""" Jarvis Agent 服务层 负责 LangGraph Agent 的调用、流式输出、对话历史管理 """ import json import uuid import logging from datetime import UTC, datetime from typing import Any, AsyncGenerator import asyncio from openai import BadRequestError from sqlalchemy.ext.asyncio import AsyncSession from sqlalchemy import select from langchain_core.messages import HumanMessage, AIMessage from app.database import async_session from app.logging_utils import summarize_llm_config from app.models.conversation import Conversation, Message from app.models.user import User from app.agents.graph import get_agent_graph from app.agents.context import set_current_user, clear_current_user from app.services import memory_service from app.services.brain_service import BrainService from app.services.llm_service import create_llm_from_config, resolve_provider_capabilities from app.agents.tools.time_reasoning import extract_reference_datetime from app.agents.state import initial_state logger = logging.getLogger(__name__) def _is_streaming_rejection_error(error: Exception, user_llm_config: dict | None) -> bool: capabilities = resolve_provider_capabilities(user_llm_config) error_text = str(error).lower() markers = [ "invalid chat setting", "invalid params", "stream", "streaming", "unsupported", "bad_request_error", "http 400", "error code: 400", ] if isinstance(error, BadRequestError): return ( getattr(capabilities, "provider", None) not in {"openai", "claude"} and any(marker in error_text for marker in markers) ) return any(marker in error_text for marker in markers) class AgentService: """对话 Agent 服务""" def __init__(self, db: AsyncSession): self.db = db async def _try_auto_summarize_background(self, user_id: str, conversation_id: str) -> None: async with async_session() as session: await memory_service.try_auto_summarize(session, user_id, conversation_id) def _build_progress_event( self, stage: str, label: str, *, agent: str | None = None, tool_name: str | None = None, step: str | None = None, steps: list[str] | None = None, ) -> dict[str, Any]: return { "type": "progress", "stage": stage, "label": label, "agent": agent, "tool_name": tool_name, "step": step, "steps": steps or [], } async def _get_user_llm_config(self, user_id: str, model_name: str | None = None) -> dict | None: """获取用户的 LLM 模型配置""" result = await self.db.execute(select(User).where(User.id == user_id)) user = result.scalar_one_or_none() if not user or not user.llm_config: return None llm_config = user.llm_config if model_name: models = llm_config.get("chat", []) for m in models: if m.get("name") == model_name: return m return None chat_models = llm_config.get("chat", []) for m in chat_models: if m.get("enabled"): return m return None async def chat( self, user_id: str, message: str, conversation_id: str | None = None, file_ids: list[str] | None = None, model_name: str | None = None, ) -> tuple[str, str, AsyncGenerator[dict[str, Any], None]]: """ 处理对话请求（流式） """ user_llm_config = await self._get_user_llm_config(user_id, model_name) model_name_used = model_name if model_name and not user_llm_config: raise ValueError("所选模型不可用于聊天，请切换到聊天模型") if user_llm_config: model_name_used = user_llm_config.get("name", model_name) logger.info( "agent_chat_started", extra={ "details": { "mode": "stream", "requested_model_name": model_name, "resolved_model_name": model_name_used, "message_length": len(message or ""), } }, ) if conversation_id: result = await self.db.execute( select(Conversation).where(Conversation.id == conversation_id) ) conv = result.scalar_one_or_none() else: conv = None if not conv: conv = Conversation(user_id=user_id, title=message[:50]) self.db.add(conv) await self.db.commit() await self.db.refresh(conv) conversation_id = conv.id else: conversation_id = conv.id file_context = "" if file_ids: from app.services.document_service import DocumentService doc_svc = DocumentService(self.db) for file_id in file_ids: content = await doc_svc.get_document_content(user_id, file_id) if content: file_context += f"\n\n[用户上传文件内容]\n{content}\n[/文件内容]" full_message = f"{message}\n{file_context}" if file_context else message user_msg = Message( conversation_id=conversation_id, role="user", content=message, attachments=[{"file_ids": file_ids}] if file_ids else None, ) self.db.add(user_msg) await self.db.commit() await self.db.refresh(user_msg) brain_service = BrainService(self.db) await brain_service.create_event( user_id, source_type="conversation", source_id=conversation_id, event_type="message_created", title="User message", content_summary=message[:500], raw_excerpt=message[:2000], metadata_={"role": "user"}, importance_signal=1.0, ) await self.db.commit() memory_ctx = await memory_service.build_memory_context( self.db, user_id, conversation_id, message ) assistant_msg = Message( conversation_id=conversation_id, role="assistant", content="", model=model_name_used or "jarvis", attachments=None, ) self.db.add(assistant_msg) await self.db.commit() await self.db.refresh(assistant_msg) def _build_current_datetime_context() -> str: now_utc = datetime.now(UTC) return ( "【当前时间】\n" f"- current_time_utc: {now_utc.isoformat()}\n" f"- current_date_utc: {now_utc.date().isoformat()}\n" "说明：解析‘今天/明天/后天/本周/下周’等相对时间时，请以 current_time_utc 为准。" ) async def run_agent(): set_current_user(user_id) try: graph = get_agent_graph() current_datetime_context = _build_current_datetime_context() # 使用 initial_state 构建状态 state = initial_state(user_id, conversation_id) state.update({ "messages": [HumanMessage(content=full_message)], "memory_context": memory_ctx, "current_datetime_context": current_datetime_context, "user_llm_config": user_llm_config, }) yield self._build_progress_event("thinking", "Jarvis 正在分析请求", agent="master", step="理解你的问题") collected = "" try: async for event in graph.astream_events(state, version="v2"): kind = event.get("event") event_name = event.get("name", "") metadata = event.get("metadata", {}) data = event.get("data", {}) if kind == "on_chain_start" and event_name in {"master", "schedule_planner", "executor", "librarian", "analyst"}: stage_map = { "master": ("thinking", "Jarvis 正在理解请求"), "schedule_planner": ("planning", "Jarvis 正在编排日程"), "executor": ("tool", "Jarvis 正在执行操作"), "librarian": ("tool", "Jarvis 正在检索知识"), "analyst": ("thinking", "Jarvis 正在分析信息"), } stage, label = stage_map.get(event_name, ("thinking", "Jarvis 正在思考")) yield self._build_progress_event(stage, label, agent=event_name, step=label) elif kind == "on_tool_start": yield self._build_progress_event( "tool", f"Jarvis 正在调用工具 {event_name}", agent="executor", tool_name=event_name, step=f"正在执行 {event_name}", ) elif kind == "on_tool_end": tool_result = data.get("output") step = f"已完成 {event_name}" if isinstance(tool_result, str) and len(tool_result) > 0: step = tool_result[:100] yield self._build_progress_event( "tool", f"工具 {event_name} 已完成", agent="executor", tool_name=event_name, step=step, ) elif kind == "on_chat_model_stream": chunk = data.get("chunk") content = getattr(chunk, "content", "") if chunk else "" if content: collected += content yield {"type": "chunk", "content": content} elif kind == "on_chain_end" and event_name == "create_agent_graph": # 最终输出通常在这里 output = data.get("output") if isinstance(output, dict) and "final_response" in output: final_resp = output["final_response"] # 如果还没流式输出完整，补全它 if final_resp and not collected: collected = final_resp yield {"type": "chunk", "content": collected} except Exception as e: if _is_streaming_rejection_error(e, user_llm_config) and not collected: yield self._build_progress_event("responding", "Jarvis 正在生成回复", agent="master", step="fallback") try: result_state = await graph.ainvoke(state) fallback_content = result_state.get("final_response") or str(result_state.get("messages", [AIMessage(content="")])[-1].content) collected = str(fallback_content) yield {"type": "chunk", "content": collected} except Exception as fallback_error: logger.exception("llm_sync_fallback_failed") yield {"type": "error", "error": "模型服务暂不可用。"} else: logger.exception("agent_streaming_failed") yield {"type": "error", "error": str(e)} finally: clear_current_user() asyncio.create_task(self._try_auto_summarize_background(user_id, conversation_id)) if collected: try: async with async_session() as session: result2 = await session.execute(select(Message).where(Message.id == assistant_msg.id)) msg = result2.scalar_one_or_none() if msg: msg.content = collected await session.commit() except Exception: logger.exception("save_assistant_message_failed") return conversation_id, assistant_msg.id, run_agent() async def chat_simple( self, user_id: str, message: str, conversation_id: str | None = None, file_ids: list[str] | None = None, model_name: str | None = None, ) -> tuple[str, str, str, str | None]: """ 简单同步版对话 """ user_llm_config = await self._get_user_llm_config(user_id, model_name) model_name_used = model_name if user_llm_config: model_name_used = user_llm_config.get("name", model_name) if not conversation_id: conv = Conversation(user_id=user_id, title=message[:50]) self.db.add(conv) await self.db.commit() await self.db.refresh(conv) conversation_id = conv.id user_msg = Message(conversation_id=conversation_id, role="user", content=message) self.db.add(user_msg) memory_ctx = await memory_service.build_memory_context(self.db, user_id, conversation_id, message) set_current_user(user_id) try: graph = get_agent_graph() state = initial_state(user_id, conversation_id) state.update({ "messages": [HumanMessage(content=message)], "memory_context": memory_ctx, "current_datetime_context": datetime.now(UTC).isoformat(), "user_llm_config": user_llm_config, }) result_state = await graph.ainvoke(state) response_content = result_state.get("final_response") or str(result_state.get("messages", [AIMessage(content="")])[-1].content) except Exception as e: logger.exception("agent_chat_simple_failed") response_content = "抱歉，发生错误。" finally: clear_current_user() assistant_msg = Message( conversation_id=conversation_id, role="assistant", content=response_content, model=model_name_used or "jarvis", ) self.db.add(assistant_msg) await self.db.commit() return conversation_id, assistant_msg.id, response_content, model_name_used