0270e17a76
1. 修复 context_rewrite 导致的 API 错误 - 删除错误的 tool 结果消息添加逻辑 - 添加 context_compressed 标记让 AI 识别压缩结果 - 将 context_rewrite 记录到 tool_calls 以便 TUI 显示 2. 优化系统提示词 - 添加强制触发条件：每5次记忆工具调用必须调用 context_rewrite - 强化指代词处理规则：必须通过工作记忆链解析 - 扩展时间指代词：添加刚刚 - 更新执行检查清单 Generated with CodeArts Agent
470 lines
17 KiB
Python
470 lines
17 KiB
Python
import threading
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import queue
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import time
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import json
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import os
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from pathlib import Path
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from typing import Any, Dict, Optional
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from dataclasses import dataclass, field
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from enum import Enum
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from .embedded_db import EmbeddedGraphDB
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class PacketType(Enum):
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PROCESS_MESSAGE = "process_message"
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EXECUTE_TOOL = "execute_tool"
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GET_STATUS = "get_status"
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GET_SETTINGS = "get_settings" # 合并：获取 api_config + tool_limits
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SET_SETTINGS = "set_settings" # 合并：设置 api_config + tool_limits
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GET_HISTORY = "get_history"
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SAVE_HISTORY = "save_history"
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SHUTDOWN = "shutdown"
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@dataclass
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class Packet:
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id: str
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type: PacketType
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body: Dict[str, Any]
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response_queue: Optional[queue.Queue] = field(default=None)
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created_at: float = field(default_factory=time.time)
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@dataclass
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class PacketResponse:
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id: str
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success: bool
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data: Any = None
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error: Optional[str] = None
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class BackendServer:
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DEFAULT_CONFIG_PATH = Path.home() / ".trulymem" / "config.json"
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def __init__(self, db_path: str = "graph_memory.db", use_embedded_db: bool = True, config_file: str = None):
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self._db_path = db_path
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self._use_embedded_db = use_embedded_db
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self._config_file = Path(config_file) if config_file else self.DEFAULT_CONFIG_PATH
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self._graph = None
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self._client = None
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self._tool_limiter = None
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self._input_queue: queue.Queue[Packet] = queue.Queue()
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self._response_queues: Dict[str, queue.Queue] = {}
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self._running = False
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self._thread: Optional[threading.Thread] = None
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self._lock = threading.Lock()
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self._config = {"api_key": "", "base_url": "https://api.deepseek.com", "model": "deepseek-chat"}
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self._tool_limits = {
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"persona_update_max": 1,
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"task_update_max": 5,
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"memory_query_max": 20,
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"memory_update_max": 10,
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}
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self._message_history: list = []
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def start(self, api_key: str = "", base_url: str = "https://api.deepseek.com", model: str = "deepseek-chat") -> None:
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if self._running:
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return
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self._load_config()
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if api_key:
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self._config["api_key"] = api_key
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if base_url:
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self._config["base_url"] = base_url
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if model:
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self._config["model"] = model
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self._init_graph()
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self._tool_limiter = self._create_tool_limiter()
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if self._config["api_key"]:
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from .graph_client import GraphMemoryClient
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self._client = GraphMemoryClient(
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api_key=self._config["api_key"],
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base_url=self._config["base_url"],
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model=self._config.get("model", "deepseek-chat"),
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graph=self._graph
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)
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self._running = True
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self._thread = threading.Thread(target=self._run_loop, daemon=True)
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self._thread.start()
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def _load_config(self) -> None:
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if self._config_file.exists():
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try:
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with open(self._config_file, 'r') as f:
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saved = json.load(f)
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self._config.update(saved)
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for key in self._tool_limits:
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if key in saved:
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self._tool_limits[key] = saved[key]
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except Exception:
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pass
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def _save_config(self) -> None:
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self._config_file.parent.mkdir(parents=True, exist_ok=True)
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saved_data = {**self._config, **self._tool_limits}
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with open(self._config_file, 'w') as f:
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json.dump(saved_data, f, indent=2)
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def _create_tool_limiter(self):
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from .tool_limiter import ToolLimiter, ToolLimits
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limits = ToolLimits(
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persona_update_max=self._tool_limits.get("persona_update_max", 1),
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task_update_max=self._tool_limits.get("task_update_max", 5),
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memory_query_max=self._tool_limits.get("memory_query_max", 20),
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memory_update_max=self._tool_limits.get("memory_update_max", 10),
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)
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return ToolLimiter(limits)
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def _init_graph(self) -> None:
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if self._use_embedded_db:
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self._graph = EmbeddedGraphDB(db_path=self._db_path)
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else:
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from .graph_client import Neo4jGraph
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self._graph = Neo4jGraph(
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uri="bolt://localhost:7687",
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user="neo4j",
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password="graphmemory123"
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)
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def _run_loop(self) -> None:
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while self._running:
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try:
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packet = self._input_queue.get(timeout=0.1)
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except queue.Empty:
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continue
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self._process_packet(packet)
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def _process_packet(self, packet: Packet) -> None:
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response_body = {"error": "not implemented"}
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try:
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if packet.type == PacketType.PROCESS_MESSAGE:
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response_body = self._handle_process_message(packet.body)
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elif packet.type == PacketType.EXECUTE_TOOL:
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response_body = self._handle_execute_tool(packet.body)
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elif packet.type == PacketType.GET_STATUS:
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response_body = self._handle_get_status()
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elif packet.type == PacketType.GET_SETTINGS:
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response_body = self._handle_get_settings()
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elif packet.type == PacketType.SET_SETTINGS:
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response_body = self._handle_set_settings(packet.body)
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elif packet.type == PacketType.GET_HISTORY:
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response_body = self._handle_get_history()
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elif packet.type == PacketType.SAVE_HISTORY:
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response_body = self._handle_save_history(packet.body)
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elif packet.type == PacketType.SHUTDOWN:
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self._running = False
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response_body = {"success": True, "status": "shutdown"}
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if "success" not in response_body:
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response_body["success"] = True
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except Exception as e:
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response_body["success"] = False
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response_body["error"] = str(e)
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self._send_response(packet.id, PacketResponse(
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id=packet.id,
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success=response_body.get("success", False),
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data=response_body if response_body.get("success") else None,
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error=response_body.get("error")
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))
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def _handle_process_message(self, body: Dict) -> Dict:
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from .tool_executor import execute_tool
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user_input = body.get("user_input", "")
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if not self._client:
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return {"success": False, "error": "API Key 未配置", "content": "请先配置 API Key"}
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self._graph.save_chat_records([{"role": "user", "content": user_input}])
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self._tool_limiter.reset()
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messages_history = [{"role": "user", "content": user_input}]
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response = self._client.send_message_with_history(messages_history)
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message = response.choices[0].message
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tool_calls = []
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accumulated_content = ""
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rejected_tools = []
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while message.tool_calls:
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if message.content:
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accumulated_content += message.content + "\n\n"
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assistant_msg = {
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"role": "assistant",
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"content": message.content,
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"tool_calls": [
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{
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"id": tc.id,
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"type": "function",
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"function": {
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"name": tc.function.name,
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"arguments": tc.function.arguments
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}
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} for tc in message.tool_calls
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]
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}
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messages_history.append(assistant_msg)
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current_tool_results = []
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for tool_call in message.tool_calls:
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args = json.loads(tool_call.function.arguments)
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allowed, reason = self._tool_limiter.can_call(tool_call.function.name, args)
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if not allowed:
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rejected_tools.append((tool_call.function.name, reason))
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result = f"工具调用被拒绝: {reason}"
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tool_result_msg = {
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"role": "tool",
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"tool_call_id": tool_call.id,
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"content": result
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}
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current_tool_results.append(tool_result_msg)
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continue
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self._tool_limiter.record_call(tool_call.function.name, args)
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if tool_call.function.name == "context_rewrite":
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result = execute_tool(self._graph, tool_call.function.name, args)
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result_data = json.loads(result)
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# 记录到 tool_calls，让 TUI 显示这个工具调用
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tool_calls.append({
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"name": tool_call.function.name,
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"arguments": args,
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"result": result
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})
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if result_data.get("status") == "success":
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user_msg = messages_history[0]
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# 添加特殊标记，让 AI 知道这是上下文压缩的结果
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compressed_content = f"<context_compressed>\n{result_data['summary']}\n</context_compressed>"
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messages_history[:] = [
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user_msg,
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{"role": "assistant", "content": compressed_content}
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]
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# context_rewrite 压缩上下文后，不需要添加 tool 结果消息
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# 因为 messages_history 已经被重写为压缩后的状态
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continue
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result = execute_tool(self._graph, tool_call.function.name, args)
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tool_calls.append({
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"name": tool_call.function.name,
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"arguments": args,
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"result": result
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})
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tool_result_msg = {
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"role": "tool",
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"tool_call_id": tool_call.id,
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"content": result
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}
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current_tool_results.append(tool_result_msg)
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messages_history.extend(current_tool_results)
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response = self._client.send_message_with_history(messages_history)
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message = response.choices[0].message
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final_content = message.content or ""
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content = accumulated_content + final_content if accumulated_content else final_content
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if not content:
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content = "(无回复)"
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if tool_calls:
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tool_names = [tc["name"] for tc in tool_calls]
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content = f"已执行工具: {', '.join(tool_names)}\n\n{content}"
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if rejected_tools:
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rejected_info = "\n".join([f"{name}: {reason}" for name, reason in rejected_tools])
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content += f"\n\n部分工具调用被限制:\n{rejected_info}"
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content += f"\n\n工具调用统计:\n{self._tool_limiter.get_summary()}"
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self._graph.save_chat_records([{"role": "assistant", "content": content}])
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return {
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"success": True,
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"content": content,
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"tool_calls": tool_calls,
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"rejected_tools": rejected_tools
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}
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def _handle_execute_tool(self, body: Dict) -> Dict:
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from .tool_executor import execute_tool
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try:
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tool_name = body.get("tool_name")
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arguments = body.get("arguments", {})
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result = execute_tool(self._graph, tool_name, arguments)
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return {"success": True, "result": result}
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except Exception as e:
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return {"success": False, "error": str(e)}
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def _handle_get_status(self) -> Dict:
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return {
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"running": self._running,
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"config": self._config,
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"graph_initialized": self._graph is not None,
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"client_initialized": self._client is not None
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}
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def _handle_get_settings(self) -> Dict:
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return {
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"api_config": self._config.copy(),
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"tool_limits": self._tool_limits.copy()
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}
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def _handle_set_settings(self, body: Dict) -> Dict:
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api_config = body.get("api_config", {})
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tool_limits = body.get("tool_limits", {})
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api_key = api_config.get("api_key", "")
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base_url = api_config.get("base_url", "https://api.deepseek.com")
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model = api_config.get("model", "deepseek-chat")
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self.update_config(api_key, base_url, model)
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limits_keys = [
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"persona_update_max",
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"task_update_max",
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"memory_query_max", "memory_update_max"
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]
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for key in limits_keys:
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if key in tool_limits:
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value = int(tool_limits[key])
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if value < 1:
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return {"success": False, "error": f"{key} must be >= 1, got {value}"}
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self._tool_limits[key] = value
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self._tool_limiter = self._create_tool_limiter()
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self._save_config()
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return {"status": "settings_updated"}
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def _handle_get_history(self) -> Dict:
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history = self._graph.get_chat_records(limit=500)
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return {"history": history}
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def _handle_save_history(self, body: Dict) -> Dict:
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messages = body.get("messages", [])
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if not messages:
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self._graph.clear_chat_records()
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return {"status": "history_cleared"}
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result = self._graph.save_chat_records(messages)
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return {"status": "history_saved"}
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def _send_response(self, request_id: str, response: PacketResponse) -> None:
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with self._lock:
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q = self._response_queues.pop(request_id, None)
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if q:
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q.put(response)
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def send(self, packet: Packet) -> Packet:
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resp_q = queue.Queue()
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with self._lock:
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self._response_queues[packet.id] = resp_q
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self._input_queue.put(packet)
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try:
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response = resp_q.get(timeout=300.0)
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return Packet(
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id=response.id,
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type=packet.type,
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body={
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"success": response.success,
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"data": response.data,
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"error": response.error
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}
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)
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except queue.Empty:
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return Packet(
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id=packet.id,
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type=packet.type,
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body={"success": False, "error": "timeout"}
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)
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finally:
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with self._lock:
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self._response_queues.pop(packet.id, None)
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def process_message(self, user_input: str) -> Dict[str, Any]:
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packet = Packet(
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id=f"{time.time()}",
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type=PacketType.PROCESS_MESSAGE,
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body={"user_input": user_input}
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)
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response = self.send(packet)
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return response.body
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def execute_tool(self, tool_name: str, arguments: Dict[str, Any]) -> Dict[str, Any]:
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packet = Packet(
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id=f"{time.time()}",
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type=PacketType.EXECUTE_TOOL,
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body={"tool_name": tool_name, "arguments": arguments}
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)
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response = self.send(packet)
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return response.body
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def update_config(self, api_key: str, base_url: str = "https://api.deepseek.com", model: str = "deepseek-chat") -> None:
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with self._lock:
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self._config["api_key"] = api_key
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self._config["base_url"] = base_url
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self._config["model"] = model
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if api_key and self._graph:
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from .graph_client import GraphMemoryClient
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self._client = GraphMemoryClient(
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api_key=api_key,
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base_url=base_url,
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model=model,
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graph=self._graph
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)
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def get_config(self) -> Dict[str, str]:
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return self._config.copy()
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def save_message_history(self, messages: list) -> None:
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self._message_history = messages
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def get_message_history(self) -> list:
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return self._message_history.copy()
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def shutdown(self) -> None:
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if not self._running:
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return
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packet = Packet(
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id=f"{time.time()}",
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type=PacketType.SHUTDOWN,
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body={}
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)
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self.send(packet)
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if self._thread:
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self._thread.join(timeout=2.0)
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if self._graph:
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self._graph.close()
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self._graph = None
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self._running = False |