531d785629
- Replace plain image URLs with `<img src="..." data-url="...">` HTML tags in multimodal and document extractor services - Propagate citations from workflow end events to client responses - Update system prompts to instruct LLMs to render images using Markdown `` with strict UUID-preserving URL copying
908 lines
37 KiB
Python
908 lines
37 KiB
Python
"""基于分享链接的聊天服务"""
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import asyncio
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import json
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import time
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import uuid
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from typing import Optional, Dict, Any, AsyncGenerator, Annotated, List
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from fastapi import Depends
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from sqlalchemy.orm import Session
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from app.core.agent.langchain_agent import LangChainAgent
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from app.core.logging_config import get_business_logger
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from app.core.memory.agent.langgraph_graph.write_graph import write_long_term
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from app.db import get_db
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from app.models import MultiAgentConfig, AgentConfig, ModelType
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from app.models import WorkflowConfig
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from app.repositories.tool_repository import ToolRepository
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from app.schemas import DraftRunRequest
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from app.schemas.app_schema import FileInput, FileType
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from app.schemas.model_schema import ModelInfo
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from app.schemas.prompt_schema import render_prompt_message, PromptMessageRole
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from app.services.conversation_service import ConversationService
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from app.services.draft_run_service import AgentRunService
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from app.services.memory_agent_service import get_end_user_connected_config
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from app.services.model_service import ModelApiKeyService
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from app.services.multi_agent_orchestrator import MultiAgentOrchestrator
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from app.services.multimodal_service import MultimodalService
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from app.services.workflow_service import WorkflowService
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from app.models.file_metadata_model import FileMetadata
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logger = get_business_logger()
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class AppChatService:
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"""基于分享链接的聊天服务"""
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def __init__(self, db: Session):
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self.db = db
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self.conversation_service = ConversationService(db)
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self.agent_service = AgentRunService(db)
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self.workflow_service = WorkflowService(db)
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async def agnet_chat(
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self,
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message: str,
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conversation_id: uuid.UUID,
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config: AgentConfig,
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files: list[FileInput],
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user_id: str,
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variables: Optional[Dict[str, Any]] = None,
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web_search: bool = False,
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memory: bool = True,
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storage_type: Optional[str] = None,
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user_rag_memory_id: Optional[str] = None,
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workspace_id: Optional[str] = None
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) -> Dict[str, Any]:
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"""聊天（非流式）"""
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start_time = time.time()
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# 应用 features 配置
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features_config: dict = config.features or {}
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if hasattr(features_config, 'model_dump'):
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features_config = features_config.model_dump()
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web_search_feature = features_config.get("web_search", {})
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if not (isinstance(web_search_feature, dict) and web_search_feature.get("enabled")):
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web_search = False
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# 校验文件上传
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self.agent_service._validate_file_upload(features_config, files)
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variables = self.agent_service.prepare_variables(variables, config.variables)
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# 获取模型配置ID
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model_config_id = config.default_model_config_id
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api_key_obj = ModelApiKeyService.get_available_api_key(self.db, model_config_id)
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# 处理系统提示词（支持变量替换）
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system_prompt = config.system_prompt
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if variables:
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system_prompt_rendered = render_prompt_message(
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system_prompt,
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PromptMessageRole.USER,
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variables
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)
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system_prompt = system_prompt_rendered.get_text_content() or system_prompt
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# 准备工具列表
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tools = []
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# 获取工具服务
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tenant_id = ToolRepository.get_tenant_id_by_workspace_id(self.db, str(workspace_id))
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tools.extend(self.agent_service.load_tools_config(config.tools, web_search, tenant_id))
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skill_tools, skill_prompts = self.agent_service.load_skill_config(config.skills, message, tenant_id)
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tools.extend(skill_tools)
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if skill_prompts:
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system_prompt = f"{system_prompt}\n\n{skill_prompts}"
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kb_tools, citations_collector = self.agent_service.load_knowledge_retrieval_config(config.knowledge_retrieval,
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user_id)
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tools.extend(kb_tools)
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memory_flag = False
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if memory:
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memory_tools, memory_flag = self.agent_service.load_memory_config(
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config.memory, user_id, storage_type, user_rag_memory_id
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)
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tools.extend(memory_tools)
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# 获取模型参数
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model_parameters = config.model_parameters
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model_info = ModelInfo(
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model_name=api_key_obj.model_name,
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provider=api_key_obj.provider,
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api_key=api_key_obj.api_key,
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api_base=api_key_obj.api_base,
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capability=api_key_obj.capability,
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is_omni=api_key_obj.is_omni,
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model_type=ModelType.LLM
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)
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# 加载历史消息（包含开场白）
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history = await self.conversation_service.get_conversation_history(
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conversation_id=conversation_id,
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max_history=10,
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current_provider=api_key_obj.provider,
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current_is_omni=api_key_obj.is_omni
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)
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# 如果是新会话且有开场白，作为第一条 assistant 消息写入数据库
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is_new_conversation = len(history) == 0
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if is_new_conversation:
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opening, suggested_questions = self.agent_service._get_opening_statement(features_config, True, variables)
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if opening:
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self.conversation_service.add_message(
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conversation_id=conversation_id,
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role="assistant",
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content=opening,
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meta_data={"suggested_questions": suggested_questions}
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)
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# 重新加载历史（包含刚写入的开场白）
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history = await self.conversation_service.get_conversation_history(
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conversation_id=conversation_id,
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max_history=10,
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current_provider=api_key_obj.provider,
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current_is_omni=api_key_obj.is_omni
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)
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# 处理多模态文件
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processed_files = None
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if files:
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multimodal_service = MultimodalService(self.db, model_info)
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fu_config = features_config.get("file_upload", {})
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if hasattr(fu_config, "model_dump"):
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fu_config = fu_config.model_dump()
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doc_img_recognition = isinstance(fu_config, dict) and fu_config.get("document_image_recognition", False)
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processed_files = await multimodal_service.process_files(
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files, document_image_recognition=doc_img_recognition,
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workspace_id=workspace_id
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)
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logger.info(f"处理了 {len(processed_files)} 个文件")
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if doc_img_recognition and "vision" in (api_key_obj.capability or []) and any(
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f.type == FileType.DOCUMENT for f in files
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):
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system_prompt += (
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"\n\n文档文字中包含图片位置标记如 [图片 第2页 第1张]: <img src=\"url\"...>，"
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"请在回答中用 Markdown 格式  展示对应图片。"
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"重要：图片 URL 中包含 UUID（如 /storage/permanent/xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx），"
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"必须将 src 属性的值原封不动复制到 Markdown 的括号中，不得增删任何字符。"
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)
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# 创建 LangChain Agent
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agent = LangChainAgent(
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model_name=api_key_obj.model_name,
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api_key=api_key_obj.api_key,
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provider=api_key_obj.provider,
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api_base=api_key_obj.api_base,
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is_omni=api_key_obj.is_omni,
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temperature=model_parameters.get("temperature", 0.7),
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max_tokens=model_parameters.get("max_tokens", 2000),
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system_prompt=system_prompt,
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tools=tools,
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deep_thinking=model_parameters.get("deep_thinking", False),
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thinking_budget_tokens=model_parameters.get("thinking_budget_tokens"),
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json_output=model_parameters.get("json_output", False),
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capability=api_key_obj.capability or [],
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)
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# 为需要运行时上下文的工具注入上下文
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for t in tools:
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if hasattr(t, 'tool_instance') and hasattr(t.tool_instance, 'set_runtime_context'):
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t.tool_instance.set_runtime_context(
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user_id=user_id or "anonymous",
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conversation_id=str(conversation_id) if conversation_id else None,
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uploaded_files=processed_files or []
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)
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# 调用 Agent（支持多模态）
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result = await agent.chat(
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message=message,
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history=history,
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context=None,
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files=processed_files # 传递处理后的文件
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)
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ModelApiKeyService.record_api_key_usage(self.db, api_key_obj.id)
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elapsed_time = time.time() - start_time
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# suggested_questions
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suggested_questions = []
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sq_config = features_config.get("suggested_questions_after_answer", {})
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if isinstance(sq_config, dict) and sq_config.get("enabled"):
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suggested_questions = await self.agent_service._generate_suggested_questions(
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features_config, result["content"],
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{"model_name": api_key_obj.model_name, "api_key": api_key_obj.api_key,
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"api_base": api_key_obj.api_base}, {}
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)
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audio_url = await self.agent_service._generate_tts(
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features_config, result["content"],
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{"model_name": api_key_obj.model_name, "api_key": api_key_obj.api_key,
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"api_base": api_key_obj.api_base, "provider": api_key_obj.provider},
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tenant_id=tenant_id, workspace_id=workspace_id
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)
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# 过滤 citations（只调用一次）
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filtered_citations = self.agent_service._filter_citations(features_config, citations_collector)
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# 构建用户消息内容（含多模态文件）
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human_meta = {
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"files": [],
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"history_files": {}
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}
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assistant_meta = {
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"model": api_key_obj.model_name,
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"usage": result.get("usage", {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}),
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"audio_url": None,
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"citations": filtered_citations,
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"reasoning_content": result.get("reasoning_content")
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}
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if files:
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local_ids = [f.upload_file_id for f in files
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if f.transfer_method.value == "local_file" and f.upload_file_id
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and (not f.name or not f.size)]
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meta_map = {}
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if local_ids:
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rows = self.db.query(FileMetadata).filter(
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FileMetadata.id.in_(local_ids),
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FileMetadata.status == "completed"
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).all()
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meta_map = {str(r.id): r for r in rows}
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for f in files:
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name, size = f.name, f.size
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if f.transfer_method.value == "local_file" and f.upload_file_id and (not name or not size):
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meta = meta_map.get(str(f.upload_file_id))
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if meta:
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name = name or meta.file_name
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size = size or meta.file_size
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human_meta["files"].append({
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"type": f.type,
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"url": f.url,
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"name": name,
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"size": size,
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"file_type": f.file_type,
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})
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if processed_files:
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human_meta["history_files"] = {
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"content": processed_files,
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"provider": api_key_obj.provider,
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"is_omni": api_key_obj.is_omni
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}
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# 保存消息
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if audio_url:
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assistant_meta["audio_url"] = audio_url
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if memory_flag:
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connected_config = get_end_user_connected_config(user_id, self.db)
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memory_config_id: str = connected_config.get("memory_config_id")
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file_list = []
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for file in files:
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file_dict = file.model_dump()
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file_dict["upload_file_id"] = str(file_dict["upload_file_id"]) if file_dict["upload_file_id"] else None
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file_list.append(file_dict)
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messages = [
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{"role": "user", "content": message, "files": file_list},
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{"role": "assistant", "content": result["content"]}
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]
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if memory_config_id:
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await write_long_term(
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storage_type,
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user_id,
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messages,
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user_rag_memory_id,
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memory_config_id
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)
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self.conversation_service.add_message(
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conversation_id=conversation_id,
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role="user",
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content=message,
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meta_data=human_meta
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)
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ai_message = self.conversation_service.add_message(
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conversation_id=conversation_id,
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role="assistant",
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content=result["content"],
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meta_data=assistant_meta
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)
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message_id = ai_message.id
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return {
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"conversation_id": conversation_id,
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"message_id": str(message_id),
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"message": result["content"],
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"reasoning_content": result.get("reasoning_content"),
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"usage": result.get("usage", {
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"prompt_tokens": 0,
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"completion_tokens": 0,
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"total_tokens": 0
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}),
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"elapsed_time": elapsed_time,
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"suggested_questions": suggested_questions,
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"citations": filtered_citations,
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"audio_url": audio_url,
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"audio_status": "pending" if audio_url else None
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}
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async def agnet_chat_stream(
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self,
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message: str,
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conversation_id: uuid.UUID,
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config: AgentConfig,
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files: list[FileInput],
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user_id: Optional[str] = None,
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variables: Optional[Dict[str, Any]] = None,
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web_search: bool = False,
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memory: bool = True,
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storage_type: Optional[str] = None,
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user_rag_memory_id: Optional[str] = None,
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workspace_id: Optional[str] = None
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) -> AsyncGenerator[str, None]:
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"""聊天（流式）"""
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try:
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start_time = time.time()
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message_id = uuid.uuid4()
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# 应用 features 配置
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features_config: dict = config.features or {}
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if hasattr(features_config, 'model_dump'):
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features_config = features_config.model_dump()
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web_search_feature = features_config.get("web_search", {})
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if not (isinstance(web_search_feature, dict) and web_search_feature.get("enabled")):
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web_search = False
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# 校验文件上传
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self.agent_service._validate_file_upload(features_config, files)
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yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation_id), 'message_id': str(message_id)}, ensure_ascii=False)}\n\n"
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variables = self.agent_service.prepare_variables(variables, config.variables)
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# 获取模型配置ID
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model_config_id = config.default_model_config_id
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api_key_obj = ModelApiKeyService.get_available_api_key(self.db, model_config_id)
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# 处理系统提示词（支持变量替换）
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system_prompt = config.system_prompt
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if variables:
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system_prompt_rendered = render_prompt_message(
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system_prompt,
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PromptMessageRole.USER,
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variables
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)
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system_prompt = system_prompt_rendered.get_text_content() or system_prompt
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# 准备工具列表
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tools = []
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# 获取工具服务
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tenant_id = ToolRepository.get_tenant_id_by_workspace_id(self.db, str(workspace_id))
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tools.extend(self.agent_service.load_tools_config(config.tools, web_search, tenant_id))
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skill_tools, skill_prompts = self.agent_service.load_skill_config(config.skills, message, tenant_id)
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tools.extend(skill_tools)
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if skill_prompts:
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system_prompt = f"{system_prompt}\n\n{skill_prompts}"
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kb_tools, citations_collector = self.agent_service.load_knowledge_retrieval_config(
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config.knowledge_retrieval, user_id)
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tools.extend(kb_tools)
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# 添加长期记忆工具
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memory_flag = False
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if memory:
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memory_tools, memory_flag = self.agent_service.load_memory_config(
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config.memory, user_id, storage_type, user_rag_memory_id
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)
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tools.extend(memory_tools)
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# 获取模型参数
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model_parameters = config.model_parameters
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model_info = ModelInfo(
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model_name=api_key_obj.model_name,
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provider=api_key_obj.provider,
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api_key=api_key_obj.api_key,
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api_base=api_key_obj.api_base,
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capability=api_key_obj.capability,
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is_omni=api_key_obj.is_omni,
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model_type=ModelType.LLM
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)
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# 加载历史消息（包含开场白）
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history = await self.conversation_service.get_conversation_history(
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conversation_id=conversation_id,
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max_history=10,
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current_provider=api_key_obj.provider,
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current_is_omni=api_key_obj.is_omni
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)
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# 如果是新会话且有开场白，作为第一条 assistant 消息写入数据库
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is_new_conversation = len(history) == 0
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if is_new_conversation:
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opening, suggested_questions = self.agent_service._get_opening_statement(features_config, True, variables)
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if opening:
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self.conversation_service.add_message(
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conversation_id=conversation_id,
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role="assistant",
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content=opening,
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meta_data={"suggested_questions": suggested_questions}
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)
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# 重新加载历史（包含刚写入的开场白）
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history = await self.conversation_service.get_conversation_history(
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conversation_id=conversation_id,
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max_history=10,
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current_provider=api_key_obj.provider,
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current_is_omni=api_key_obj.is_omni
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)
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# 处理多模态文件
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processed_files = None
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if files:
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multimodal_service = MultimodalService(self.db, model_info)
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fu_config = features_config.get("file_upload", {})
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if hasattr(fu_config, "model_dump"):
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fu_config = fu_config.model_dump()
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doc_img_recognition = isinstance(fu_config, dict) and fu_config.get("document_image_recognition", False)
|
||
processed_files = await multimodal_service.process_files(
|
||
files, document_image_recognition=doc_img_recognition,
|
||
workspace_id=workspace_id
|
||
)
|
||
logger.info(f"处理了 {len(processed_files)} 个文件")
|
||
if doc_img_recognition and "vision" in (api_key_obj.capability or []) and any(
|
||
f.type == FileType.DOCUMENT for f in files
|
||
):
|
||
from langchain.agents import create_agent
|
||
system_prompt += (
|
||
"\n\n文档文字中包含图片位置标记如 [图片 第2页 第1张]: <img src=\"url\"...>，"
|
||
"请在回答中用 Markdown 格式  展示对应图片。"
|
||
"重要：图片 URL 中包含 UUID（如 /storage/permanent/xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx），"
|
||
"必须将 src 属性的值原封不动复制到 Markdown 的括号中，不得增删任何字符。"
|
||
)
|
||
|
||
# 创建 LangChain Agent
|
||
agent = LangChainAgent(
|
||
model_name=api_key_obj.model_name,
|
||
api_key=api_key_obj.api_key,
|
||
provider=api_key_obj.provider,
|
||
api_base=api_key_obj.api_base,
|
||
is_omni=api_key_obj.is_omni,
|
||
temperature=model_parameters.get("temperature", 0.7),
|
||
max_tokens=model_parameters.get("max_tokens", 2000),
|
||
system_prompt=system_prompt,
|
||
tools=tools,
|
||
streaming=True,
|
||
deep_thinking=model_parameters.get("deep_thinking", False),
|
||
thinking_budget_tokens=model_parameters.get("thinking_budget_tokens"),
|
||
json_output=model_parameters.get("json_output", False),
|
||
capability=api_key_obj.capability or [],
|
||
)
|
||
|
||
# 为需要运行时上下文的工具注入上下文
|
||
for t in tools:
|
||
if hasattr(t, 'tool_instance') and hasattr(t.tool_instance, 'set_runtime_context'):
|
||
t.tool_instance.set_runtime_context(
|
||
user_id=user_id or "anonymous",
|
||
conversation_id=str(conversation_id) if conversation_id else None,
|
||
uploaded_files=processed_files or []
|
||
)
|
||
|
||
# 流式调用 Agent（支持多模态），同时并行启动 TTS
|
||
full_content = ""
|
||
full_reasoning = ""
|
||
total_tokens = 0
|
||
|
||
text_queue: asyncio.Queue = asyncio.Queue()
|
||
api_key_config = {
|
||
"model_name": api_key_obj.model_name,
|
||
"api_key": api_key_obj.api_key,
|
||
"api_base": api_key_obj.api_base,
|
||
"provider": api_key_obj.provider,
|
||
}
|
||
stream_audio_url, tts_task = await self.agent_service._generate_tts_streaming(
|
||
features_config, api_key_config,
|
||
text_queue=text_queue,
|
||
tenant_id=tenant_id, workspace_id=workspace_id
|
||
)
|
||
|
||
async for chunk in agent.chat_stream(
|
||
message=message,
|
||
history=history,
|
||
context=None,
|
||
files=processed_files
|
||
):
|
||
if isinstance(chunk, int):
|
||
total_tokens = chunk
|
||
elif isinstance(chunk, dict) and chunk.get("type") == "reasoning":
|
||
full_reasoning += chunk['content']
|
||
yield f"event: reasoning\ndata: {json.dumps({'content': chunk['content']}, ensure_ascii=False)}\n\n"
|
||
else:
|
||
full_content += chunk
|
||
yield f"event: message\ndata: {json.dumps({'content': chunk}, ensure_ascii=False)}\n\n"
|
||
if tts_task is not None:
|
||
await text_queue.put(chunk)
|
||
|
||
if tts_task is not None:
|
||
await text_queue.put(None)
|
||
|
||
elapsed_time = time.time() - start_time
|
||
ModelApiKeyService.record_api_key_usage(self.db, api_key_obj.id)
|
||
|
||
# 发送结束事件（包含 suggested_questions、tts、audio_status、citations）
|
||
end_data: dict = {"elapsed_time": elapsed_time, "message_length": len(full_content), "error": None}
|
||
sq_config = features_config.get("suggested_questions_after_answer", {})
|
||
if isinstance(sq_config, dict) and sq_config.get("enabled"):
|
||
end_data["suggested_questions"] = await self.agent_service._generate_suggested_questions(
|
||
features_config, full_content,
|
||
{"model_name": api_key_obj.model_name, "api_key": api_key_obj.api_key,
|
||
"api_base": api_key_obj.api_base}, {}
|
||
)
|
||
end_data["audio_url"] = stream_audio_url
|
||
# 检查TTS是否已完成（非阻塞，不取消任务）
|
||
audio_status = "pending"
|
||
if tts_task is not None and tts_task.done():
|
||
# 任务已完成，检查是否有异常
|
||
try:
|
||
tts_task.result()
|
||
audio_status = "completed"
|
||
except Exception as e:
|
||
logger.warning(f"TTS任务异常: {e}")
|
||
audio_status = "failed"
|
||
end_data["audio_status"] = audio_status if stream_audio_url else None
|
||
# 过滤 citations（只调用一次）
|
||
filtered_citations = self.agent_service._filter_citations(features_config, citations_collector)
|
||
end_data["citations"] = filtered_citations
|
||
|
||
# 保存消息
|
||
human_meta = {
|
||
"files": [],
|
||
"history_files": {}
|
||
}
|
||
assistant_meta = {
|
||
"model": api_key_obj.model_name,
|
||
"usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens},
|
||
"audio_url": None,
|
||
"citations": filtered_citations,
|
||
"reasoning_content": full_reasoning or None
|
||
}
|
||
|
||
if files:
|
||
local_ids = [f.upload_file_id for f in files
|
||
if f.transfer_method.value == "local_file" and f.upload_file_id
|
||
and (not f.name or not f.size)]
|
||
meta_map = {}
|
||
if local_ids:
|
||
rows = self.db.query(FileMetadata).filter(
|
||
FileMetadata.id.in_(local_ids),
|
||
FileMetadata.status == "completed"
|
||
).all()
|
||
meta_map = {str(r.id): r for r in rows}
|
||
for f in files:
|
||
name, size = f.name, f.size
|
||
if f.transfer_method.value == "local_file" and f.upload_file_id and (not name or not size):
|
||
meta = meta_map.get(str(f.upload_file_id))
|
||
if meta:
|
||
name = name or meta.file_name
|
||
size = size or meta.file_size
|
||
human_meta["files"].append({
|
||
"type": f.type,
|
||
"url": f.url,
|
||
"name": name,
|
||
"size": size,
|
||
"file_type": f.file_type,
|
||
})
|
||
if processed_files:
|
||
human_meta["history_files"] = {
|
||
"content": processed_files,
|
||
"provider": api_key_obj.provider,
|
||
"is_omni": api_key_obj.is_omni
|
||
}
|
||
|
||
if stream_audio_url:
|
||
assistant_meta["audio_url"] = stream_audio_url
|
||
|
||
if memory_flag:
|
||
connected_config = get_end_user_connected_config(user_id, self.db)
|
||
memory_config_id: str = connected_config.get("memory_config_id")
|
||
file_list = []
|
||
for file in files:
|
||
file_dict = file.model_dump()
|
||
file_dict["upload_file_id"] = str(file_dict["upload_file_id"]) if file_dict["upload_file_id"] else None
|
||
file_list.append(file_dict)
|
||
messages = [
|
||
{"role": "user", "content": message, "files": file_list},
|
||
{"role": "assistant", "content": full_content}
|
||
]
|
||
if memory_config_id:
|
||
await write_long_term(
|
||
storage_type,
|
||
user_id,
|
||
messages,
|
||
user_rag_memory_id,
|
||
memory_config_id
|
||
)
|
||
self.conversation_service.add_message(
|
||
conversation_id=conversation_id,
|
||
role="user",
|
||
content=message,
|
||
meta_data=human_meta
|
||
)
|
||
self.conversation_service.add_message(
|
||
message_id=message_id,
|
||
conversation_id=conversation_id,
|
||
role="assistant",
|
||
content=full_content,
|
||
meta_data=assistant_meta
|
||
)
|
||
yield f"event: end\ndata: {json.dumps(end_data, ensure_ascii=False)}\n\n"
|
||
|
||
logger.info(
|
||
"流式聊天完成",
|
||
extra={
|
||
"conversation_id": str(conversation_id),
|
||
"elapsed_time": elapsed_time,
|
||
"message_length": len(full_content)
|
||
}
|
||
)
|
||
|
||
except (GeneratorExit, asyncio.CancelledError):
|
||
# 生成器被关闭或任务被取消，正常退出
|
||
logger.debug("流式聊天被中断")
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"流式聊天失败: {str(e)}", exc_info=True)
|
||
# 发送错误事件
|
||
yield f"event: end\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
|
||
|
||
async def multi_agent_chat(
|
||
self,
|
||
message: str,
|
||
conversation_id: uuid.UUID,
|
||
config: MultiAgentConfig,
|
||
user_id: Optional[str] = None,
|
||
variables: Optional[Dict[str, Any]] = None,
|
||
web_search: bool = False,
|
||
memory: bool = True,
|
||
storage_type: Optional[str] = None,
|
||
user_rag_memory_id: Optional[str] = None,
|
||
) -> Dict[str, Any]:
|
||
"""多 Agent 聊天（非流式）"""
|
||
|
||
start_time = time.time()
|
||
actual_config_id = None
|
||
config_id = actual_config_id
|
||
|
||
if variables is None:
|
||
variables = {}
|
||
|
||
# 2. 创建编排器
|
||
orchestrator = MultiAgentOrchestrator(self.db, config)
|
||
|
||
# 3. 执行任务
|
||
result = await orchestrator.execute(
|
||
message=message,
|
||
conversation_id=conversation_id,
|
||
user_id=user_id,
|
||
variables=variables,
|
||
use_llm_routing=True, # 默认启用 LLM 路由
|
||
web_search=web_search, # 网络搜索参数
|
||
memory=memory # 记忆功能参数
|
||
)
|
||
|
||
elapsed_time = time.time() - start_time
|
||
|
||
# 保存消息
|
||
self.conversation_service.add_message(
|
||
conversation_id=conversation_id,
|
||
role="user",
|
||
content=message
|
||
)
|
||
|
||
ai_message = self.conversation_service.add_message(
|
||
conversation_id=conversation_id,
|
||
role="assistant",
|
||
content=result.get("message", ""),
|
||
meta_data={
|
||
"mode": result.get("mode"),
|
||
"elapsed_time": result.get("elapsed_time"),
|
||
"usage": result.get("usage", {
|
||
"prompt_tokens": 0,
|
||
"completion_tokens": 0,
|
||
"total_tokens": 0
|
||
})
|
||
}
|
||
)
|
||
|
||
return {
|
||
"conversation_id": conversation_id,
|
||
"message": result.get("message", ""),
|
||
"message_id": str(ai_message.id),
|
||
"usage": {
|
||
"prompt_tokens": 0,
|
||
"completion_tokens": 0,
|
||
"total_tokens": 0
|
||
},
|
||
"elapsed_time": elapsed_time
|
||
}
|
||
|
||
async def multi_agent_chat_stream(
|
||
self,
|
||
message: str,
|
||
conversation_id: uuid.UUID,
|
||
config: MultiAgentConfig,
|
||
user_id: Optional[str] = None,
|
||
variables: Optional[Dict[str, Any]] = None,
|
||
web_search: bool = False,
|
||
memory: bool = True,
|
||
storage_type: Optional[str] = None,
|
||
user_rag_memory_id: Optional[str] = None,
|
||
) -> AsyncGenerator[str, None]:
|
||
"""多 Agent 聊天（流式）"""
|
||
|
||
start_time = time.time()
|
||
|
||
if variables is None:
|
||
variables = {}
|
||
|
||
try:
|
||
message_id = uuid.uuid4()
|
||
# 发送开始事件
|
||
yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation_id), 'message_id': str(message_id)}, ensure_ascii=False)}\n\n"
|
||
|
||
full_content = ""
|
||
total_tokens = 0
|
||
|
||
# 2. 创建编排器
|
||
orchestrator = MultiAgentOrchestrator(self.db, config)
|
||
|
||
# 3. 流式执行任务
|
||
async for event in orchestrator.execute_stream(
|
||
message=message,
|
||
conversation_id=conversation_id,
|
||
user_id=user_id,
|
||
variables=variables,
|
||
use_llm_routing=True,
|
||
web_search=web_search, # 网络搜索参数
|
||
memory=memory, # 记忆功能参数
|
||
storage_type=storage_type,
|
||
user_rag_memory_id=user_rag_memory_id
|
||
):
|
||
# 拦截 sub_usage 事件，累加 token
|
||
if "event: sub_usage" in event:
|
||
if "data:" in event:
|
||
try:
|
||
data_line = event.split("data: ", 1)[1].strip()
|
||
data = json.loads(data_line)
|
||
total_tokens += data.get("total_tokens", 0)
|
||
except:
|
||
pass
|
||
else:
|
||
yield event
|
||
# 尝试提取内容（用于保存）
|
||
if "data:" in event:
|
||
try:
|
||
data_line = event.split("data: ", 1)[1].strip()
|
||
data = json.loads(data_line)
|
||
if "content" in data:
|
||
full_content += data["content"]
|
||
except:
|
||
pass
|
||
|
||
elapsed_time = time.time() - start_time
|
||
|
||
# 保存消息
|
||
self.conversation_service.add_message(
|
||
conversation_id=conversation_id,
|
||
role="user",
|
||
content=message
|
||
)
|
||
|
||
self.conversation_service.add_message(
|
||
message_id=message_id,
|
||
conversation_id=conversation_id,
|
||
role="assistant",
|
||
content=full_content,
|
||
meta_data={
|
||
"elapsed_time": elapsed_time,
|
||
"usage": {
|
||
"prompt_tokens": 0,
|
||
"completion_tokens": 0,
|
||
"total_tokens": total_tokens
|
||
}
|
||
}
|
||
)
|
||
|
||
logger.info(
|
||
"多 Agent 流式聊天完成",
|
||
extra={
|
||
"conversation_id": str(conversation_id),
|
||
"elapsed_time": elapsed_time,
|
||
"message_length": len(full_content)
|
||
}
|
||
)
|
||
|
||
except (GeneratorExit, asyncio.CancelledError):
|
||
# 生成器被关闭或任务被取消，正常退出
|
||
logger.debug("多 Agent 流式聊天被中断")
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"多 Agent 流式聊天失败: {str(e)}", exc_info=True)
|
||
# 发送错误事件
|
||
yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
|
||
|
||
async def workflow_chat(
|
||
self,
|
||
message: str,
|
||
conversation_id: uuid.UUID,
|
||
config: WorkflowConfig,
|
||
app_id: uuid.UUID,
|
||
release_id: uuid.UUID,
|
||
workspace_id: uuid.UUID,
|
||
files: Optional[List[FileInput]] = None,
|
||
user_id: Optional[str] = None,
|
||
variables: Optional[Dict[str, Any]] = None,
|
||
web_search: bool = False,
|
||
memory: bool = True,
|
||
storage_type: Optional[str] = None,
|
||
user_rag_memory_id: Optional[str] = None,
|
||
) -> Dict[str, Any]:
|
||
"""聊天（非流式）"""
|
||
payload = DraftRunRequest(
|
||
message=message,
|
||
variables=variables,
|
||
conversation_id=str(conversation_id),
|
||
stream=True,
|
||
user_id=user_id,
|
||
files=files
|
||
)
|
||
return await self.workflow_service.run(
|
||
app_id=app_id,
|
||
payload=payload,
|
||
config=config,
|
||
workspace_id=workspace_id,
|
||
release_id=release_id,
|
||
)
|
||
|
||
async def workflow_chat_stream(
|
||
self,
|
||
message: str,
|
||
conversation_id: uuid.UUID,
|
||
config: WorkflowConfig,
|
||
app_id: uuid.UUID,
|
||
release_id: uuid.UUID,
|
||
workspace_id: uuid.UUID,
|
||
user_id: str = None,
|
||
variables: Optional[Dict[str, Any]] = None,
|
||
files: Optional[List[FileInput]] = None,
|
||
web_search: bool = False,
|
||
memory: bool = True,
|
||
storage_type: Optional[str] = None,
|
||
user_rag_memory_id: Optional[str] = None,
|
||
public=False
|
||
|
||
) -> AsyncGenerator[dict, None]:
|
||
"""聊天（流式）"""
|
||
payload = DraftRunRequest(
|
||
message=message,
|
||
variables=variables,
|
||
conversation_id=str(conversation_id),
|
||
stream=True,
|
||
user_id=user_id,
|
||
files=files
|
||
)
|
||
async for event in self.workflow_service.run_stream(
|
||
app_id=app_id,
|
||
payload=payload,
|
||
config=config,
|
||
workspace_id=workspace_id,
|
||
release_id=release_id,
|
||
public=public
|
||
):
|
||
yield event
|
||
|
||
|
||
# ==================== 依赖注入函数 ====================
|
||
|
||
def get_app_chat_service(
|
||
db: Annotated[Session, Depends(get_db)]
|
||
) -> AppChatService:
|
||
"""获取工作流服务（依赖注入）"""
|
||
return AppChatService(db)
|