""" 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) def _coerce_event_text(content: Any) -> str: if isinstance(content, str): return content if isinstance(content, list): parts: list[str] = [] for item in content: if isinstance(item, str): parts.append(item) elif isinstance(item, dict): text = item.get("text") if isinstance(text, str): parts.append(text) return "".join(parts) return str(content) if content else "" _CONTINUITY_STATE_VERSION = 1 _CONTINUITY_SNAPSHOT_FIELDS = ( "turn_context", "routing_decision", "continuity_state", "pending_action", "last_completed_action", "clarification_context", "tool_outcomes", "pending_tasks", "completed_tasks", "created_entities", "current_agent", "next_step", "agent_trace", ) def _build_continuity_snapshot(state: dict[str, Any]) -> dict[str, Any] | None: snapshot = { field: state.get(field) for field in _CONTINUITY_SNAPSHOT_FIELDS if state.get(field) is not None } if not snapshot: return None return { "version": _CONTINUITY_STATE_VERSION, "state": snapshot, } def _extract_continuity_snapshot(payload: Any) -> dict[str, Any] | None: if isinstance(payload, list): for item in payload: snapshot = _extract_continuity_snapshot(item) if snapshot: return snapshot return None if not isinstance(payload, dict): return None if payload.get("kind") != "agent_continuity_state": return None if payload.get("version") != _CONTINUITY_STATE_VERSION: return None state = payload.get("state") if isinstance(state, dict): return state return None 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 [], } def _build_current_datetime_context(self) -> tuple[str, dict[str, str]]: now_utc = datetime.now(UTC) reference = { "current_time_iso": now_utc.isoformat(), "current_date_iso": now_utc.date().isoformat(), } context = ( "【当前时间】\n" f"- current_time_utc: {reference['current_time_iso']}\n" f"- current_date_utc: {reference['current_date_iso']}\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。" ) return context, reference 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""" user = await self.db.get(User, user_id) 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 _load_continuity_snapshot(self, conversation: Conversation) -> dict[str, Any] | None: snapshot = _extract_continuity_snapshot(conversation.agent_state) if snapshot: return snapshot result = await self.db.execute( select(Message) .where(Message.conversation_id == conversation.id, Message.role == "assistant") .order_by(Message.created_at.desc()) ) for message in result.scalars(): snapshot = _extract_continuity_snapshot(message.attachments) if snapshot: return snapshot return None async def _build_agent_state( self, *, user_id: str, conversation: Conversation, full_message: str, memory_context: str | None, current_datetime_context: str, current_datetime_reference: dict[str, str], user_llm_config: dict | None, ) -> dict[str, Any]: state = initial_state(user_id, conversation.id) state.update({ "messages": [HumanMessage(content=full_message)], "memory_context": memory_context, "current_datetime_context": current_datetime_context, "current_datetime_reference": current_datetime_reference, "user_llm_config": user_llm_config, }) previous_snapshot = await self._load_continuity_snapshot(conversation) if previous_snapshot: state.update(previous_snapshot) state["messages"] = [HumanMessage(content=full_message)] return state 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, Conversation.user_id == user_id, ) ) conv = result.scalar_one_or_none() if conv is None: raise ValueError("会话不存在或无权访问") 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_assistant_event_payload(content: str) -> dict[str, Any]: return { "source_type": "conversation", "source_id": conversation_id, "event_type": "message_created", "title": "Assistant message", "content_summary": content[:500], "raw_excerpt": content[:2000], "metadata_": {"role": "assistant"}, "importance_signal": 0.8, } async def run_agent(): collected = "" state: dict[str, Any] | None = None set_current_user(user_id) try: graph = get_agent_graph() current_datetime_context, current_datetime_reference = self._build_current_datetime_context() state = await self._build_agent_state( user_id=user_id, conversation=conv, full_message=full_message, memory_context=memory_ctx, current_datetime_context=current_datetime_context, current_datetime_reference=current_datetime_reference, 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") 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 = _coerce_event_text(getattr(chunk, "content", "") if chunk else "") if content: collected += content yield {"type": "chunk", "content": content} elif kind == "on_chain_end": output = data.get("output") final_resp = None if isinstance(output, dict): state.update(output) final_resp = output.get("final_response") if final_resp: final_text = str(final_resp) if final_text != collected: collected = final_text yield {"type": "chunk", "content": final_text} elif kind == "on_chat_model_end": output = data.get("output") final_content = _coerce_event_text(getattr(output, "content", "") if output else "") if final_content: final_text = final_content if final_text != collected: collected = final_text yield {"type": "chunk", "content": final_text} 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) if isinstance(result_state, dict): state.update(result_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: logger.exception("llm_sync_fallback_failed") safe_error = "模型服务暂不可用，请稍后再试。" yield {"type": "error", "error": safe_error} collected = f"抱歉，发生错误: {safe_error}" yield {"type": "chunk", "content": collected} else: logger.exception("agent_streaming_failed") if not collected: safe_error = "模型服务暂不可用，请稍后再试。" yield {"type": "error", "error": safe_error} collected = f"抱歉，发生错误: {safe_error}" yield {"type": "chunk", "content": collected} else: yield {"type": "error", "error": str(e)} finally: clear_current_user() try: if collected: assistant_msg.content = collected continuity_snapshot = _build_continuity_snapshot(state or {}) assistant_msg.attachments = ([{ "kind": "agent_continuity_state", **continuity_snapshot, }] if continuity_snapshot else None) conv.agent_state = continuity_snapshot await BrainService(self.db).create_event( user_id, **_build_assistant_event_payload(collected), ) await self.db.commit() await self.db.refresh(assistant_msg) except Exception: logger.exception("save_assistant_message_failed") asyncio.create_task(self._try_auto_summarize_background(user_id, conversation_id)) 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 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) if conversation_id: result = await self.db.execute( select(Conversation).where( Conversation.id == conversation_id, Conversation.user_id == user_id, ) ) conv = result.scalar_one_or_none() if conv is None: raise ValueError("会话不存在或无权访问") 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 user_msg = Message(conversation_id=conversation_id, role="user", content=message) self.db.add(user_msg) assistant_msg = Message( conversation_id=conversation_id, role="assistant", content="", model=model_name_used or "jarvis", attachments=None, ) self.db.add(assistant_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, ) 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() current_datetime_context, current_datetime_reference = self._build_current_datetime_context() state = await self._build_agent_state( user_id=user_id, conversation=conv, full_message=message, memory_context=memory_ctx, current_datetime_context=current_datetime_context, current_datetime_reference=current_datetime_reference, 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() brain_service = BrainService(self.db) await brain_service.create_event( user_id, source_type="conversation", source_id=conversation_id, event_type="message_created", title="Assistant message", content_summary=response_content[:500], raw_excerpt=response_content[:2000], metadata_={"role": "assistant"}, importance_signal=0.8, ) assistant_msg.content = response_content continuity_snapshot = _build_continuity_snapshot(result_state) if 'result_state' in locals() else None assistant_msg.attachments = ([{ "kind": "agent_continuity_state", **continuity_snapshot, }] if continuity_snapshot else None) conv.agent_state = continuity_snapshot await self.db.commit() await self.db.refresh(assistant_msg) return conversation_id, assistant_msg.id, response_content, model_name_used