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 Proactive Participant Handler — bridges the kommunikation plugin with the proactive AI.
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When a message is received in a conversation that includes the 'ai_proactive' participant,
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this handler generates a proactive suggestion based on the message context.
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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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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 AIProactiveParticipantHandler(ParticipantHandler):
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"""Handles proactive AI suggestions 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 a proactive suggestion when ai_proactive is a participant.
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Checks if 'ai_proactive' is in the conversation participants as a
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participant_type. If yes, generates a suggestion based on the message.
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Returns a list with one message dict containing the suggestion as
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an action card, or None on error.
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"""
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# Check if 'ai_proactive' is a participant in this conversation
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participants = conversation.get("participants", [])
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ai_proactive_is_participant = any(
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p.get("participant_type") == "ai_proactive" for p in participants
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)
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if not ai_proactive_is_participant:
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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_proactive":
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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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user_id_str = context.get("user_id")
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if not tenant_id_str:
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logger.warning("AIProactiveParticipantHandler: 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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user_id = uuid.UUID(str(user_id_str)) if user_id_str else None
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except (ValueError, TypeError):
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logger.warning("AIProactiveParticipantHandler: invalid UUID in context")
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return None
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# Generate suggestion using the existing generate_suggestion function
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try:
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from app.plugins.builtins.ai_proactive.services import (
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generate_suggestion,
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get_user_settings,
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)
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async with create_db_session(tenant_id) as db:
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# Get user settings for the proactive AI
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if user_id:
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settings = await get_user_settings(db, tenant_id, user_id)
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else:
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# Create minimal default settings if no user_id
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from app.plugins.builtins.ai_proactive.models import ProactiveSettings
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settings = ProactiveSettings(
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tenant_id=tenant_id,
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user_id=user_id or uuid.uuid4(),
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enabled=True,
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suggestion_categories=["mail", "tasks", "contacts", "companies", "insights"],
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confidence_threshold=0.5,
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rate_limit_seconds=10,
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model="ollama/deepseek-v4-flash",
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)
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if not settings.enabled:
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return None
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# Build context data from the message
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context_data: dict[str, Any] = {
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"entity_type": "message",
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"message": message.get("content", ""),
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"conversation_id": str(conversation_id),
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"sender_type": message.get("sender_type", "user"),
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}
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# Generate suggestion via LLM
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suggestion = await generate_suggestion(
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context_data, settings, db=db, tenant_id=tenant_id
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)
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if not suggestion:
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return None
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# Build the response message with an action card block
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suggestion_text = suggestion.get("content", "")
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title = suggestion.get("title", "KI Vorschlag")
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suggestion_type = suggestion.get("suggestion_type", "info")
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confidence = suggestion.get("confidence", 0.5)
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actions = suggestion.get("actions", [])
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return [
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{
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"content": suggestion_text,
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"content_format": "markdown",
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"blocks": [
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{
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"block_type": "action_card",
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"block_data": {
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"title": title,
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"suggestion_type": suggestion_type,
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"confidence": confidence,
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"actions": actions,
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},
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}
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],
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}
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]
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except Exception:
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logger.exception("AIProactiveParticipantHandler: error generating suggestion")
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return None
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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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sender_id_str = payload.get("sender_id")
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if not conversation_id_str or not tenant_id_str:
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logger.warning("AIProactiveParticipantHandler.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("AIProactiveParticipantHandler.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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if not sender_id_str:
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logger.warning("AIProactiveParticipantHandler.handle_event: missing sender_id")
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return
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user_id = uuid.UUID(str(sender_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("AIProactiveParticipantHandler.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": sender_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_proactive",
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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("AIProactiveParticipantHandler.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": "Live KI",
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"capabilities": ["proactive", "context_aware", "heartbeat"],
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"description": "Proaktive KI mit Kontextbewusstsein und Heartbeat",
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}
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