallPromptWebSearch
+        )
+        import json
+        
+        # Discover and register connectors if not already registered
+        if not modelRegistry._connectors:
+            discovered_connectors = modelRegistry.discoverConnectors()
+            for connector in discovered_connectors:
+                modelRegistry.registerConnector(connector)
+        
+        # Refresh models to ensure Tavily is available
+        modelRegistry.refreshModels()
+        
+        # Get available Tavily models (without RBAC filtering since tools don't have user context)
+        available_models = modelRegistry.getAvailableModels()
+        tavily_models = [m for m in available_models if m.connectorType == "tavily"]
+        
+        if not tavily_models:
+            return "Error: Tavily model not available in AI Center. Please check configuration."
+        
+        # Select the best Tavily model for web search
+        options = AiCallOptions(
+            operationType=OperationTypeEnum.WEB_SEARCH_DATA,
+            processingMode=ProcessingModeEnum.BASIC
+        )
+        
+        # Use model selector to choose the best Tavily model
+        # Since we only have Tavily models, we can just pick the first one
+        # or use selector if multiple Tavily models exist
+        if len(tavily_models) == 1:
+            selected_model = tavily_models[0]
+        else:
+            selected_model = modelSelector.selectModel(
+                prompt=query,
+                context="",
+                options=options,
+                availableModels=tavily_models
+            )
+        
+        if not selected_model:
+            return "Error: Could not select Tavily model for web search."
+        
+        # Create web search prompt with more results and deeper research
+        web_search_prompt = AiCallPromptWebSearch(
+            instruction=query,
+            maxNumberPages=15,  # Request more results for comprehensive information
+            country=None,  # No country filter by default
+            language=None,  # No language filter by default
+            researchDepth="deep"  # Deep research for comprehensive results
+        )
+        
+        # Create model call with JSON prompt
+        model_call = AiModelCall(
+            messages=[
+                {
+                    "role": "user",
+                    "content": json.dumps(web_search_prompt.model_dump())
+                }
+            ],
+            model=selected_model,
+            options=options
+        )
+        
+        # Call the model's functionCall (which routes to _routeWebOperation)
+        if not selected_model.functionCall:
+            return "Error: Tavily model has no functionCall defined."
+        
+        response: AiModelResponse = await selected_model.functionCall(model_call)
+        
+        if not response.success:
+            error_msg = response.error or "Unknown error"
+            logger.error(f"Tavily search failed: {error_msg}")
+            return f"Error performing Tavily search: {error_msg}"
+        
+        # Parse response content (should be JSON with URLs and content)
+        try:
+            result_data = json.loads(response.content) if response.content else {}
+            
+            # Handle different response formats
+            if isinstance(result_data, list):
+                # List of URLs or results
+                results = result_data
+            elif isinstance(result_data, dict):
+                # Dictionary with URLs or results key
+                results = result_data.get("urls", []) or result_data.get("results", []) or []
+            else:
+                results = []
+            
+            if not results:
+                return f"No results found for query: {query}"
+            
+            # Format results with full content (not truncated)
+            lines = [f"Internet search results for: {query}\n"]
+            
+            # Return all results with full content (up to 15 results)
+            for i, result in enumerate(results[:15], 1):
+                if isinstance(result, str):
+                    # Simple URL string
+                    lines.append(f"{i}. {result}")
+                    lines.append(f"   URL: {result}")
+                elif isinstance(result, dict):
+                    # Dictionary with url, title, content
+                    url = result.get("url", "")
+                    title = result.get("title", url)
+                    content = result.get("content", "")
+                    
+                    lines.append(f"{i}. {title}")
+                    lines.append(f"   URL: {url}")
+                    if content:
+                        # Return FULL content, not truncated - let the LLM decide what to use
+                        lines.append(f"   Content: {content}")
+                else:
+                    # Fallback
+                    lines.append(f"{i}. {str(result)}")
+                lines.append("")
+            
+            return "\n".join(lines)
+            
+        except json.JSONDecodeError:
+            # If response is not JSON, try to parse as plain text
+            if response.content:
+                return f"Internet search results for: {query}\n\n{response.content}"
+            return f"No results found for query: {query}"
+            
+    except Exception as e:
+        error_msg = f"Error performing Tavily search via AI Center: {str(e)}"
+        logger.error(error_msg, exc_info=True)
+        return error_msg
+
+
+# Note: send_streaming_message will be created in the LangGraph integration
+# where it has access to the event manager. For now, we define it here as a placeholder.
+
+def create_send_streaming_message_tool(event_manager=None):
+    """
+    Create the send_streaming_message tool with access to event manager.
+    
+    Args:
+        event_manager: Event manager instance for emitting events (not used directly,
+                      events are captured via LangGraph tool events)
+        
+    Returns:
+        LangChain tool for sending streaming messages
+    """
+    @tool
+    async def send_streaming_message(message: str) -> str:
+        """
+        Send a streaming status update to the user.
+        
+        Use this tool frequently to keep the user informed about what you are doing.
+        This helps provide a better user experience by showing progress updates.
+        
+        Args:
+            message: A short message describing what you are currently doing.
+                    Examples:
+                    - "Durchsuche Datenbank nach Lampen, LED, Leuchten, und Ähnlichem."
+                    - "Suche im Internet nach Produktinformationen."
+                    - "Analysiere Suchergebnisse und bereite Antwort vor."
+        
+        Returns:
+            Confirmation that the message was sent.
+        """
+        # This tool doesn't actually do anything in the tool execution
+        # The actual event emission happens in the streaming bridge
+        # This is just for LangGraph to recognize it as a tool call
+        return f"Status-Update gesendet: {message}"
+    
+    return send_streaming_message

modules/features/chatbot/chatbot.py

@@ -0,0 +1,348 @@
+# Copyright (c) 2025 Patrick Motsch
+# All rights reserved.
+"""Chatbot domain logic."""
+
+import logging
+from dataclasses import dataclass
+from typing import Annotated, AsyncIterator, Any, List
+from pydantic import BaseModel
+
+from langchain_core.messages import (
+    BaseMessage,
+    HumanMessage,
+    SystemMessage,
+    trim_messages,
+)
+from langgraph.graph.message import add_messages
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.state import CompiledStateGraph
+from langgraph.prebuilt import ToolNode
+
+from modules.features.chatbot.bridges.ai import AICenterChatModel
+from modules.features.chatbot.bridges.memory import DatabaseCheckpointer
+from modules.features.chatbot.bridges.tools import (
+    sqlite_query,
+    tavily_search,
+    create_send_streaming_message_tool,
+)
+from modules.features.chatbot.streaming.helpers import ChatStreamingHelper
+from modules.features.chatbot.streaming.events import get_event_manager
+from modules.datamodels.datamodelUam import User
+
+logger = logging.getLogger(__name__)
+
+
+class ChatState(BaseModel):
+    """Represents the state of a chat session."""
+
+    messages: Annotated[List[BaseMessage], add_messages]
+
+
+@dataclass
+class Chatbot:
+    """Represents a chatbot."""
+
+    model: AICenterChatModel
+    memory: DatabaseCheckpointer
+    app: CompiledStateGraph = None
+    system_prompt: str = "You are a helpful assistant."
+    workflow_id: str = "default"
+
+    @classmethod
+    async def create(
+        cls,
+        model: AICenterChatModel,
+        memory: DatabaseCheckpointer,
+        system_prompt: str,
+        workflow_id: str = "default",
+    ) -> "Chatbot":
+        """Factory method to create and configure a Chatbot instance.
+
+        Args:
+            model: The chat model to use (AICenterChatModel).
+            memory: The chat memory to use (DatabaseCheckpointer).
+            system_prompt: The system prompt to initialize the chatbot.
+            workflow_id: The workflow ID (maps to thread_id).
+
+        Returns:
+            A configured Chatbot instance.
+        """
+        instance = Chatbot(
+            model=model,
+            memory=memory,
+            system_prompt=system_prompt,
+            workflow_id=workflow_id,
+        )
+        configured_tools = await instance._configure_tools()
+        instance.app = instance._build_app(memory, configured_tools)
+        return instance
+
+    async def _configure_tools(self) -> List[Any]:
+        """Configure tools for the chatbot.
+
+        Returns:
+            List of configured tools.
+        """
+        tools = []
+        
+        # SQL query tool
+        tools.append(sqlite_query)
+        
+        # Tavily search tool
+        tools.append(tavily_search)
+        
+        # Streaming status tool (needs event manager)
+        event_manager = get_event_manager()
+        send_streaming_message = create_send_streaming_message_tool(event_manager)
+        tools.append(send_streaming_message)
+        
+        return tools
+
+    def _build_app(
+        self, memory: DatabaseCheckpointer, tools: List[Any]
+    ) -> CompiledStateGraph[ChatState, None, ChatState, ChatState]:
+        """Builds the chatbot application workflow using LangGraph.
+
+        Args:
+            memory: The chat memory to use.
+            tools: The list of tools the chatbot can use.
+
+        Returns:
+            A compiled state graph representing the chatbot application.
+        """
+        llm_with_tools = self.model.bind_tools(tools=tools)
+
+        def select_window(msgs: List[BaseMessage]) -> List[BaseMessage]:
+            """Selects a window of messages that fit within the context window size.
+
+            Args:
+                msgs: The list of messages to select from.
+
+            Returns:
+                A list of messages that fit within the context window size.
+            """
+
+            def approx_counter(items: List[BaseMessage]) -> int:
+                """Approximate token counter for messages.
+
+                Args:
+                    items: List of messages to count tokens for.
+
+                Returns:
+                    Approximate number of tokens in the messages.
+                """
+                return sum(len(getattr(m, "content", "") or "") for m in items)
+
+            # Use model's context length if available, otherwise default
+            max_tokens = getattr(self.model._selected_model, "contextLength", 128000) if hasattr(self.model, "_selected_model") and self.model._selected_model else 128000
+            
+            return trim_messages(
+                msgs,
+                strategy="last",
+                token_counter=approx_counter,
+                max_tokens=int(max_tokens * 0.8),  # Use 80% of context window
+                start_on="human",
+                end_on=("human", "tool"),
+                include_system=True,
+            )
+
+        async def agent_node(state: ChatState) -> dict:
+            """Agent node for the chatbot workflow.
+
+            Args:
+                state: The current chat state.
+
+            Returns:
+                The updated chat state after processing.
+            """
+            # Select the message window to fit in context (trim if needed)
+            window = select_window(state.messages)
+
+            # Ensure the system prompt is present at the start
+            if not window or not isinstance(window[0], SystemMessage):
+                window = [SystemMessage(content=self.system_prompt)] + window
+
+            # Call the LLM with tools (use ainvoke for async)
+            response = await llm_with_tools.ainvoke(window)
+
+            # Return the new state
+            return {"messages": [response]}
+
+        def should_continue(state: ChatState) -> str:
+            """Determines whether to continue the workflow or end it.
+
+            This conditional edge is called after the agent node to decide
+            whether to continue to the tools node (if the last message contains
+            tool calls) or to end the workflow (if no tool calls are present).
+
+            Args:
+                state: The current chat state.
+
+            Returns:
+                The next node to transition to ("tools" or END).
+            """
+            # Get the last message
+            last_message = state.messages[-1]
+
+            # Check if the last message contains tool calls
+            # If so, continue to the tools node; otherwise, end the workflow
+            return "tools" if getattr(last_message, "tool_calls", None) else END
+
+        async def tools_with_retry(state: ChatState) -> dict:
+            """Tools node with retry logic.
+
+            Args:
+                state: The current chat state.
+
+            Returns:
+                The updated chat state after tool execution.
+            """
+            # Execute tools normally
+            tool_node = ToolNode(tools=tools)
+            result = await tool_node.ainvoke(state)
+            
+            # Check if we got no results and should retry
+            no_results_keywords = [
+                "returned 0 rows",
+                "no data",
+                "keine artikel gefunden",
+                "keine ergebnisse"
+            ]
+            
+            # Check tool results for no data
+            for msg in result.get("messages", []):
+                content = getattr(msg, "content", "")
+                if isinstance(content, str):
+                    content_lower = content.lower()
+                    if any(keyword in content_lower for keyword in no_results_keywords):
+                        # Check if we haven't retried yet (avoid infinite loops)
+                        retry_count = sum(1 for m in state.messages if "retry" in str(getattr(m, "content", "")).lower())
+                        if retry_count < 2:  # Allow max 2 retries
+                            logger.info("No results found in tool output, adding retry instruction")
+                            retry_message = HumanMessage(
+                                content="WICHTIG: Die vorherige Suche hat keine Ergebnisse gefunden. "
+                                        "Bitte versuche eine alternative Suchstrategie:\n"
+                                        "1. Wenn die Frage im Format 'X von Y' war (z.B. 'Lampen von Eaton'), "
+                                        "verwende IMMER eine Kombination aus Lieferanten-Filter (WHERE a.\"Lieferant\" LIKE '%Y%') "
+                                        "UND Produkttyp-Filter (WHERE a.\"Artikelbezeichnung\" LIKE '%X%' OR ...)\n"
+                                        "2. Verwende mehrere Synonyme für den Produkttyp (z.B. bei 'Lampen': Lampe, LED, Beleuchtung, Licht, Leuchte, Strahler)\n"
+                                        "3. Führe zuerst eine COUNT-Abfrage durch, dann die Detail-Abfrage mit Lagerbeständen\n"
+                                        "4. Verwende LIKE '%Lieferant%' für den Lieferanten-Filter, um auch Varianten zu finden"
+                            )
+                            result["messages"].append(retry_message)
+                            break
+            
+            return result
+
+        # Compose the workflow
+        workflow = StateGraph(ChatState)
+        workflow.add_node("agent", agent_node)
+        workflow.add_node("tools", tools_with_retry)
+        workflow.add_edge(START, "agent")
+        workflow.add_conditional_edges("agent", should_continue)
+        workflow.add_edge("tools", "agent")
+        return workflow.compile(checkpointer=memory)
+
+    async def chat(self, message: str, chat_id: str = "default") -> List[BaseMessage]:
+        """Processes a chat message by calling the LLM and tools and returns the chat history.
+
+        Args:
+            message: The user message to process.
+            chat_id: The chat thread ID.
+
+        Returns:
+            The list of messages in the chat history.
+        """
+        # Set the right thread ID for memory
+        config = {"configurable": {"thread_id": chat_id}}
+
+        # Single-turn chat (non-streaming)
+        result = await self.app.ainvoke(
+            {"messages": [HumanMessage(content=message)]}, config=config
+        )
+
+        # Extract and return the messages from the result
+        return result["messages"]
+
+    async def stream_events(
+        self, *, message: str, chat_id: str = "default"
+    ) -> AsyncIterator[dict]:
+        """Stream UI-focused events using astream_events v2.
+
+        Args:
+            message: The user message to process.
+            chat_id: Logical thread identifier; forwarded in the runnable config so
+                memory and tools are scoped per thread.
+
+        Yields:
+            dict: One of:
+                - ``{"type": "status", "label": str}`` for short progress updates.
+                - ``{"type": "final", "response": {"thread": str, "chat_history": list[dict]}}``
+                where ``chat_history`` only includes ``user``/``assistant`` roles.
+                - ``{"type": "error", "message": str}`` if an exception occurs.
+        """
+        # Thread-aware config for LangGraph/LangChain
+        config = {"configurable": {"thread_id": chat_id}}
+
+        def _is_root(ev: dict) -> bool:
+            """Return True if the event is from the root run (v2: empty parent_ids)."""
+            return not ev.get("parent_ids")
+
+        try:
+            async for event in self.app.astream_events(
+                {"messages": [HumanMessage(content=message)]},
+                config=config,
+                version="v2",
+            ):
+                etype = event.get("event")
+                ename = event.get("name") or ""
+                edata = event.get("data") or {}
+
+                # Stream human-readable progress via the special send_streaming_message tool
+                # Match the legacy implementation exactly (line 267-272 in legacy/chatbot.py)
+                if etype == "on_tool_start":
+                    # Log all tool starts to debug
+                    logger.debug(f"Tool start event: name='{ename}', event='{etype}'")
+                    if ename == "send_streaming_message":
+                        tool_in = edata.get("input") or {}
+                        msg = tool_in.get("message")
+                        logger.info(f"send_streaming_message tool called with input: {tool_in}")
+                        if isinstance(msg, str) and msg.strip():
+                            logger.info(f"Status-Update gesendet: {msg.strip()}")
+                            yield {"type": "status", "label": msg.strip()}
+                        continue
+
+                # Emit the final payload when the root run finishes
+                if etype == "on_chain_end" and _is_root(event):
+                    output_obj = edata.get("output")
+
+                    # Extract message list from the graph's final output
+                    final_msgs = ChatStreamingHelper.extract_messages_from_output(
+                        output_obj=output_obj
+                    )
+
+                    # Normalize for the frontend (only user/assistant with text content)
+                    chat_history_payload: List[dict] = []
+                    for m in final_msgs:
+                        if isinstance(m, BaseMessage):
+                            d = ChatStreamingHelper.message_to_dict(msg=m)
+                        elif isinstance(m, dict):
+                            d = ChatStreamingHelper.dict_message_to_dict(obj=m)
+                        else:
+                            continue
+                        if d.get("role") in ("user", "assistant") and d.get("content"):
+                            chat_history_payload.append(d)
+
+                    yield {
+                        "type": "final",
+                        "response": {
+                            "thread": chat_id,
+                            "chat_history": chat_history_payload,
+                        },
+                    }
+                    return
+
+        except Exception as exc:
+            # Emit a single error envelope and end the stream
+            logger.error(f"Exception in stream_events: {exc}", exc_info=True)
+            yield {"type": "error", "message": f"Fehler beim Verarbeiten: {exc}"}

modules/features/chatbot/chatbotConstants.py

@@ -1,731 +1,170 @@
 # Copyright (c) 2025 Patrick Motsch
 # All rights reserved.
 """
-Constants and utility functions for the chatbot module.
-Contains system prompts and conversation name generation.
+Chatbot constants and helper functions.
 """
 
 import logging
-import re
-import datetime
-from typing import Optional, List
+from typing import Optional
 
-from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, OperationTypeEnum, ProcessingModeEnum
+from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, OperationTypeEnum, PriorityEnum, ProcessingModeEnum
 
 logger = logging.getLogger(__name__)
 
 
-def get_analysis_system_prompt() -> str:
-    """
-    Get the system prompt for analyzing user input and creating queries.
-    Focuses on understanding the question and determining what queries are needed.
-    """
-    current_date = datetime.datetime.now().strftime("%d.%m.%Y")
-    
-    return f"""Heute ist der {current_date}.
-
-Du bist ein Chatbot der Althaus AG.
-Deine Aufgabe ist es, Benutzeranfragen zu analysieren und zu bestimmen, welche Datenbankabfragen oder Web-Recherchen benötigt werden, um die Frage zu beantworten.
-
-DATENBANK-INFORMATIONEN:
-- Datenbankdatei: /data/database.db (SQLite)
-- Tabellen: Artikel, Einkaufspreis, Lagerplatz_Artikel, Lagerplatz
-
-Die Datenbank besteht aus vier Tabellen, die über Beziehungen verbunden sind:
-- **Artikel**: Enthält alle Produktinformationen (I_ID, Artikelbezeichnung, Artikelnummer, etc.)
-- **Einkaufspreis**: Enthält Preisdaten (m_Artikel, EP_CHF)
-- **Lagerplatz_Artikel**: Enthält Lagerbestands- und Lagerplatzinformationen (R_ARTIKEL, R_LAGERPLATZ, Bestände, etc.)
-- **Lagerplatz**: Enthält die tatsächlichen Lagerplatznamen und -informationen (I_ID, Lagerplatz, R_LAGER, R_LAGERORT)
-- **Beziehungen**: 
-  - Artikel.I_ID = Einkaufspreis.m_Artikel
-  - Artikel.I_ID = Lagerplatz_Artikel.R_ARTIKEL
-  - Lagerplatz_Artikel.R_LAGERPLATZ = Lagerplatz.I_ID (WICHTIG: R_LAGERPLATZ enthält die ID, nicht den Namen!)
-
-TABELLEN-SCHEMA (WICHTIG - Spalten mit Leerzeichen/Sonderzeichen IMMER in doppelte Anführungszeichen setzen):
-
-Tabelle 1: Artikel
-CREATE TABLE Artikel (
-  "I_ID" INTEGER PRIMARY KEY,
-  "Artikelbeschrieb" TEXT,
-  "Artikelbezeichnung" TEXT,
-  "Artikelgruppe" TEXT,
-  "Artikelkategorie" TEXT,
-  "Artikelkürzel" TEXT,
-  "Artikelnummer" TEXT,
-  "Einheit" TEXT,
-  "Gesperrt" TEXT,
-  "Keywords" TEXT,
-  "Lieferant" TEXT,
-  "Warengruppe" TEXT
-)
-
-Tabelle 2: Einkaufspreis
-CREATE TABLE Einkaufspreis (
-  "m_Artikel" INTEGER,
-  "EP_CHF" FLOAT
-)
-
-Tabelle 3: Lagerplatz_Artikel
-CREATE TABLE Lagerplatz_Artikel (
-  "R_ARTIKEL" INTEGER,
-  "R_LAGERPLATZ" TEXT,
-  "S_BESTELLTER__BESTAND" INTEGER,
-  "S_IST_BESTAND" TEXT,
-  "S_MAXIMALBESTAND" INTEGER,
-  "S_MINDESTBESTAND" INTEGER,
-  "S_RESERVIERTER__BESTAND" INTEGER,
-  "S_SOLL_BESTAND" INTEGER
-)
-
-Tabelle 4: Lagerplatz
-CREATE TABLE Lagerplatz (
-  "I_ID" INTEGER PRIMARY KEY,
-  "Lagerplatz" TEXT,
-  "R_LAGER" TEXT,
-  "R_LAGERORT" TEXT
-)
-
-⚠️⚠️⚠️ KRITISCH - LAGERBESTANDSABFRAGEN - ABSOLUT VERBINDLICH ⚠️⚠️⚠️
-JEDE SQL-Abfrage, die Lagerbestände (S_IST_BESTAND) zeigt oder verwendet, MUSS IMMER auch enthalten:
-- l."S_RESERVIERTER__BESTAND" (Reservierte Bestände) - OBLIGATORISCH!
-- Berechnung des verfügbaren Bestands - OBLIGATORISCH!
-- JOIN mit Lagerplatz-Tabelle für den Lagerplatznamen - OBLIGATORISCH!
-
-VERBOTEN: Abfragen ohne reservierte Bestände - auch nicht als "korrigierte Abfrage"!
-VERBOTEN: Zwischenschritte ohne reservierte Bestände!
-VERBOTEN: "Korrigierte Abfragen ohne reservierte Bestände" - das ist KEINE Korrektur, das ist FALSCH!
-
-SQL-ANFORDERUNGEN - ABSOLUT VERBINDLICH:
-JEDE Abfrage, die Lagerbestände zeigt, MUSS diese Struktur haben:
-- JOIN mit Lagerplatz-Tabelle: LEFT JOIN Lagerplatz lp ON l."R_LAGERPLATZ" = lp."I_ID"
-- Lagerplatzname anzeigen: lp."Lagerplatz" as "Lagerplatzname" (NICHT l."R_LAGERPLATZ"!)
-- Ist-Bestand: l."S_IST_BESTAND"
-- Reservierte Bestände: IMMER l."S_RESERVIERTER__BESTAND" hinzufügen (OBLIGATORISCH!)
-- Verfügbarer Bestand berechnen: CASE WHEN l."S_IST_BESTAND" != 'Unbekannt' THEN CAST(l."S_IST_BESTAND" AS INTEGER) - COALESCE(l."S_RESERVIERTER__BESTAND", 0) ELSE NULL END as "Verfügbarer Bestand" (OBLIGATORISCH!)
-
-SQL-HINWEISE:
-- Verwende IMMER doppelte Anführungszeichen für Spaltennamen: "Artikelkürzel", "Artikelnummer", etc.
-- Für Textsuche verwende LIKE mit Wildcards: WHERE a."Artikelbezeichnung" LIKE '%suchbegriff%'
-- Für Preisabfragen: Nutze JOINs um auf e."EP_CHF" zuzugreifen
-- Für Lagerbestände: Nutze JOINs um auf l."S_IST_BESTAND", l."S_SOLL_BESTAND", etc. zuzugreifen
-- WICHTIG bei S_IST_BESTAND: Dieser Wert kann "Unbekannt" sein (TEXT), nicht nur Zahlen! Prüfe mit WHERE l."S_IST_BESTAND" != 'Unbekannt' wenn du nur numerische Werte willst
-- Sortierung oft sinnvoll: ORDER BY a."Artikelnummer" ASC, ORDER BY e."EP_CHF" DESC, oder ORDER BY l."S_IST_BESTAND" DESC
-- Verwende Tabellenaliase (a für Artikel, e für Einkaufspreis, l für Lagerplatz_Artikel, lp für Lagerplatz) für bessere Lesbarkeit
-- WICHTIG: Du kannst bis zu 50 Ergebnisse pro Abfrage abrufen
-
-ARTIKELKÜRZEL vs ARTIKELNUMMER - WICHTIG:
-Es gibt zwei verschiedene Identifikatoren für Artikel:
-
-1. **Artikelkürzel**: Numerisches Format (z.B. "131741", "141215")
-   - Besteht aus reinen Zahlen
-   - Format: Nur Ziffern, keine Buchstaben, keine Bindestriche, keine Leerzeichen
-   - Beispiel: "131741", "141215"
-
-2. **Artikelnummer**: Alphanumerisches Format (z.B. "6AV2 181-8XP00-0AX0", "AX5206")
-   - Kann Buchstaben, Zahlen, Bindestriche und Leerzeichen enthalten
-   - Format: Alphanumerisch, kann Bindestriche und Leerzeichen enthalten
-   - Beispiel: "6AV2 181-8XP00-0AX0", "AX5206", "SIE.6ES7500"
-
-WICHTIG - RICHTIGE SPALTE VERWENDEN:
-- Wenn der Nutzer eine rein numerische Zahl angibt (z.B. "131741", "141215") → Suche in a."Artikelkürzel"
-- Wenn der Nutzer eine alphanumerische Bezeichnung angibt mit Buchstaben, Bindestrichen oder Leerzeichen (z.B. "6AV2 181-8XP00-0AX0", "AX5206") → Suche in a."Artikelnummer"
-
-Beispiele:
-- "Wie viele von 141215 haben wir auf Lager?" → Artikelkürzel "141215" → WHERE a."Artikelkürzel" = '141215'
-- "Wie viel von 6AV2 181-8XP00-0AX0 haben wir auf Lager?" → Artikelnummer "6AV2 181-8XP00-0AX0" → WHERE a."Artikelnummer" = '6AV2 181-8XP00-0AX0'
-- "Zeig mir Informationen zu AX5206" → Artikelnummer "AX5206" → WHERE a."Artikelnummer" = 'AX5206'
-
-Bei Fragen nach Lagerbestand: Kombiniere mit der Lagerplatz_Artikel Tabelle über JOIN und beachte die Anforderungen aus dem Abschnitt "LAGERBESTANDSABFRAGEN"
-
-Du antwortest ausschliesslich auf Deutsch. Nutze kein sz(ß) sondern immer ss.
-"""
-
-
-def get_final_answer_system_prompt() -> str:
-    """
-    Get the system prompt for generating the final answer.
-    Focuses on formatting, presenting results, and user engagement.
-    """
-    current_date = datetime.datetime.now().strftime("%d.%m.%Y")
-    
-    return f"""Heute ist der {current_date}.
-
-Du bist ein Chatbot der Althaus AG.
-Deine Aufgabe ist es, auf Basis von Datenbank-Ergebnissen und Web-Recherchen hilfreiche, präzise Antworten zu geben.
-
-QUELLENANGABE - DATENBANK:
-WICHTIG: Wenn du Informationen aus der Datenbank präsentierst, kennzeichne dies IMMER klar für den Nutzer.
-- Beginne deine Antwort mit einer klaren Kennzeichnung, z.B.: "Aus der Datenbank habe ich folgende Artikel gefunden:"
-- Bei kombinierten Informationen (Datenbank + Internet): Trenne klar zwischen beiden Quellen
-
-⚠️⚠️⚠️ QUELLENANGABE - INTERNET - ABSOLUT VERBINDLICH ⚠️⚠️⚠️
-Wenn du Informationen aus einer Web-Recherche präsentierst, MUSS du dies IMMER explizit kennzeichnen und die Quellen angeben:
-- ❌ VERBOTEN: Informationen aus Web-Recherchen ohne explizite Kennzeichnung zu präsentieren
-- ❌ VERBOTEN: Informationen aus Web-Recherchen ohne Quellenangabe zu präsentieren
-- ❌ VERBOTEN: Quellen nur am Ende als Liste zu präsentieren
-- ✓ OBLIGATORISCH: Beginne IMMER mit einer expliziten Kennzeichnung, z.B.:
-  * "Aus meiner Web-Recherche habe ich folgende Informationen gefunden:"
-  * "Laut meiner Internet-Recherche:"
-  * "Aus meiner Online-Suche:"
-- ✓ OBLIGATORISCH: Gib IMMER die konkreten Quellen DIREKT NACH der jeweiligen Information an (nicht am Ende!)
-- ✓ OBLIGATORISCH: Format: [Information] ([Quelle: Website-Name](URL))
-- ✓ OBLIGATORISCH: Bei mehreren Informationen: Gib nach JEDER Information die entsprechende Quelle an
-- ✓ OBLIGATORISCH: Trenne klar zwischen Datenbank-Informationen und Web-Recherchen
-- ✓ OBLIGATORISCH: Wenn sowohl Datenbank- als auch Web-Informationen vorhanden sind, trenne diese klar in separaten Abschnitten
-
-⚠️⚠️⚠️ DATENBLATT-LINKS - ABSOLUT VERBINDLICH ⚠️⚠️⚠️
-Wenn Web-Recherche-Ergebnisse vorhanden sind, MUSS du IMMER:
-- ✓ OBLIGATORISCH: Explizit erwähnen, dass Datenblätter verfügbar sind
-- ✓ OBLIGATORISCH: ALLE verfügbaren Datenblatt-Links angeben (vollständige URLs)
-- ✓ OBLIGATORISCH: Format: "Datenblätter verfügbar: [Link 1](URL1), [Link 2](URL2)"
-- ✓ OBLIGATORISCH: Wenn keine direkten Datenblatt-Links vorhanden sind, gib Links zu Seiten mit technischen Informationen an
-- ❌ VERBOTEN: Datenblatt-Links zu verschweigen oder nicht explizit zu erwähnen
-
-⚠️⚠️⚠️ AUSFÜHRLICHE INFORMATIONEN - ABSOLUT VERBINDLICH ⚠️⚠️⚠️
-Wenn Web-Recherche-Ergebnisse vorhanden sind, MUSS du:
-- ✓ OBLIGATORISCH: AUSFÜHRLICHE Informationen präsentieren (nicht nur kurze Zusammenfassungen!)
-- ✓ OBLIGATORISCH: Alle relevanten technischen Details angeben:
-  * Technische Spezifikationen (Größe, Gewicht, Abmessungen, etc.)
-  * Betriebsbedingungen (Temperatur, Spannung, etc.)
-  * Kompatibilität und Anwendungsbereiche
-  * Zertifizierungen und Normen
-  * Installation und Verwendung
-  * Weitere relevante Produktdetails
-- ✓ OBLIGATORISCH: Strukturiere die Informationen übersichtlich (z.B. mit Abschnitten oder Aufzählungen)
-- ❌ VERBOTEN: Nur oberflächliche Informationen zu geben
-- ❌ VERBOTEN: Wichtige Details auszulassen
-
-BEISPIEL FÜR KORREKTE QUELLENANGABE MIT INLINE-QUELLEN:
-"Aus meiner Web-Recherche habe ich folgende Informationen gefunden:
-
-**Technische Spezifikationen:**
-- Speicherkapazität: 2 GB ([Quelle: Siemens Support](https://...))
-- Format: Secure Digital (SD) Card ([Quelle: Best4Automation](https://...))
-- Betriebsspannung: 3,3 V DC ([Quelle: Automation24](https://...))
-
-**Kompatibilität:**
-- Geeignet für SIMATIC HMI Comfort Panels ([Quelle: Siemens Support](https://...))
-- Montage im Hoch- und Querformat möglich ([Quelle: Best4Automation](https://...))
-
-**Zertifizierungen:**
-- CE-zertifiziert ([Quelle: Automation24](https://...))
-- Für ATEX-Zonen geeignet ([Quelle: Elit](https://...))
-
-**Datenblätter verfügbar:**
-- [Siemens Produktdatenblatt](https://...)
-- [Technische Dokumentation](https://...)"
-
-NIEMALS Informationen aus Web-Recherchen präsentieren, ohne explizit zu erwähnen, dass es sich um eine Web-Recherche handelt und ohne die Quellen DIREKT NACH der jeweiligen Information anzugeben!
-
-TABELLENLÄNGE UND ARTIKELANZAHL - KRITISCH:
-WICHTIG: Zeige MAXIMAL 20 Artikel in Tabellen. Du darfst und sollst aber ausführliche Erklärungen liefern!
-
-STRATEGIE FÜR VIELE TREFFER (> 20):
-✓ Zeige Zusammenfassung mit Statistiken (Anzahl, Lieferanten, Preisspanne, Kategorien, Lagerbestände)
-✓ Dann: Tabelle mit den 20 relevantesten/ersten Artikeln
-✓ Unter der Tabelle: Hinweis dass weitere Artikel existieren
-✓ Biete Filteroptionen an (nach Lieferant, Preis, Lagerbestand, etc.)
-
-WICHTIG: 
-- Tabellen: MAXIMAL 20 Zeilen
-- Erklärungen: Dürfen AUSFÜHRLICH sein!
-- Du darfst viele Daten abfragen und analysieren
-- Präsentiere Tabellen aber KOMPAKT (max. 20 Zeilen)
-- Ergänze mit detaillierten Erklärungen, Statistiken, Zusammenfassungen
-
-ZAHLEN-PRÜFUNG - ABSOLUT KRITISCH:
-BEVOR du deine finale Antwort zurückgibst, MUSST du diese Schritte befolgen:
-
-1. ZÄHLE die TATSÄCHLICHEN Zeilen in deiner finalen Tabelle
-2. Diese Zahl ist die EINZIGE korrekte Anzahl für deine Antwort
-3. Verwende diese Zahl KONSISTENT überall in deiner Antwort:
-   - In der Tabellenüberschrift
-   - In Texten unter der Tabelle
-   - In der Zusammenfassung
-   - Überall wo du die Anzahl erwähnst
-
-VERBOTEN - Inkonsistente Zahlen:
-❌ FALSCH: "Verfügbare Lampen (50 Artikel)" + "Zeige die ersten 30 Artikel"
-✓ RICHTIG: "Verfügbare Lampen (30 Artikel)" + "Zeige 30 Artikel"
-
-Falls du dem User strukturierte Daten zurückgibst, formatiere sie bitte als Tabelle.
-WICHTIG! Falls deine Tabelle nur ein Teil der Daten anzeigt, die du gefunden hast, dann vermerke dies bitte in deiner Antwort unter der Tabelle in markdown _italic_.
-
-Wenn immer du eine Artikelnummer innerhalb einer Tabelle zurückgibst bitte markiere diese als Markdownlink:
-[ARTIKELNUMMER](/details/ARTIKELNUMMER). ARTIKELNUMMER ist hierbei der Platzhalter, den du ersetzen musst.
-WICHTIG! Du musst im Link die ARTIKELNUMMER sicher URL-encodieren. Encodiere aber NICHT die Artikelnummer in eckigen Klammern. Also encodiere den Ankertext nicht!
-Ausserhalb einer Tabelle musst du keine Links auf Artikelnummern setzen.
-
-Die erste Nachricht das Nutzers ist eine Antwort auf die folgende Nachricht:
-"Hallo! Ich bin Ihr KI-Assistent für die Materialverwaltung. Wie kann ich Ihnen heute helfen?"
-
-⚠️⚠️⚠️ ABSOLUT KRITISCH - KEINE DATEN ERFINDEN ⚠️⚠️⚠️
-
-NIEMALS Daten erfinden oder halluzinieren:
-- ❌ VERBOTEN: Preise erfinden (z.B. "Der Preis beträgt 1200 CHF" wenn kein Preis in den Daten ist)
-- ❌ VERBOTEN: Lagerplätze erfinden (z.B. "Lager A-01" wenn dieser nicht in den Daten steht)
-- ❌ VERBOTEN: Lagerbestände erfinden (z.B. "50 Stück" wenn dieser Wert nicht in den Daten ist)
-- ❌ VERBOTEN: Artikelbezeichnungen erfinden oder ändern
-- ❌ VERBOTEN: Lieferanten erfinden oder ändern
-- ❌ VERBOTEN: Jegliche Werte erfinden, die nicht explizit in den Datenbank-Ergebnissen stehen
-
-✓ RICHTIG: Wenn Daten fehlen, schreibe "Nicht verfügbar" oder "N/A"
-✓ RICHTIG: Verwende NUR die tatsächlichen Werte aus den Datenbank-Ergebnissen
-✓ RICHTIG: Wenn ein Wert NULL oder leer ist, schreibe "Nicht verfügbar"
-
-FORMATIERUNGSREGELN FÜR ARTIKEL-ANFRAGEN:
-1. Beginne mit: "Aus der Datenbank habe ich den Artikel [ARTIKELNUMMER] gefunden. Es handelt sich um [ARTIKELBEZEICHNUNG] von [LIEFERANT]."
-   - Verwende die tatsächlichen Werte aus den Datenbank-Ergebnissen (Artikelbezeichnung und Lieferant)
-   - Beispiel: "Aus der Datenbank habe ich den Artikel 6AV2 181-8XP00-0AX0 gefunden. Es handelt sich um eine Simatic HMI Speicherkarte 2GB SD Card von Siemens Schweiz AG."
-   - Falls Artikelbezeichnung oder Lieferant fehlen, verwende "Nicht verfügbar"
-2. Zeige Artikelinformationen als Liste (Artikelkürzel, Artikelnummer, Bezeichnung, Lieferant, Einkaufspreis)
-3. Zeige Lagerbestände als Tabelle mit ALLEN Lagerplätzen
-4. Berechne Gesamtbestand aus den tatsächlichen Daten
-5. Biete nächste Schritte an
-
-WICHTIG: Wenn du dir nicht sicher bist, ob ein Wert korrekt ist, schreibe "Nicht verfügbar" statt zu erfinden!
-
-⚠️⚠️⚠️ ABSOLUT KRITISCH - KEINE PLANUNGSSCHRITTE IN DER ANTWORT ⚠️⚠️⚠️
-
-NIEMALS Planungsschritte, SQL-Queries oder Zwischenschritte in deine finale Antwort einbauen:
-- ❌ VERBOTEN: "Ich werde jetzt die Datenbank durchsuchen..."
-- ❌ VERBOTEN: "Suche in der Datenbank nach..."
-- ❌ VERBOTEN: "Führe SQL-Abfrage aus..."
-- ❌ VERBOTEN: SQL-Queries (SELECT-Statements) zeigen
-- ❌ VERBOTEN: "Analysiere die Ergebnisse..."
-- ❌ VERBOTEN: "Bereite die Abfrageergebnisse auf..."
-- ❌ VERBOTEN: Jegliche Erklärungen über den Prozess oder die Methode
-
-✓ RICHTIG: Beginne DIREKT mit "Aus der Datenbank habe ich den Artikel [ARTIKELNUMMER] gefunden:"
-✓ RICHTIG: Zeige NUR die finale Antwort mit den Daten
-✓ RICHTIG: Keine Planungsschritte, keine Queries, keine Zwischenschritte
-
-Deine Antwort soll NUR die finale Antwort enthalten - keine Planung, keine Queries, keine Zwischenschritte!
-
-⚠️⚠️⚠️ ABSOLUT KRITISCH - KEINE BEISPIELDATEN ERFINDEN ⚠️⚠️⚠️
-
-NIEMALS Beispielartikel oder Testdaten erfinden:
-- ❌ VERBOTEN: Beispielartikel wie "123456", "789012", "Beispielartikel 1", etc.
-- ❌ VERBOTEN: Erfundene Lieferanten wie "Lieferant A", "Lieferant B"
-- ❌ VERBOTEN: Erfundene Preise oder Bestände
-- ❌ VERBOTEN: Jegliche Testdaten oder Beispieldaten
-
-Wenn KEINE echten Daten aus der Datenbank vorhanden sind:
-- ✓ Schreibe: "Es wurden keine Artikel in der Datenbank gefunden."
-- ✓ Oder: "Die Datenbankabfrage hat keine Ergebnisse zurückgegeben."
-- ✓ Oder: "Keine Daten verfügbar für diese Anfrage."
-
-ERFINDE NIEMALS Daten, auch nicht als "Beispiel" oder "Test"!
-
-NUTZER-ENGAGEMENT - NÄCHSTE SCHRITTE VORSCHLAGEN:
-Am Ende jeder Antwort sollst du dem Nutzer immer hilfreiche Optionen für nächste Schritte anbieten. Zeige dem Nutzer, was alles möglich ist und halte die Konversation aktiv.
-
-Beispiele für Vorschläge:
-- "Möchten Sie mehr Details zu einem bestimmten Artikel erfahren?"
-- "Soll ich nach ähnlichen Produkten oder alternativen Lieferanten suchen?"
-- "Interessieren Sie Lagerstände oder Preisinformationen zu diesen Artikeln?"
-- "Soll ich die aktuellen Lagerbestände und Lagerplätze zu diesen Artikeln anzeigen?"
-- "Möchten Sie Artikel mit niedrigem Lagerbestand oder unter Mindestbestand sehen?"
-- "Kann ich Ihnen bei einer spezifischeren Suche helfen?"
-- "Benötigen Sie technische Datenblätter oder weitere Produktinformationen aus dem Internet?"
-
-Passe deine Vorschläge an den Kontext der Anfrage an und sei kreativ. Ziel ist es, dem Nutzer zu zeigen, welche Möglichkeiten er hat und ihn zur weiteren Interaktion zu ermutigen.
-
-Du antwortest ausschliesslich auf Deutsch. Nutze kein sz(ß) sondern immer ss.
-"""
-
-
-def get_system_prompt() -> str:
-    """
-    DEPRECATED: Use get_analysis_system_prompt() or get_final_answer_system_prompt() instead.
-    Kept for backward compatibility.
-    """
-    return get_final_answer_system_prompt()
-
-
-def get_initial_analysis_prompt(user_prompt: str, context: str) -> str:
-    """
-    Get the prompt for initial user input analysis.
-    
-    Args:
-        user_prompt: User's input prompt
-        context: Conversation context
-        
-    Returns:
-        Formatted prompt string
-    """
-    system_prompt = get_analysis_system_prompt()
-    return f"""{system_prompt}
-
-User question: {user_prompt}{context}
-
-Analysiere die Benutzeranfrage und bestimme:
-1. Ob eine Datenbankabfrage benötigt wird (needsDatabaseQuery)
-2. Ob eine Web-Recherche benötigt wird (needsWebResearch)
-3. Falls eine Datenbankabfrage benötigt wird: Erstelle MEHRERE separate, vollständige, ausführbare SQL-Abfragen
-   - Eine Abfrage pro benötigter Tabelle/Datenquelle
-   - Beispiel: Für Lagerbestandsabfragen: eine Abfrage für Artikel-Informationen, eine für Lagerplatz-Informationen
-   - Jede Abfrage sollte fokussiert sein und die benötigten Informationen aus einer spezifischen Tabelle/Datenquelle abrufen
-4. Begründung für deine Entscheidung
-
-⚠️⚠️⚠️ WICHTIG - WEB-RECHERCHE BEI ZUSÄTZLICHEN INFORMATIONEN ⚠️⚠️⚠️
-Wenn der Nutzer nach zusätzlichen Informationen fragt oder explizit eine Recherche anfordert, MUSS IMMER eine Web-Recherche durchgeführt werden (needsWebResearch = true).
-Beispiele für solche Anfragen:
-- "recherchier nach weiteren informationen zu diesem produkt"
-- "suche nach zusätzlichen informationen"
-- "finde mehr details"
-- "recherchiere im internet"
-- "suche online nach"
-- Ähnliche Formulierungen, die eine Recherche oder zusätzliche Informationen anfordern
-In diesen Fällen IMMER needsWebResearch auf true setzen!
-
-WICHTIG für SQL-Abfragen:
-- Verwende IMMER doppelte Anführungszeichen für Spaltennamen
-- Bei Lagerbestandsabfragen: IMMER S_RESERVIERTER__BESTAND und verfügbaren Bestand einbeziehen
-- Bei Lagerplatzabfragen: IMMER JOIN mit Lagerplatz-Tabelle für den Namen
-- Abfragen müssen direkt ausführbar sein (keine Platzhalter)
-- Erstelle SEPARATE Abfragen für verschiedene Tabellen/Datenquellen, nicht eine große JOIN-Abfrage
-
-STRATEGIE FÜR MEHRERE ABFRAGEN:
-- Analysiere welche Informationen benötigt werden
-- Identifiziere welche Tabellen diese Informationen enthalten
-- Erstelle für jede Tabelle/Datenquelle eine separate, fokussierte Abfrage
-- Beispiel für "wie viel von 6AV2 181-8XP00-0AX0 haben wir auf lager":
-  * Abfrage 1: Artikel-Informationen (Artikelbezeichnung, Lieferant, etc.) aus Artikel-Tabelle
-  * Abfrage 2: Lagerbestände und Lagerplätze aus Lagerplatz_Artikel + Lagerplatz-Tabellen
-
-Return ONLY valid JSON:
-{{
-  "needsDatabaseQuery": boolean,
-  "needsWebResearch": boolean,
-  "sqlQueries": [
-    {{
-      "query": string (ready-to-execute SQL with double quotes for column names),
-      "purpose": string (description of what this query retrieves, e.g., "Get product information from Artikel table"),
-      "table": string (primary table name, e.g., "Artikel", "Lagerplatz_Artikel")
-    }}
-  ] (array of query objects, empty array if needsDatabaseQuery is false),
-  "reasoning": string
-}}"""
-
-
-def get_query_needs_analysis_prompt(
-    user_prompt: str,
-    context: str,
-    query_history: List[str],
-    results_summary: str,
-    validation_summary: str,
-    empty_results_instructions: str
-) -> str:
-    """
-    Get the prompt for analyzing if more database queries are needed.
-    
-    Args:
-        user_prompt: Original user prompt
-        context: Conversation context
-        query_history: List of SQL queries already executed
-        results_summary: Summary of current query results
-        validation_summary: Summary of validation issues
-        empty_results_instructions: Instructions for handling empty results
-        
-    Returns:
-        Formatted prompt string
-    """
-    system_prompt = get_analysis_system_prompt()
-    history_summary = "\n".join([f"- {q[:100]}..." for q in query_history]) if query_history else "No queries executed yet."
-    
-    return f"""{system_prompt}
-
-User question: {user_prompt}{context}
-
-Bisher ausgeführte Abfragen:
-{history_summary}
-
-Aktuelle Abfrageergebnisse:
-{results_summary}{validation_summary}{empty_results_instructions}
-
-Analysiere, ob weitere Datenbankabfragen nötig sind:
-- Sind alle relevanten Tabellen abgefragt worden? (Artikel, Einkaufspreis, Lagerplatz_Artikel, Lagerplatz)
-- Sind die Ergebnisse ausreichend, um die Frage zu beantworten?
-- Fehlen JOINs oder Beziehungen zwischen Tabellen?
-- Gibt es Fehler, die korrigiert werden müssen?
-- Werden alle benötigten Informationen abgerufen (z.B. Lagerplatzname statt nur ID, reservierte Bestände, verfügbarer Bestand)?
-- Gibt es Validierungsprobleme, die durch zusätzliche Queries behoben werden können?
-- **WICHTIG**: Wenn Queries 0 Zeilen zurückgegeben haben, MUSS eine alternative Strategie versucht werden!
-
-WICHTIG: Wenn Validierungsprobleme vorhanden sind, MUSS eine korrigierte Query erstellt werden, die diese Probleme behebt!
-WICHTIG: Wenn leere Ergebnisse erkannt wurden, MUSS eine alternative Query-Strategie verwendet werden!
-
-Return ONLY valid JSON:
-{{
-  "needsMoreQueries": boolean,
-  "sqlQuery": string (ready-to-execute SQL if needsMoreQueries is true, empty string otherwise),
-  "reasoning": string (explanation of decision)
-}}"""
-
-
-def get_empty_results_retry_instructions(empty_count: int) -> str:
-    """
-    Get retry instructions when empty results are detected.
-    
-    Args:
-        empty_count: Number of queries that returned empty results
-        
-    Returns:
-        Formatted instructions string
-    """
-    if empty_count == 0:
-        return ""
-    
-    return f"""
-⚠️⚠️⚠️ KRITISCH - LEERE ERGEBNISSE ERKANNT ⚠️⚠️⚠️
-
-Es wurden {empty_count} Query(s) ausgeführt, die 0 Zeilen zurückgegeben haben. Dies bedeutet, dass die bisherige Query-Strategie nicht erfolgreich war.
-
-DU MUSST JETZT EINE ALTERNATIVE QUERY-STRATEGIE VERSUCHEN!
-
-Verfügbare Tabellen im System:
-1. Artikel - Enthält alle Produktinformationen (I_ID, Artikelbezeichnung, Artikelnummer, etc.)
-2. Einkaufspreis - Enthält Preisdaten (m_Artikel, EP_CHF)
-3. Lagerplatz_Artikel - Enthält Lagerbestands- und Lagerplatzinformationen (R_ARTIKEL, R_LAGERPLATZ, Bestände, etc.)
-4. Lagerplatz - Enthält die tatsächlichen Lagerplatznamen und -informationen (I_ID, Lagerplatz, R_LAGER, R_LAGERORT)
-
-ALTERNATIVE STRATEGIEN ZUM AUSPROBIEREN:
-
-1. **Direkte Lagerplatz-Suche**: Prüfe zuerst, ob der Lagerplatzname in der Lagerplatz-Tabelle existiert:
-   SELECT * FROM Lagerplatz WHERE "Lagerplatz" LIKE '%[Suchbegriff]%'
-   
-2. **Verschiedene Schreibweisen**: Versuche verschiedene Schreibweisen (Groß-/Kleinschreibung, Teilstrings):
-   - UPPER/LOWER Funktionen verwenden
-   - Verschiedene LIKE-Patterns: '%term%', 'term%', '%term'
-   
-3. **JOIN-Strategie überprüfen**: Stelle sicher, dass R_LAGERPLATZ korrekt mit Lagerplatz.I_ID gejoint wird:
-   - R_LAGERPLATZ in Lagerplatz_Artikel enthält die ID (nicht den Namen!)
-   - Verwende: LEFT JOIN Lagerplatz lp ON l."R_LAGERPLATZ" = lp."I_ID"
-   
-4. **Breitere Suche**: Versuche eine breitere Suche ohne exakte Filter:
-   - Entferne zu spezifische WHERE-Bedingungen
-   - Verwende OR-Bedingungen für verschiedene Suchvarianten
-   
-5. **Andere Tabellen zuerst**: Versuche zuerst eine einfache Abfrage auf einer einzelnen Tabelle, dann JOINs:
-   - Starte mit Lagerplatz-Tabelle direkt
-   - Dann JOIN mit Lagerplatz_Artikel
-   - Dann JOIN mit Artikel
-
-WICHTIG: Wenn alle bisherigen Queries 0 Zeilen zurückgegeben haben, MUSS eine alternative Query-Strategie versucht werden!
-Erstelle eine neue Query, die eine der oben genannten Strategien verwendet. Versuche verschiedene Ansätze, bis Ergebnisse gefunden werden.
-"""
-
-
-def get_formatting_instructions() -> str:
-    """
-    Get formatting instructions for the final answer.
-    
-    Returns:
-        Formatted instructions string
-    """
-    return """
-WICHTIGSTE REGELN - ABSOLUT VERBINDLICH:
-
-0. VERBOTEN IN DER ANTWORT - ABSOLUT NICHT ZEIGEN:
-   ❌ KEINE Planungsschritte ("Ich werde jetzt...", "Suche in der Datenbank...", etc.)
-   ❌ KEINE SQL-Queries (SELECT-Statements)
-   ❌ KEINE Zwischenschritte ("Führe SQL-Abfrage aus...", "Analysiere Ergebnisse...", etc.)
-   ❌ KEINE Erklärungen über den Prozess oder die Methode
-   ❌ KEINE "Ich werde..."- oder "Ich suche..."-Sätze
-   ❌ NUR die finale Antwort mit den Daten!
-
-1. VERWENDE NUR DIE TATSÄCHLICHEN DATEN AUS DEN DATENBANK-ERGEBNISSEN
-   - Erfinde KEINE Preise, Lagerplätze, Bestände oder andere Daten
-   - Wenn ein Wert fehlt, schreibe "Nicht verfügbar" oder "N/A"
-   - Verwende KEINE Platzhalter oder geschätzte Werte
-
-2. FORMATIERUNG FÜR ARTIKEL-ANFRAGEN:
-   Beginne DIREKT mit: "Aus der Datenbank habe ich den Artikel [ARTIKELNUMMER] gefunden. Es handelt sich um [ARTIKELBEZEICHNUNG] von [LIEFERANT]."
-   - Verwende die tatsächlichen Werte aus den Datenbank-Ergebnissen (Artikelbezeichnung und Lieferant)
-   - Beispiel: "Aus der Datenbank habe ich den Artikel 6AV2 181-8XP00-0AX0 gefunden. Es handelt sich um eine Simatic HMI Speicherkarte 2GB SD Card von Siemens Schweiz AG."
-   - Falls Artikelbezeichnung oder Lieferant fehlen, verwende "Nicht verfügbar"
-   
-   Dann zeige:
-   Artikelinformationen
-   - Artikelkürzel: [Wert aus Datenbank oder "Nicht verfügbar"]
-   - Artikelnummer: [Wert aus Datenbank oder "Nicht verfügbar"]
-   - Bezeichnung: [Wert aus Datenbank oder "Nicht verfügbar"]
-   - Lieferant: [Wert aus Datenbank oder "Nicht verfügbar"]
-   - Einkaufspreis: [Wert aus Datenbank oder "Nicht verfügbar"]
-   
-   Lagerbestände nach Lagerplätzen
-   [Tabelle mit ALLEN Lagerplätzen aus den Daten]
-   Lagerplatz | Ist-Bestand | Soll-Bestand | Min-Bestand | Max-Bestand | Reservierter Bestand | Verfügbarer Bestand
-   
-   Gesamtbestand: [Summe aller Ist-Bestände] Stück (alle am Lagerplatz "[Lagerplatzname]")
-   
-   Möchten Sie:
-   - Mehr technische Details zu diesem Artikel erfahren?
-   - Nach ähnlichen Artikeln suchen?
-   - Informationen zu anderen Artikeln im Lager anzeigen?
-   - Den aktuellen Preis oder Lieferzeiten prüfen?
-
-3. STELLE SICHER, DASS ALLE LAGERPLÄTZE ANGEZEIGT WERDEN
-   - Wenn mehrere Lagerplätze vorhanden sind, zeige ALLE in der Tabelle
-   - Gruppiere nicht - zeige jeden Lagerplatz als separate Zeile
-
-4. VERWENDE NUR DIE TATSÄCHLICHEN WERTE
-   - Wenn Einkaufspreis fehlt: "Nicht verfügbar" (NICHT erfinden!)
-   - Wenn Lagerplatz fehlt: "Nicht verfügbar" (NICHT erfinden!)
-   - Wenn Bestand fehlt: "Nicht verfügbar" (NICHT erfinden!)
-"""
-
-
-def get_final_answer_prompt(
-    user_prompt: str,
-    context: str,
-    formatting_instructions: str,
-    structured_data_part: str,
-    db_results_part: str,
-    web_results_part: str
-) -> str:
-    """
-    Get the prompt for generating the final answer.
-    
-    Args:
-        user_prompt: User's original prompt
-        context: Conversation context
-        formatting_instructions: Formatting instructions
-        structured_data_part: Structured data section
-        db_results_part: Database results section
-        web_results_part: Web research results section
-        
-    Returns:
-        Formatted prompt string
-    """
-    system_prompt = get_final_answer_system_prompt()
-    
-    return f"""{system_prompt}
-
-Antworte auf die folgende Frage des Nutzers: {user_prompt}{context}
-
-{formatting_instructions}
-
-{structured_data_part}
-
-{db_results_part}{web_results_part}
-
-KRITISCH: Verwende NUR die oben angegebenen Daten. Erfinde KEINE Werte. Wenn Daten fehlen, schreibe "Nicht verfügbar".
-
-⚠️⚠️⚠️ ABSOLUT KRITISCH - WEB-RECHERCHE QUELLENANGABE ⚠️⚠️⚠️
-Wenn WEB-RECHERCHE-ERGEBNISSE oben vorhanden sind, MUSS du:
-- ✓ IMMER explizit erwähnen, dass die Informationen aus einer Web-Recherche stammen
-- ✓ IMMER alle Quellen DIREKT NACH der jeweiligen Information angeben (INLINE, nicht am Ende!)
-- ✓ Format: [Information] ([Quelle: Website-Name](URL))
-- ✓ IMMER AUSFÜHRLICHE Informationen präsentieren (nicht nur kurze Zusammenfassungen!)
-- ✓ IMMER alle verfügbaren Datenblatt-Links explizit erwähnen und angeben
-- ✓ Format für Datenblätter: "Datenblätter verfügbar: [Link 1](URL1), [Link 2](URL2)"
-- ✓ Die Web-Recherche-Informationen klar von Datenbank-Informationen trennen
-- ❌ VERBOTEN: Web-Recherche-Informationen ohne explizite Kennzeichnung zu präsentieren
-- ❌ VERBOTEN: Web-Recherche-Informationen ohne Quellenangabe zu präsentieren
-- ❌ VERBOTEN: Quellen nur am Ende als Liste zu präsentieren
-- ❌ VERBOTEN: Datenblatt-Links zu verschweigen oder nicht explizit zu erwähnen
-- ❌ VERBOTEN: Nur oberflächliche Informationen zu geben
-
-⚠️⚠️⚠️ ABSOLUT VERBOTEN - KEINE DATEN ERFINDEN ⚠️⚠️⚠️
-Wenn KEINE Datenbank-Ergebnisse vorhanden sind (keine DATENBANK-ERGEBNISSE oder STRUKTURIERTE DATEN oben), dann:
-- ❌ ERFINDE KEINE Artikelnummern, Artikelbezeichnungen, Preise oder Lagerbestände!
-- ❌ ERFINDE KEINE Beispielartikel wie "123456", "789012", "Beispielartikel 1", "Lieferant A", etc.!
-- ❌ ERFINDE KEINE Daten, auch nicht als "Beispiel"!
-- ❌ Wenn DATENBANK-FEHLER vorhanden sind, bedeutet das: KEINE DATEN VERFÜGBAR - ERFINDE NICHTS!
-- ✓ Schreibe stattdessen: "Es wurden keine Artikel in der Datenbank gefunden." oder "Die Datenbankabfrage ist fehlgeschlagen."
-- ✓ Wenn Fehler vorhanden sind: "Die Datenbankabfrage konnte nicht ausgeführt werden. Bitte versuchen Sie es später erneut oder kontaktieren Sie den Administrator."
-
-WICHTIG: Deine Antwort soll NUR die finale Antwort enthalten - KEINE Planungsschritte, KEINE SQL-Queries, KEINE Zwischenschritte!
-Beginne DIREKT mit "Aus der Datenbank habe ich..." (wenn Daten vorhanden) oder "Es wurden keine Artikel gefunden" (wenn keine Daten vorhanden).
-Entferne ALLE Planungsschritte, SQL-Queries und Zwischenschritte aus deiner Antwort - zeige NUR die finale Antwort mit den Daten!"""
-
-
 async def generate_conversation_name(
     services,
-    userPrompt: str,
-    userLanguage: str = "en"
+    prompt: str,
+    user_language: Optional[str] = None
 ) -> str:
     """
-    Generate a short, descriptive conversation name based on user's prompt.
+    Generate a conversation name from the user's prompt using AI.
+    Creates a concise, informative summary name in German based on the user input.
     
     Args:
-        services: Services instance with AI access
-        userPrompt: The user's input prompt
-        userLanguage: User's preferred language (for prompt localization)
+        services: Services object with AI service
+        prompt: User's input prompt (always in German)
+        user_language: User's language preference (not used, always German)
         
     Returns:
-        Short conversation name (max 60 characters)
+        A short, informative conversation name in German
     """
+    if not prompt or not prompt.strip():
+        return "Neue Unterhaltung"
+    
     try:
-        truncated_prompt = userPrompt[:200] if len(userPrompt) > 200 else userPrompt
-        
-        name_prompt = f"""Create a professional conversation title in THE SAME LANGUAGE as the user's question.
-
-Question: "{truncated_prompt}"
-
-Rules:
-- Title MUST be in the same language as the question (German→German, French→French, English→English)
-- Max 60 characters, no punctuation (?, !, .)
-- Professional and concise
-- Respond ONLY with the title, nothing else"""
+        # Check if AI service is available
+        if not hasattr(services, 'ai') or services.ai is None:
+            logger.warning("AI service not available, generating name from prompt")
+            return _generate_name_from_prompt(prompt)
         
+        # Ensure AI service is initialized before use
         await services.ai.ensureAiObjectsInitialized()
         
-        nameRequest = AiCallRequest(
-            prompt=name_prompt,
+        # Create AI prompt - very explicit that answer must be in German
+        ai_prompt = f"""Du bist ein deutscher Assistent. Der Benutzer hat folgende Anfrage auf Deutsch gestellt:
+
+"{prompt.strip()}"
+
+Erstelle einen kurzen, zusammenfassenden Titel für diese Unterhaltung. Der Titel muss:
+- Auf Deutsch sein (KEIN Englisch!)
+- Maximal 50 Zeichen lang sein
+- Das Hauptthema zusammenfassen
+- Informativ sein
+
+Beispiele für gute deutsche Titel:
+- "LED-Artikel Suche"
+- "Lagerbestandsabfrage"  
+- "Produktinformationen"
+- "Artikel-Suche"
+
+Antworte NUR mit dem deutschen Titel, ohne Anführungszeichen oder Erklärungen."""
+
+        # Create AI request
+        request = AiCallRequest(
+            prompt=ai_prompt,
+            context="",
             options=AiCallOptions(
-                resultFormat="txt",
                 operationType=OperationTypeEnum.DATA_GENERATE,
-                processingMode=ProcessingModeEnum.DETAILED,
-                temperature=0.7
+                priority=PriorityEnum.SPEED,
+                processingMode=ProcessingModeEnum.BASIC,
+                compressPrompt=False,
+                compressContext=False,
+                temperature=0.3  # Lower temperature for more consistent German output
             )
         )
         
-        nameResponse = await services.ai.callAi(nameRequest)
-        generated_name = nameResponse.content.strip()
+        # Call AI service
+        logger.info(f"Calling AI to generate conversation name for prompt: {prompt[:50]}...")
+        response = await services.ai.callAi(request)
         
-        # Extract first line and clean up
-        generated_name = generated_name.split('\n')[0].strip()
-        generated_name = re.sub(r'^(Title|Titel|Titre|Name|Name:):\s*', '', generated_name, flags=re.IGNORECASE)
-        generated_name = re.sub(r'^["\']|["\']$', '', generated_name)
-        generated_name = re.sub(r'[?!.]+$', '', generated_name)  # Remove trailing punctuation
+        if not response or not hasattr(response, 'content') or not response.content:
+            logger.warning("AI response invalid, generating name from prompt")
+            return _generate_name_from_prompt(prompt)
         
-        # Apply title case
-        if generated_name:
-            words = generated_name.split()
-            capitalized_words = []
-            for word in words:
-                if word.isupper() and len(word) > 1:
-                    capitalized_words.append(word)  # Keep acronyms
-                else:
-                    capitalized_words.append(word.capitalize())
-            generated_name = " ".join(capitalized_words).strip()
+        logger.info(f"AI response received: {response.content[:100]}...")
         
-        # Validate and truncate if needed
-        if not generated_name or len(generated_name) < 3:
-            if userLanguage == "de":
-                generated_name = "Chatbot Konversation"
-            elif userLanguage == "fr":
-                generated_name = "Conversation Chatbot"
-            else:
-                generated_name = "Chatbot Conversation"
+        # Clean up the AI response
+        name = str(response.content).strip()
+        name = name.strip('"\'')
         
-        if len(generated_name) > 60:
-            truncated = generated_name[:57]
-            last_space = truncated.rfind(' ')
-            generated_name = truncated[:last_space] + "..." if last_space > 30 else truncated + "..."
+        # Remove markdown code blocks if present
+        if name.startswith('```'):
+            lines = name.split('\n')
+            if len(lines) > 1:
+                name = '\n'.join(lines[1:-1]) if lines[-1].strip() == '```' else '\n'.join(lines[1:])
         
-        logger.info(f"Generated conversation name: '{generated_name}'")
-        return generated_name
+        # Remove newlines and extra spaces
+        name = " ".join(name.split())
+        
+        # Check if name contains English words - if so, generate from prompt instead
+        name_lower = name.lower()
+        english_words = ["search", "find", "show", "display", "query", "article", "product", "item", "led articles", "product search"]
+        if any(word in name_lower for word in english_words):
+            logger.warning(f"AI generated English name '{name}', generating from prompt instead")
+            return _generate_name_from_prompt(prompt)
+        
+        # Limit to 50 characters
+        if len(name) > 50:
+            name = name[:47] + "..."
+        
+        # If we got a valid name, return it
+        if name and len(name) >= 3:
+            logger.info(f"Successfully generated conversation name via AI: '{name}'")
+            return name
+        else:
+            logger.warning(f"Generated name is too short: '{name}', generating from prompt")
+            return _generate_name_from_prompt(prompt)
         
     except Exception as e:
-        logger.error(f"Error generating conversation name: {e}", exc_info=True)
-        if userLanguage == "de":
-            return "Chatbot Konversation"
-        elif userLanguage == "fr":
-            return "Conversation Chatbot"
-        else:
-            return "Chatbot Conversation"
+        logger.error(f"Error generating conversation name with AI: {e}", exc_info=True)
+        return _generate_name_from_prompt(prompt)
 
+
+def _generate_name_from_prompt(prompt: str) -> str:
+    """
+    Generate a conversation name directly from the German prompt.
+    Creates a concise title by extracting key words and formatting them.
+    
+    Args:
+        prompt: User's input prompt in German
+        
+    Returns:
+        A short conversation name in German
+    """
+    if not prompt or not prompt.strip():
+        return "Neue Unterhaltung"
+    
+    # Clean up the prompt
+    name = prompt.strip()
+    
+    # Remove newlines and extra spaces
+    name = " ".join(name.split())
+    
+    # Remove common question words and phrases
+    question_words = ["wie", "was", "wo", "wann", "wer", "welche", "welcher", "welches"]
+    words = name.split()
+    filtered_words = [w for w in words if w.lower() not in question_words]
+    
+    if filtered_words:
+        name = " ".join(filtered_words)
+    
+    # Capitalize first letter
+    if name:
+        name = name[0].upper() + name[1:] if len(name) > 1 else name.upper()
+    
+    # Limit to 50 characters
+    if len(name) > 50:
+        # Try to cut at word boundary
+        truncated = name[:47]
+        last_space = truncated.rfind(' ')
+        if last_space > 20:  # Only cut at word boundary if reasonable
+            name = truncated[:last_space] + "..."
+        else:
+            name = truncated + "..."
+    
+    # If name is empty or too short, use default
+    if not name or len(name) < 3:
+        return "Neue Unterhaltung"
+    
+    return name

modules/features/chatbot/config.py

@@ -0,0 +1,130 @@
+# Copyright (c) 2025 Patrick Motsch
+# All rights reserved.
+"""
+Configuration system for chatbot instances.
+Loads JSON configuration files from configs/ directory.
+"""
+
+import logging
+import json
+from pathlib import Path
+from dataclasses import dataclass
+from typing import Optional, Dict, Any
+
+logger = logging.getLogger(__name__)
+
+# Cache for loaded configs
+_config_cache: Dict[str, 'ChatbotConfig'] = {}
+
+
+@dataclass
+class DatabaseConfig:
+    """Database configuration for a chatbot instance."""
+    schema: Dict[str, Any]
+    connector: str = "preprocessor"
+
+
+@dataclass
+class ToolConfig:
+    """Tool configuration for a chatbot instance."""
+    sql: Dict[str, Any]
+    tavily: Optional[Dict[str, Any]] = None
+    streaming: Dict[str, Any] = None
+
+
+@dataclass
+class ModelConfig:
+    """Model configuration for a chatbot instance."""
+    operationType: str = "DATA_ANALYSE"
+    processingMode: str = "DETAILED"
+
+
+@dataclass
+class ChatbotConfig:
+    """Configuration for a chatbot instance."""
+    id: str
+    name: str
+    systemPrompt: str
+    database: DatabaseConfig
+    tools: ToolConfig
+    model: ModelConfig
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> 'ChatbotConfig':
+        """Create ChatbotConfig from dictionary."""
+        return cls(
+            id=data.get("id", "default"),
+            name=data.get("name", "Default Chatbot"),
+            systemPrompt=data.get("systemPrompt", "You are a helpful assistant."),
+            database=DatabaseConfig(
+                schema=data.get("database", {}).get("schema", {}),
+                connector=data.get("database", {}).get("connector", "preprocessor")
+            ),
+            tools=ToolConfig(
+                sql=data.get("tools", {}).get("sql", {"enabled": True}),
+                tavily=data.get("tools", {}).get("tavily"),
+                streaming=data.get("tools", {}).get("streaming", {"enabled": True})
+            ),
+            model=ModelConfig(
+                operationType=data.get("model", {}).get("operationType", "DATA_ANALYSE"),
+                processingMode=data.get("model", {}).get("processingMode", "DETAILED")
+            )
+        )
+
+
+def load_chatbot_config(config_id: str) -> ChatbotConfig:
+    """
+    Load chatbot configuration from JSON file.
+    
+    Args:
+        config_id: Configuration ID (e.g., "althaus", "default")
+        
+    Returns:
+        ChatbotConfig instance
+        
+    Raises:
+        FileNotFoundError: If config file not found
+        ValueError: If config file is invalid
+    """
+    # Check cache first
+    if config_id in _config_cache:
+        logger.debug(f"Returning cached config for {config_id}")
+        return _config_cache[config_id]
+    
+    # Get path to configs directory
+    current_dir = Path(__file__).parent
+    configs_dir = current_dir / "configs"
+    config_file = configs_dir / f"{config_id}.json"
+    
+    if not config_file.exists():
+        # Try default config if requested config not found
+        if config_id != "default":
+            logger.warning(f"Config {config_id} not found, trying default")
+            return load_chatbot_config("default")
+        raise FileNotFoundError(f"Chatbot config file not found: {config_file}")
+    
+    try:
+        with open(config_file, 'r', encoding='utf-8') as f:
+            data = json.load(f)
+        
+        config = ChatbotConfig.from_dict(data)
+        
+        # Cache the config
+        _config_cache[config_id] = config
+        logger.info(f"Loaded chatbot config: {config_id} ({config.name})")
+        
+        return config
+        
+    except json.JSONDecodeError as e:
+        logger.error(f"Error parsing chatbot config JSON {config_file}: {e}")
+        raise ValueError(f"Invalid JSON in config file {config_file}: {e}")
+    except Exception as e:
+        logger.error(f"Error loading chatbot config {config_file}: {e}")
+        raise
+
+
+def clear_config_cache():
+    """Clear the configuration cache."""
+    global _config_cache
+    _config_cache.clear()
+    logger.debug("Cleared chatbot config cache")

modules/features/chatbot/configs/default.json

@@ -0,0 +1,31 @@
+{
+  "id": "default",
+  "name": "Default Chatbot",
+  "systemPrompt": "You are a helpful assistant. You have access to SQL query tools and web search tools. Use them to help answer user questions.",
+  "database": {
+    "schema": {
+      "database": {
+        "path": "/data/database.db",
+        "type": "SQLite"
+      },
+      "tables": {},
+      "relationships": []
+    },
+    "connector": "preprocessor"
+  },
+  "tools": {
+    "sql": {
+      "enabled": true
+    },
+    "tavily": {
+      "enabled": false
+    },
+    "streaming": {
+      "enabled": true
+    }
+  },
+  "model": {
+    "operationType": "DATA_ANALYSE",
+    "processingMode": "DETAILED"
+  }
+}

modules/features/chatbot/eventManager.py

@@ -1,225 +0,0 @@
-# Copyright (c) 2025 Patrick Motsch
-# All rights reserved.
-"""
-Generic streaming event manager for real-time updates.
-Manages event queues for SSE streaming across all features (chatbot, workflows, documents, etc.).
-Supports event-driven streaming instead of polling.
-"""
-
-import logging
-import asyncio
-from typing import Dict, Optional, Any, List, AsyncIterator, Set
-from datetime import datetime
-
-logger = logging.getLogger(__name__)
-
-
-class StreamingEventManager:
-    """
-    Generic event manager for real-time streaming across all features.
-    Supports multiple event types and contexts (workflows, documents, tasks, etc.).
-    Thread-safe event emission and queue management.
-    """
-    
-    def __init__(self):
-        """Initialize the event manager."""
-        self._queues: Dict[str, asyncio.Queue] = {}
-        self._locks: Dict[str, asyncio.Lock] = {}
-        self._cleanup_tasks: Dict[str, asyncio.Task] = {}
-        self._subscribers: Dict[str, Set[str]] = {}  # context_id -> set of queue_ids (for future multi-subscriber support)
-    
-    def create_queue(self, context_id: str) -> asyncio.Queue:
-        """
-        Create a new event queue for a context.
-        
-        Args:
-            context_id: Context ID (workflow_id, document_id, task_id, etc.)
-            
-        Returns:
-            Event queue for the context
-        """
-        if context_id not in self._queues:
-            self._queues[context_id] = asyncio.Queue()
-            self._locks[context_id] = asyncio.Lock()
-            self._subscribers[context_id] = set()
-            logger.debug(f"Created event queue for context {context_id}")
-        return self._queues[context_id]
-    
-    def get_queue(self, context_id: str) -> Optional[asyncio.Queue]:
-        """
-        Get existing event queue for a context.
-        
-        Args:
-            context_id: Context ID
-            
-        Returns:
-            Event queue if exists, None otherwise
-        """
-        return self._queues.get(context_id)
-    
-    async def emit_event(
-        self,
-        context_id: str,
-        event_type: str,
-        data: Dict[str, Any],
-        event_category: str = "default",
-        message: Optional[str] = None,
-        step: Optional[str] = None
-    ):
-        """
-        Emit an event to the context's event queue.
-        
-        Args:
-            context_id: Context ID (workflow_id, document_id, etc.)
-            event_type: Type of event ("message", "log", "status", "progress", "complete", "error", "chatdata")
-            data: Event data dictionary (will be included in event)
-            event_category: Category of event for filtering ("chat", "workflow", "document", etc.)
-            message: Optional event message (for backward compatibility)
-            step: Optional processing step (for backward compatibility)
-        """
-        queue = self.get_queue(context_id)
-        if not queue:
-            logger.debug(f"No event queue found for context {context_id}, skipping event")
-            return
-        
-        event = {
-            "type": event_type,
-            "category": event_category,
-            "timestamp": datetime.now().timestamp(),
-            "data": data,
-            "message": message,  # For backward compatibility
-            "step": step  # For backward compatibility
-        }
-        
-        try:
-            await queue.put(event)
-            logger.debug(f"Emitted {event_type} event (category: {event_category}) for context {context_id}")
-        except Exception as e:
-            logger.error(f"Error emitting event for context {context_id}: {e}")
-    
-    async def stream_events(
-        self,
-        context_id: str,
-        event_categories: Optional[List[str]] = None,
-        timeout: Optional[float] = None
-    ) -> AsyncIterator[Dict[str, Any]]:
-        """
-        Async generator for streaming events from a context.
-        
-        Args:
-            context_id: Context ID to stream events from
-            event_categories: Optional list of event categories to filter by
-            timeout: Optional timeout in seconds (None = no timeout)
-            
-        Yields:
-            Event dictionaries
-        """
-        queue = self.get_queue(context_id)
-        if not queue:
-            logger.warning(f"No queue found for context {context_id}")
-            return
-        
-        start_time = asyncio.get_event_loop().time() if timeout else None
-        
-        while True:
-            # Check timeout
-            if timeout and start_time:
-                elapsed = asyncio.get_event_loop().time() - start_time
-                if elapsed > timeout:
-                    logger.debug(f"Stream timeout for context {context_id}")
-                    break
-            
-            try:
-                # Wait for event with timeout
-                wait_timeout = 1.0  # Check timeout every second
-                if timeout and start_time:
-                    remaining = timeout - (asyncio.get_event_loop().time() - start_time)
-                    if remaining <= 0:
-                        break
-                    wait_timeout = min(wait_timeout, remaining)
-                
-                event = await asyncio.wait_for(queue.get(), timeout=wait_timeout)
-                
-                # Filter by category if specified
-                if event_categories and event.get("category") not in event_categories:
-                    continue
-                
-                yield event
-                
-            except asyncio.TimeoutError:
-                # Check if we should continue or timeout
-                if timeout and start_time:
-                    elapsed = asyncio.get_event_loop().time() - start_time
-                    if elapsed >= timeout:
-                        break
-                continue
-            except Exception as e:
-                logger.error(f"Error in stream_events for context {context_id}: {e}")
-                break
-    
-    async def cleanup(self, context_id: str, delay: float = 60.0):
-        """
-        Schedule cleanup of event queue after delay.
-        This allows time for any remaining events to be consumed.
-        
-        Args:
-            context_id: Context ID
-            delay: Delay in seconds before cleanup (default: 60 seconds)
-        """
-        if context_id in self._cleanup_tasks:
-            # Cancel existing cleanup task
-            self._cleanup_tasks[context_id].cancel()
-        
-        async def _cleanup():
-            try:
-                await asyncio.sleep(delay)
-                if context_id in self._queues:
-                    # Drain remaining events
-                    queue = self._queues[context_id]
-                    while not queue.empty():
-                        try:
-                            queue.get_nowait()
-                        except asyncio.QueueEmpty:
-                            break
-                    
-                    del self._queues[context_id]
-                    if context_id in self._locks:
-                        del self._locks[context_id]
-                    if context_id in self._subscribers:
-                        del self._subscribers[context_id]
-                    logger.info(f"Cleaned up event queue for context {context_id}")
-            except asyncio.CancelledError:
-                pass
-            except Exception as e:
-                logger.error(f"Error during cleanup for context {context_id}: {e}")
-            finally:
-                if context_id in self._cleanup_tasks:
-                    del self._cleanup_tasks[context_id]
-        
-        self._cleanup_tasks[context_id] = asyncio.create_task(_cleanup())
-    
-    def has_queue(self, context_id: str) -> bool:
-        """
-        Check if a queue exists for a context.
-        
-        Args:
-            context_id: Context ID
-            
-        Returns:
-            True if queue exists, False otherwise
-        """
-        return context_id in self._queues
-
-
-# Backward compatibility: ChatbotEventManager is an alias
-ChatbotEventManager = StreamingEventManager
-
-# Global singleton instance
-_event_manager = StreamingEventManager()
-
-
-def get_event_manager() -> StreamingEventManager:
-    """Get the global event manager instance."""
-    return _event_manager
-
-

modules/features/chatbot/mainChatbot.py

@@ -1,999 +0,0 @@
-# Copyright (c) 2025 Patrick Motsch
-# All rights reserved.
-"""
-Simple chatbot feature - basic implementation.
-User input is processed by AI to create list of needed queries.
-Those queries get streamed back.
-"""
-
-import logging
-import json
-import uuid
-import asyncio
-import re
-from typing import Optional, Dict, Any, List
-
-from modules.datamodels.datamodelChat import ChatWorkflow, UserInputRequest, WorkflowModeEnum, ChatLog
-from modules.datamodels.datamodelUam import User
-from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, OperationTypeEnum, ProcessingModeEnum
-from modules.shared.timeUtils import getUtcTimestamp, parseTimestamp
-from modules.services import getInterface as getServices
-from modules.features.chatbot.eventManager import get_event_manager
-from modules.workflows.methods.methodAi.methodAi import MethodAi
-from modules.connectors.connectorPreprocessor import PreprocessorConnector
-from modules.features.chatbot.chatbotConstants import (
-    get_initial_analysis_prompt,
-    generate_conversation_name,
-    get_final_answer_system_prompt
-)
-
-logger = logging.getLogger(__name__)
-
-
-def _extractJsonFromResponse(content: str) -> Optional[dict]:
-    """Extract JSON from AI response, handling markdown code blocks."""
-    # Try direct JSON parse first
-    try:
-        return json.loads(content.strip())
-    except json.JSONDecodeError:
-        pass
-    
-    # Try to extract JSON from markdown code blocks
-    json_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', content, re.DOTALL)
-    if json_match:
-        try:
-            return json.loads(json_match.group(1))
-        except json.JSONDecodeError:
-            pass
-    
-    # Try to find JSON object in the text
-    json_match = re.search(r'\{.*\}', content, re.DOTALL)
-    if json_match:
-        try:
-            return json.loads(json_match.group(0))
-        except json.JSONDecodeError:
-            pass
-    
-    return None
-
-
-async def chatProcess(
-    currentUser: User,
-    userInput: UserInputRequest,
-    workflowId: Optional[str] = None
-) -> ChatWorkflow:
-    """
-    Simple chatbot processing - analyze user input and generate queries.
-    
-    Flow:
-    1. Create or load workflow
-    2. Store user message
-    3. AI analyzes user input to create list of needed queries
-    4. Stream queries back
-    
-    Args:
-        currentUser: Current user
-        userInput: User input request
-        workflowId: Optional workflow ID to continue existing conversation
-        
-    Returns:
-        ChatWorkflow instance
-    """
-    try:
-        # Get services
-        services = getServices(currentUser, None)
-        interfaceDbChat = services.interfaceDbChat
-        
-        # Get event manager and create queue if needed
-        event_manager = get_event_manager()
-        
-        # Create or load workflow
-        if workflowId:
-            workflow = interfaceDbChat.getWorkflow(workflowId)
-            if not workflow:
-                raise ValueError(f"Workflow {workflowId} not found")
-            
-            # Resume workflow: increment round number
-            new_round = workflow.currentRound + 1
-            interfaceDbChat.updateWorkflow(workflowId, {
-                "status": "running",
-                "currentRound": new_round,
-                "lastActivity": getUtcTimestamp()
-            })
-            workflow = interfaceDbChat.getWorkflow(workflowId)
-            logger.info(f"Resumed workflow {workflowId}, round incremented to {new_round}")
-            
-            # Create event queue if it doesn't exist (for streaming)
-            if not event_manager.has_queue(workflowId):
-                event_manager.create_queue(workflowId)
-        else:
-            # Generate conversation name based on user's prompt
-            conversation_name = await generate_conversation_name(
-                services,
-                userInput.prompt,
-                userInput.userLanguage
-            )
-            
-            # Create new workflow
-            workflowData = {
-                "id": str(uuid.uuid4()),
-                "mandateId": currentUser.mandateId,
-                "status": "running",
-                "name": conversation_name,
-                "currentRound": 1,
-                "currentTask": 0,
-                "currentAction": 0,
-                "totalTasks": 0,
-                "totalActions": 0,
-                "workflowMode": WorkflowModeEnum.WORKFLOW_CHATBOT.value,
-                "startedAt": getUtcTimestamp(),
-                "lastActivity": getUtcTimestamp()
-            }
-            workflow = interfaceDbChat.createWorkflow(workflowData)
-            logger.info(f"Created new chatbot workflow: {workflow.id} with name: {conversation_name}")
-            
-            # Create event queue for new workflow (for streaming)
-            event_manager.create_queue(workflow.id)
-        
-        # Reload workflow to get current message count
-        workflow = interfaceDbChat.getWorkflow(workflow.id)
-        
-        # Store user message
-        userMessageData = {
-            "id": f"msg_{uuid.uuid4()}",
-            "workflowId": workflow.id,
-            "message": userInput.prompt,
-            "role": "user",
-            "status": "first" if workflowId is None else "step",
-            "sequenceNr": len(workflow.messages) + 1,
-            "publishedAt": getUtcTimestamp(),
-            "roundNumber": workflow.currentRound,
-            "taskNumber": 0,
-            "actionNumber": 0
-        }
-        
-        userMessage = interfaceDbChat.createMessage(userMessageData)
-        logger.info(f"Stored user message: {userMessage.id}")
-        
-        # Emit message event for streaming (exact chatData format)
-        message_timestamp = parseTimestamp(userMessage.publishedAt, default=getUtcTimestamp())
-        await event_manager.emit_event(
-            context_id=workflow.id,
-            event_type="chatdata",
-            data={
-                "type": "message",
-                "createdAt": message_timestamp,
-                "item": userMessage.dict()
-            },
-            event_category="chat"
-        )
-        
-        # Update workflow status
-        interfaceDbChat.updateWorkflow(workflow.id, {
-            "status": "running",
-            "lastActivity": getUtcTimestamp()
-        })
-        
-        # Process in background (async)
-        asyncio.create_task(_processChatbotMessage(
-            services,
-            workflow.id,
-            userInput,
-            userMessage.id
-        ))
-        
-        # Reload workflow to include new message
-        workflow = interfaceDbChat.getWorkflow(workflow.id)
-        return workflow
-        
-    except Exception as e:
-        logger.error(f"Error in chatProcess: {str(e)}", exc_info=True)
-        raise
-
-
-async def _execute_queries_parallel(queries: List[Dict[str, Any]]) -> Dict[str, Any]:
-    """
-    Execute multiple SQL queries in parallel.
-    
-    Args:
-        queries: List of query dictionaries, each containing:
-            - "query": SQL query string
-            - "purpose": Description of what the query retrieves
-            - "table": Primary table name
-    
-    Returns:
-        Dictionary mapping query indices to results:
-        - "query_1", "query_2", etc.: Success result text
-        - "query_1_data", "query_2_data", etc.: Raw data arrays
-        - "query_1_error", "query_2_error", etc.: Error messages if query failed
-    """
-    async def execute_single_query(idx: int, query_info: Dict[str, Any]):
-        """Execute a single query and return result."""
-        connector = PreprocessorConnector()
-        try:
-            query_text = query_info.get("query", "")
-            result = await connector.executeQuery(query_text, return_json=True)
-            await connector.close()
-            return idx, result, None
-        except Exception as e:
-            await connector.close()
-            return idx, None, str(e)
-    
-    # Execute all queries in parallel
-    tasks = [execute_single_query(i, q) for i, q in enumerate(queries)]
-    results = await asyncio.gather(*tasks, return_exceptions=True)
-    
-    # Process results into dictionary
-    query_results = {}
-    for result in results:
-        if isinstance(result, Exception):
-            # Handle exceptions from gather
-            logger.error(f"Exception in parallel query execution: {result}")
-            continue
-        
-        idx, result_data, error = result
-        
-        if error:
-            query_results[f"query_{idx+1}_error"] = error
-            logger.error(f"Query {idx+1} failed: {error}")
-        else:
-            if result_data and not result_data.get("text", "").startswith(("Error:", "Query failed:")):
-                query_results[f"query_{idx+1}"] = result_data.get("text", "")
-                query_results[f"query_{idx+1}_data"] = result_data.get("data", [])
-                row_count = len(result_data.get('data', []))
-                logger.info(f"Query {idx+1} executed successfully, returned {row_count} rows")
-            else:
-                error_text = result_data.get("text", "Query failed") if result_data else "Query failed: No response"
-                query_results[f"query_{idx+1}_error"] = error_text
-                logger.error(f"Query {idx+1} failed: {error_text}")
-    
-    return query_results
-
-
-async def _emit_log_and_event(
-    interfaceDbChat,
-    workflowId: str,
-    event_manager,
-    message: str,
-    log_type: str = "info",
-    status: str = "running",
-    round_number: Optional[int] = None
-) -> None:
-    """
-    Store log in database. The route's periodic chat data fetch will handle emitting it.
-    This avoids duplicate log emissions.
-    
-    Args:
-        interfaceDbChat: Database interface
-        workflowId: Workflow ID
-        event_manager: Event manager (unused, kept for compatibility)
-        message: Log message
-        log_type: Log type (info, warning, error)
-        status: Status string
-        round_number: Optional round number (will be fetched from workflow if not provided)
-    """
-    try:
-        # Get round number from workflow if not provided
-        if round_number is None:
-            workflow = interfaceDbChat.getWorkflow(workflowId)
-            if workflow:
-                round_number = workflow.currentRound
-        
-        log_timestamp = getUtcTimestamp()
-        log_data = {
-            "id": f"log_{uuid.uuid4()}",
-            "workflowId": workflowId,
-            "message": message,
-            "type": log_type,
-            "timestamp": log_timestamp,
-            "status": status,
-            "roundNumber": round_number
-        }
-        # Only store in database - route's periodic fetch will emit it
-        interfaceDbChat.createLog(log_data)
-    except Exception as e:
-        logger.error(f"Error storing log: {e}")
-
-
-async def _check_workflow_stopped(interfaceDbChat, workflowId: str) -> bool:
-    """
-    Check if workflow was stopped.
-    
-    Args:
-        interfaceDbChat: Database interface
-        workflowId: Workflow ID
-        
-    Returns:
-        True if workflow is stopped, False otherwise
-    """
-    try:
-        workflow = interfaceDbChat.getWorkflow(workflowId)
-        return workflow and workflow.status == "stopped"
-    except Exception as e:
-        logger.warning(f"Error checking workflow status: {e}")
-        return False
-
-
-def _buildWebResearchQuery(userPrompt: str, workflowMessages: List, queryResults: Optional[Dict[str, Any]] = None) -> str:
-    """
-    Build enriched web research query by extracting product context from conversation history and current prompt.
-    
-    Extracts product information from:
-    1. Current user prompt (article numbers, product mentions)
-    2. Database query results (if available)
-    3. Previous assistant messages (conversation history)
-    
-    Args:
-        userPrompt: Current user prompt
-        workflowMessages: List of workflow messages (conversation history)
-        queryResults: Optional database query results to extract product info from
-        
-    Returns:
-        Enriched search query string
-    """
-    # Normalize user prompt for detection
-    prompt_lower = userPrompt.lower().strip()
-    
-    # Patterns that indicate a search request
-    search_patterns = [
-        "ja", "yes", "oui", "si",
-        "such", "suche", "search", "recherche", "recherchier",
-        "internet", "web", "online",
-        "datenblatt", "datasheet", "fiche technique",
-        "mehr informationen", "more information", "plus d'information",
-        "weitere informationen", "further information", "additional information"
-    ]
-    
-    # Check if current prompt contains search-related keywords
-    has_search_intent = any(pattern in prompt_lower for pattern in search_patterns)
-    
-    # Extract product information - try multiple sources
-    article_number = None
-    article_description = None
-    supplier = None
-    
-    # Pattern for article numbers like "6AV2 181-8XP00-0AX0" or "6AV2181-8XP00-0AX0"
-    article_patterns = [
-        r'\b[A-Z0-9]{2,}\s+[0-9]{3,}-[A-Z0-9-]+\b',  # With space: "6AV2 181-8XP00-0AX0"
-        r'\b[A-Z0-9]{4,}[\s-][A-Z0-9-]{6,}\b',  # General pattern
-        r'\b[A-Z]{2,}[0-9]+\s+[0-9]+-[A-Z0-9-]+\b',  # Specific Siemens pattern
-    ]
-    
-    # 1. First, try to extract from current user prompt
-    for pattern in article_patterns:
-        matches = re.findall(pattern, userPrompt)
-        if matches:
-            article_number = matches[0]
-            logger.info(f"Extracted article number from user prompt: {article_number}")
-            break
-    
-    # 2. Try to extract from database query results if available
-    # Always check queryResults to enrich with product description and supplier, even if article_number was already found
-    if queryResults:
-        # Look for article numbers in query result text (if not already found)
-        if not article_number:
-            for key in queryResults.keys():
-                if key.startswith("query_") and not key.endswith("_error") and not key.endswith("_data"):
-                    result_text = queryResults.get(key, "")
-                    if isinstance(result_text, str):
-                        for pattern in article_patterns:
-                            matches = re.findall(pattern, result_text)
-                            if matches:
-                                article_number = matches[0]
-                                logger.info(f"Extracted article number from query results: {article_number}")
-                                break
-                        if article_number:
-                            break
-        
-        # Always check data arrays for product description and supplier (even if article_number already found)
-        for key in queryResults.keys():
-            if key.startswith("query_") and not key.endswith("_error") and not key.endswith("_data"):
-                data_key = f"{key}_data"
-                if data_key in queryResults:
-                    data_array = queryResults[data_key]
-                    if isinstance(data_array, list) and len(data_array) > 0:
-                        # Look for article number in first row (if not already found)
-                        first_row = data_array[0]
-                        if isinstance(first_row, dict):
-                            # Check common article number fields (if not already found)
-                            if not article_number:
-                                for field in ["Artikelnummer", "Artikelkürzel", "article_number", "articleNumber"]:
-                                    if field in first_row and first_row[field]:
-                                        article_number = str(first_row[field])
-                                        logger.info(f"Extracted article number from query data: {article_number}")
-                                        break
-                            
-                            # Always check article description (can enrich even if article_number already found)
-                            if not article_description:
-                                for field in ["Artikelbezeichnung", "Bezeichnung", "article_description", "description"]:
-                                    if field in first_row and first_row[field]:
-                                        article_description = str(first_row[field])
-                                        logger.info(f"Extracted article description from query data: {article_description}")
-                                        break
-                            
-                            # Always check supplier (can enrich even if article_number already found)
-                            if not supplier:
-                                for field in ["Lieferant", "Supplier", "supplier"]:
-                                    if field in first_row and first_row[field]:
-                                        supplier = str(first_row[field])
-                                        logger.info(f"Extracted supplier from query data: {supplier}")
-                                        break
-                            
-                            # If we found all needed info, we can stop
-                            if article_number and article_description and supplier:
-                                break
-    
-    # 3. Extract from previous assistant messages (conversation history)
-    if not article_number or not article_description:
-        for msg in reversed(workflowMessages[-10:]):
-            if msg.role == "assistant":
-                message_text = msg.message
-                
-                # Extract article number if not found yet
-                if not article_number:
-                    for pattern in article_patterns:
-                        matches = re.findall(pattern, message_text)
-                        if matches:
-                            article_number = matches[0]
-                            break
-                
-                # Extract article description if not found yet
-                if not article_description:
-                    description_patterns = [
-                        r'Es handelt sich um\s+([^\.]+)',
-                        r'It is a\s+([^\.]+)',
-                        r'C\'est\s+([^\.]+)',
-                        r'Bezeichnung:\s*([^\n]+)',
-                        r'Description:\s*([^\n]+)',
-                        r'Artikelbezeichnung:\s*([^\n]+)',
-                        r'Artikelbezeichnung:\s*([^\n]+)'
-                    ]
-                    for pattern in description_patterns:
-                        match = re.search(pattern, message_text, re.IGNORECASE)
-                        if match:
-                            article_description = match.group(1).strip()
-                            break
-                
-                # Extract supplier if not found yet
-                if not supplier:
-                    supplier_patterns = [
-                        r'von\s+([A-Z][A-Za-z\s]+(?:AG|GmbH|Ltd|Inc|Corp)?)',
-                        r'from\s+([A-Z][A-Za-z\s]+(?:AG|GmbH|Ltd|Inc|Corp)?)',
-                        r'Lieferant:\s*([^\n]+)',
-                        r'Supplier:\s*([^\n]+)'
-                    ]
-                    for pattern in supplier_patterns:
-                        match = re.search(pattern, message_text, re.IGNORECASE)
-                        if match:
-                            supplier = match.group(1).strip()
-                            break
-                
-                # Stop if we found everything
-                if article_number and article_description and supplier:
-                    break
-    
-    # Build enriched search query
-    query_parts = []
-    
-    # If we have search intent but no product info, try to use the user prompt intelligently
-    if has_search_intent and not article_number and not article_description:
-        # Try to extract meaningful parts from the prompt
-        # Remove common search phrases and keep the product-related parts
-        cleaned_prompt = userPrompt
-        for phrase in ["recherchier nach", "recherche", "suche nach", "search for", "find", "informationen zu", "information about", "weitere informationen", "further information"]:
-            cleaned_prompt = re.sub(phrase, "", cleaned_prompt, flags=re.IGNORECASE)
-        cleaned_prompt = cleaned_prompt.strip()
-        
-        # If cleaned prompt still has content and is different, use it
-        if cleaned_prompt and cleaned_prompt != userPrompt and len(cleaned_prompt) > 10:
-            query_parts.append(cleaned_prompt)
-    
-    # Add article description if found
-    if article_description:
-        query_parts.append(article_description)
-    
-    # Add article number if found
-    if article_number:
-        query_parts.append(article_number)
-    
-    # Add supplier if found
-    if supplier:
-        query_parts.append(supplier)
-    
-    # Add "Datenblatt" or "datasheet" if user requested it or if we have product info
-    if "datenblatt" in prompt_lower or "datasheet" in prompt_lower or "fiche technique" in prompt_lower:
-        query_parts.append("Datenblatt")
-    elif query_parts:
-        # If we have product info but no explicit request for datasheet, add it anyway
-        query_parts.append("Datenblatt")
-    
-    # If we found product information or built a meaningful query, use it
-    if query_parts:
-        enriched_query = " ".join(query_parts)
-        logger.info(f"Built enriched search query: '{enriched_query}' from context (original: '{userPrompt}')")
-        return enriched_query
-    else:
-        # Fall back to original prompt, but try to clean it up
-        logger.info(f"No product context found, using original prompt: '{userPrompt}'")
-        return userPrompt
-
-
-async def _processChatbotMessage(
-    services,
-    workflowId: str,
-    userInput: UserInputRequest,
-    userMessageId: str
-):
-    """
-    Process chatbot message in background.
-    Analyzes user input and generates list of queries, then streams them back.
-    """
-    event_manager = get_event_manager()
-    
-    try:
-        interfaceDbChat = services.interfaceDbChat
-        
-        # Reload workflow to get current messages
-        workflow = interfaceDbChat.getWorkflow(workflowId)
-        if not workflow:
-            logger.error(f"Workflow {workflowId} not found during processing")
-            await event_manager.emit_event(
-                context_id=workflowId,
-                event_type="error",
-                data={"error": f"Workflow {workflowId} nicht gefunden"},
-                event_category="workflow",
-                message=f"Workflow {workflowId} nicht gefunden",
-                step="error"
-            )
-            return
-        
-        # Check if workflow was stopped before starting
-        if await _check_workflow_stopped(interfaceDbChat, workflowId):
-            logger.info(f"Workflow {workflowId} was stopped, aborting processing")
-            return
-        
-        # Build conversation context from history
-        context = ""
-        if workflow.messages:
-            recent_messages = workflow.messages[-5:]
-            context = "\n\nPrevious conversation:\n"
-            for msg in recent_messages:
-                if msg.role == "user":
-                    context += f"User: {msg.message}\n"
-                elif msg.role == "assistant":
-                    context += f"Assistant: {msg.message}\n"
-        
-        await services.ai.ensureAiObjectsInitialized()
-        
-        # Step 1: Analyze user input to generate queries
-        logger.info("Analyzing user input to generate queries...")
-        await _emit_log_and_event(interfaceDbChat, workflowId, event_manager, "Analysiere Benutzeranfrage...")
-        
-        analysisPrompt = get_initial_analysis_prompt(userInput.prompt, context)
-        
-        # AI call for analysis
-        method_ai = MethodAi(services)
-        analysis_result = await method_ai.process({
-            "aiPrompt": analysisPrompt,
-            "documentList": None,
-            "resultType": "json",
-            "simpleMode": True
-        })
-        
-        # Extract content from ActionResult
-        analysis_content = None
-        if analysis_result.success and analysis_result.documents:
-            analysis_content = analysis_result.documents[0].documentData
-            if isinstance(analysis_content, bytes):
-                analysis_content = analysis_content.decode('utf-8')
-        
-        if not analysis_content:
-            logger.warning("Analysis failed, using fallback")
-            analysis = {}
-        else:
-            analysis = _extractJsonFromResponse(analysis_content)
-        
-        # Extract analysis results
-        needsDatabaseQuery = analysis.get("needsDatabaseQuery", False) if analysis else False
-        needsWebResearch = analysis.get("needsWebResearch", False) if analysis else False
-        sql_queries = analysis.get("sqlQueries", [])
-        # Support legacy single query format for backward compatibility
-        if not sql_queries and analysis.get("sqlQuery"):
-            sql_queries = [{
-                "query": analysis.get("sqlQuery", ""),
-                "purpose": "Database query",
-                "table": "Unknown"
-            }]
-        reasoning = analysis.get("reasoning", "")
-        
-        logger.info(f"Analysis: DB={needsDatabaseQuery}, Web={needsWebResearch}, SQL queries={len(sql_queries)}")
-        
-        # Build initial enriched web research query if needed (for logging, will be rebuilt after DB queries)
-        enriched_web_query = None
-        if needsWebResearch:
-            enriched_web_query = _buildWebResearchQuery(userInput.prompt, workflow.messages)
-        
-        # Build list of queries to stream back
-        queries = []
-        
-        if needsDatabaseQuery and sql_queries:
-            for i, sql_query_info in enumerate(sql_queries, 1):
-                queries.append({
-                    "type": "database",
-                    "query": sql_query_info.get("query", ""),
-                    "purpose": sql_query_info.get("purpose", f"Query {i}"),
-                    "table": sql_query_info.get("table", "Unknown"),
-                    "reasoning": reasoning
-                })
-        
-        if needsWebResearch:
-            queries.append({
-                "type": "web",
-                "query": enriched_web_query or userInput.prompt,
-                "reasoning": reasoning
-            })
-        
-        # Format queries as log text
-        log_lines = []
-        if queries:
-            db_queries = [q for q in queries if q["type"] == "database"]
-            log_lines.append(f"Generiert: {len(db_queries)} Datenbankabfrage(n) und {len(queries) - len(db_queries)} Web-Recherche(n)\n\n")
-            for i, q in enumerate(queries, 1):
-                if q["type"] == "database":
-                    log_lines.append(f"{i}. Datenbankabfrage ({q.get('table', 'Unknown')}):\n")
-                    log_lines.append(f"   Zweck: {q.get('purpose', 'Nicht angegeben')}\n")
-                    log_lines.append(f"```sql\n{q['query']}\n```\n")
-                elif q["type"] == "web":
-                    log_lines.append(f"{i}. Web-Recherche:\n")
-                    log_lines.append(f"   Suchbegriff: {q['query']}\n")
-                if q.get("reasoning"):
-                    log_lines.append(f"   Begründung: {q['reasoning']}\n")
-                log_lines.append("\n")
-        else:
-            log_lines.append("Keine Abfragen erforderlich.")
-        
-        log_text = "".join(log_lines)
-        
-        # Stream queries as a log
-        await _emit_log_and_event(interfaceDbChat, workflowId, event_manager, log_text)
-        
-        # Check if workflow was stopped before executing queries
-        if await _check_workflow_stopped(interfaceDbChat, workflowId):
-            logger.info(f"Workflow {workflowId} was stopped, aborting query execution")
-            return
-        
-        # Step 2: Execute queries
-        queryResults = {}
-        webResearchResults = ""
-        
-        # Execute database queries in parallel
-        if needsDatabaseQuery and sql_queries:
-            logger.info(f"Executing {len(sql_queries)} database queries in parallel...")
-            await _emit_log_and_event(interfaceDbChat, workflowId, event_manager, f"Führe {len(sql_queries)} Datenbankabfrage(n) parallel aus...")
-            
-            try:
-                queryResults = await _execute_queries_parallel(sql_queries)
-                
-                # Log results summary
-                successful_queries = [k for k in queryResults.keys() if k.startswith("query_") and not k.endswith("_error") and not k.endswith("_data")]
-                failed_queries = [k for k in queryResults.keys() if k.endswith("_error")]
-                
-                if successful_queries:
-                    total_rows = sum(len(queryResults.get(f"{k}_data", [])) for k in successful_queries)
-                    logger.info(f"Successfully executed {len(successful_queries)} query/queries, total {total_rows} rows")
-                    await _emit_log_and_event(
-                        interfaceDbChat, 
-                        workflowId, 
-                        event_manager, 
-                        f"Abgeschlossen: {len(successful_queries)} Abfrage(n) erfolgreich, {total_rows} Ergebnis{'e' if total_rows != 1 else ''} gefunden"
-                    )
-                
-                if failed_queries:
-                    logger.warning(f"{len(failed_queries)} query/queries failed")
-                    await _emit_log_and_event(
-                        interfaceDbChat, 
-                        workflowId, 
-                        event_manager, 
-                        f"Warnung: {len(failed_queries)} Abfrage(n) fehlgeschlagen",
-                        log_type="warning"
-                    )
-            except Exception as e:
-                logger.error(f"Error executing parallel queries: {e}")
-                queryResults["error"] = f"Error executing queries: {str(e)}"
-                await _emit_log_and_event(
-                    interfaceDbChat,
-                    workflowId,
-                    event_manager,
-                    "Fehler bei parallelen Datenbankabfragen",
-                    log_type="error"
-                )
-        
-        # Execute web research
-        if needsWebResearch:
-            logger.info("Performing web research...")
-            await _emit_log_and_event(interfaceDbChat, workflowId, event_manager, "Suche im Internet nach Informationen...")
-            
-            try:
-                # Rebuild enriched query with database results if available (better product context)
-                web_research_query = _buildWebResearchQuery(
-                    userInput.prompt, 
-                    workflow.messages, 
-                    queryResults if queryResults else None
-                )
-                
-                logger.info(f"Using enriched web research query: '{web_research_query}'")
-                
-                researchResult = await services.web.performWebResearch(
-                    prompt=web_research_query,
-                    urls=[],
-                    country=None,
-                    language=userInput.userLanguage or "de",
-                    researchDepth="general",
-                    operationId=None
-                )
-                webResearchResults = json.dumps(researchResult, ensure_ascii=False, indent=2) if isinstance(researchResult, dict) else str(researchResult)
-                await _emit_log_and_event(interfaceDbChat, workflowId, event_manager, "Internet-Recherche abgeschlossen")
-            except Exception as e:
-                logger.error(f"Web research failed: {e}", exc_info=True)
-                webResearchResults = f"Web research error: {str(e)}"
-                await _emit_log_and_event(interfaceDbChat, workflowId, event_manager, "Internet-Recherche fehlgeschlagen", log_type="warning")
-        
-        # Check if workflow was stopped before generating final answer
-        if await _check_workflow_stopped(interfaceDbChat, workflowId):
-            logger.info(f"Workflow {workflowId} was stopped, aborting final answer generation")
-            return
-        
-        # Step 3: Generate final answer using AI
-        logger.info("Generating final answer with AI...")
-        await _emit_log_and_event(interfaceDbChat, workflowId, event_manager, "Formuliere finale Antwort...")
-        
-        # Build prompt for final answer
-        system_prompt = get_final_answer_system_prompt()
-        
-        # Build answer context with query results
-        answerContext = f"User question: {userInput.prompt}{context}\n\n"
-        
-        # Add database results - organize by query with metadata
-        db_results_part = ""
-        if queryResults:
-            successful_results = []
-            error_results = []
-            
-            # Extract query metadata from sql_queries if available
-            query_metadata = {}
-            if sql_queries:
-                for i, q_info in enumerate(sql_queries, 1):
-                    query_metadata[f"query_{i}"] = {
-                        "purpose": q_info.get("purpose", f"Query {i}"),
-                        "table": q_info.get("table", "Unknown")
-                    }
-            
-            # Organize results by query number
-            query_numbers = set()
-            for key in queryResults.keys():
-                if key.startswith("query_") and not key.endswith("_data"):
-                    # Extract query number (e.g., "query_1" -> 1)
-                    try:
-                        num = int(key.split("_")[1])
-                        query_numbers.add(num)
-                    except (ValueError, IndexError):
-                        pass
-            
-            # Build results with metadata
-            for query_num in sorted(query_numbers):
-                query_key = f"query_{query_num}"
-                error_key = f"{query_key}_error"
-                
-                if error_key in queryResults:
-                    error_msg = queryResults[error_key]
-                    metadata = query_metadata.get(query_key, {})
-                    purpose = metadata.get("purpose", f"Query {query_num}")
-                    table = metadata.get("table", "Unknown")
-                    error_results.append(f"Abfrage {query_num} ({table} - {purpose}): {error_msg}")
-                elif query_key in queryResults:
-                    result_text = queryResults[query_key]
-                    metadata = query_metadata.get(query_key, {})
-                    purpose = metadata.get("purpose", f"Query {query_num}")
-                    table = metadata.get("table", "Unknown")
-                    successful_results.append(f"=== Abfrage {query_num}: {purpose} (Tabelle: {table}) ===\n{result_text}")
-            
-            # Handle general error if present
-            if "error" in queryResults:
-                error_results.append(f"Allgemeiner Fehler: {queryResults['error']}")
-            
-            if successful_results:
-                db_results_part = "\n\nDATENBANK-ERGEBNISSE:\n" + "\n\n".join(successful_results)
-                answerContext += "DATENBANK-ERGEBNISSE:\n" + "\n\n".join(successful_results) + "\n\n"
-            
-            if error_results:
-                db_results_part += "\n\nDATENBANK-FEHLER:\n" + "\n".join(error_results)
-                answerContext += "DATENBANK-FEHLER:\n" + "\n".join(error_results) + "\n\n"
-        
-        # Add web research results
-        web_results_part = ""
-        if webResearchResults:
-            web_results_part = f"\n\nINTERNET-RECHERCHE:\n{webResearchResults}"
-            answerContext += f"INTERNET-RECHERCHE:\n{webResearchResults}\n\n"
-        
-        # Check if we have any actual data
-        successful_query_keys = [k for k in queryResults.keys() if k.startswith("query_") and not k.endswith("_error") and not k.endswith("_data")]
-        has_query_results = bool(successful_query_keys)
-        error_query_keys = [k for k in queryResults.keys() if k.endswith("_error")]
-        has_only_errors = bool(error_query_keys and not successful_query_keys)
-        
-        if not has_query_results and needsDatabaseQuery:
-            db_results_part = "\n\nWICHTIG: Es wurden KEINE Datenbank-Ergebnisse gefunden. Die Datenbankabfrage wurde nicht ausgeführt oder hat keine Ergebnisse zurückgegeben."
-        
-        if has_only_errors:
-            db_results_part += "\n\n⚠️⚠️⚠️ KRITISCH - ALLE QUERIES FEHLGESCHLAGEN ⚠️⚠️⚠️\n" + \
-                              "ALLE Datenbankabfragen sind fehlgeschlagen. Es gibt KEINE gültigen Daten aus der Datenbank.\n" + \
-                              "DU DARFST KEINE DATEN ERFINDEN! Schreibe stattdessen: 'Es wurden keine Artikel gefunden' oder 'Die Datenbankabfrage ist fehlgeschlagen'."
-        
-        answer_prompt = f"""{system_prompt}
-
-Antworte auf die folgende Frage des Nutzers: {userInput.prompt}{context}
-
-{db_results_part}{web_results_part}
-
-KRITISCH: Verwende NUR die oben angegebenen Daten. Erfinde KEINE Werte. Wenn Daten fehlen, schreibe "Nicht verfügbar".
-
-WICHTIG - MEHRERE ABFRAGEN:
-Die oben angegebenen DATENBANK-ERGEBNISSE können aus mehreren separaten Abfragen stammen. Jede Abfrage ist mit "=== Abfrage X ===" markiert und enthält Informationen zu einem spezifischen Aspekt (z.B. Artikel-Informationen, Lagerbestände, etc.).
-- Kombiniere die Informationen aus ALLEN erfolgreichen Abfragen zu einer umfassenden Antwort
-- Beispiel: Wenn Abfrage 1 Artikel-Informationen liefert und Abfrage 2 Lagerbestände liefert, kombiniere beide in deiner Antwort
-- Verwende ALLE verfügbaren Informationen aus den verschiedenen Abfragen
-
-⚠️⚠️⚠️ ABSOLUT VERBOTEN - KEINE DATEN ERFINDEN ⚠️⚠️⚠️
-Wenn KEINE Datenbank-Ergebnisse vorhanden sind, dann:
-- ❌ ERFINDE KEINE Artikelnummern, Artikelbezeichnungen, Preise oder Lagerbestände!
-- ❌ ERFINDE KEINE Beispielartikel!
-- ✓ Schreibe stattdessen: "Es wurden keine Artikel in der Datenbank gefunden." oder "Die Datenbankabfrage ist fehlgeschlagen."
-
-WICHTIG: Deine Antwort soll NUR die finale Antwort enthalten - KEINE Planungsschritte, KEINE SQL-Queries, KEINE Zwischenschritte!
-Beginne DIREKT mit "Aus der Datenbank habe ich..." (wenn Daten vorhanden) oder "Es wurden keine Artikel gefunden" (wenn keine Daten vorhanden)."""
-
-        answerRequest = AiCallRequest(
-            prompt=answer_prompt,
-            context=answerContext if (queryResults or webResearchResults) else None,
-            options=AiCallOptions(
-                resultFormat="txt",
-                operationType=OperationTypeEnum.DATA_ANALYSE,
-                processingMode=ProcessingModeEnum.DETAILED
-            )
-        )
-        
-        answerResponse = await services.ai.callAi(answerRequest)
-        finalAnswer = answerResponse.content
-        
-        logger.info("Final answer generated")
-        
-        # Check if workflow was stopped during AI call - if so, don't store the message
-        if await _check_workflow_stopped(interfaceDbChat, workflowId):
-            logger.info(f"Workflow {workflowId} was stopped during final answer generation, not storing message")
-            return
-        
-        # Reload workflow to get current message count
-        workflow = interfaceDbChat.getWorkflow(workflowId)
-        
-        # Double-check workflow wasn't stopped while we were reloading
-        if workflow and workflow.status == "stopped":
-            logger.info(f"Workflow {workflowId} was stopped, not storing final message")
-            return
-        
-        # Create assistant message with final answer
-        assistantMessageData = {
-            "id": f"msg_{uuid.uuid4()}",
-            "workflowId": workflowId,
-            "parentMessageId": userMessageId,
-            "message": finalAnswer,
-            "role": "assistant",
-            "status": "last",
-            "sequenceNr": len(workflow.messages) + 1,
-            "publishedAt": getUtcTimestamp(),
-            "success": True,
-            "roundNumber": workflow.currentRound,
-            "taskNumber": 0,
-            "actionNumber": 0
-        }
-        
-        assistantMessage = interfaceDbChat.createMessage(assistantMessageData)
-        logger.info(f"Stored assistant message with final answer: {assistantMessage.id}")
-        
-        # Emit message event for streaming (exact chatData format)
-        message_timestamp = parseTimestamp(assistantMessage.publishedAt, default=getUtcTimestamp())
-        await event_manager.emit_event(
-            context_id=workflowId,
-            event_type="chatdata",
-            data={
-                "type": "message",
-                "createdAt": message_timestamp,
-                "item": assistantMessage.dict()
-            },
-            event_category="chat"
-        )
-        
-        # Update workflow status to completed (only if not stopped)
-        if not await _check_workflow_stopped(interfaceDbChat, workflowId):
-            interfaceDbChat.updateWorkflow(workflowId, {
-                "status": "completed",
-                "lastActivity": getUtcTimestamp()
-            })
-        else:
-            logger.info(f"Workflow {workflowId} was stopped, not updating status to completed")
-        
-        logger.info(f"Chatbot processing completed for workflow {workflowId}, generated {len(queries)} queries and final answer")
-        
-        # Emit completion event only if workflow wasn't stopped
-        if not await _check_workflow_stopped(interfaceDbChat, workflowId):
-            await event_manager.emit_event(
-                context_id=workflowId,
-                event_type="complete",
-                data={"workflowId": workflowId},
-                event_category="workflow",
-                message="Chatbot-Verarbeitung abgeschlossen",
-                step="complete"
-            )
-        
-        # Schedule cleanup
-        await event_manager.cleanup(workflowId)
-        
-    except Exception as e:
-        logger.error(f"Error processing chatbot message: {str(e)}", exc_info=True)
-        
-        # Check if workflow was stopped - if so, don't store error message
-        if await _check_workflow_stopped(interfaceDbChat, workflowId):
-            logger.info(f"Workflow {workflowId} was stopped, not storing error message")
-            return
-        
-        # Store error message
-        try:
-            # Reload workflow to get current message count
-            workflow = interfaceDbChat.getWorkflow(workflowId)
-            
-            # Double-check workflow wasn't stopped while we were reloading
-            if workflow and workflow.status == "stopped":
-                logger.info(f"Workflow {workflowId} was stopped, not storing error message")
-                return
-            
-            errorMessageData = {
-                "id": f"msg_{uuid.uuid4()}",
-                "workflowId": workflowId,
-                "parentMessageId": userMessageId,
-                "message": f"Sorry, I encountered an error: {str(e)}",
-                "role": "assistant",
-                "status": "last",
-                "sequenceNr": len(workflow.messages) + 1,
-                "publishedAt": getUtcTimestamp(),
-                "success": False,
-                "roundNumber": workflow.currentRound if workflow else 1,
-                "taskNumber": 0,
-                "actionNumber": 0
-            }
-            errorMessage = interfaceDbChat.createMessage(errorMessageData)
-            
-            # Emit message event for streaming (exact chatData format)
-            message_timestamp = parseTimestamp(errorMessage.publishedAt, default=getUtcTimestamp())
-            await event_manager.emit_event(
-                context_id=workflowId,
-                event_type="chatdata",
-                data={
-                    "type": "message",
-                    "createdAt": message_timestamp,
-                    "item": errorMessage.dict()
-                },
-                event_category="chat"
-            )
-            
-            # Update workflow status to error (only if not stopped)
-            if not await _check_workflow_stopped(interfaceDbChat, workflowId):
-                interfaceDbChat.updateWorkflow(workflowId, {
-                    "status": "error",
-                    "lastActivity": getUtcTimestamp()
-                })
-            else:
-                logger.info(f"Workflow {workflowId} was stopped, not updating status to error")
-            
-            # Schedule cleanup
-            await event_manager.cleanup(workflowId)
-        except Exception as storeError:
-            logger.error(f"Error storing error message: {storeError}")

modules/features/chatbot/streaming/__init__.py

@@ -0,0 +1,3 @@
+# Copyright (c) 2025 Patrick Motsch
+# All rights reserved.
+"""Streaming infrastructure for chatbot events."""

modules/features/chatbot/streaming/events.py

@@ -0,0 +1,159 @@
+# Copyright (c) 2025 Patrick Motsch
+# All rights reserved.
+"""
+Event manager for chatbot streaming.
+Manages event queues for Server-Sent Events (SSE) streaming.
+"""
+
+import logging
+import asyncio
+from typing import Dict, Optional, Any
+from collections import defaultdict
+
+logger = logging.getLogger(__name__)
+
+
+class EventManager:
+    """
+    Manages event queues for chatbot streaming.
+    Each workflow has its own async queue for events.
+    """
+    
+    def __init__(self):
+        """Initialize the event manager."""
+        self._queues: Dict[str, asyncio.Queue] = {}
+        self._cleanup_tasks: Dict[str, asyncio.Task] = {}
+    
+    def create_queue(self, workflow_id: str) -> asyncio.Queue:
+        """
+        Create an event queue for a workflow.
+        
+        Args:
+            workflow_id: Workflow ID
+            
+        Returns:
+            Async queue for events
+        """
+        if workflow_id not in self._queues:
+            self._queues[workflow_id] = asyncio.Queue()
+            logger.debug(f"Created event queue for workflow {workflow_id}")
+        return self._queues[workflow_id]
+    
+    def get_queue(self, workflow_id: str) -> Optional[asyncio.Queue]:
+        """
+        Get the event queue for a workflow.
+        
+        Args:
+            workflow_id: Workflow ID
+            
+        Returns:
+            Async queue if exists, None otherwise
+        """
+        return self._queues.get(workflow_id)
+    
+    def has_queue(self, workflow_id: str) -> bool:
+        """
+        Check if a queue exists for a workflow.
+        
+        Args:
+            workflow_id: Workflow ID
+            
+        Returns:
+            True if queue exists, False otherwise
+        """
+        return workflow_id in self._queues
+    
+    async def emit_event(
+        self,
+        context_id: str,
+        event_type: str,
+        data: Dict[str, Any],
+        event_category: str = "chat",
+        message: Optional[str] = None,
+        step: Optional[str] = None
+    ) -> None:
+        """
+        Emit an event to the queue for a workflow.
+        
+        Args:
+            context_id: Workflow ID (context)
+            event_type: Type of event (e.g., "chatdata", "complete", "error")
+            data: Event data dictionary
+            event_category: Category of event (e.g., "chat", "workflow")
+            message: Optional message string
+            step: Optional step identifier
+        """
+        queue = self._queues.get(context_id)
+        if not queue:
+            logger.warning(f"No queue found for workflow {context_id}, event not emitted")
+            return
+        
+        event = {
+            "type": event_type,
+            "data": data,
+            "category": event_category,
+            "message": message,
+            "step": step
+        }
+        
+        try:
+            await queue.put(event)
+            logger.debug(f"Emitted {event_type} event for workflow {context_id}")
+        except Exception as e:
+            logger.error(f"Error emitting event for workflow {context_id}: {e}", exc_info=True)
+    
+    async def cleanup(self, workflow_id: str, delay: float = 60.0) -> None:
+        """
+        Schedule cleanup of a queue after a delay.
+        
+        Args:
+            workflow_id: Workflow ID
+            delay: Delay in seconds before cleanup
+        """
+        # Cancel existing cleanup task if any
+        if workflow_id in self._cleanup_tasks:
+            self._cleanup_tasks[workflow_id].cancel()
+        
+        async def _cleanup():
+            try:
+                await asyncio.sleep(delay)
+                if workflow_id in self._queues:
+                    # Drain remaining events
+                    queue = self._queues[workflow_id]
+                    while not queue.empty():
+                        try:
+                            queue.get_nowait()
+                        except asyncio.QueueEmpty:
+                            break
+                    
+                    # Remove queue
+                    del self._queues[workflow_id]
+                    logger.info(f"Cleaned up event queue for workflow {workflow_id}")
+            except asyncio.CancelledError:
+                logger.debug(f"Cleanup cancelled for workflow {workflow_id}")
+            except Exception as e:
+                logger.error(f"Error during cleanup for workflow {workflow_id}: {e}", exc_info=True)
+            finally:
+                if workflow_id in self._cleanup_tasks:
+                    del self._cleanup_tasks[workflow_id]
+        
+        # Schedule cleanup
+        task = asyncio.create_task(_cleanup())
+        self._cleanup_tasks[workflow_id] = task
+
+
+# Global event manager instance
+_event_manager: Optional[EventManager] = None
+
+
+def get_event_manager() -> EventManager:
+    """
+    Get the global event manager instance.
+    
+    Returns:
+        EventManager instance
+    """
+    global _event_manager
+    if _event_manager is None:
+        _event_manager = EventManager()
+    return _event_manager

modules/features/chatbot/streaming/helpers.py

@@ -0,0 +1,242 @@
+# Copyright (c) 2025 Patrick Motsch
+# All rights reserved.
+"""Streaming helper utilities for chat message processing and normalization."""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Literal, Mapping, Optional
+
+from langchain_core.messages import (
+    AIMessage,
+    BaseMessage,
+    HumanMessage,
+    SystemMessage,
+    ToolMessage,
+)
+
+Role = Literal["user", "assistant", "system", "tool"]
+
+
+class ChatStreamingHelper:
+    """Pure helper methods for streaming and message normalization.
+
+    This class provides static utility methods for converting between different
+    message formats, extracting content, and normalizing message structures
+    for streaming chat applications.
+    """
+
+    @staticmethod
+    def role_from_message(*, msg: BaseMessage) -> Role:
+        """Extract the role from a BaseMessage instance.
+
+        Args:
+            msg: The BaseMessage instance to extract the role from.
+
+        Returns:
+            The role as a string literal: "user", "assistant", "system", or "tool".
+            Defaults to "assistant" if the message type is not recognized.
+
+        Examples:
+            >>> from langchain_core.messages import HumanMessage
+            >>> msg = HumanMessage(content="Hello")
+            >>> ChatStreamingHelper.role_from_message(msg=msg)
+            'user'
+        """
+        if isinstance(msg, HumanMessage):
+            return "user"
+        if isinstance(msg, AIMessage):
+            return "assistant"
+        if isinstance(msg, SystemMessage):
+            return "system"
+        if isinstance(msg, ToolMessage):
+            return "tool"
+        return getattr(msg, "role", "assistant")
+
+    @staticmethod
+    def flatten_content(*, content: Any) -> str:
+        """Convert complex content structures to plain text.
+
+        This method handles various content formats including strings, lists of
+        content parts, and dictionaries with text fields. It's designed to
+        normalize content from different message sources into a consistent
+        plain text format.
+
+        Args:
+            content: The content to flatten. Can be:
+                - str: Returned as-is after stripping whitespace
+                - list: Each item processed and joined with newlines
+                - dict: Text extracted from "text" or "content" fields
+                - None: Returns empty string
+                - Any other type: Converted to string
+
+        Returns:
+            The flattened content as a plain text string with whitespace stripped.
+
+        Examples:
+            >>> content = [{"type": "text", "text": "Hello"}, {"type": "text", "text": "world"}]
+            >>> ChatStreamingHelper.flatten_content(content=content)
+            'Hello\nworld'
+
+            >>> content = {"text": "Simple message"}
+            >>> ChatStreamingHelper.flatten_content(content=content)
+            'Simple message'
+        """
+        if content is None:
+            return ""
+        if isinstance(content, str):
+            return content.strip()
+        if isinstance(content, list):
+            parts: List[str] = []
+            for part in content:
+                if isinstance(part, dict):
+                    if "text" in part and isinstance(part["text"], str):
+                        parts.append(part["text"])
+                    elif part.get("type") == "text" and isinstance(
+                        part.get("text"), str
+                    ):
+                        parts.append(part["text"])
+                    elif "content" in part and isinstance(part["content"], str):
+                        parts.append(part["content"])
+                    else:
+                        # Fallback for unknown dictionary structures
+                        val = part.get("value")
+                        if isinstance(val, str):
+                            parts.append(val)
+                else:
+                    parts.append(str(part))
+            return "\n".join(p.strip() for p in parts if p is not None)
+        if isinstance(content, dict):
+            if "text" in content and isinstance(content["text"], str):
+                return content["text"].strip()
+            if "content" in content and isinstance(content["content"], str):
+                return content["content"].strip()
+        return str(content).strip()
+
+    @staticmethod
+    def message_to_dict(*, msg: BaseMessage) -> Dict[str, Any]:
+        """Convert a BaseMessage instance to a dictionary for streaming output.
+
+        This method normalizes BaseMessage instances into a consistent dictionary
+        format suitable for JSON serialization and streaming to clients.
+
+        Args:
+            msg: The BaseMessage instance to convert.
+
+        Returns:
+            A dictionary containing:
+                - "role": The message role (user, assistant, system, tool)
+                - "content": The flattened message content as plain text
+                - "tool_calls": Tool calls if present (optional)
+                - "name": Message name if present (optional)
+
+        Examples:
+            >>> from langchain_core.messages import HumanMessage
+            >>> msg = HumanMessage(content="Hello there")
+            >>> result = ChatStreamingHelper.message_to_dict(msg=msg)
+            >>> result["role"]
+            'user'
+            >>> result["content"]
+            'Hello there'
+        """
+        payload: Dict[str, Any] = {
+            "role": ChatStreamingHelper.role_from_message(msg=msg),
+            "content": ChatStreamingHelper.flatten_content(
+                content=getattr(msg, "content", "")
+            ),
+        }
+        tool_calls = getattr(msg, "tool_calls", None)
+        if tool_calls:
+            payload["tool_calls"] = tool_calls
+        name = getattr(msg, "name", None)
+        if name:
+            payload["name"] = name
+        return payload
+
+    @staticmethod
+    def dict_message_to_dict(*, obj: Mapping[str, Any]) -> Dict[str, Any]:
+        """Convert a dictionary-shaped message to a normalized dictionary.
+
+        This method handles messages that come from serialized state and are
+        represented as dictionaries rather than BaseMessage instances. It
+        normalizes various dictionary formats into a consistent structure.
+
+        Args:
+            obj: The dictionary-shaped message to convert. Expected to contain
+                fields like "role", "type", "content", "text", etc.
+
+        Returns:
+            A normalized dictionary containing:
+                - "role": The message role (user, assistant, system, tool)
+                - "content": The flattened message content as plain text
+                - "tool_calls": Tool calls if present (optional)
+                - "name": Message name if present (optional)
+
+        Examples:
+            >>> obj = {"type": "human", "content": "Hello"}
+            >>> result = ChatStreamingHelper.dict_message_to_dict(obj=obj)
+            >>> result["role"]
+            'user'
+            >>> result["content"]
+            'Hello'
+        """
+        role: Optional[str] = obj.get("role")
+        if not role:
+            # Handle alternative type field mappings
+            typ = obj.get("type")
+            if typ in ("human", "user"):
+                role = "user"
+            elif typ in ("ai", "assistant"):
+                role = "assistant"
+            elif typ in ("system",):
+                role = "system"
+            elif typ in ("tool", "function"):
+                role = "tool"
+
+        content = obj.get("content")
+        if content is None and "text" in obj:
+            content = obj["text"]
+
+        out: Dict[str, Any] = {
+            "role": role or "assistant",
+            "content": ChatStreamingHelper.flatten_content(content=content),
+        }
+        if "tool_calls" in obj:
+            out["tool_calls"] = obj["tool_calls"]
+        if obj.get("name"):
+            out["name"] = obj["name"]
+        return out
+
+    @staticmethod
+    def extract_messages_from_output(*, output_obj: Any) -> List[Any]:
+        """Extract messages from LangGraph output objects.
+
+        This method handles various output formats from LangGraph execution,
+        extracting the messages list from different possible structures.
+
+        Args:
+            output_obj: The output object from LangGraph execution. Can be:
+                - An object with a "messages" attribute
+                - A dictionary with a "messages" key
+                - Any other object (returns empty list)
+
+        Returns:
+            A list of extracted messages, or an empty list if no messages
+            are found or if the output object is None.
+
+        Examples:
+            >>> output = {"messages": [{"role": "user", "content": "Hello"}]}
+            >>> messages = ChatStreamingHelper.extract_messages_from_output(output_obj=output)
+            >>> len(messages)
+            1
+        """
+        if output_obj is None:
+            return []
+
+        # Try to parse dicts first
+        if isinstance(output_obj, dict):
+            msgs = output_obj.get("messages")
+            return msgs if isinstance(msgs, list) else []
+
+        # Then try to get messages attribute
+        msgs = getattr(output_obj, "messages", None)
+        return msgs if isinstance(msgs, list) else []

modules/interfaces/interfaceDbChatObjects.py

@@ -1060,7 +1060,7 @@ class ChatObjects:
             
             # Emit message event for streaming (if event manager is available)
             try:
-                from modules.features.chatbot.eventManager import get_event_manager
+                from modules.features.chatbot.streaming.events import get_event_manager
                 event_manager = get_event_manager()
                 message_timestamp = parseTimestamp(chat_message.publishedAt, default=getUtcTimestamp())
                 # Emit message event in exact chatData format: {type, createdAt, item}
@@ -1434,7 +1434,7 @@ class ChatObjects:
         
         # Emit log event for streaming (if event manager is available)
         try:
-            from modules.features.chatbot.eventManager import get_event_manager
+            from modules.features.chatbot.streaming.events import get_event_manager
             event_manager = get_event_manager()
             log_timestamp = parseTimestamp(createdLog.get("timestamp"), default=getUtcTimestamp())
             # Emit log event in exact chatData format: {type, createdAt, item}

modules/routes/routeChatbot.py

@@ -28,7 +28,7 @@ from modules.datamodels.datamodelPagination import PaginationParams, PaginatedRe
 
 # Import chatbot feature
 from modules.features.chatbot import chatProcess
-from modules.features.chatbot.eventManager import get_event_manager
+from modules.features.chatbot.streaming.events import get_event_manager
 
 # Import workflow control functions
 from modules.features.workflow import chatStop
@@ -174,17 +174,26 @@ async def stream_chatbot_start(
                         event_type = event.get("type")
                         event_data = event.get("data", {})
                         
-                        # Emit chatdata events (messages, logs, stats) in exact chatData format
+                        # Emit chatdata events (messages, logs, stats, status) in exact chatData format
                         if event_type == "chatdata" and event_data:
-                            # Emit item directly in exact chatData format: {type, createdAt, item}
-                            chatdata_item = event_data
-                            # Ensure item field is serializable (convert Pydantic models to dicts)
-                            if isinstance(chatdata_item, dict) and "item" in chatdata_item:
-                                item_obj = chatdata_item.get("item")
-                                if hasattr(item_obj, "dict"):
-                                    chatdata_item = chatdata_item.copy()
-                                    chatdata_item["item"] = item_obj.dict()
-                            yield f"data: {json.dumps(chatdata_item)}\n\n"
+                            # Handle status events (transient UI feedback)
+                            if event_data.get("type") == "status":
+                                # Status events have simple structure: {type: "status", label: "..."}
+                                status_item = {
+                                    "type": "status",
+                                    "label": event_data.get("label", "")
+                                }
+                                yield f"data: {json.dumps(status_item)}\n\n"
+                            else:
+                                # Emit other chatdata items (messages, logs, stats) in exact chatData format
+                                chatdata_item = event_data
+                                # Ensure item field is serializable (convert Pydantic models to dicts)
+                                if isinstance(chatdata_item, dict) and "item" in chatdata_item:
+                                    item_obj = chatdata_item.get("item")
+                                    if hasattr(item_obj, "dict"):
+                                        chatdata_item = chatdata_item.copy()
+                                        chatdata_item["item"] = item_obj.dict()
+                                yield f"data: {json.dumps(chatdata_item)}\n\n"
                         
                         # Handle completion/stopped events to close stream
                         elif event_type == "complete":

requirements.txt

@@ -78,6 +78,9 @@ azure-communication-email>=1.0.0  # Azure Communication Services Email
 pytest>=8.0.0
 pytest-asyncio>=0.21.0
 
+## Configuration Validation
+jsonschema>=4.0.0  # Required for chatbot workflow config validation
+
 ## For Scheduling / Repeated Tasks
 APScheduler==3.11.0
 

tests/functional/chatbot/__init__.py

@@ -0,0 +1,3 @@
+# Copyright (c) 2025 Patrick Motsch
+# All rights reserved.
+"""Chatbot functional tests."""

tests/functional/chatbot/test_chatbot.py

@@ -0,0 +1,217 @@
+#!/usr/bin/env python3
+# Copyright (c) 2025 Patrick Motsch
+# All rights reserved.
+"""
+Chatbot Functional Tests
+
+Tests the chatbot implementation to ensure:
+1. Chatbot initialization works correctly
+2. Streaming events are emitted properly
+3. Tool calls execute correctly
+4. Messages are stored in database
+5. No infinite loops occur
+"""
+
+import asyncio
+import os
+import sys
+from pathlib import Path
+
+# Add the gateway to path (go up 2 levels from tests/functional/chatbot/)
+_gateway_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
+if _gateway_path not in sys.path:
+    sys.path.insert(0, _gateway_path)
+
+import pytest
+from modules.features.chatbot.chatbot import Chatbot
+from modules.features.chatbot.chatbotAIBridge import AICenterChatModel
+from modules.features.chatbot.chatbotMemory import DatabaseCheckpointer
+from modules.features.chatbot.chatbotConfig import load_chatbot_config
+from modules.features.chatbot.streamingHelper import ChatStreamingHelper
+from modules.datamodels.datamodelUam import User
+from modules.datamodels.datamodelAi import OperationTypeEnum, ProcessingModeEnum
+
+
+class TestChatbot:
+    """Test suite for chatbot functionality."""
+    
+    @pytest.fixture
+    def test_user(self):
+        """Create a test user."""
+        return User(
+            id="test_user_chatbot",
+            username="test_chatbot",
+            email="test@example.com",
+            fullName="Test Chatbot User",
+            language="de",
+            mandateId="test_mandate",
+        )
+    
+    @pytest.fixture
+    def workflow_id(self):
+        """Generate a test workflow ID."""
+        import uuid
+        return str(uuid.uuid4())
+    
+    @pytest.mark.asyncio
+    async def test_chatbot_initialization(self, test_user, workflow_id):
+        """Test that chatbot can be initialized correctly."""
+        # Load config
+        config = load_chatbot_config("althaus")
+        
+        # Create system prompt
+        from datetime import datetime
+        system_prompt = config.systemPrompt.replace(
+            "{{DATE}}",
+            datetime.now().strftime("%d.%m.%Y")
+        )
+        
+        # Create AI center model
+        operation_type = OperationTypeEnum[config.model.operationType]
+        processing_mode = ProcessingModeEnum[config.model.processingMode]
+        
+        model = AICenterChatModel(
+            user=test_user,
+            operation_type=operation_type,
+            processing_mode=processing_mode
+        )
+        
+        # Create memory/checkpointer
+        memory = DatabaseCheckpointer(user=test_user, workflow_id=workflow_id)
+        
+        # Create chatbot instance
+        chatbot = await Chatbot.create(
+            model=model,
+            memory=memory,
+            system_prompt=system_prompt,
+            workflow_id=workflow_id
+        )
+        
+        assert chatbot is not None
+        assert chatbot.model is not None
+        assert chatbot.memory is not None
+        assert chatbot.app is not None
+        assert chatbot.system_prompt == system_prompt
+    
+    @pytest.mark.asyncio
+    async def test_streaming_helper_role_from_message(self):
+        """Test ChatStreamingHelper.role_from_message."""
+        from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
+        
+        human_msg = HumanMessage(content="Hello")
+        assert ChatStreamingHelper.role_from_message(msg=human_msg) == "user"
+        
+        ai_msg = AIMessage(content="Hi there")
+        assert ChatStreamingHelper.role_from_message(msg=ai_msg) == "assistant"
+        
+        system_msg = SystemMessage(content="You are a helpful assistant")
+        assert ChatStreamingHelper.role_from_message(msg=system_msg) == "system"
+    
+    @pytest.mark.asyncio
+    async def test_streaming_helper_flatten_content(self):
+        """Test ChatStreamingHelper.flatten_content."""
+        # Test string
+        assert ChatStreamingHelper.flatten_content(content="Hello") == "Hello"
+        
+        # Test list
+        content_list = [{"type": "text", "text": "Hello"}, {"type": "text", "text": "World"}]
+        result = ChatStreamingHelper.flatten_content(content=content_list)
+        assert "Hello" in result
+        assert "World" in result
+        
+        # Test dict
+        content_dict = {"text": "Simple message"}
+        assert ChatStreamingHelper.flatten_content(content=content_dict) == "Simple message"
+        
+        # Test None
+        assert ChatStreamingHelper.flatten_content(content=None) == ""
+    
+    @pytest.mark.asyncio
+    async def test_streaming_helper_message_to_dict(self):
+        """Test ChatStreamingHelper.message_to_dict."""
+        from langchain_core.messages import HumanMessage
+        
+        msg = HumanMessage(content="Hello there")
+        result = ChatStreamingHelper.message_to_dict(msg=msg)
+        
+        assert result["role"] == "user"
+        assert result["content"] == "Hello there"
+    
+    @pytest.mark.asyncio
+    async def test_streaming_helper_extract_messages_from_output(self):
+        """Test ChatStreamingHelper.extract_messages_from_output."""
+        # Test dict with messages
+        output_dict = {"messages": [{"role": "user", "content": "Hello"}]}
+        messages = ChatStreamingHelper.extract_messages_from_output(output_obj=output_dict)
+        assert len(messages) == 1
+        
+        # Test None
+        messages = ChatStreamingHelper.extract_messages_from_output(output_obj=None)
+        assert len(messages) == 0
+        
+        # Test object with messages attribute
+        class MockOutput:
+            def __init__(self):
+                self.messages = [{"role": "assistant", "content": "Hi"}]
+        
+        mock_output = MockOutput()
+        messages = ChatStreamingHelper.extract_messages_from_output(output_obj=mock_output)
+        assert len(messages) == 1
+    
+    @pytest.mark.asyncio
+    async def test_chatbot_should_continue_logic(self, test_user, workflow_id):
+        """Test that should_continue logic works correctly (no infinite loops)."""
+        # Load config
+        config = load_chatbot_config("althaus")
+        
+        # Create system prompt
+        from datetime import datetime
+        system_prompt = config.systemPrompt.replace(
+            "{{DATE}}",
+            datetime.now().strftime("%d.%m.%Y")
+        )
+        
+        # Create AI center model
+        operation_type = OperationTypeEnum[config.model.operationType]
+        processing_mode = ProcessingModeEnum[config.model.processingMode]
+        
+        model = AICenterChatModel(
+            user=test_user,
+            operation_type=operation_type,
+            processing_mode=processing_mode
+        )
+        
+        # Create memory/checkpointer
+        memory = DatabaseCheckpointer(user=test_user, workflow_id=workflow_id)
+        
+        # Create chatbot instance
+        chatbot = await Chatbot.create(
+            model=model,
+            memory=memory,
+            system_prompt=system_prompt,
+            workflow_id=workflow_id
+        )
+        
+        # The should_continue logic is internal to the workflow
+        # We can test that the workflow compiles successfully
+        assert chatbot.app is not None
+        
+        # Test that we can invoke the workflow (this will test should_continue internally)
+        # Use a simple message that shouldn't cause infinite loops
+        try:
+            result = await chatbot.chat(
+                message="Hallo",
+                chat_id=workflow_id
+            )
+            # Should return messages without infinite loop
+            assert result is not None
+            assert isinstance(result, list)
+        except Exception as e:
+            # If there's an error, it shouldn't be an infinite loop error
+            # (infinite loops would timeout or hit max iterations)
+            assert "infinite" not in str(e).lower()
+            assert "loop" not in str(e).lower()
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])