feat: Unified Messaging System — kommunikation plugin, AI/Proactive/System participants, MessageSidebar, Rich Content Renderer
Phase 1: Backend plugin kommunikation (13 files, 10 tables, REST API, WebSocket, RBAC, DMS Bridge, Participant Registry, Mini-App Registry, Search Provider) Phase 2: AI plugins as participants (ai_assistant + ai_proactive dock as participants, heartbeat job) Phase 3: system_notif plugin (system events → chat messages, pinned System room) Phase 4: Frontend MessageSidebar (replaces AISidebar, same design, comm API client, WebSocket hook, commStore) Phase 5: Rich Content Block Renderer (11 components: Markdown, HTML, Image, Audio, Video, File, ActionCard, ContactCard, MiniApp, BlockRenderer) BasePlugin: added services property + _container in on_activate
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Agent Zero
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"""AI Participant Handler — bridges the kommunikation plugin with the AI Assistant.
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When a message is received in a conversation that includes the 'ai' participant,
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this handler generates an LLM response using litellm.acompletion (non-streaming)
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and returns it as a new message in the conversation.
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"""
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from __future__ import annotations
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import logging
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import uuid
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from typing import Any
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import litellm
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from app.core.db import create_db_session
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from app.plugins.builtins.kommunikation.participant_registry import ParticipantHandler
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logger = logging.getLogger(__name__)
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class AIParticipantHandler(ParticipantHandler):
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"""Handles AI responses as a participant in kommunikation conversations."""
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def __init__(self, container: Any) -> None:
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self._container = container
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async def on_message_received(
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self,
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conversation_id: Any,
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message: dict[str, Any],
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conversation: dict[str, Any],
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mentions: list[str],
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context: dict[str, Any],
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) -> list[dict[str, Any]] | None:
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"""Generate an AI response when the AI is mentioned or in a direct chat.
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Checks:
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1. 'ai' is in the conversation participants as a participant_type
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2. '@KI' is in mentions OR the conversation is_direct with only user+ai
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Returns a list with one message dict containing the AI response.
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"""
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# Check if 'ai' is a participant in this conversation
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participants = conversation.get("participants", [])
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ai_is_participant = any(
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p.get("participant_type") == "ai" for p in participants
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)
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if not ai_is_participant:
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return None
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# Check if AI is mentioned or it's a direct chat with only user + ai
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ai_mentioned = "KI" in mentions or "ai" in mentions
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is_direct = conversation.get("is_direct", False)
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if is_direct:
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# For direct chats, check that only user and ai are participants
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non_system_participants = [
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p for p in participants
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if p.get("participant_type") in ("user", "ai")
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]
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if len(non_system_participants) <= 2:
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ai_mentioned = True
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if not ai_mentioned:
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return None
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# Don't respond to our own messages
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if message.get("sender_type") == "ai":
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return None
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# Get tenant_id and user_id from context
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tenant_id_str = context.get("tenant_id") or message.get("tenant_id")
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if not tenant_id_str:
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logger.warning("AIParticipantHandler: missing tenant_id in context")
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return None
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try:
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tenant_id = uuid.UUID(str(tenant_id_str))
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except (ValueError, TypeError):
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logger.warning("AIParticipantHandler: invalid tenant_id: %s", tenant_id_str)
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return None
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# Build messages from conversation history and generate response
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try:
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from app.plugins.builtins.ai_assistant.services import (
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build_litellm_params,
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get_default_agent,
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)
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async with create_db_session(tenant_id) as db:
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# Get default agent for system prompt and preset configuration
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agent = await get_default_agent(db, tenant_id)
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# Build message history from conversation messages
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messages = await self._build_message_history(
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db, conversation_id, tenant_id, message
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)
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if agent:
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params, model_id = await build_litellm_params(
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db, agent, messages, tenant_id
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)
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else:
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# Fallback: use default provider without agent
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from app.plugins.builtins.ai_assistant.services import (
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get_default_provider,
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)
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provider = await get_default_provider(db, tenant_id)
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if not provider:
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return [
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{
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"content": "Kein AI-Provider konfiguriert. Bitte konfigurieren Sie einen Provider in den KI-Einstellungen.",
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"content_format": "text",
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}
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]
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model_id = "gpt-4o-mini"
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litellm_model = f"{provider.provider_type}/{model_id}"
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params: dict[str, Any] = {
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"model": litellm_model,
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"messages": messages,
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"temperature": 0.7,
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"max_tokens": 2048,
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"stream": False,
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}
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if provider.api_key:
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params["api_key"] = provider.api_key
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if provider.base_url:
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params["api_base"] = provider.base_url
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# Ensure non-streaming for acompletion
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params["stream"] = False
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response = await litellm.acompletion(**params)
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response_text = response.choices[0].message.content or ""
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if not response_text.strip():
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response_text = "*(keine Antwort generiert)*"
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return [
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{
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"content": response_text,
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"content_format": "markdown",
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}
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]
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except Exception as exc:
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logger.exception("AIParticipantHandler: error generating AI response")
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return [
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{
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"content": f"Fehler bei der KI-Antwort: {exc}",
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"content_format": "text",
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}
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]
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async def _build_message_history(
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self,
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db: Any,
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conversation_id: Any,
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tenant_id: uuid.UUID,
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current_message: dict[str, Any],
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) -> list[dict[str, str]]:
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"""Build a messages array from the conversation history for the LLM."""
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from app.plugins.builtins.kommunikation.services import get_messages
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messages: list[dict[str, str]] = []
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# Get conversation history (last 50 messages)
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try:
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result = await get_messages(
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db, tenant_id, uuid.UUID(str(conversation_id)),
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page=1, page_size=50,
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)
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items = result.get("items", [])
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for item in items:
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role = "assistant" if item.get("sender_type") == "ai" else "user"
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content = item.get("content", "")
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if content:
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messages.append({"role": role, "content": content})
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except Exception:
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logger.debug("Could not load conversation history, using current message only")
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# Ensure the current message is included
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current_content = current_message.get("content", "")
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if current_content and (
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not messages
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or messages[-1].get("content") != current_content
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):
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messages.append({"role": "user", "content": current_content})
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return messages
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async def handle_event(self, payload: dict[str, Any]) -> None:
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"""Handle a message.received event from the event bus.
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Extracts conversation_id from the payload, loads the conversation,
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and calls on_message_received. If a response is generated, sends it
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back to the conversation.
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"""
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conversation_id_str = payload.get("conversation_id")
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tenant_id_str = payload.get("tenant_id")
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message_content = payload.get("content", "")
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message_id = payload.get("message_id")
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sender_type = payload.get("sender_type", "user")
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if not conversation_id_str or not tenant_id_str:
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logger.warning("AIParticipantHandler.handle_event: missing conversation_id or tenant_id")
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return
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try:
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tenant_id = uuid.UUID(str(tenant_id_str))
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conversation_id = uuid.UUID(str(conversation_id_str))
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except (ValueError, TypeError):
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logger.warning("AIParticipantHandler.handle_event: invalid UUID in payload")
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return
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# Build the message dict
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message: dict[str, Any] = {
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"id": message_id,
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"content": message_content,
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"sender_type": sender_type,
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"tenant_id": tenant_id_str,
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}
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# Load conversation
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try:
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from app.plugins.builtins.kommunikation.services import get_conversation
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async with create_db_session(tenant_id) as db:
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# We need a user_id to load the conversation — use the sender_id from payload
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user_id_str = payload.get("sender_id")
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if not user_id_str:
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logger.warning("AIParticipantHandler.handle_event: missing sender_id")
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return
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user_id = uuid.UUID(str(user_id_str))
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conversation = await get_conversation(db, tenant_id, conversation_id, user_id)
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if not conversation:
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logger.warning("AIParticipantHandler.handle_event: conversation not found")
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return
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# Parse mentions from message content
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from app.plugins.builtins.kommunikation.services import parse_mentions
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mentions = parse_mentions(message_content)
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context: dict[str, Any] = {
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"tenant_id": tenant_id_str,
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"user_id": user_id_str,
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}
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# Call on_message_received
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response_messages = await self.on_message_received(
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conversation_id, message, conversation, mentions, context
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)
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# If we got a response, send it to the conversation
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if response_messages:
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from app.plugins.builtins.kommunikation.services import send_message
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for resp_msg in response_messages:
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await send_message(
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db=db,
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tenant_id=tenant_id,
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conversation_id=conversation_id,
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sender_id=None,
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sender_type="ai",
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content=resp_msg.get("content", ""),
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content_format=resp_msg.get("content_format", "text"),
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blocks=resp_msg.get("blocks"),
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)
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await db.commit()
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except Exception:
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logger.exception("AIParticipantHandler.handle_event: error processing event")
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def get_participant_info(self) -> dict[str, Any]:
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"""Return metadata about this participant."""
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return {
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"display_name": "KI Assistent",
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"capabilities": ["chat", "tools", "streaming"],
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"description": "KI Assistent für Chat und Tool-Nutzung",
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}
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