import os import dotenv import datetime from pathlib import Path from langchain.agents import create_agent, AgentState from langchain_openai import ChatOpenAI from langchain_core.messages import SystemMessage, HumanMessage, BaseMessage from tools.tool_factory import get_all_tools from langchain_core.runnables import RunnableConfig from langchain.agents.middleware import before_model from langgraph.runtime import Runtime from typing import Any, List, Sequence from langchain.messages import RemoveMessage from langgraph.graph.message import REMOVE_ALL_MESSAGES import sqlite3 from config.settings import settings dotenv.load_dotenv() def create_system_prompt( backend_url: str = "", token: str = "", username: str = "default" ) -> str: auth_status = "已认证" if token else "未认证" backend_available = "API可用" if backend_url and token else "仅知识库" system_prompt = f"""小龙助手（龙嘉软件）- 用户:{username} 认证:{auth_status} 服务:{backend_available} 职责：ERP问题解答，按用户语言回答。 工作流： 1. 分析问题意图，提取模块关键词 2. {"优先知识库搜索，需要时调用API" if token else "仅使用知识库搜索"} 3. 关键词要精准，避免无意义词 回答规则： - 知识库优先，找不到时提示"正在学习该问题" - {"需要个人数据时验证认证状态" if backend_url else "仅提供知识库支持"} - 保护隐私，专业准确 {"- 后端地址: " + backend_url if backend_url else ""} {"- API用户的认证令牌: " + token if token else ""} 时间：{datetime.datetime.now().strftime("%m-%d %H:%M")} """ return system_prompt def get_day_number(date=None): """获取日期编号 (YYYYMMDD 格式)""" if date is None: date = datetime.datetime.now() return date.strftime("%Y%m%d") # 格式: 20251229 def get_sqlite_checkpointer(): """创建按天分割的SQLite检查点保存器""" try: from langgraph.checkpoint.sqlite import SqliteSaver # 获取当前日期编号 current_day = get_day_number() # 数据库文件存放目录 project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) base_dir = os.path.join(project_root, "data", "checkpoints") os.makedirs(base_dir, exist_ok=True) # 数据库文件名格式: checkpoints_20251229.db db_filename = f"checkpoints_{current_day}.db" db_path = os.path.join(base_dir, db_filename) # checkpointer = SqliteSaver.from_conn_string(db_path) conn = sqlite3.connect(db_path, check_same_thread=False) conn.execute("PRAGMA wal_autocheckpoint=500") # 2MB 就提交 conn.execute("PRAGMA journal_size_limit=52428800") # 最大 50MB checkpointer = SqliteSaver(conn) return checkpointer except Exception as e: print(f"❌❌ 创建 SQLite 检查器失败: {e}") import traceback traceback.print_exc() # 回退到内存保存器 from langgraph.checkpoint.memory import InMemorySaver print("⚠️ 使用 InMemorySaver 作为回退") return InMemorySaver() def cleanup_old_checkpoints(max_days=7): """清理超过指定天数的旧检查点文件（可选功能）""" try: project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) base_dir = os.path.join(project_root, "data", "checkpoints") if not os.path.exists(base_dir): return # 获取当前日期 current_date = datetime.datetime.now() # 遍历目录中的所有.db文件 for filename in os.listdir(base_dir): if filename.startswith("checkpoints_") and filename.endswith(".db"): try: print(f"检查旧检查点文件: {filename}") # 提取日期 (checkpoints_day_20251229.db -> 20251229) date_str = filename.replace("checkpoints_day_", "").replace( ".db", "" ) file_date = datetime.datetime.strptime(date_str, "%Y%m%d") # 计算天数差 days_diff = (current_date - file_date).days # 删除超过 max_days 天的旧数据 if days_diff > max_days: file_path = os.path.join(base_dir, filename) os.remove(file_path) print(f"🧹🧹 清理旧检查点文件: {filename} (超过 {max_days} 天)") except (ValueError, IndexError): # 文件名不符合预期，跳过 continue except Exception as e: print(f"⚠️ 清理旧检查点失败: {e}") # 创建agent def create_langchain_agent( backend_url: str = "", token: str = "", username: str = "default", thread_id: str = "default", ): llm = ChatOpenAI( model=settings.LLM_MODEL, temperature=settings.LLM_TEMPERATURE, api_key=settings.DEEPSEEK_API_KEY, base_url=settings.DEEPSEEK_BASE_URL, max_tokens=settings.LLM_MAX_TOKENS, ) tools = get_all_tools() # 添加调试信息 print(f"🔧🔧🔧🔧 Agent 创建调试信息:") print(f" - 用户: {username}") print(f" - Thread ID: {thread_id}") print(f" - 后端地址: {backend_url}") print(f" - Token: {'已提供' if token else '未提供'}") print(f" - 工具数量: {len(tools)}") for i, tool in enumerate(tools): print(f" - 工具 {i+1}: {tool.name}") # 获取动态的system_prompt system_prompt = create_system_prompt(backend_url, token, username) def simple_turn_based_trim( messages: Sequence[BaseMessage], keep_turns: int = 3, system_message: BaseMessage = None, ) -> List[BaseMessage]: """ 修正版：按完整对话轮次修剪消息 每轮对话从Human开始，到下一个Human之前结束 """ if not messages: return [] # 分离系统消息（始终保留） system_messages = [] other_messages = [] for msg in messages: if ( isinstance(msg, SystemMessage) or getattr(msg, "type", None) == "system" or getattr(msg, "role", None) == "system" or msg.__class__.__name__ == "SystemMessage" ): system_messages.append(msg) else: other_messages.append(msg) if len(other_messages) <= 1: return system_messages + other_messages # 找出所有Human消息的位置 human_indices = [] for i, msg in enumerate(other_messages): if ( isinstance(msg, HumanMessage) or getattr(msg, "type", None) == "human" or getattr(msg, "role", None) == "user" ): human_indices.append(i) # 如果Human消息不足keep_turns轮，返回所有 if not human_indices or len(human_indices) <= keep_turns: return system_messages + other_messages # 计算起始索引 start_idx = human_indices[-keep_turns] # 获取要保留的消息 preserved_messages = other_messages[start_idx:] # 4. 返回从该索引开始的所有消息 result = system_messages + preserved_messages # print(f"修剪后消息数: {len(result)}") return result @before_model def trim_messages(state: AgentState, runtime: Runtime) -> dict[str, Any] | None: """Keep only the last few messages to fit context window.""" messages = state["messages"] if len(messages) <= 3: return None # No changes needed # 保留最后4轮对话 trimmed_messages = simple_turn_based_trim(messages, keep_turns=4) return {"messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES)] + trimmed_messages} # 使用SQLiteSaver（按天分割） checkpointer = get_sqlite_checkpointer() # print(f"打印检查点保存器: {checkpointer}") # 可选：清理旧检查点（可配置为定期执行） if os.getenv("AUTO_CLEANUP", "false").lower() == "true": cleanup_old_checkpoints(max_days=7) # 保留最近7天数据 agent = create_agent( llm, tools, checkpointer=checkpointer, system_prompt=system_prompt, middleware=[trim_messages], ) return agent