7b99a32a1e
1.The end users are still bound to the app. 2. Multi-modal file support includes xlsx, csv, and json. 3. The file routing protocol is consistent with the page routing.
302 lines
12 KiB
Python
302 lines
12 KiB
Python
"""App 服务接口 - 基于 API Key 认证"""
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import json
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from typing import Annotated
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from fastapi import APIRouter, Depends, Request, Body
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from sqlalchemy.orm import Session
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from starlette.responses import StreamingResponse
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from app.core.api_key_auth import require_api_key
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from app.core.error_codes import BizCode
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from app.core.exceptions import BusinessException
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from app.core.logging_config import get_business_logger
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from app.core.response_utils import success
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from app.db import get_db
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from app.models.app_model import App
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from app.models.app_model import AppType
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from app.repositories import knowledge_repository
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from app.repositories.end_user_repository import EndUserRepository
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from app.schemas import AppChatRequest, conversation_schema
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from app.schemas.api_key_schema import ApiKeyAuth
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from app.services import workspace_service
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from app.services.app_chat_service import AppChatService, get_app_chat_service
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from app.services.app_service import get_app_service, AppService
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from app.services.conversation_service import ConversationService, get_conversation_service
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from app.utils.app_config_utils import workflow_config_4_app_release, \
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agent_config_4_app_release, multi_agent_config_4_app_release
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router = APIRouter(prefix="/app", tags=["V1 - App API"])
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logger = get_business_logger()
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@router.get("")
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async def list_apps():
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"""列出可访问的应用（占位）"""
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return success(data=[], msg="App API - Coming Soon")
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# /v1/app/chat
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# @router.post("/chat")
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# @require_api_key(scopes=["app"])
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# async def chat2(
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# request: Request,
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# api_key_auth: ApiKeyAuth = None,
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# db: Session = Depends(get_db),
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# message: str = Body(..., description="聊天消息内容"),
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# ):
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# """
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# Agent 聊天接口demo
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# scopes: 所需的权限范围列表["app", "rag", "memory"]
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# Args:
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# message: 请求参数
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# request: 声明请求
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# api_key_auth: 包含验证后的API Key 信息
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# db: db_session
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# """
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# logger.info(f"API Key Auth: {api_key_auth}")
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# logger.info(f"Message: {message}")
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# return success(data={"received": True}, msg="消息已接收")
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def _checkAppConfig(app: App):
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if app.type == AppType.AGENT:
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if not app.current_release.config:
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raise BusinessException("Agent 应用未配置模型", BizCode.AGENT_CONFIG_MISSING)
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elif app.type == AppType.MULTI_AGENT:
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if not app.current_release.config:
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raise BusinessException("Multi-Agent 应用未配置模型", BizCode.AGENT_CONFIG_MISSING)
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elif app.type == AppType.WORKFLOW:
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if not app.current_release.config:
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raise BusinessException("工作流应用未配置模型", BizCode.AGENT_CONFIG_MISSING)
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else:
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raise BusinessException("不支持的应用类型", BizCode.AGENT_CONFIG_MISSING)
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@router.post("/chat")
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@require_api_key(scopes=["app"])
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async def chat(
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request: Request,
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api_key_auth: ApiKeyAuth = None,
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db: Session = Depends(get_db),
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conversation_service: Annotated[ConversationService, Depends(get_conversation_service)] = None,
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app_chat_service: Annotated[AppChatService, Depends(get_app_chat_service)] = None,
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app_service: Annotated[AppService, Depends(get_app_service)] = None,
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message: str = Body(..., description="聊天消息内容"),
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):
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body = await request.json()
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payload = AppChatRequest(**body)
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app = app_service.get_app(api_key_auth.resource_id, api_key_auth.workspace_id)
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other_id = payload.user_id
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workspace_id = app.workspace_id
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end_user_repo = EndUserRepository(db)
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new_end_user = end_user_repo.get_or_create_end_user(
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app_id=app.id,
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workspace_id=workspace_id,
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other_id=other_id,
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)
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end_user_id = str(new_end_user.id)
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web_search = True
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memory = True
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# 提前验证和准备（在流式响应开始前完成）
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storage_type = workspace_service.get_workspace_storage_type_without_auth(
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db=db,
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workspace_id=workspace_id
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)
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if storage_type is None:
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storage_type = 'neo4j'
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user_rag_memory_id = ''
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if storage_type == 'rag':
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if workspace_id:
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knowledge = knowledge_repository.get_knowledge_by_name(
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db=db,
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name="USER_RAG_MERORY",
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workspace_id=workspace_id
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)
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if knowledge:
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user_rag_memory_id = str(knowledge.id)
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else:
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logger.warning(
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f"未找到名为 'USER_RAG_MERORY' 的知识库，workspace_id: {workspace_id}，将使用 neo4j 存储")
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storage_type = 'neo4j'
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else:
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logger.warning("workspace_id 为空，无法使用 rag 存储，将使用 neo4j 存储")
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storage_type = 'neo4j'
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app_type = app.type
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# check app config
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_checkAppConfig(app)
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# 获取或创建会话（提前验证）
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conversation = conversation_service.create_or_get_conversation(
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app_id=app.id,
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workspace_id=workspace_id,
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user_id=end_user_id,
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is_draft=False,
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conversation_id=payload.conversation_id
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)
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if app_type == AppType.AGENT:
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# print("="*50)
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# print(app.current_release.default_model_config_id)
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agent_config = agent_config_4_app_release(app.current_release)
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# print(agent_config.default_model_config_id)
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# 流式返回
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if payload.stream:
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async def event_generator():
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async for event in app_chat_service.agnet_chat_stream(
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message=payload.message,
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conversation_id=conversation.id, # 使用已创建的会话 ID
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user_id=end_user_id, # 转换为字符串
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variables=payload.variables,
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web_search=web_search,
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config=agent_config,
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memory=memory,
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storage_type=storage_type,
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user_rag_memory_id=user_rag_memory_id,
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workspace_id=workspace_id,
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files=payload.files # 传递多模态文件
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):
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yield event
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return StreamingResponse(
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event_generator(),
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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"
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}
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)
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# 非流式返回
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result = await app_chat_service.agnet_chat(
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message=payload.message,
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conversation_id=conversation.id, # 使用已创建的会话 ID
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user_id=end_user_id, # 转换为字符串
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variables=payload.variables,
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config=agent_config,
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web_search=web_search,
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memory=memory,
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storage_type=storage_type,
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user_rag_memory_id=user_rag_memory_id,
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workspace_id=workspace_id,
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files=payload.files # 传递多模态文件
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)
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return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
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elif app_type == AppType.MULTI_AGENT:
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# 多 Agent 流式返回
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config = multi_agent_config_4_app_release(app.current_release)
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if payload.stream:
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async def event_generator():
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async for event in app_chat_service.multi_agent_chat_stream(
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message=payload.message,
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conversation_id=conversation.id, # 使用已创建的会话 ID
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user_id=end_user_id, # 转换为字符串
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variables=payload.variables,
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config=config,
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web_search=web_search,
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memory=memory,
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storage_type=storage_type,
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user_rag_memory_id=user_rag_memory_id
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):
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yield event
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return StreamingResponse(
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event_generator(),
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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"
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}
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)
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# 多 Agent 非流式返回
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result = await app_chat_service.multi_agent_chat(
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message=payload.message,
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conversation_id=conversation.id, # 使用已创建的会话 ID
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user_id=end_user_id, # 转换为字符串
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variables=payload.variables,
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config=config,
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web_search=web_search,
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memory=memory,
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storage_type=storage_type,
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user_rag_memory_id=user_rag_memory_id
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)
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return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
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elif app_type == AppType.WORKFLOW:
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# 多 Agent 流式返回
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config = workflow_config_4_app_release(app.current_release)
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if payload.stream:
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async def event_generator():
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async for event in app_chat_service.workflow_chat_stream(
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message=payload.message,
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conversation_id=conversation.id, # 使用已创建的会话 ID
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user_id=end_user_id, # 转换为字符串
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variables=payload.variables,
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files=payload.files,
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config=config,
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web_search=web_search,
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memory=memory,
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storage_type=storage_type,
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user_rag_memory_id=user_rag_memory_id,
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app_id=app.id,
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workspace_id=workspace_id,
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release_id=app.current_release.id,
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public=True
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):
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event_type = event.get("event", "message")
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event_data = event.get("data", {})
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# 转换为标准 SSE 格式（字符串）
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sse_message = f"event: {event_type}\ndata: {json.dumps(event_data)}\n\n"
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yield sse_message
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return StreamingResponse(
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event_generator(),
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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"
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}
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)
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# 多 Agent 非流式返回
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result = await app_chat_service.workflow_chat(
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message=payload.message,
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conversation_id=conversation.id, # 使用已创建的会话 ID
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user_id=end_user_id, # 转换为字符串
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variables=payload.variables,
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config=config,
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web_search=web_search,
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memory=memory,
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storage_type=storage_type,
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user_rag_memory_id=user_rag_memory_id,
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app_id=app.id,
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workspace_id=workspace_id,
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release_id=app.current_release.id
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)
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logger.debug(
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"工作流试运行返回结果",
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extra={
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"result_type": str(type(result)),
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"has_response": "response" in result if isinstance(result, dict) else False
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}
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)
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return success(
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data=result,
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msg="工作流任务执行成功"
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)
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else:
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from app.core.exceptions import BusinessException
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from app.core.error_codes import BizCode
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raise BusinessException(f"不支持的应用类型: {app_type}", BizCode.APP_TYPE_NOT_SUPPORTED)
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