修复上下文编排问题、优化子agent消息队列展示UI
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+20
-3
@@ -331,6 +331,21 @@ function stopFastAPI() {
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}
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// ==================== 窗口管理 ====================
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// 缓存清理标记：仅首次运行清一次（应用页资源经 FastAPI no-cache 头加载，
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// 后续启动无需反复清磁盘缓存，省掉每次启动的等待）
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const CACHE_CLEARED_FLAG = () => path.join(app.getPath('userData'), '.cache-cleared');
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/** 返回 true 表示本次应清理缓存（首次运行且已写标记）；标记写入失败时保持旧行为 */
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function ensureInitialCacheClear() {
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try {
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if (fs.existsSync(CACHE_CLEARED_FLAG())) return false;
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fs.writeFileSync(CACHE_CLEARED_FLAG(), String(Date.now()), 'utf-8');
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return true;
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} catch {
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return true;
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}
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}
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async function createMainWindow(themeMode) {
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themeMode = themeMode || getThemeMode();
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@@ -350,9 +365,11 @@ async function createMainWindow(themeMode) {
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show: false,
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});
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// 清除缓存后再加载，确保获取最新 JS/CSS
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await mainWindow.webContents.session.clearCache();
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await mainWindow.webContents.session.clearStorageData({ storages: ['caches', 'serviceworkers'] });
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// 仅首次运行清除缓存（本地文件与应用页资源不走 HTTP 缓存，无需每次清理）
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if (ensureInitialCacheClear()) {
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await mainWindow.webContents.session.clearCache();
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await mainWindow.webContents.session.clearStorageData({ storages: ['caches', 'serviceworkers'] });
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}
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// 先显示本地过渡页：毛玻璃 + 中央图标（无任何按钮），主题随 theme_config.json 变化；
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// FastAPI 就绪后再切到应用页；应用页自带的同款玻璃覆盖层会无缝接手
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@@ -17,7 +17,7 @@ from agent.core.config_manager import load_agent_configs, load_models, AgentConf
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from agent.core.graph import build_agent_graph
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from agent.core.runtime import execute_agent as execute_runtime_agent
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from agent.core.runtime import AgentRuntime
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from agent.tools import get_tools
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from agent.tools import get_tools, ensure_tool_registered
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AgentMode = Literal["agent", "plan", "cron"]
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@@ -75,6 +75,10 @@ def _build_agent_runtime(cfg: AgentConfig) -> AgentRuntime:
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# 2. 获取所有可用工具并按配置过滤
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all_tools = get_tools()
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if cfg.tools:
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# 动态注册：配置显式引用但未静态注册的按需工具（如 rag_tool）
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# 在此现场加载（其 langchain/chromadb 重依赖不占用启动时间）
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for name in cfg.tools:
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ensure_tool_registered(name)
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agent_tools = [t for t in all_tools if t.name in cfg.tools]
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else:
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agent_tools = all_tools
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@@ -99,7 +99,6 @@
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"RAG_CHUNK_OVERLAP": "50",
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"GROUNDING_WIDTH": "1000",
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"GROUNDING_HEIGHT": "1000",
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"LOOP_DETECT_REPEATED_TOOL_WARN": "3",
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"SEND_FILE_SIZE_LIMIT": "30",
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"STORAGE_BACKEND": "json",
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"CRON_TIME_PERIOD_MINUTES": 30,
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@@ -453,7 +453,6 @@ def _default_env_config() -> dict:
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"RAG_CHUNK_OVERLAP": "50",
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"GROUNDING_WIDTH": "1000",
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"GROUNDING_HEIGHT": "1000",
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"LOOP_DETECT_REPEATED_TOOL_WARN": "3",
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"SEND_FILE_SIZE_LIMIT": "30",
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# 浏览器控制工具持久 Profile 目录（空 = 每次启动一次性临时 Profile）
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"BROWSER_PROFILE_DIR": "",
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@@ -511,9 +510,13 @@ def save_env_config(config: dict) -> bool:
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# ---------- 获取所有已注册工具的基础信息 ----------
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def get_registered_tool_names() -> list[str]:
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"""返回所有可用工具名称（含数据库工具）。"""
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from agent.tools import get_all_available_tool_names
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return get_all_available_tool_names()
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"""返回所有可选工具名称（静态注册 + 按需工具名，含数据库工具）。
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按需工具（如 rag_tool）仅返回名字供前端勾选，不触发其重依赖加载；
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实际构建 Agent 时如该工具在配置中被引用，才由 ensure_tool_registered 现场注册。
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"""
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from agent.tools import get_selectable_tool_names
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return get_selectable_tool_names()
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# ---------- GUI 显示器配置 ----------
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@@ -374,7 +374,8 @@ _SQLITE_SESSION_TABLE_DDLS = [
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session_id TEXT NOT NULL,
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turn_id TEXT NOT NULL,
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tool_calls TEXT NOT NULL,
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meta TEXT
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meta TEXT,
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assistant_units TEXT
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)
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""",
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"CREATE INDEX IF NOT EXISTS idx_sess_tc_turn ON session_tool_calls (session_id, turn_id)",
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@@ -418,7 +419,8 @@ _SQLITE_CRON_TABLE_DDLS = [
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task_id TEXT NOT NULL,
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turn_id TEXT NOT NULL,
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tool_calls TEXT NOT NULL,
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meta TEXT
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meta TEXT,
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assistant_units TEXT
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)
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""",
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"CREATE INDEX IF NOT EXISTS idx_task_tc_turn ON cron_tool_calls (task_id, turn_id)",
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@@ -1,173 +0,0 @@
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"""Agent 重复工具调用检测模块。
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系统定位:
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在 ReAct 循环的 agent 节点调用 LLM 前，检测连续相同工具调用的异常模式，
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提供 Level 1 响应：注入反思提示引导模型自我纠正（不再强制终止，Level 2 已移除）。
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配置来源:
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env_config.json 中的 LOOP_DETECT_REPEATED_TOOL_WARN 参数，可由前端动态修改。
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可扩展性：
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可增加更多循环模式检测器、自定义检测规则。
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"""
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from __future__ import annotations
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import hashlib
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import json
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from typing import Any
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from langchain_core.messages import AIMessage
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from agent.utils.tool_call_utils import normalize_tool_call, format_args_summary
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# ============================================================
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# 配置加载（每次检测时实时读取，支持前端热更新）
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# ============================================================
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def get_loop_config() -> dict:
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"""从 env_config.json 读取循环检测参数（每次调用实时读取）。"""
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try:
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from agent.core.config_manager import load_env_config
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env = load_env_config()
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except Exception:
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env = {}
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return {
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"repeated_tool_warn": int(env.get("LOOP_DETECT_REPEATED_TOOL_WARN", 3)),
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}
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# ============================================================
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# 工具调用历史分析
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# ============================================================
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def _tool_call_fingerprint(tc: dict) -> str:
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"""计算单个工具调用的指纹 (tool_name, args_hash)。
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输入:
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tc: 已 normalize 的工具调用 dict。
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输出:
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"tool_name:md5hash" 格式的指纹字符串。
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"""
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tc_name = tc.get("name", "") if isinstance(tc, dict) else getattr(tc, "name", "")
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tc_args = tc.get("args", {}) if isinstance(tc, dict) else getattr(tc, "args", {})
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args_str = json.dumps(tc_args, sort_keys=True, default=str)
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args_hash = hashlib.md5(args_str.encode()).hexdigest()[:12]
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return f"{tc_name}:{args_hash}"
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def get_recent_tool_calls_from_messages(
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messages: list,
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count: int = 10,
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) -> list[dict]:
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"""从消息列表中提取最近 N 个工具调用（含指纹）。
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输入:
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messages: 消息列表。
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count: 需要提取的最近工具调用数量。
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输出:
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[{"name": str, "args": dict, "fingerprint": str}, ...]，按时间正序排列。
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"""
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result = []
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for msg in messages:
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if isinstance(msg, AIMessage) and getattr(msg, "tool_calls", None):
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for tc in msg.tool_calls:
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tc_dict = normalize_tool_call(tc)
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fingerprint = _tool_call_fingerprint(tc_dict)
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result.append({
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"name": tc_dict.get("name", "unknown"),
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"args": tc_dict.get("args", {}),
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"fingerprint": fingerprint,
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})
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# 按时间正序，取最近 count 个
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return result[-count:] if len(result) > count else result
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def _compute_consecutive_same(recent_calls: list[dict]) -> int:
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"""计算从末尾向前连续相同的工具调用次数（基于指纹）。
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输入:
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recent_calls: 工具调用列表（时间正序）。
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输出:
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末尾连续相同指纹的次数。
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"""
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if not recent_calls:
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return 0
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last_fp = recent_calls[-1]["fingerprint"]
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count = 0
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for call in reversed(recent_calls):
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if call["fingerprint"] == last_fp:
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count += 1
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else:
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break
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return count
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def detect_repeated_tool_calls(
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messages: list,
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session_id: str = "",
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) -> tuple[int, str, str | None]:
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"""检测连续相同工具调用。
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输入:
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messages: 当前消息列表。
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session_id: 会话 ID。
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输出:
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(连续次数, 工具名, 参数摘要 | None)
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连续次数为 0 表示无重复。
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"""
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cfg = get_loop_config()
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warn_threshold = max(cfg["repeated_tool_warn"], 2)
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lookback = warn_threshold + 2
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recent = get_recent_tool_calls_from_messages(messages, count=lookback)
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consecutive = _compute_consecutive_same(recent)
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if consecutive < warn_threshold:
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return 0, "", None
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last_call = recent[-1] if recent else {}
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args_summary = format_args_summary(last_call.get("args", {}))
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return consecutive, last_call.get("name", "unknown"), args_summary
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# ============================================================
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# 反循环反思提示生成
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# ============================================================
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def build_loop_reflection_prompt(
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messages: list,
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session_id: str = "",
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) -> str | None:
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"""根据当前状态构建反循环反思提示（Level 1 响应）。
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输入:
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messages: 当前消息列表。
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session_id: 会话 ID。
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输出:
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反思提示文本；无需注入时返回 None。
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"""
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parts = []
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# 检查 1: 连续相同工具调用 (Level 1 - 警告)
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tool_count, tool_name, args_summary = detect_repeated_tool_calls(messages, session_id)
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cfg = get_loop_config()
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warn_threshold = max(cfg["repeated_tool_warn"], 2)
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if tool_count >= warn_threshold:
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parts.append(
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f"【循环警告】你已经连续 {tool_count} 次调用工具 `{tool_name}` "
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f"且参数基本相同（{args_summary}），但似乎没有取得实质进展。\n"
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f"请反思当前策略是否有效，考虑：\n"
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f"1. 更换工具或策略\n"
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f"2. 检查之前的工具返回结果是否有误\n"
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f"3. 如果确实无法推进，向用户说明当前困难并请求帮助"
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)
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if not parts:
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return None
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return "\n\n".join(parts)
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+34
-165
@@ -20,7 +20,6 @@ from typing import Any, Callable
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from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
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from agent.core.loop_detector import build_loop_reflection_prompt
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from agent.core.state import AgentState
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from agent.memory.system_prompt import SUB_AGENT_OOM_PROMPT
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from agent.core.llm import LLMRetryExhausted, is_timeout_error
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@@ -64,7 +63,8 @@ def _filter_short_term_memory(messages: list, trajectory_rounds: int) -> list:
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- 仅 process_tool_artifact 生成的合成图片 HumanMessage 按 trajectory_rounds 裁剪
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- 只对实际包含图片截图的轮次计数，无截图的轮次跳过不计数
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- 上一/几轮的反思提示词（reflection 标记）不进入本轮上下文
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- 带 tool_calls 的历史 AIMessage 仅保留工具调用，content 中的思考文本不进入上下文
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- 带 tool_calls 的 AIMessage 完整保留（content + reasoning_content + tool_calls
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三元组原样进入上下文，不做剥离/合成，保证前缀缓存一致）
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"""
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if trajectory_rounds <= 0:
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return list(messages)
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@@ -103,93 +103,7 @@ def _filter_short_term_memory(messages: list, trajectory_rounds: int) -> list:
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kept_indices.add(i)
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result = [messages[i] for i in sorted(kept_indices)]
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# 剥离历史模型思考文本：上一/几轮的思考不进入本轮上下文。
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# 带 tool_calls 的 AIMessage，其 content 仅是思维链，保留 tool_calls 即可
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# （思考/反思已由 extract_thought/extract_reflection 单独记录到 think 面板，
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# 并由 build_native_history_messages 以原生 AIMessage 形式注入）。
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cleaned: list = []
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for m in result:
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if isinstance(m, AIMessage) and getattr(m, "tool_calls", None) and getattr(m, "content", None):
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cleaned.append(m.model_copy(update={"content": ""}))
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else:
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cleaned.append(m)
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return cleaned
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def _build_dynamic_tail_prompt(messages: list, session_id: str = "") -> str:
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"""构建每轮追加到消息末尾的动态尾部提示（仅变化部分）。
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输入:
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messages: 当前消息列表（已过滤短期记忆），供循环检测使用。
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session_id: 会话 ID，供循环检测与 TodoList 状态重建使用。
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输出:
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动态尾部文本；无 TodoList 且无循环提醒时返回空串
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（此时不加任何占位提示，省 token）。
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系统定位:
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- 固定反思引导已移入系统提示词前缀（system_prompt._reflection_section），
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不再在节点中间构造固定提示；
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- 循环警告：思考关闭档位注入（Level 1 仅提醒，不强制终止）；
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- TodoList 状态：思考开/关均注入（事实状态，供模型跟踪计划进度）。
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"""
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from agent.utils.env_utils import thinking_enabled
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parts: list[str] = []
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# 思考关闭：循环警告（Level 1 仅提醒，不强制终止）
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if not thinking_enabled():
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loop_warning = build_loop_reflection_prompt(messages, session_id)
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if loop_warning:
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parts.append(loop_warning)
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# TodoList 状态：思考开/关均注入（事实状态，供模型跟踪计划进度）
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try:
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from agent.tools.todo_list import get_todo_status_text
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todo_status = get_todo_status_text(session_id)
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except Exception:
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todo_status = ""
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if todo_status:
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parts.append(todo_status)
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return "\n\n".join(parts)
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def _dynamic_tail_agent_key() -> str:
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"""动态尾部长期记忆的 agent 键：主 Agent 为 "main"，子 Agent 用其名称。
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主/子 Agent 的动态尾部各自隔离，避免子 Agent 覆盖主 Agent 的长期记忆。
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"""
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try:
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from agent.core.human_request import get_current_agent_name
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name = get_current_agent_name()
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except Exception:
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name = ""
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return "main" if name in ("", "__main__") else name
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def _persist_dynamic_tail(session_id: str, text: str) -> None:
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"""持久化当前轮动态尾部到 session_meta（长期记忆，供后续轮次注入查看）。"""
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if not session_id:
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return
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try:
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from agent.utils.agent_utils import update_session_dynamic_tail
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update_session_dynamic_tail(session_id, _dynamic_tail_agent_key(), text)
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except Exception:
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pass
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|
||||
def _load_dynamic_tail_history(session_id: str) -> list[str]:
|
||||
"""读取当前 Agent 的动态尾部长期记忆历史（时间正序，最新在末尾）。"""
|
||||
if not session_id:
|
||||
return []
|
||||
try:
|
||||
from agent.utils.agent_utils import get_session_dynamic_tail_history
|
||||
return get_session_dynamic_tail_history(session_id, _dynamic_tail_agent_key())
|
||||
except Exception:
|
||||
return []
|
||||
return [messages[i] for i in sorted(kept_indices)]
|
||||
|
||||
|
||||
# ---------- ReAct 循环内上下文压缩 ----------
|
||||
@@ -539,58 +453,6 @@ def maybe_compress_context(state: AgentState) -> dict:
|
||||
return {"_need_compress": False}
|
||||
|
||||
|
||||
def _inject_turn_thinking(messages: list[Any]) -> list[Any]:
|
||||
"""把本轮图消息中携带的思考/反思（reasoning_content / content）以合成消息插回上下文。
|
||||
|
||||
reasoning_content 不被 LLM API 回传（additional_kwargs 不在标准消息
|
||||
schema 内），跨步骤调用时模型会丢失自己的思考链；此处在其所属的
|
||||
工具调用 AIMessage 之前插入 ``[思考]`` / ``[反思]`` 合成消息
|
||||
（因果交错），使模型在后续步骤中仍能看到本轮已产生的思考与反思。
|
||||
|
||||
注意:
|
||||
- 仅注入到本次调用的 messages_for_llm，不进图状态，无副作用；
|
||||
- 思考与反思独立提取（extract_thought / extract_reflection），
|
||||
可同时存在（既有深度思考又有反思的通用情况），分别注入；
|
||||
- 思考文本与位置确定性构建（同一图状态下结果恒定），id 稳定，
|
||||
不破坏 LLM 前缀缓存；
|
||||
- trim_messages 已剥空 content 时反思自然跳过（思考存于
|
||||
additional_kwargs，不受影响）。
|
||||
"""
|
||||
out: list[Any] = []
|
||||
think_seq = 0
|
||||
reflect_seq = 0
|
||||
for m in messages:
|
||||
if isinstance(m, AIMessage) and getattr(m, "tool_calls", None):
|
||||
# 原生重建历史（hist_*）已是 AIMessage 格式：思考在
|
||||
# additional_kwargs.reasoning_content、反思在 content，
|
||||
# 由 llm._get_request_payload 以原生字段透传 → 不在此合成文本。
|
||||
# 图中当前轮响应同理：reasoning_content 随之透传后
|
||||
# [思考] 合成为重复内容，跳过；[反思] 保持原行为兜底。
|
||||
if not _msg_id(m).startswith(HISTORY_MSG_PREFIX):
|
||||
try:
|
||||
thought = extract_thought(m)
|
||||
reflection = extract_reflection(m)
|
||||
except Exception:
|
||||
thought = reflection = ""
|
||||
has_reasoning = bool((getattr(m, "additional_kwargs", None) or {}).get("reasoning_content"))
|
||||
if thought and not has_reasoning:
|
||||
out.append(HumanMessage(
|
||||
content=f"[思考] {thought}",
|
||||
additional_kwargs={"synthetic": True},
|
||||
id=f"turn_think_{think_seq}",
|
||||
))
|
||||
think_seq += 1
|
||||
if reflection:
|
||||
out.append(HumanMessage(
|
||||
content=f"[反思] {reflection}",
|
||||
additional_kwargs={"synthetic": True},
|
||||
id=f"turn_reflect_{reflect_seq}",
|
||||
))
|
||||
reflect_seq += 1
|
||||
out.append(m)
|
||||
return out
|
||||
|
||||
|
||||
def _invoke_llm_with_retry(model: Any, messages: list[Any], session_id: str = "") -> Any:
|
||||
"""调用已绑定工具的模型，LLM 服务超时时自动重试（默认 3 次，LLM_MAX_RETRIES 配置）。
|
||||
|
||||
@@ -647,12 +509,13 @@ def make_call_model_node(
|
||||
model_with_tools: Any,
|
||||
trajectory_rounds: int = 3,
|
||||
) -> Callable[[AgentState], dict]:
|
||||
"""创建带按思考档位注入提示 + 上下文中压缩的调用已绑定工具的 LLM 的图节点闭包。
|
||||
"""创建调用已绑定工具的 LLM 的图节点闭包（含上下文中压缩）。
|
||||
|
||||
每次调用 LLM 前按思考档位注入提示（思考关闭：反思引导 + TodoList 状态 +
|
||||
循环警告；思考开启：仅 TodoList 状态）；调用后提取模型产出的思考/反思内容
|
||||
（思考开启取深度思考，思考关闭取一句话反思），记录到 think store，
|
||||
供前端实时展示思维链。
|
||||
消息上下文 = 图内消息原样（AIMessage 三元组 content/reasoning_content/
|
||||
tool_calls 完整保留，json 原生透传，不做合成/剥离/尾部注入）；
|
||||
TodoList 状态由 process_tool_artifact 拼入各轮工具返回值。
|
||||
调用后提取模型产出的思考/反思内容（思考开启取深度思考，思考关闭取
|
||||
一句话反思），记录到 think store 供前端实时展示思维链。
|
||||
|
||||
在每次 LLM 调用后，根据 response 的 ``usage_metadata.total_tokens`` 判断
|
||||
是否触发消息列表就地压缩——确保 ReAct 循环中的上下文永不溢出。
|
||||
@@ -675,31 +538,17 @@ def make_call_model_node(
|
||||
|
||||
filtered_messages = _filter_short_term_memory(all_messages, trajectory_rounds)
|
||||
|
||||
# 获取 session_id 供循环检测使用
|
||||
# 获取 session_id（token 统计、工具调用记录使用）
|
||||
try:
|
||||
from agent.history.tool_call_recorder import get_current_session
|
||||
_sid = get_current_session()
|
||||
except Exception:
|
||||
_sid = ""
|
||||
|
||||
# 构建动态尾部提示并注入到消息列表末尾（仅作引导，不记录到前端）。
|
||||
# 固定反思引导已并入系统提示词前缀，此处仅注入变化部分：
|
||||
# TodoList 状态与循环提醒；无 TodoList 且无循环提醒时不注入（省 token）。
|
||||
# 上下文 = 图内消息原样（AIMessage 三元组 content/reasoning_content/tool_calls
|
||||
# 完整保留，由 llm._get_request_payload 原生透传，不做合成/剥离/尾部注入）。
|
||||
# TodoList 状态已由 process_tool_artifact 拼入各轮工具返回值。
|
||||
messages_for_llm = list(filtered_messages)
|
||||
# 注入本轮已产生的思考（reasoning_content 不被 API 回传，跨步骤需回插；
|
||||
# 因果交错：思考位于其工具调用 AIMessage 之前）
|
||||
messages_for_llm = _inject_turn_thinking(messages_for_llm)
|
||||
tail = _build_dynamic_tail_prompt(filtered_messages, _sid)
|
||||
if tail:
|
||||
# 长期记忆：持久化本轮动态尾部，供后续轮次注入查看
|
||||
_persist_dynamic_tail(_sid, tail)
|
||||
# 注入全部历史动态尾部（含本轮），使 Agent 能看到之前所有轮的
|
||||
# todo 状态与循环提醒；这些消息仅存在于本次调用，不进图状态。
|
||||
for hist_text in _load_dynamic_tail_history(_sid):
|
||||
messages_for_llm.append(HumanMessage(
|
||||
content=hist_text,
|
||||
additional_kwargs={"synthetic": True, "reflection": True},
|
||||
))
|
||||
|
||||
# 超时自动重试：仅 ReAct 调用的模型调用（工具内部 LLM 与压缩节点不受影响）
|
||||
response = _invoke_llm_with_retry(model_with_tools, messages_for_llm, _sid)
|
||||
@@ -790,13 +639,33 @@ def process_tool_artifact(state: AgentState) -> dict:
|
||||
if not isinstance(msg, ToolMessage):
|
||||
break
|
||||
trailing.append(msg)
|
||||
# 按原始顺序处理（保证 record_tool_result_live 的 reverse 查找正确）
|
||||
# TodoList 状态拼入本轮最后一条工具返回值（存在未完成计划时）。
|
||||
# 状态文本随工具返回值一起被 record/save_turn 持久化，
|
||||
# build_native_history_messages 重建时原样带出——无额外存储与注入逻辑；
|
||||
# 计划全部完成后不再拼接，之后的任务状态变更（新建计划）自动恢复。
|
||||
if trailing:
|
||||
try:
|
||||
from agent.tools.todo_list import get_todo_status
|
||||
status_text, all_done = get_todo_status(sid)
|
||||
if status_text and not all_done:
|
||||
last_tool_msg = trailing[0]
|
||||
raw = str(last_tool_msg.content) if last_tool_msg.content else ""
|
||||
suffix = f"\n\n【任务进度】\n{status_text}"
|
||||
last_tool_msg.content = raw + suffix if raw else status_text
|
||||
except Exception:
|
||||
pass
|
||||
# 按原始顺序处理（保证 record_tool_result_live 的 reverse 查找正确）；
|
||||
# 最后一条（拼接了任务进度）强制按 tool_call_id 更新，覆盖后台任务等
|
||||
# 已标记 done 的 live 记录，确保进度文本同步进实时记录与存档。
|
||||
for msg in reversed(trailing):
|
||||
is_last = msg is trailing[0]
|
||||
record_tool_result_live(
|
||||
sid,
|
||||
msg.name or "unknown",
|
||||
str(msg.content) if msg.content else "",
|
||||
msg.artifact,
|
||||
force_update=is_last,
|
||||
tool_call_id=getattr(msg, "tool_call_id", ""),
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
+106
-39
@@ -86,7 +86,7 @@ class ConversationRecordRepository(Protocol):
|
||||
def list_turn_ids(self, session_id: str) -> list[str]: ...
|
||||
def save_tool_calls(
|
||||
self, session_id: str, turn_id: str, tool_calls: list[dict], meta: dict | None = None,
|
||||
reflections: list[dict] | None = None,
|
||||
reflections: list[dict] | None = None, assistant_units: list[dict] | None = None,
|
||||
) -> None: ...
|
||||
def get_turn_record(self, session_id: str, turn_id: str) -> dict[str, Any] | None: ...
|
||||
def get_latest_turn_record(self, session_id: str) -> dict[str, Any] | None: ...
|
||||
@@ -149,6 +149,7 @@ _SESSION_TABLE_DDLS = [
|
||||
turn_id VARCHAR(64) NOT NULL,
|
||||
tool_calls JSON NOT NULL,
|
||||
meta JSON DEFAULT NULL,
|
||||
assistant_units JSON DEFAULT NULL,
|
||||
INDEX idx_sess_turn (session_id, turn_id)
|
||||
) CHARACTER SET utf8mb4
|
||||
""",
|
||||
@@ -192,6 +193,7 @@ _CRON_TABLE_DDLS = [
|
||||
task_id VARCHAR(64) NOT NULL,
|
||||
turn_id VARCHAR(64) NOT NULL,
|
||||
tool_calls JSON NOT NULL,
|
||||
assistant_units JSON DEFAULT NULL,
|
||||
INDEX idx_task_turn (task_id, turn_id)
|
||||
) CHARACTER SET utf8mb4
|
||||
""",
|
||||
@@ -353,7 +355,7 @@ class JsonRecordRepository:
|
||||
# ---- 工具调用记录 ----
|
||||
def save_tool_calls(
|
||||
self, session_id: str, turn_id: str, tool_calls: list[dict], meta: dict | None = None,
|
||||
reflections: list[dict] | None = None,
|
||||
reflections: list[dict] | None = None, assistant_units: list[dict] | None = None,
|
||||
) -> None:
|
||||
if not session_id or not turn_id:
|
||||
return
|
||||
@@ -369,8 +371,12 @@ class JsonRecordRepository:
|
||||
record.update(meta) # meta 全量透传，消费方新增字段无需改动存储层
|
||||
if reflections:
|
||||
# 尾部思考记录（未被任何工具调用消费的反思），随 turn 一并持久化，
|
||||
# 供后续轮次 build_native_history_messages 重建原生消息注入上下文
|
||||
# 供前端 think 面板历史回放
|
||||
record["reflections"] = reflections
|
||||
if assistant_units:
|
||||
# 每轮 AIMessage 三元组（content/reasoning_content/tool_calls），
|
||||
# 供 build_native_history_messages 1:1 重建上下文
|
||||
record["assistant_units"] = assistant_units
|
||||
with open(self._tool_path(session_id, turn_id), "w", encoding="utf-8") as f:
|
||||
json.dump(record, f, ensure_ascii=False, indent=2)
|
||||
|
||||
@@ -596,6 +602,7 @@ class MysqlRecordRepository:
|
||||
else:
|
||||
_ensure_session_tables()
|
||||
self._ensure_extra_column()
|
||||
self._ensure_assistant_units_column()
|
||||
|
||||
# ---- 表名 / 连接 ----
|
||||
def _messages_table(self) -> str:
|
||||
@@ -627,6 +634,21 @@ class MysqlRecordRepository:
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
def _ensure_assistant_units_column(self) -> None:
|
||||
"""幂等添加 *_tool_calls.assistant_units JSON 列（每轮 AIMessage 三元组）。"""
|
||||
conn = mysql_conn()
|
||||
try:
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
f"ALTER TABLE {self._tool_table()} "
|
||||
"ADD COLUMN assistant_units JSON DEFAULT NULL"
|
||||
)
|
||||
conn.commit()
|
||||
except Exception:
|
||||
pass # 列已存在 → 忽略
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
@staticmethod
|
||||
def _loads(raw: Any) -> Any:
|
||||
"""pymysql 对 JSON 列可能返回 dict 或 str，统一反序列化。"""
|
||||
@@ -773,11 +795,13 @@ class MysqlRecordRepository:
|
||||
# ---- 工具调用记录 ----
|
||||
def save_tool_calls(
|
||||
self, session_id: str, turn_id: str, tool_calls: list[dict], meta: dict | None = None,
|
||||
reflections: list[dict] | None = None,
|
||||
reflections: list[dict] | None = None, assistant_units: list[dict] | None = None,
|
||||
) -> None:
|
||||
if not session_id or not turn_id:
|
||||
return
|
||||
sid_col = self._sid_col()
|
||||
# 每轮 AIMessage 三元组（content/reasoning_content/tool_calls），独立列存储
|
||||
units_json = json.dumps(assistant_units or [], ensure_ascii=False)
|
||||
conn = mysql_conn()
|
||||
try:
|
||||
with conn.cursor() as cur:
|
||||
@@ -789,9 +813,13 @@ class MysqlRecordRepository:
|
||||
# cron_tool_calls 无 meta 列（与既有表结构一致），
|
||||
# 尾部思考记录无法入库；JSON 后端下完整持久化
|
||||
cur.execute(
|
||||
f"INSERT INTO {self._tool_table()} ({sid_col}, turn_id, tool_calls) "
|
||||
"VALUES (%s,%s,%s)",
|
||||
(session_id, turn_id, json.dumps(tool_calls or [], ensure_ascii=False)),
|
||||
f"INSERT INTO {self._tool_table()} "
|
||||
f"({sid_col}, turn_id, tool_calls, assistant_units) VALUES (%s,%s,%s,%s)",
|
||||
(
|
||||
session_id, turn_id,
|
||||
json.dumps(tool_calls or [], ensure_ascii=False),
|
||||
units_json,
|
||||
),
|
||||
)
|
||||
else:
|
||||
# 尾部思考并入 meta（_row_to_record 全量透传，读取时合并回记录顶层）
|
||||
@@ -799,12 +827,14 @@ class MysqlRecordRepository:
|
||||
meta = {**(meta or {}), "reflections": reflections}
|
||||
cur.execute(
|
||||
f"INSERT INTO {self._tool_table()} "
|
||||
"(session_id, turn_id, tool_calls, meta) VALUES (%s,%s,%s,%s)",
|
||||
f"({sid_col}, turn_id, tool_calls, meta, assistant_units) "
|
||||
"VALUES (%s,%s,%s,%s,%s)",
|
||||
(
|
||||
session_id,
|
||||
turn_id,
|
||||
json.dumps(tool_calls or [], ensure_ascii=False),
|
||||
json.dumps(meta, ensure_ascii=False) if meta else None,
|
||||
units_json,
|
||||
),
|
||||
)
|
||||
conn.commit()
|
||||
@@ -816,7 +846,7 @@ class MysqlRecordRepository:
|
||||
meta_key: str | None = None) -> dict[str, Any]:
|
||||
"""把工具调用表行还原为与 tool_*.json 同构的 record dict。
|
||||
|
||||
meta 全量透传（不做字段白名单）：消费方新增 meta 字段无需改动存储层。
|
||||
meta / assistant_units 全量透传（不做字段白名单）：消费方新增字段无需改动存储层。
|
||||
"""
|
||||
tool_calls = cls._loads(row.get("tool_calls")) or []
|
||||
record: dict[str, Any] = {
|
||||
@@ -829,6 +859,9 @@ class MysqlRecordRepository:
|
||||
meta = cls._loads(row.get(meta_key))
|
||||
if isinstance(meta, dict):
|
||||
record.update(meta)
|
||||
if row.get("assistant_units") is not None:
|
||||
units = cls._loads(row.get("assistant_units"))
|
||||
record["assistant_units"] = units if isinstance(units, list) else []
|
||||
return record
|
||||
|
||||
def get_turn_record(self, session_id: str, turn_id: str) -> dict[str, Any] | None:
|
||||
@@ -838,22 +871,29 @@ class MysqlRecordRepository:
|
||||
with conn.cursor() as cur:
|
||||
if self.scope == "cron":
|
||||
cur.execute(
|
||||
f"SELECT tool_calls FROM {self._tool_table()} "
|
||||
f"SELECT tool_calls, assistant_units FROM {self._tool_table()} "
|
||||
f"WHERE {sid_col}=%s AND turn_id=%s ORDER BY id",
|
||||
(session_id, turn_id),
|
||||
)
|
||||
rows = cur.fetchall() or []
|
||||
out: list[dict] = []
|
||||
units: list[dict] = []
|
||||
for r in rows:
|
||||
tc = self._loads(r.get("tool_calls"))
|
||||
if isinstance(tc, list):
|
||||
out.extend(tc)
|
||||
u = self._loads(r.get("assistant_units"))
|
||||
if isinstance(u, list):
|
||||
units.extend(u)
|
||||
if not out:
|
||||
return None
|
||||
return {"session_id": session_id, "turn_id": turn_id,
|
||||
"tool_calls": out, "timestamp": turn_id}
|
||||
rec = {"session_id": session_id, "turn_id": turn_id,
|
||||
"tool_calls": out, "timestamp": turn_id}
|
||||
if units:
|
||||
rec["assistant_units"] = units
|
||||
return rec
|
||||
cur.execute(
|
||||
f"SELECT tool_calls, meta FROM {self._tool_table()} "
|
||||
f"SELECT tool_calls, meta, assistant_units FROM {self._tool_table()} "
|
||||
f"WHERE session_id=%s AND turn_id=%s",
|
||||
(session_id, turn_id),
|
||||
)
|
||||
@@ -864,6 +904,21 @@ class MysqlRecordRepository:
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
def _cron_record_from_row(self, session_id: str, row: dict[str, Any]) -> dict[str, Any]:
|
||||
"""cron 行 → record（无 meta 列，assistant_units 独立列带出）。"""
|
||||
tc = self._loads(row.get("tool_calls"))
|
||||
tid = row.get("turn_id", "")
|
||||
rec: dict[str, Any] = {
|
||||
"session_id": session_id,
|
||||
"turn_id": tid,
|
||||
"tool_calls": tc if isinstance(tc, list) else [],
|
||||
"timestamp": tid,
|
||||
}
|
||||
units = self._loads(row.get("assistant_units"))
|
||||
if isinstance(units, list) and units:
|
||||
rec["assistant_units"] = units
|
||||
return rec
|
||||
|
||||
def get_latest_turn_record(self, session_id: str) -> dict[str, Any] | None:
|
||||
sid_col = self._sid_col()
|
||||
conn = mysql_conn()
|
||||
@@ -871,19 +926,16 @@ class MysqlRecordRepository:
|
||||
with conn.cursor() as cur:
|
||||
if self.scope == "cron":
|
||||
cur.execute(
|
||||
f"SELECT turn_id, tool_calls FROM {self._tool_table()} "
|
||||
f"SELECT turn_id, tool_calls, assistant_units FROM {self._tool_table()} "
|
||||
f"WHERE {sid_col}=%s ORDER BY turn_id DESC LIMIT 1",
|
||||
(session_id,),
|
||||
)
|
||||
row = cur.fetchone()
|
||||
if not row:
|
||||
return None
|
||||
tc = self._loads(row.get("tool_calls"))
|
||||
return {"session_id": session_id, "turn_id": row.get("turn_id", ""),
|
||||
"tool_calls": tc if isinstance(tc, list) else [],
|
||||
"timestamp": row.get("turn_id", "")}
|
||||
return self._cron_record_from_row(session_id, row)
|
||||
cur.execute(
|
||||
f"SELECT turn_id, tool_calls, meta FROM {self._tool_table()} "
|
||||
f"SELECT turn_id, tool_calls, meta, assistant_units FROM {self._tool_table()} "
|
||||
f"WHERE session_id=%s ORDER BY turn_id DESC LIMIT 1",
|
||||
(session_id,),
|
||||
)
|
||||
@@ -902,20 +954,14 @@ class MysqlRecordRepository:
|
||||
with conn.cursor() as cur:
|
||||
if self.scope == "cron":
|
||||
cur.execute(
|
||||
f"SELECT turn_id, tool_calls FROM {self._tool_table()} "
|
||||
f"SELECT turn_id, tool_calls, assistant_units FROM {self._tool_table()} "
|
||||
f"WHERE {sid_col}=%s ORDER BY turn_id DESC LIMIT %s",
|
||||
(session_id, limit),
|
||||
)
|
||||
rows = cur.fetchall() or []
|
||||
out: list[dict] = []
|
||||
for r in rows:
|
||||
tc = self._loads(r.get("tool_calls"))
|
||||
out.append({"session_id": session_id, "turn_id": r.get("turn_id", ""),
|
||||
"tool_calls": tc if isinstance(tc, list) else [],
|
||||
"timestamp": r.get("turn_id", "")})
|
||||
return out
|
||||
return [self._cron_record_from_row(session_id, r) for r in rows]
|
||||
cur.execute(
|
||||
f"SELECT turn_id, tool_calls, meta FROM {self._tool_table()} "
|
||||
f"SELECT turn_id, tool_calls, meta, assistant_units FROM {self._tool_table()} "
|
||||
f"WHERE session_id=%s ORDER BY turn_id DESC LIMIT %s",
|
||||
(session_id, limit),
|
||||
)
|
||||
@@ -1117,10 +1163,25 @@ class SqliteRecordRepository:
|
||||
def __init__(self, scope: str = "session", db_path: str | None = None) -> None:
|
||||
self.scope = scope if scope == "cron" else "session"
|
||||
self._path_override = db_path
|
||||
self._db_path_actual = self._db_path()
|
||||
if self.scope == "cron":
|
||||
ensure_sqlite_tables(self._db_path(), _SQLITE_CRON_TABLE_DDLS)
|
||||
ensure_sqlite_tables(self._db_path_actual, _SQLITE_CRON_TABLE_DDLS)
|
||||
else:
|
||||
ensure_sqlite_tables(self._db_path(), _SQLITE_SESSION_TABLE_DDLS)
|
||||
ensure_sqlite_tables(self._db_path_actual, _SQLITE_SESSION_TABLE_DDLS)
|
||||
self._ensure_assistant_units_column()
|
||||
|
||||
def _ensure_assistant_units_column(self) -> None:
|
||||
"""幂等添加 *_tool_calls.assistant_units TEXT 列（旧库升级）。"""
|
||||
conn = sqlite_conn(self._db_path_actual)
|
||||
try:
|
||||
conn.execute(
|
||||
f"ALTER TABLE {self._tool_table()} ADD COLUMN assistant_units TEXT "
|
||||
)
|
||||
conn.commit()
|
||||
except Exception:
|
||||
pass # 列已存在 → 忽略
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
# ---- 表名 / 连接 ----
|
||||
def _db_path(self) -> str:
|
||||
@@ -1297,7 +1358,7 @@ class SqliteRecordRepository:
|
||||
# ---- 工具调用记录 ----
|
||||
def save_tool_calls(
|
||||
self, session_id: str, turn_id: str, tool_calls: list[dict], meta: dict | None = None,
|
||||
reflections: list[dict] | None = None,
|
||||
reflections: list[dict] | None = None, assistant_units: list[dict] | None = None,
|
||||
) -> None:
|
||||
if not session_id or not turn_id:
|
||||
return
|
||||
@@ -1314,12 +1375,14 @@ class SqliteRecordRepository:
|
||||
)
|
||||
conn.execute(
|
||||
f"INSERT INTO {self._tool_table()} "
|
||||
f"({sid_col}, turn_id, tool_calls, meta) VALUES (?,?,?,?)",
|
||||
f"({sid_col}, turn_id, tool_calls, meta, assistant_units) "
|
||||
"VALUES (?,?,?,?,?)",
|
||||
(
|
||||
session_id,
|
||||
turn_id,
|
||||
self._dumps(tool_calls or []),
|
||||
self._dumps(meta) if meta else None,
|
||||
self._dumps(assistant_units or []),
|
||||
),
|
||||
)
|
||||
finally:
|
||||
@@ -1330,7 +1393,7 @@ class SqliteRecordRepository:
|
||||
meta_key: str | None = None) -> dict[str, Any]:
|
||||
"""把工具调用表行还原为与 tool_*.json 同构的 record dict。
|
||||
|
||||
meta 全量透传（不做字段白名单）：消费方新增 meta 字段无需改动存储层。
|
||||
meta / assistant_units 全量透传（不做字段白名单）：消费方新增字段无需改动存储层。
|
||||
"""
|
||||
tool_calls = cls._loads(row.get("tool_calls")) or []
|
||||
record: dict[str, Any] = {
|
||||
@@ -1343,6 +1406,9 @@ class SqliteRecordRepository:
|
||||
meta = cls._loads(row.get(meta_key))
|
||||
if isinstance(meta, dict):
|
||||
record.update(meta)
|
||||
if row.get("assistant_units") is not None:
|
||||
units = cls._loads(row.get("assistant_units"))
|
||||
record["assistant_units"] = units if isinstance(units, list) else []
|
||||
return record
|
||||
|
||||
def get_turn_record(self, session_id: str, turn_id: str) -> dict[str, Any] | None:
|
||||
@@ -1350,7 +1416,7 @@ class SqliteRecordRepository:
|
||||
conn = self._conn()
|
||||
try:
|
||||
row = conn.execute(
|
||||
f"SELECT turn_id, tool_calls, meta FROM {self._tool_table()} "
|
||||
f"SELECT turn_id, tool_calls, meta, assistant_units FROM {self._tool_table()} "
|
||||
f"WHERE {sid_col}=? AND turn_id=?",
|
||||
(session_id, turn_id),
|
||||
).fetchone()
|
||||
@@ -1366,7 +1432,7 @@ class SqliteRecordRepository:
|
||||
conn = self._conn()
|
||||
try:
|
||||
row = conn.execute(
|
||||
f"SELECT turn_id, tool_calls, meta FROM {self._tool_table()} "
|
||||
f"SELECT turn_id, tool_calls, meta, assistant_units FROM {self._tool_table()} "
|
||||
f"WHERE {sid_col}=? ORDER BY turn_id DESC LIMIT 1",
|
||||
(session_id,),
|
||||
).fetchone()
|
||||
@@ -1383,7 +1449,7 @@ class SqliteRecordRepository:
|
||||
conn = self._conn()
|
||||
try:
|
||||
rows = conn.execute(
|
||||
f"SELECT turn_id, tool_calls, meta FROM {self._tool_table()} "
|
||||
f"SELECT turn_id, tool_calls, meta, assistant_units FROM {self._tool_table()} "
|
||||
f"WHERE {sid_col}=? ORDER BY turn_id DESC LIMIT ?",
|
||||
(session_id, limit),
|
||||
).fetchall() or []
|
||||
@@ -2078,11 +2144,12 @@ def list_turn_ids(session_id: str) -> list[str]:
|
||||
|
||||
def save_tool_calls(
|
||||
session_id: str, turn_id: str, tool_calls: list[dict], meta: dict | None = None,
|
||||
reflections: list[dict] | None = None,
|
||||
reflections: list[dict] | None = None, assistant_units: list[dict] | None = None,
|
||||
) -> None:
|
||||
"""持久化一轮工具调用记录（meta 全量透传；reflections 为尾部思考记录）。"""
|
||||
"""持久化一轮工具调用记录（meta 全量透传；reflections 为尾部思考记录；
|
||||
assistant_units 为每轮 AIMessage 三元组，供历史重建）。"""
|
||||
try:
|
||||
_repo().save_tool_calls(session_id, turn_id, tool_calls, meta, reflections)
|
||||
_repo().save_tool_calls(session_id, turn_id, tool_calls, meta, reflections, assistant_units)
|
||||
except Exception as e:
|
||||
print(f"[storage] save_tool_calls 失败: {e}", file=sys.stderr)
|
||||
|
||||
|
||||
@@ -361,8 +361,15 @@ def record_tool_result_live(
|
||||
name: str,
|
||||
result_text: str,
|
||||
artifact: Any | None = None,
|
||||
force_update: bool = False,
|
||||
tool_call_id: str = "",
|
||||
) -> None:
|
||||
"""记录工具返回结果，供前端实时展示文本、图片预览、音频信息与文件信息。"""
|
||||
"""记录工具返回结果，供前端实时展示文本、图片预览、音频信息与文件信息。
|
||||
|
||||
默认仅更新 status 仍为 "running" 的条目（幂等）；``force_update=True`` 时
|
||||
按 ``tool_call_id`` 精确匹配并允许覆盖已 done 条目 —— 供 process_tool_artifact
|
||||
在工具返回值末尾拼接任务进度后强制同步 live 记录。
|
||||
"""
|
||||
# 子 Agent 上下文：路由到 _sub_live_records（按 (session, tool_call_id) 隔离）
|
||||
if is_sub_agent_context():
|
||||
key = _sub_key(session_id)
|
||||
@@ -389,24 +396,30 @@ def record_tool_result_live(
|
||||
with _live_lock:
|
||||
records = _live_records.get(session_id, [])
|
||||
for rec in reversed(records):
|
||||
if rec["name"] == name and rec["status"] == "running":
|
||||
rec["status"] = "done"
|
||||
rec["result_text"] = (result_text or "")[:4000]
|
||||
# 图片/文件字节立即落盘为会话目录下的文件，记录只存文件名，
|
||||
# 不内嵌 base64（实时推送由 get_live_tool_calls 转成
|
||||
# /api/tool-image URL；aborted 轮次经 save_aborted_turn 落盘时
|
||||
# 文件已存在，历史回放按 result_files 即可还原）
|
||||
_persist_artifact_files(rec, artifact, records, session_id)
|
||||
audio_info = _artifact_audio_info(artifact)
|
||||
if audio_info:
|
||||
rec["result_audio"] = audio_info
|
||||
file_infos = _artifact_file_infos(artifact)
|
||||
if file_infos:
|
||||
rec["result_file_info"] = file_infos
|
||||
# 标记变更（增量推送用），并增量持久化到磁盘防止崩溃丢失
|
||||
_live_dirty.setdefault(session_id, set()).add(records.index(rec))
|
||||
_schedule_live_persist(session_id)
|
||||
break
|
||||
# force_update：按 tool_call_id 精确匹配（允许覆盖已 done 的返回值）
|
||||
if force_update and tool_call_id and rec.get("id") == tool_call_id:
|
||||
pass
|
||||
elif rec["name"] == name and rec["status"] == "running":
|
||||
pass
|
||||
else:
|
||||
continue
|
||||
rec["status"] = "done"
|
||||
rec["result_text"] = (result_text or "")[:4000]
|
||||
# 图片/文件字节立即落盘为会话目录下的文件，记录只存文件名，
|
||||
# 不内嵌 base64（实时推送由 get_live_tool_calls 转成
|
||||
# /api/tool-image URL；aborted 轮次经 save_aborted_turn 落盘时
|
||||
# 文件已存在，历史回放按 result_files 即可还原）
|
||||
_persist_artifact_files(rec, artifact, records, session_id)
|
||||
audio_info = _artifact_audio_info(artifact)
|
||||
if audio_info:
|
||||
rec["result_audio"] = audio_info
|
||||
file_infos = _artifact_file_infos(artifact)
|
||||
if file_infos:
|
||||
rec["result_file_info"] = file_infos
|
||||
# 标记变更（增量推送用），并增量持久化到磁盘防止崩溃丢失
|
||||
_live_dirty.setdefault(session_id, set()).add(records.index(rec))
|
||||
_schedule_live_persist(session_id)
|
||||
break
|
||||
_notify_live(session_id)
|
||||
|
||||
|
||||
@@ -1226,6 +1239,29 @@ def extract_tool_calls_from_messages(messages: list[BaseMessage]) -> list[dict[s
|
||||
return records
|
||||
|
||||
|
||||
def extract_assistant_units(messages: list[BaseMessage]) -> list[dict[str, Any]]:
|
||||
"""从图执行后的消息列表中提取每轮模型返回的 AIMessage 三元组单元。
|
||||
|
||||
每个 AIMessage（含无 tool_calls 的最终回复）按原始顺序保存为一条单元：
|
||||
{"content": str, "reasoning_content": str, "tool_calls": [{id,name,args}, ...]}
|
||||
|
||||
上下文历史重建（agent_utils._rebuild_turn_messages）按此单元 1:1 还原
|
||||
AIMessage（content + reasoning_content + tool_calls），不做内容剥离与合成，
|
||||
保证送入模型的历史与当轮转入时逐字节一致（前缀缓存命中）。
|
||||
"""
|
||||
units: list[dict[str, Any]] = []
|
||||
for msg in messages:
|
||||
if not isinstance(msg, AIMessage):
|
||||
continue
|
||||
rc = (getattr(msg, "additional_kwargs", None) or {}).get("reasoning_content")
|
||||
units.append({
|
||||
"content": str(getattr(msg, "content", "") or ""),
|
||||
"reasoning_content": str(rc or ""),
|
||||
"tool_calls": [_json_safe(normalize_tool_call(tc)) for tc in (msg.tool_calls or [])],
|
||||
})
|
||||
return units
|
||||
|
||||
|
||||
def _json_safe(value: Any) -> Any:
|
||||
try:
|
||||
json.dumps(value, ensure_ascii=False)
|
||||
@@ -1348,9 +1384,16 @@ def save_turn(session_id: str, turn_id: str, user_message: dict[str, Any],
|
||||
"duration": (ended_at - started_at) if started_at else None,
|
||||
}
|
||||
# 尾部思考（未被任何工具调用消费的反思）：end_live_turn 暂存 → 此处持久化，
|
||||
# 供后续轮次 build_native_history_messages 重建原生消息注入上下文。
|
||||
# 供前端历史回放/think 面板展示（上下文重建已改由 assistant_units 承载）。
|
||||
_reflections = _take_stashed_reflections(session_id)
|
||||
session_storage.save_tool_calls(session_id, turn_id, tool_calls, meta=_meta, reflections=_reflections)
|
||||
# 每轮模型返回的 AIMessage 三元组（content/reasoning_content/tool_calls）原样保存，
|
||||
# 供 build_native_history_messages 1:1 重建上下文（前缀缓存一致）。
|
||||
_assistant_units = extract_assistant_units(messages)
|
||||
session_storage.save_tool_calls(
|
||||
session_id, turn_id, tool_calls,
|
||||
meta=_meta, reflections=_reflections,
|
||||
assistant_units=_assistant_units,
|
||||
)
|
||||
return None
|
||||
|
||||
|
||||
|
||||
@@ -6,7 +6,6 @@ from .human_interaction import HumanInteraction
|
||||
from .todo_list import TodoList
|
||||
from .software_control import SoftwareControl
|
||||
from .gui import GUITool
|
||||
from .rag import RAGTool
|
||||
from .agent_call import CallSubAgentTool
|
||||
from .doc import DocTool
|
||||
from .skill_router import SkillRouter
|
||||
@@ -28,7 +27,6 @@ _all_tools = [
|
||||
WebPage(),
|
||||
HumanInteraction(),
|
||||
SoftwareControl(),
|
||||
RAGTool(),
|
||||
TodoList(),
|
||||
GUITool(),
|
||||
CallSubAgentTool(),
|
||||
@@ -68,3 +66,50 @@ def get_all_available_tool_descriptions():
|
||||
desc = getattr(inst, "description", "")
|
||||
result[name] = (cls.__name__, (desc or "").split("\n")[0][:200])
|
||||
return result
|
||||
|
||||
|
||||
# ---------- 按需工具（动态注册） ----------
|
||||
# 重依赖工具（如 RAG 的 langchain/chromadb）不在启动时加载：
|
||||
# 仅当 Agent 配置显式引用其名字时，由 ensure_tool_registered 现场实例化并注册。
|
||||
def _load_rag_tool():
|
||||
"""构造 RAGTool（现场导入 langchain/chromadb，仅 rag_tool 被配置引用时执行）。"""
|
||||
from .rag import RAGTool
|
||||
return RAGTool()
|
||||
|
||||
|
||||
_LAZY_TOOL_LOADERS: dict[str, object] = {
|
||||
"rag_tool": _load_rag_tool,
|
||||
}
|
||||
|
||||
|
||||
def ensure_tool_registered(tool_name: str):
|
||||
"""确保按需工具已注册（动态注册）。
|
||||
|
||||
输入:
|
||||
tool_name: 工具名字符串（如 "rag_tool"）。
|
||||
|
||||
输出:
|
||||
已注册的工具实例；非按需工具返回 None。
|
||||
|
||||
常规工具启动时静态注册；本函数用于配置显式引用但未静态注册的
|
||||
按需工具，其重依赖不占用启动时间。
|
||||
"""
|
||||
loader = _LAZY_TOOL_LOADERS.get(tool_name)
|
||||
if loader is None:
|
||||
return None
|
||||
existing = next((t for t in _all_tools if t.name == tool_name), None)
|
||||
if existing is not None:
|
||||
return existing
|
||||
tool = loader()
|
||||
_all_tools.append(tool)
|
||||
_ALL_AVAILABLE_TOOL_CLASSES[tool.name] = type(tool)
|
||||
return tool
|
||||
|
||||
|
||||
def get_selectable_tool_names():
|
||||
"""返回前端设置面板可选工具名单（静态注册 + 按需工具名）。
|
||||
|
||||
仅返回名称字符串，不触发按需工具加载（langchain/chromadb 等
|
||||
重依赖在 Agent 实际配置引用时才现场注册）。
|
||||
"""
|
||||
return list(_ALL_AVAILABLE_TOOL_CLASSES.keys()) + list(_LAZY_TOOL_LOADERS.keys())
|
||||
@@ -9,7 +9,6 @@ import os
|
||||
from datetime import datetime
|
||||
from typing import List, Optional, Literal, Union, Tuple
|
||||
|
||||
import chromadb
|
||||
import requests
|
||||
from langchain.tools import BaseTool
|
||||
from pydantic import BaseModel, Field, create_model
|
||||
@@ -118,9 +117,14 @@ GLOBAL_COLLECTION_NAME = "rag_global"
|
||||
|
||||
|
||||
def get_chroma_client():
|
||||
"""获取或懒初始化 Chroma 持久化客户端单例。"""
|
||||
"""获取或懒初始化 Chroma 持久化客户端单例。
|
||||
|
||||
chromadb 仅在真正使用 RAG 工具时才导入（体积大、拖慢应用启动），
|
||||
模块级不再提前加载。
|
||||
"""
|
||||
global _chroma_client
|
||||
if _chroma_client is None:
|
||||
import chromadb
|
||||
_chroma_client = chromadb.PersistentClient(path=CHROMA_PERSIST_DIR)
|
||||
return _chroma_client
|
||||
|
||||
|
||||
@@ -84,27 +84,36 @@ def _reconstruct_state(sid: str, steps_arg: Optional[list[str]], done_step_arg:
|
||||
return steps, done_steps
|
||||
|
||||
|
||||
def get_todo_status_text(session_id: str) -> str:
|
||||
"""构建当前会话的 TodoList 状态文本（供每轮注入到模型上下文）。
|
||||
def get_todo_status(session_id: str) -> tuple[str, bool]:
|
||||
"""构建当前会话的 TodoList 状态文本与是否全部完成标记。
|
||||
|
||||
从已落盘 turn + 当前 live 记录重建累计状态，输出形如：
|
||||
当前TodoList进度：2/5 已完成
|
||||
1. [已完成] 打开哔哩哔哩网站
|
||||
2. [待完成] 点击顶部搜索框，输入"燕三嘤嘤嘤"并点击搜索
|
||||
无计划时返回空字符串。
|
||||
无计划时返回 ("", False)。
|
||||
|
||||
返回: (状态文本, 是否全部完成)。全部完成指存在计划且 done_steps 覆盖全部步骤；
|
||||
此后调用方不再把状态拼入工具返回值。
|
||||
"""
|
||||
if not session_id:
|
||||
return ""
|
||||
return "", False
|
||||
steps, done_steps = _accumulate_todo_args(_collect_todo_args(session_id))
|
||||
if not steps:
|
||||
return ""
|
||||
return "", False
|
||||
total = len(steps)
|
||||
done_count = len(done_steps)
|
||||
all_done = done_count >= total
|
||||
lines = [f"当前TodoList进度：{done_count}/{total} 已完成"]
|
||||
for i, s in enumerate(steps):
|
||||
status = "[已完成]" if i in done_steps else "[待完成]"
|
||||
lines.append(f" {i + 1}. {status} {s}")
|
||||
return "\n".join(lines)
|
||||
return "\n".join(lines), all_done
|
||||
|
||||
|
||||
def get_todo_status_text(session_id: str) -> str:
|
||||
"""构建当前会话的 TodoList 状态文本（兼容入口，供历史注入路径使用）。"""
|
||||
return get_todo_status(session_id)[0]
|
||||
|
||||
|
||||
class TodoListInput(BaseModel):
|
||||
@@ -140,7 +149,7 @@ class TodoList(BaseTool):
|
||||
|
||||
调用时会随 live 工具记录推送状态，前端据此实时渲染 Todo 浮窗；
|
||||
返回值只包含创建/更新结果（成功或失败+原因），**不返回完整步骤列表**
|
||||
——当前计划状态由系统每轮注入模型上下文（get_todo_status_text）。
|
||||
——当前计划状态由系统每轮拼入工具返回值供模型感知（见 process_tool_artifact）。
|
||||
"""
|
||||
|
||||
args_schema: type[BaseModel] = TodoListInput
|
||||
|
||||
@@ -227,63 +227,19 @@ def set_session_thinking_level(session_id: str, level: str) -> None:
|
||||
_save_session_meta(session_id, meta)
|
||||
|
||||
|
||||
# ---------- 动态尾部长期记忆（TodoList 状态 + 循环提醒） ----------
|
||||
def update_session_dynamic_tail(session_id: str, agent_key: str, text: str, max_entries: int = 100) -> None:
|
||||
"""持久化当前轮的动态尾部提示（TodoList 状态 + 循环提醒）为长期记忆。
|
||||
|
||||
输入:
|
||||
session_id: 会话 ID。
|
||||
agent_key: session_meta 中的 agent 键（主 Agent 为 "main"，子 Agent 用其名称）。
|
||||
text: 本轮动态尾部文本。
|
||||
max_entries: 历史条目上限（防止无界增长），超出丢弃最旧。
|
||||
|
||||
说明:
|
||||
独立于 compressed_context 保存（不覆盖）；连续相同内容去重，
|
||||
使 todo 进度等状态变更形成可追溯的历史记录，供后续轮次注入查看
|
||||
（充分利用前缀缓存：变化部分始终位于消息末尾）。
|
||||
"""
|
||||
if not session_id or not agent_key or not text:
|
||||
return
|
||||
meta = _load_session_meta(session_id)
|
||||
entry = dict(meta.get(agent_key) or {})
|
||||
history = list(entry.get("dynamic_tail_history") or [])
|
||||
if history and history[-1] == text:
|
||||
return # 去重：内容未变化则不重复记录
|
||||
history.append(text)
|
||||
if len(history) > max_entries:
|
||||
history = history[-max_entries:]
|
||||
entry["dynamic_tail_history"] = history
|
||||
meta[agent_key] = entry
|
||||
_save_session_meta(session_id, meta)
|
||||
|
||||
|
||||
def get_session_dynamic_tail_history(session_id: str, agent_key: str) -> list[str]:
|
||||
"""读取指定 Agent 的动态尾部长期记忆历史（时间正序，最新在末尾）。
|
||||
|
||||
输出:
|
||||
[tail_text, ...]；无记录返回空列表。
|
||||
"""
|
||||
if not session_id or not agent_key:
|
||||
return []
|
||||
meta = _load_session_meta(session_id)
|
||||
entry = meta.get(agent_key) or {}
|
||||
return list(entry.get("dynamic_tail_history") or [])
|
||||
|
||||
|
||||
def build_native_history_messages(session_id: str, msg_cursor: int = 0) -> list:
|
||||
"""重建压缩游标之后的历史为原生 LangChain 消息（链式压缩块的输入/输出视图）。
|
||||
|
||||
单轮拼接顺序（每条消息 = 1 个重建序号单位，压缩块按此区间计数）:
|
||||
HumanMessage(用户原生 content 部分: text / image_url / file) hist_user_{turn_id}
|
||||
AIMessage(思考→additional_kwargs.reasoning_content，反思→content，
|
||||
tool_calls=[真实 id/name/args]) × 每个 assistant 组 hist_assist_{turn_id}_{g}
|
||||
—— 同一模型响应（AIMessage）的并行调用共享 group_index，合并为一条
|
||||
AIMessage(content + reasoning_content + tool_calls) × 每个 assistant 单元
|
||||
—— 1:1 复现当轮模型返回的三元组（assistant_units 原样保存） hist_assist_{turn_id}_{i}
|
||||
ToolMessage(result_text, tool_call_id/name 严格匹配) × 每个 call hist_tool_{turn_id}_{g}_{i}
|
||||
...未消费的尾部反思（与组内思考/反思去重后） hist_tail_{turn_id}_{i}
|
||||
AIMessage(最终无工具回复文本) hist_final_{turn_id}
|
||||
AIMessage(最终无工具回复，三元组无 tool_calls) hist_final_{turn_id}_{i}
|
||||
|
||||
所有消息带稳定 id 与 synthetic/tool_ctx 标记（兼容 _filter_short_term_memory
|
||||
与压缩节点的 RemoveMessage）。旧记录无 group_index 时每组回退为单 call。
|
||||
与压缩节点的 RemoveMessage）。无 assistant_units 的记录（子 Agent 轨迹、
|
||||
abort 轮、旧数据）回退为"按 group_index 拼装 thinking/reflection 字段"的旧逻辑。
|
||||
|
||||
输入:
|
||||
session_id: 会话 ID。
|
||||
@@ -319,7 +275,8 @@ def build_native_history_messages(session_id: str, msg_cursor: int = 0) -> list:
|
||||
record = records_by_turn.get(turn_id) or {}
|
||||
calls = [c for c in (record.get("tool_calls") or []) if isinstance(c, dict)]
|
||||
reflections = record.get("reflections") or []
|
||||
for m in _rebuild_turn_messages(turn_id, turn_msgs, calls, reflections):
|
||||
units = [u for u in (record.get("assistant_units") or []) if isinstance(u, dict)]
|
||||
for m in _rebuild_turn_messages(turn_id, turn_msgs, calls, reflections, units):
|
||||
if msg_cursor > 0:
|
||||
msg_cursor -= 1
|
||||
continue
|
||||
@@ -381,8 +338,14 @@ def _tool_result_content(entry: dict) -> str:
|
||||
return "\n".join(x for x in lines if x)
|
||||
|
||||
|
||||
def _rebuild_turn_messages(turn_id: str, turn_msgs: list, calls: list[dict], reflections: list) -> list:
|
||||
"""按原生格式重建单轮消息序列（顺序：user → (组AI+ToolMessage)* → 尾部反思 → 最终回复）。"""
|
||||
def _rebuild_turn_messages(turn_id: str, turn_msgs: list, calls: list[dict], reflections: list, assistant_units: list[dict] | None = None) -> list:
|
||||
"""按原生格式重建单轮消息序列（顺序：user → (组AI+ToolMessage)* → 尾部反思 → 最终回复）。
|
||||
|
||||
存在 assistant_units（当前记录）时按其 1:1 复现当轮 AIMessage 三元组
|
||||
（content + reasoning_content + tool_calls），工具结果按 unit 内 tool_calls
|
||||
的数量顺序消费 calls —— 不做内容剥离与合成，保证重建字节与当轮转入一致；
|
||||
无 assistant_units（子 Agent 轨迹 / abort 轮 / 旧数据）回退为旧拼装逻辑。
|
||||
"""
|
||||
from langchain_core.messages import AIMessage, ToolMessage # noqa: F401
|
||||
msgs: list = []
|
||||
# 1) 用户消息
|
||||
@@ -391,6 +354,36 @@ def _rebuild_turn_messages(turn_id: str, turn_msgs: list, calls: list[dict], ref
|
||||
hu = _native_user_message(turn_id, (user_entry.get("user") or {}).get("content") or "")
|
||||
if hu:
|
||||
msgs.append(hu)
|
||||
|
||||
units = [u for u in (assistant_units or []) if isinstance(u, dict)]
|
||||
if units:
|
||||
# 三元组 1:1 重建：第 i 个 unit = 第 i 条 AIMessage；
|
||||
# 带 tool_calls 的 unit 后接与该 unit 等数量、顺序一致的工具结果消息。
|
||||
call_iter = iter(calls)
|
||||
for i, u in enumerate(units):
|
||||
rc = str(u.get("reasoning_content") or "")
|
||||
kwargs = {"additional_kwargs": {"synthetic": True, "tool_ctx": True}}
|
||||
if rc:
|
||||
kwargs["additional_kwargs"]["reasoning_content"] = rc
|
||||
content = str(u.get("content") or "")
|
||||
tc_list = [t for t in (u.get("tool_calls") or []) if isinstance(t, dict)]
|
||||
if tc_list:
|
||||
msgs.append(AIMessage(content=content, tool_calls=tc_list, id=f"hist_assist_{turn_id}_{i}", **kwargs))
|
||||
for j in range(len(tc_list)):
|
||||
c = next(call_iter, None)
|
||||
content_t = _tool_result_content(c) if c else "[无返回内容]"
|
||||
msgs.append(ToolMessage(
|
||||
content=content_t if content_t else "[无返回内容]",
|
||||
tool_call_id=str((c or {}).get("id") or ""),
|
||||
name=str((c or {}).get("name") or "unknown"),
|
||||
id=f"hist_tool_{turn_id}_{i}_{j}",
|
||||
additional_kwargs={"synthetic": True, "tool_ctx": True},
|
||||
))
|
||||
else:
|
||||
msgs.append(AIMessage(content=content, id=f"hist_final_{turn_id}_{i}", **kwargs))
|
||||
return msgs
|
||||
|
||||
# ---- 回退：无 assistant_units（子 Agent 轨迹 / abort 轮 / 旧数据）----
|
||||
# 2) 工具调用分组：group_index 相同 → 同一模型响应（并行调用合并为一条 AIMessage）
|
||||
group_order: list = []
|
||||
group_map: dict = {}
|
||||
|
||||
@@ -341,7 +341,7 @@ def format_args_summary(args: dict, max_len: int = 80) -> str:
|
||||
截断后的 JSON 字符串。
|
||||
|
||||
系统定位:
|
||||
供 ``core/loop_detector`` 死循环检测、``history/tool_call_recorder`` 工具调用记录展示。
|
||||
供 ``history/tool_call_recorder`` 工具调用记录展示。
|
||||
"""
|
||||
s = json.dumps(args, ensure_ascii=False, default=str)
|
||||
if len(s) > max_len:
|
||||
|
||||
@@ -22,8 +22,8 @@ const TOOL_META = {
|
||||
gui_tool: { icon: '/image/gui.svg' },
|
||||
rag: { icon: '/image/rag.svg' },
|
||||
human_interaction: { icon: '/image/人为干预.svg' },
|
||||
call_subagent: { icon: '/image/人机交互.svg', defaultExpanded: true },
|
||||
agent_call: { icon: '/image/人机交互.svg', defaultExpanded: true },
|
||||
call_subagent: { icon: '/image/人机交互.svg' },
|
||||
agent_call: { icon: '/image/人机交互.svg' },
|
||||
date_query: { icon: '/image/时钟.svg', noParam: true },
|
||||
cron_manager: { icon: '/image/时钟.svg' },
|
||||
text2sql: { icon: '/image/SQL.svg' },
|
||||
@@ -592,7 +592,7 @@ function _renderSubReplyRow(rawText) {
|
||||
const isEmpty = !text.trim();
|
||||
|
||||
const row = document.createElement("div");
|
||||
row.className = "sub-reply-row" + (isEmpty ? "" : " sub-reply-row--expanded");
|
||||
row.className = "sub-reply-row";
|
||||
|
||||
const icon = document.createElement("img");
|
||||
icon.className = "sub-reply-row-icon";
|
||||
@@ -839,7 +839,9 @@ function _calcLiveDuration(toolCalls, turnStartedAt) {
|
||||
/* ===== 思考行（独立可展开折叠，类似工具调用行） ===== */
|
||||
function renderThinkRow(content, options = {}) {
|
||||
const isLive = !!options.live;
|
||||
const defaultExpanded = isLive || !!options.defaultExpanded;
|
||||
// 思考/反思默认折叠（live 与历史一致）：live 时仅可见 Thought/Reflection 行，
|
||||
// 内容按需点击展开，避免主 Agent 思考链实时占满气泡
|
||||
const defaultExpanded = !!options.defaultExpanded;
|
||||
const index = options.index || 1;
|
||||
const isReflection = options.type === "reflection";
|
||||
|
||||
|
||||
+8
-4
@@ -535,13 +535,14 @@ async def lifespan(app):
|
||||
"""应用生命周期：预热重依赖 + 启动定时任务调度器，退出时关闭。
|
||||
|
||||
预热目的:
|
||||
agent.tools（含 chromadb 等重依赖）与 Agent 运行时原先在首次
|
||||
工具调用/首次打开设置页时惰性导入，首次请求要现场付数秒导入成本。
|
||||
agent.tools 重依赖与 Agent 运行时原先在首次工具调用/首次打开设置页
|
||||
时惰性导入，首次请求要现场付数秒导入成本。
|
||||
这里在启动阶段统一预热（成本被 Electron 健康检查吸收），
|
||||
使设置页与首次聊天即时响应。
|
||||
chromadb 等按需重型依赖（RAG 工具）保持惰性导入，不占用启动时间。
|
||||
"""
|
||||
try:
|
||||
# 预热：构建全部 Agent 运行时（顺带导入 agent.tools/chromadb 等重依赖）
|
||||
# 预热：构建全部 Agent 运行时（顺带导入 agent.tools 常规依赖）
|
||||
from agent.agents import agent_runtime # noqa: F401
|
||||
# 预热工具参数缓存（实例化全部工具类读取 args_schema）
|
||||
from agent.core.config_manager import _get_tool_parameters
|
||||
@@ -768,8 +769,11 @@ def api_session_branch(
|
||||
rec = session_storage.get_turn_record(session_id, tid)
|
||||
if rec:
|
||||
_meta = {k: rec[k] for k in ("started_at", "ended_at", "duration", "aborted") if k in rec}
|
||||
# 保留 AIMessage 三元组与尾部思考：分支会话的历史重建与原会话一致
|
||||
session_storage.save_tool_calls(
|
||||
new_sid, tid, rec.get("tool_calls") or [], meta=_meta or None
|
||||
new_sid, tid, rec.get("tool_calls") or [], meta=_meta or None,
|
||||
reflections=rec.get("reflections"),
|
||||
assistant_units=rec.get("assistant_units"),
|
||||
)
|
||||
except Exception:
|
||||
shutil.rmtree(dst_dir, ignore_errors=True)
|
||||
|
||||
Reference in new issue
Block a user