457 lines
20 KiB
Python
457 lines
20 KiB
Python
# Copyright (c) 2025 Patrick Motsch
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# All rights reserved.
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"""
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Chatbot routes for the backend API.
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Implements simple chatbot endpoints using direct AI center calls via chatbot feature.
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"""
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import logging
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import json
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import asyncio
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import math
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from typing import Optional, Any, Dict, Union
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from fastapi import APIRouter, HTTPException, Depends, Body, Path, Query, Request, status
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from fastapi.responses import StreamingResponse
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from modules.shared.timeUtils import parseTimestamp
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# Import auth modules
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from modules.auth import limiter, getRequestContext, RequestContext
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# Import interfaces
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import modules.interfaces.interfaceDbChatbot as interfaceDbChatbot
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from modules.interfaces.interfaceRbac import getRecordsetWithRBAC
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# Import models
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from modules.datamodels.datamodelChat import ChatWorkflow, UserInputRequest, WorkflowModeEnum
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from modules.datamodels.datamodelPagination import PaginationParams, PaginatedResponse
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# Import chatbot feature
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from modules.features.chatbot import chatProcess
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from modules.features.chatbot.eventManager import get_event_manager
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# Import workflow control functions
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from modules.features.workflow import chatStop
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# Configure logger
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logger = logging.getLogger(__name__)
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# Create router for chatbot endpoints
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router = APIRouter(
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prefix="/api/chatbot",
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tags=["Chatbot"],
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responses={404: {"description": "Not found"}}
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)
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def _getServiceChat(context: RequestContext):
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return interfaceDbChatbot.getInterface(context.user, mandateId=str(context.mandateId) if context.mandateId else None)
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# Chatbot streaming endpoint (SSE)
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@router.post("/start/stream")
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@limiter.limit("120/minute")
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async def stream_chatbot_start(
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request: Request,
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workflowId: Optional[str] = Query(None, description="Optional ID of the workflow to continue (can also be in request body)"),
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userInput: UserInputRequest = Body(...),
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context: RequestContext = Depends(getRequestContext)
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) -> StreamingResponse:
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"""
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Starts a new chatbot workflow or continues an existing one with SSE streaming.
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Streams progress updates in real-time via Server-Sent Events.
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workflowId can be provided either:
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- As a query parameter: /api/chatbot/start/stream?workflowId=xxx
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- In the request body as part of UserInputRequest
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- Query parameter takes precedence if both are provided
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"""
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event_manager = get_event_manager()
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try:
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# Use workflowId from query parameter if provided, otherwise from request body
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final_workflow_id = workflowId or userInput.workflowId
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# Start background processing (this will create the workflow and event queue)
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workflow = await chatProcess(context.user, str(context.mandateId), userInput, final_workflow_id)
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# Get event queue for the workflow
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queue = event_manager.get_queue(workflow.id)
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if not queue:
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# Create queue if it doesn't exist
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queue = event_manager.create_queue(workflow.id)
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async def event_stream():
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"""Async generator for SSE events - pure event-driven streaming (no polling)."""
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try:
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# Get interface for initial data and status checks
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interfaceDbChat = _getServiceChat(context)
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# Get current workflow to check if resuming and get current round
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current_workflow = interfaceDbChat.getWorkflow(workflow.id)
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current_round = current_workflow.currentRound if current_workflow else None
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is_resuming = final_workflow_id is not None and current_round and current_round > 1
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# Send initial chat data (exact format as chatData endpoint) - only once at start
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try:
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chatData = interfaceDbChat.getUnifiedChatData(workflow.id, None)
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if chatData.get("items"):
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# Filter items by round number if resuming
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filtered_items = []
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for item in chatData["items"]:
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if is_resuming and current_round:
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# Get round number from item
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item_round = None
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item_data = item.get("item")
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if item_data:
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# Handle both dict and object access
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if isinstance(item_data, dict):
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item_round = item_data.get("roundNumber")
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elif hasattr(item_data, "roundNumber"):
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item_round = item_data.roundNumber
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# When resuming, only include items from current round onwards
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# Exclude items without roundNumber (they're from old rounds before roundNumber was added)
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# Exclude items with roundNumber < current_round (from previous rounds)
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if item_round is None or item_round < current_round:
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continue # Skip items from previous rounds or without round info
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filtered_items.append(item)
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# Emit filtered items
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for item in filtered_items:
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# Convert Pydantic models to dicts for JSON serialization
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serializable_item = {
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"type": item.get("type"),
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"createdAt": item.get("createdAt"),
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"item": item.get("item").dict() if hasattr(item.get("item"), "dict") else item.get("item")
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}
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# Emit item directly in exact chatData format: {type, createdAt, item}
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yield f"data: {json.dumps(serializable_item)}\n\n"
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except Exception as e:
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logger.warning(f"Error fetching initial chat data: {e}")
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# Keepalive interval (30 seconds)
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keepalive_interval = 30.0
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last_keepalive = asyncio.get_event_loop().time()
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# Status check interval (check workflow status every 5 seconds - less frequent since we're event-driven)
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status_check_interval = 5.0
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last_status_check = asyncio.get_event_loop().time()
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# Stream events until completion or timeout - pure event-driven (no polling)
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timeout = 300.0 # 5 minutes max
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start_time = asyncio.get_event_loop().time()
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while True:
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# Check timeout
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elapsed = asyncio.get_event_loop().time() - start_time
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if elapsed > timeout:
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logger.info(f"Stream timeout for workflow {workflow.id}")
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break
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# Check for client disconnection
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if await request.is_disconnected():
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logger.info(f"Client disconnected for workflow {workflow.id}")
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break
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current_time = asyncio.get_event_loop().time()
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# Periodically check workflow status (less frequent since we're event-driven)
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if current_time - last_status_check >= status_check_interval:
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try:
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current_workflow = interfaceDbChat.getWorkflow(workflow.id)
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if current_workflow and current_workflow.status == "stopped":
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logger.info(f"Workflow {workflow.id} was stopped, closing stream")
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break
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except Exception as e:
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logger.warning(f"Error checking workflow status: {e}")
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last_status_check = current_time
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# Get event from queue (pure event-driven - no polling database)
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try:
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event = await asyncio.wait_for(queue.get(), timeout=1.0)
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# Handle different event types
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event_type = event.get("type")
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event_data = event.get("data", {})
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# Emit chatdata events (messages, logs, stats) in exact chatData format
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if event_type == "chatdata" and event_data:
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# Emit item directly in exact chatData format: {type, createdAt, item}
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chatdata_item = event_data
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# Ensure item field is serializable (convert Pydantic models to dicts)
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if isinstance(chatdata_item, dict) and "item" in chatdata_item:
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item_obj = chatdata_item.get("item")
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if hasattr(item_obj, "dict"):
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chatdata_item = chatdata_item.copy()
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chatdata_item["item"] = item_obj.dict()
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yield f"data: {json.dumps(chatdata_item)}\n\n"
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# Handle completion/stopped events to close stream
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elif event_type == "complete":
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logger.info(f"Workflow {workflow.id} completed, closing stream")
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break
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elif event_type == "stopped":
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logger.info(f"Workflow {workflow.id} stopped, closing stream")
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break
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elif event_type == "error" and event.get("step") == "error":
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logger.warning(f"Workflow {workflow.id} error, closing stream")
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break
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last_keepalive = current_time
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except asyncio.TimeoutError:
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# Send keepalive if needed (no events received, but keep connection alive)
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current_time = asyncio.get_event_loop().time()
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if current_time - last_keepalive >= keepalive_interval:
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yield f": keepalive\n\n"
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last_keepalive = current_time
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continue
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except Exception as e:
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logger.error(f"Error in event stream: {e}")
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break
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except Exception as e:
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logger.error(f"Error in event stream generator: {e}", exc_info=True)
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finally:
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# Stream ends - cleanup handled by event manager
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pass
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return StreamingResponse(
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event_stream(),
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media_type="text/event-stream",
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"X-Accel-Buffering": "no" # Disable buffering for nginx
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}
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)
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except Exception as e:
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logger.error(f"Error in stream_chatbot_start: {str(e)}", exc_info=True)
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raise HTTPException(
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status_code=500,
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detail=str(e)
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)
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# Workflow stop endpoint
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@router.post("/{workflowId}/stop", response_model=ChatWorkflow)
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@limiter.limit("120/minute")
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async def stop_chatbot(
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request: Request,
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workflowId: str = Path(..., description="ID of the workflow to stop"),
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context: RequestContext = Depends(getRequestContext)
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) -> ChatWorkflow:
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"""Stops a running chatbot workflow."""
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try:
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workflow = await chatStop(context.user, workflowId)
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# Emit stopped event to active streams
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event_manager = get_event_manager()
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await event_manager.emit_event(
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context_id=workflowId,
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event_type="stopped",
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data={"workflowId": workflowId},
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event_category="workflow",
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message="Workflow stopped by user",
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step="stopped"
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)
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logger.info(f"Emitted stopped event for workflow {workflowId}")
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return workflow
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except Exception as e:
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logger.error(f"Error in stop_chatbot: {str(e)}")
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raise HTTPException(
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status_code=500,
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detail=str(e)
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)
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# Delete chatbot workflow endpoint
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@router.delete("/{workflowId}", response_model=Dict[str, Any])
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@limiter.limit("120/minute")
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async def delete_chatbot(
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request: Request,
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workflowId: str = Path(..., description="ID of the workflow to delete"),
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context: RequestContext = Depends(getRequestContext)
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) -> Dict[str, Any]:
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"""Deletes a chatbot workflow and its associated data."""
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try:
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# Get service center
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interfaceDbChat = _getServiceChat(context)
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# Check workflow access and permission using RBAC
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workflows = getRecordsetWithRBAC(
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interfaceDbChat.db,
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ChatWorkflow,
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context.user,
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recordFilter={"id": workflowId}
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)
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if not workflows:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail=f"Workflow with ID {workflowId} not found"
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)
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workflow_data = workflows[0]
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# Check if workflow is a chatbot workflow
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if workflow_data.get("workflowMode") != WorkflowModeEnum.WORKFLOW_CHATBOT.value:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=f"Workflow {workflowId} is not a chatbot workflow"
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)
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# Check if user has permission to delete using RBAC
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if not interfaceDbChat.checkRbacPermission(ChatWorkflow, "delete", workflowId):
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raise HTTPException(
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status_code=status.HTTP_403_FORBIDDEN,
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detail="You don't have permission to delete this workflow"
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)
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# Delete workflow
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success = interfaceDbChat.deleteWorkflow(workflowId)
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if not success:
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail="Failed to delete workflow"
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)
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return {
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"id": workflowId,
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"message": "Chatbot workflow and associated data deleted successfully"
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}
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error in delete_chatbot: {str(e)}", exc_info=True)
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raise HTTPException(
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status_code=500,
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detail=f"Error deleting chatbot workflow: {str(e)}"
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)
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# List chatbot threads/workflows or get specific thread details
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@router.get("/threads")
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@limiter.limit("120/minute")
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async def get_chatbot_threads(
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request: Request,
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workflowId: Optional[str] = Query(None, description="Optional workflow ID to get details and chat data for a specific thread"),
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pagination: Optional[str] = Query(None, description="JSON-encoded PaginationParams object (only used when workflowId is not provided)"),
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context: RequestContext = Depends(getRequestContext)
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) -> Union[PaginatedResponse[ChatWorkflow], Dict[str, Any]]:
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"""
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List all chatbot workflows (threads) for the current user, or get details and chat data for a specific thread.
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- If workflowId is provided: Returns the workflow details and all chat data (messages, logs, stats)
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- If workflowId is not provided: Returns a paginated list of all workflows
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"""
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try:
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interfaceDbChat = _getServiceChat(context)
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# If workflowId is provided, return single workflow with chat data
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if workflowId:
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workflow = interfaceDbChat.getWorkflow(workflowId)
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if not workflow:
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raise HTTPException(
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status_code=404,
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detail=f"Workflow with ID {workflowId} not found"
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)
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# Normalize workflow data to match ChatWorkflow model requirements
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# Convert workflow object to dict if needed, and normalize None values
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if hasattr(workflow, 'model_dump'):
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workflow_dict = workflow.model_dump()
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elif hasattr(workflow, 'dict'):
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workflow_dict = workflow.dict()
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elif isinstance(workflow, dict):
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workflow_dict = dict(workflow)
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else:
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workflow_dict = workflow
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# Set maxSteps to default value of 10 if None (as per ChatWorkflow model)
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if workflow_dict.get("maxSteps") is None:
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workflow_dict["maxSteps"] = 10
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# Get unified chat data for this workflow
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chatData = interfaceDbChat.getUnifiedChatData(workflowId, None)
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return {
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"workflow": workflow_dict,
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"chatData": chatData
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}
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# Otherwise, return paginated list of workflows
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# Parse pagination parameter
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paginationParams = None
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if pagination:
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try:
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paginationDict = json.loads(pagination)
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paginationParams = PaginationParams(**paginationDict) if paginationDict else None
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except (json.JSONDecodeError, ValueError) as e:
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raise HTTPException(
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status_code=400,
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detail=f"Invalid pagination parameter: {str(e)}"
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)
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# Get all workflows filtered by mandateId (RBAC handles this automatically)
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# We get all workflows first to filter by workflowMode before pagination
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all_workflows = interfaceDbChat.getWorkflows(pagination=None)
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# Filter to only include chatbot workflows
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chatbot_workflows_data = [
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wf for wf in all_workflows
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if wf.get("workflowMode") == WorkflowModeEnum.WORKFLOW_CHATBOT.value
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]
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# Apply pagination if requested
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if paginationParams:
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# Apply sorting if provided
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if paginationParams.sort:
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chatbot_workflows_data = interfaceDbChat._applySorting(chatbot_workflows_data, paginationParams.sort)
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# Count total items after filtering
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totalItems = len(chatbot_workflows_data)
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totalPages = math.ceil(totalItems / paginationParams.pageSize) if totalItems > 0 else 0
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# Apply pagination (skip/limit)
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startIdx = (paginationParams.page - 1) * paginationParams.pageSize
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endIdx = startIdx + paginationParams.pageSize
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workflows = chatbot_workflows_data[startIdx:endIdx]
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else:
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workflows = chatbot_workflows_data
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totalItems = len(chatbot_workflows_data)
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totalPages = 1
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# Normalize workflow data to match ChatWorkflow model requirements
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# Convert None values to defaults before response validation
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normalized_workflows = []
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for wf in workflows:
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normalized_wf = dict(wf) # Create a copy
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# Set maxSteps to default value of 10 if None (as per ChatWorkflow model)
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if normalized_wf.get("maxSteps") is None:
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normalized_wf["maxSteps"] = 10
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normalized_workflows.append(normalized_wf)
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# Create paginated response
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from modules.datamodels.datamodelPagination import PaginationMetadata
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metadata = PaginationMetadata(
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currentPage=paginationParams.page if paginationParams else 1,
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pageSize=paginationParams.pageSize if paginationParams else len(workflows),
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totalItems=totalItems,
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totalPages=totalPages,
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sort=paginationParams.sort if paginationParams else [],
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filters=paginationParams.filters if paginationParams else None
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)
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return PaginatedResponse(
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items=normalized_workflows,
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pagination=metadata
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error getting chatbot threads: {str(e)}", exc_info=True)
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raise HTTPException(
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status_code=500,
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detail=f"Error getting chatbot threads: {str(e)}"
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)
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