89d188fbf3
# Conflicts: # api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/embedding_generation.py # api/app/repositories/neo4j/add_nodes.py # api/app/repositories/neo4j/cypher_queries.py # api/app/repositories/neo4j/graph_saver.py # api/app/services/memory_agent_service.py # api/app/services/multimodal_service.py
728 lines
27 KiB
Python
728 lines
27 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.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
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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.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.schemas import FileType
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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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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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files: Optional[List[FileInput]] = 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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config_id = None
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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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tools.extend(self.agent_service.load_knowledge_retrieval_config(config.knowledge_retrieval, user_id))
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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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# 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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)
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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
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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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processed_files = await multimodal_service.process_files(files)
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logger.info(f"处理了 {len(processed_files)} 个文件")
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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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end_user_id=user_id,
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storage_type=storage_type,
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user_rag_memory_id=user_rag_memory_id,
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config_id=config_id,
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memory_flag=memory_flag,
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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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# 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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}
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if files:
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for f in files:
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# url = await MultimodalService(self.db).get_file_url(f)
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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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})
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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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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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"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": self.agent_service._filter_citations(features_config, result.get("citations", [])),
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"audio_url": audio_url,
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"audio_status": "pending"
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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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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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files: Optional[List[FileInput]] = 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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config_id = None
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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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tools.extend(self.agent_service.load_knowledge_retrieval_config(config.knowledge_retrieval, user_id))
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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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# 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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streaming=True
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)
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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
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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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processed_files = await multimodal_service.process_files(files)
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logger.info(f"处理了 {len(processed_files)} 个文件")
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# 流式调用 Agent（支持多模态），同时并行启动 TTS
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full_content = ""
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total_tokens = 0
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text_queue: asyncio.Queue = asyncio.Queue()
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api_key_config = {
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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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"api_base": api_key_obj.api_base,
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"provider": api_key_obj.provider,
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}
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stream_audio_url, tts_task = await self.agent_service._generate_tts_streaming(
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features_config, api_key_config,
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text_queue=text_queue,
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tenant_id=tenant_id, workspace_id=workspace_id
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)
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async for chunk in agent.chat_stream(
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message=message,
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history=history,
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context=None,
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end_user_id=user_id,
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storage_type=storage_type,
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user_rag_memory_id=user_rag_memory_id,
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config_id=config_id,
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memory_flag=memory_flag,
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files=processed_files
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):
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if isinstance(chunk, int):
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total_tokens = chunk
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else:
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full_content += chunk
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yield f"event: message\ndata: {json.dumps({'content': chunk}, ensure_ascii=False)}\n\n"
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if tts_task is not None:
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await text_queue.put(chunk)
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if tts_task is not None:
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await text_queue.put(None)
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elapsed_time = time.time() - start_time
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ModelApiKeyService.record_api_key_usage(self.db, api_key_obj.id)
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# 发送结束事件（包含 suggested_questions、tts、audio_status、citations）
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end_data: dict = {"elapsed_time": elapsed_time, "message_length": len(full_content), "error": None}
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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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end_data["suggested_questions"] = await self.agent_service._generate_suggested_questions(
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features_config, full_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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end_data["audio_url"] = stream_audio_url
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# 检查TTS是否已完成（非阻塞，不取消任务）
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audio_status = "pending"
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if tts_task is not None and tts_task.done():
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# 任务已完成，检查是否有异常
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try:
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tts_task.result()
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audio_status = "completed"
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except Exception as e:
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logger.warning(f"TTS任务异常: {e}")
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audio_status = "failed"
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end_data["audio_status"] = audio_status if stream_audio_url else None
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end_data["citations"] = self.agent_service._filter_citations(features_config, [])
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# 保存消息
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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": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens},
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"audio_url": None
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}
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if files:
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for f in files:
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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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})
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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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if stream_audio_url:
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assistant_meta["audio_url"] = stream_audio_url
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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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self.conversation_service.add_message(
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message_id=message_id,
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conversation_id=conversation_id,
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role="assistant",
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content=full_content,
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meta_data=assistant_meta
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)
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yield f"event: end\ndata: {json.dumps(end_data, ensure_ascii=False)}\n\n"
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logger.info(
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"流式聊天完成",
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extra={
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"conversation_id": str(conversation_id),
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"elapsed_time": elapsed_time,
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"message_length": len(full_content)
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}
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)
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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
|
||
):
|
||
if "sub_usage" in event:
|
||
if "data:" in event:
|
||
try:
|
||
data_line = event.split("data: ", 1)[1].strip()
|
||
data = json.loads(data_line)
|
||
if "total_tokens" in data:
|
||
total_tokens += data["total_tokens"]
|
||
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)
|