diff --git a/config/config.yaml b/config/config.yaml index 62a2ed1..8e95342 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -88,8 +88,8 @@ runtime: cpu: {ngl: 0, ctx: 8192} architect: # 大模型（API） - model: deepseek-chat - base_url: https://api.deepseek.com/v1 + model: deepseek-v4-flash + base_url: https://api.deepseek.com api_key_env: DEEPSEEK_API_KEY temperature: 0.2 timeout_s: 60 diff --git a/gateway/jobs.py b/gateway/jobs.py new file mode 100644 index 0000000..016329a --- /dev/null +++ b/gateway/jobs.py @@ -0,0 +1,328 @@ +"""异步后台任务注册表（T1：后端异步化核心）。 + +设计原则（对齐 v3 方案 D1-D6）： +- asyncio 原生，无 Celery/Redis/外部队列 +- 每个 request_id -> TaskInfo（状态/开始时间/结果或错误） +- 任务写 runs/{id}/workspace.json，SSE 生成器只读该文件（D3：不改 pipeline） +- 定期清理已完成任务（防内存泄漏） +""" +from __future__ import annotations + +import asyncio +import json +import time +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, AsyncGenerator, Dict, Optional + +# runs/ 目录（与 pipeline.py 默认一致） +RUNS_DIR = Path("runs") + +# 任务状态 +STATE_PENDING = "pending" +STATE_RUNNING = "running" +STATE_DONE = "done" +STATE_FAILED = "failed" + + +@dataclass +class TaskInfo: + """一个后台任务的状态快照。""" + request_id: str + state: str = STATE_PENDING # pending | running | done | failed + started_at: float = 0.0 # time.time() + finished_at: float = 0.0 # time.time()（done/failed 时） + error: Optional[str] = None # failed 时错误信息 + # PipelineResult 字段（done 时填充） + response: Optional[str] = None + status: Optional[str] = None # done | fast_path | escalated | failed + fast_path: bool = False + rounds_used: int = 0 + api_input_tokens: int = 0 + api_output_tokens: int = 0 + cost_est: float = 0.0 + model_used: Optional[str] = None + latency_ms: float = 0.0 + workspace_path: Optional[str] = None + error_detail: Optional[str] = None + route: list = field(default_factory=list) + + def to_dict(self) -> Dict[str, Any]: + return { + "request_id": self.request_id, + "state": self.state, + "started_at": self.started_at, + "finished_at": self.finished_at, + "error": self.error, + "response": self.response, + "status": self.status, + "fast_path": self.fast_path, + "rounds_used": self.rounds_used, + "api_input_tokens": self.api_input_tokens, + "api_output_tokens": self.api_output_tokens, + "cost_est": self.cost_est, + "model_used": self.model_used, + "latency_ms": round(self.latency_ms, 2), + "workspace_path": self.workspace_path, + "route": self.route, + "error_detail": self.error_detail, + } + + def to_result_event(self) -> Dict[str, Any]: + """终态 SSE result 事件 payload。""" + return { + "type": "result", + "request_id": self.request_id, + "response": self.response or "", + "status": self.status, + "fast_path": self.fast_path, + "rounds_used": self.rounds_used, + "api_input_tokens": self.api_input_tokens, + "api_output_tokens": self.api_output_tokens, + "cost_est": self.cost_est, + "model_used": self.model_used, + "latency_ms": round(self.latency_ms, 2), + "route": self.route, + "error": self.error, + } + + +class JobStore: + """asyncio 后台任务注册表。 + + 线程安全（asyncio 事件循环单线程，不需要额外锁）。 + 任务由 register() 注册、由 _task_done() 填充结果。 + SSE 生成器调用 watch_file() 轮询 workspace.json 变化。 + """ + + def __init__(self, max_age_seconds: float = 3600.0, max_running: int = 50): + """ + Args: + max_age_seconds: 已完成任务在内存中保留时间（秒），超时后自动清理 + max_running: 最大同时运行任务数，超出后拒绝新任务 + """ + self._tasks: Dict[str, TaskInfo] = {} + self._asyncio_tasks: Dict[str, "asyncio.Task[None]"] = {} # request_id -> asyncio.Task + self._cancel_events: Dict[str, asyncio.Event] = {} # request_id -> cancel Event + self.max_age_seconds = max_age_seconds + self.max_running = max_running + self._poll_interval = 0.3 # workspace.json 轮询间隔（秒） + + # ───────────────────────────────────────────────────────────────── + # 公共 API + # ───────────────────────────────────────────────────────────────── + + def register(self, request_id: str) -> tuple[bool, str]: + """注册一个 pending 任务。返回 (True, "") 成功，(False, reason) 容量满。""" + running = [t for t in self._tasks.values() if t.state == STATE_RUNNING] + if len(running) >= self.max_running: + return False, f"同时运行任务已达上限 {self.max_running}，请稍后重试" + if request_id in self._tasks: + return False, f"任务 {request_id} 已存在" + self._tasks[request_id] = TaskInfo( + request_id=request_id, + state=STATE_PENDING, + started_at=time.time(), + ) + return True, "" + + def get(self, request_id: str) -> Optional[TaskInfo]: + """查询任务状态。""" + return self._tasks.get(request_id) + + def list_all(self) -> Dict[str, TaskInfo]: + """列出所有任务（含已完成）。""" + return dict(self._tasks) + + def list_running(self) -> Dict[str, TaskInfo]: + """只列出运行中任务。""" + return {k: v for k, v in self._tasks.items() if v.state == STATE_RUNNING} + + def submit( + self, + request_id: str, + coro, # type: asyncio.coroutine + ) -> "asyncio.Task[None]": + """提交协程到后台运行；内部注册 asyncio.Task。""" + task = asyncio.create_task(self._run_wrapper(request_id, coro)) + self._asyncio_tasks[request_id] = task + return task + + def new_cancel(self, request_id: str) -> asyncio.Event: + """为 SSE 连接创建一个取消事件。""" + evt = asyncio.Event() + self._cancel_events[request_id] = evt + return evt + + def get_cancel(self, request_id: str) -> Optional[asyncio.Event]: + """获取已有取消事件。""" + return self._cancel_events.get(request_id) + + # ───────────────────────────────────────────────────────────────── + # 内部：任务运行包装 + # ───────────────────────────────────────────────────────────────── + + async def _run_wrapper(self, request_id: str, coro): + """把用户协程包装成可追踪的任务：更新状态、捕获异常、清理。""" + info = self._tasks.get(request_id) + if info is None: + return + info.state = STATE_RUNNING + try: + await coro + except Exception as exc: # pragma: no cover + if info: + info.state = STATE_FAILED + info.finished_at = time.time() + info.error = f"unhandled:{type(exc).__name__}:{exc}" + finally: + # 清理 asyncio task 引用 + self._asyncio_tasks.pop(request_id, None) + # 定期 GC 已完成任务 + self._cleanup_aged() + + def _task_done( + self, + request_id: str, + result: "PipelineResult", # from pipeline.PipelineResult + ) -> None: + """任务正常完成时由调用方调用，写入结果。""" + info = self._tasks.get(request_id) + if info is None: + return + info.state = STATE_DONE + info.finished_at = time.time() + info.response = result.response + info.status = result.status + info.fast_path = result.fast_path + info.rounds_used = result.rounds_used + info.api_input_tokens = result.api_input_tokens + info.api_output_tokens = result.api_output_tokens + info.cost_est = result.cost_est + info.model_used = result.model_used + info.latency_ms = result.latency_ms + info.workspace_path = result.workspace_path + info.route = list(result.route) if result.route else [] + if result.error: + info.error_detail = result.error + + def _task_failed( + self, + request_id: str, + error: str, + detail: Optional[str] = None, + ) -> None: + """任务异常结束时调用。""" + info = self._tasks.get(request_id) + if info is None: + return + info.state = STATE_FAILED + info.finished_at = time.time() + info.error = error + info.error_detail = detail + + # ───────────────────────────────────────────────────────────────── + # SSE 专用：workspace.json 文件轮询 + # ───────────────────────────────────────────────────────────────── + + async def watch_workspace( + self, + request_id: str, + cancel_event: asyncio.Event, + ) -> AsyncGenerator[Dict[str, Any], None]: + """监视 runs/{id}/workspace.json，yield 单条事件。 + + 事件类型： + - {"type": "status", "value": , "request_id": } + - {"type": "workspace", "version": , "workspace": } + - {"type": "error", "detail": } + + 当任务进入 done/failed 状态或 cancel_event 被 set 时停止。 + """ + ws_path = RUNS_DIR / request_id / "workspace.json" + seen_mtime: float = 0.0 + seen_size: int = 0 + + while not cancel_event.is_set(): + info = self.get(request_id) + # 检查终态 + if info and info.state in (STATE_DONE, STATE_FAILED): + if info.state == STATE_FAILED: + yield {"type": "error", "detail": info.error or "任务失败"} + # result 由 /runs/{id}/status 提供，此处只推送终态 status + yield {"type": "status", "value": info.state, "request_id": request_id} + break + + # 读文件变化（mtime + size 双检） + if ws_path.exists(): + try: + stat = ws_path.stat() + if stat.st_mtime != seen_mtime or stat.st_size != seen_size: + raw = ws_path.read_text(encoding="utf-8") + data = json.loads(raw) + version = int(data.get("meta", {}).get("round", 0)) + seen_mtime = stat.st_mtime + seen_size = stat.st_size + yield { + "type": "workspace", + "version": version, + "state": info.state if info else STATE_RUNNING, + "request_id": request_id, + "workspace": data, + } + except (json.JSONDecodeError, OSError): + pass # 文件正在写入，忽略 + + await asyncio.sleep(self._poll_interval) + + # 连接关闭前最后推一次终态 + info = self.get(request_id) + if info: + yield {"type": "status", "value": info.state, "request_id": request_id} + + # ───────────────────────────────────────────────────────────────── + # 内存清理 + # ───────────────────────────────────────────────────────────────── + + def _cleanup_aged(self) -> None: + """删除超过 max_age_seconds 的已完成任务引用。""" + now = time.time() + to_remove = [ + rid for rid, info in self._tasks.items() + if info.state in (STATE_DONE, STATE_FAILED) + and (now - info.finished_at) > self.max_age_seconds + ] + for rid in to_remove: + self._tasks.pop(rid, None) + + def cancel(self, request_id: str) -> bool: + """取消运行中的任务。返回 True 找到并取消，False 未找到。""" + task = self._asyncio_tasks.get(request_id) + if task is None: + return False + task.cancel() + info = self.get(request_id) + if info: + info.state = STATE_FAILED + info.finished_at = time.time() + info.error = "cancelled_by_user" + return True + + +# ───────────────────────────────────────────────────────────────────────────── +# 全局单例（gateway 进程内共享） +# ───────────────────────────────────────────────────────────────────────────── +_store: Optional[JobStore] = None + + +def get_job_store() -> JobStore: + global _store + if _store is None: + _store = JobStore(max_age_seconds=3600.0, max_running=50) + return _store + + +def reset_job_store() -> None: + """测试用：重置全局注册表。""" + global _store + _store = None diff --git a/gateway/llama_manager.py b/gateway/llama_manager.py new file mode 100644 index 0000000..915fe9f --- /dev/null +++ b/gateway/llama_manager.py @@ -0,0 +1,453 @@ +"""llama-server 进程管理 & 模型下载。 + +职责： +- 启动/停止本地 llama-server 子进程（Windows 兼容） +- 探测已有 .gguf 模型文件 +- 从 HuggingFace URL 下载模型（支持 huggingface.co 路径别名） +- 下载进度可通过 SSE /llama/download/stream 订阅 + +用法： + from gateway.llama_manager import get_llama_manager + lm = get_llama_manager() + await lm.start(model="models/qwen3.5-4b-q4_k_m.gguf") + await lm.stop() +""" +from __future__ import annotations + +import asyncio +import json +import os +import re +import subprocess +import sys +import threading +import time +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, AsyncGenerator, Optional + +# ───────────────────────────────────────────────────────────────────────────── +# 路径配置（与 config.yaml runtime.llama_server 段保持一致） +# ───────────────────────────────────────────────────────────────────────────── +_ROOT = Path(__file__).resolve().parent.parent # E:\projectAIpopular +BIN_DIR = _ROOT / "bin" +MODELS_DIR = _ROOT / "models" +PID_FILE = _ROOT / "data" / "llama-server.pid" +LOG_FILE = _ROOT / "data" / "llama-server.log" + +# 确保目录存在 +BIN_DIR.mkdir(parents=True, exist_ok=True) +MODELS_DIR.mkdir(parents=True, exist_ok=True) +PID_FILE.parent.mkdir(parents=True, exist_ok=True) + + +# ───────────────────────────────────────────────────────────────────────────── +# 数据模型 +# ───────────────────────────────────────────────────────────────────────────── + +@dataclass +class LlamaServerStatus: + running: bool + pid: Optional[int] = None + model: Optional[str] = None + port: Optional[int] = None + base_url: Optional[str] = None + started_at: Optional[float] = None + error: Optional[str] = None + + +@dataclass +class DownloadProgress: + url: str + dest: str + total_bytes: Optional[int] = None + downloaded_bytes: int = 0 + progress_pct: float = 0.0 + speed: str = "" + eta: str = "" + done: bool = False + error: Optional[str] = None + + +# ───────────────────────────────────────────────────────────────────────────── +# llama_manager 单例 +# ───────────────────────────────────────────────────────────────────────────── + +class LlamaManager: + _instance: Optional["LlamaManager"] = None + + def __init__(self): + self._proc: Optional[subprocess.Popen] = None + self._pid: Optional[int] = None + self._model: Optional[str] = None + self._port: Optional[int] = None + self._started_at: Optional[float] = None + self._downloading: dict[str, DownloadProgress] = {} # url -> progress + self._dl_lock = threading.Lock() + # 加载已有进程 + self._load_pid() + + # ── 进程持久化 ───────────────────────────────────────────────────────── + + def _load_pid(self) -> None: + """从 pid 文件恢复进程引用（进程仍在运行时）。""" + if not PID_FILE.exists(): + return + try: + pid = int(PID_FILE.read_text().strip()) + os.kill(pid, 0) # 检查进程是否存活 + # 进程还在，尝试接管（通过 cmdline 判断是否是 llama-server） + self._pid = pid + self._proc = self._attach_to_process(pid) + except (ValueError, FileNotFoundError, OSError): + PID_FILE.unlink(missing_ok=True) + + def _attach_to_process(self, pid: int) -> Optional[subprocess.Popen]: + """通过 pid 重新关联到 Popen（仅作状态恢复，不拥有 stdout）。""" + try: + return subprocess.Popen( + [sys.executable, "-c", + f"import os; os.kill({pid}, 0)"], # 存活检查 + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + except Exception: + return None + + def _write_pid(self, pid: int) -> None: + PID_FILE.write_text(str(pid), encoding="utf-8") + + def _clear_pid(self) -> None: + PID_FILE.unlink(missing_ok=True) + + # ── 进程管理 ───────────────────────────────────────────────────────── + + def find_binary(self) -> Optional[Path]: + """查找 llama-server 可执行文件。""" + candidates = [ + BIN_DIR / "llama-server.exe", + BIN_DIR / "llama-server", + _ROOT / "llama-server.exe", + _ROOT / "llama-server", + ] + for p in candidates: + if p.exists(): + return p + # PATH 中查找 + import shutil + found = shutil.which("llama-server") or shutil.which("llama-server.exe") + if found: + return Path(found) + return None + + def status(self) -> LlamaServerStatus: + """返回当前服务状态。""" + if self._proc is None or self._pid is None: + return LlamaServerStatus(running=False) + try: + # 检查进程是否存活 + os.kill(self._pid, 0) + except OSError: + # 进程已死 + self._proc = None + self._pid = None + self._model = None + self._port = None + self._started_at = None + self._clear_pid() + return LlamaServerStatus(running=False) + return LlamaServerStatus( + running=True, + pid=self._pid, + model=self._model, + port=self._port, + base_url=f"http://127.0.0.1:{self._port}/v1", + started_at=self._started_at, + ) + + async def start( + self, + model: str, + port: int = 8901, + ngl: int = 99, + ctx: int = 4096, + extra_args: Optional[list] = None, + ) -> LlamaServerStatus: + """启动 llama-server，阻塞直到监听就绪或超时。""" + if self.status().running: + s = self.status() + if s.model == model and s.port == port: + return s # 已是同一模型，无需重启 + await self.stop() + + binary = self.find_binary() + if binary is None: + return LlamaServerStatus( + running=False, + error="未找到 llama-server 可执行文件。" + "请将 llama-server.exe 放入 bin/ 目录，" + "或从 https://github.com/ggerganov/llama.cpp/releases 下载。", + ) + + model_path = Path(model) + if not model_path.is_absolute(): + model_path = MODELS_DIR / model + if not model_path.exists(): + return LlamaServerStatus( + running=False, + error=f"模型文件不存在：{model_path}。" + "请先下载模型，或在设置页填写 HuggingFace URL 下载。", + ) + + args = [ + str(binary), + "-m", str(model_path), + "-c", str(ctx), + "-ngl", str(ngl), + "--port", str(port), + "--host", "127.0.0.1", + ] + if extra_args: + args.extend(extra_args) + + LOG_FILE.parent.mkdir(parents=True, exist_ok=True) + log_f = open(LOG_FILE, "w", encoding="utf-8", buffering=1) + + try: + self._proc = subprocess.Popen( + args, + stdout=log_f, + stderr=subprocess.STDOUT, + cwd=str(_ROOT), + creationflags=subprocess.CREATE_NEW_PROCESS_GROUP if sys.platform == "win32" else 0, + ) + except OSError as e: + log_f.close() + return LlamaServerStatus(running=False, error=f"启动失败：{e}") + + self._pid = self._proc.pid + self._model = str(model_path) + self._port = port + self._started_at = time.time() + self._write_pid(self._pid) + + # 等待服务就绪 + ok = await self._wait_until_ready(port, timeout=30) + if not ok: + await self.stop() + return LlamaServerStatus( + running=False, + error=f"llama-server 启动后 {port} 端口在 30 秒内未响应", + ) + + return self.status() + + async def stop(self) -> None: + """优雅停止 llama-server。""" + if self._pid is None: + self._proc = None + return + + try: + if sys.platform == "win32": + # Windows: CTRL_BREAK_EVENT 或 taskkill + subprocess.run( + ["taskkill", "/PID", str(self._pid), "/T", "/F"], + capture_output=True, + timeout=10, + ) + else: + os.kill(self._pid, 15) # SIGTERM + time.sleep(1) + try: + os.kill(self._pid, 0) + os.kill(self._pid, 9) + except OSError: + pass + except Exception: + pass + finally: + self._proc = None + self._pid = None + self._model = None + self._port = None + self._started_at = None + self._clear_pid() + + async def _wait_until_ready(self, port: int, timeout: float = 30) -> bool: + """轮询检查端口是否开始监听。""" + import httpx + url = f"http://127.0.0.1:{port}/v1/models" + deadline = time.time() + timeout + async with httpx.AsyncClient(timeout=5.0) as client: + while time.time() < deadline: + try: + r = await client.get(url) + if r.status_code < 500: + return True + except (httpx.ConnectError, httpx.ReadTimeout, OSError): + pass + await asyncio.sleep(0.5) + return False + + # ── 模型列表 ───────────────────────────────────────────────────────── + + def list_local_models(self) -> list[dict[str, str]]: + """列出 models/ 目录下所有 .gguf 文件。""" + models = [] + for p in MODELS_DIR.glob("*.gguf"): + size_mb = p.stat().st_size // (1024 * 1024) + models.append({ + "id": p.name, + "name": p.name, + "size_mb": size_mb, + "path": str(p), + }) + return sorted(models, key=lambda m: m["name"]) + + # ── 模型下载 ───────────────────────────────────────────────────────── + + async def download_model( + self, + url: str, + dest: Optional[str] = None, + ) -> DownloadProgress: + """从 HuggingFace 或直链下载 .gguf 模型文件。 + + HuggingFace 路径别名：用户输入 "Qwen/Qwen3-4B-GGUF/Qwen3-4B-Q4_K_M.gguf" + 自动转换为 "https://huggingface.co//resolve/main/" + + 支持断点续传（Content-Range）。 + + 返回 DownloadProgress 对象（含当前进度），进度通过 get_download_progress() 查询。 + """ + import httpx + + # 路径别名转换 + if not url.startswith("http"): + url = f"https://huggingface.co/{url}/resolve/main" + + # 解析文件名 + filename = url.rstrip("/").split("/")[-1] + if not filename.endswith(".gguf"): + filename += ".gguf" + + if dest: + dest_path = Path(dest) + else: + dest_path = MODELS_DIR / filename + + # 构造 HTTP 头 + headers = {} + resume_bytes = 0 + if dest_path.exists(): + resume_bytes = dest_path.stat().st_size + headers["Range"] = f"bytes={resume_bytes}-" + + # 获取文件大小 + total_bytes: Optional[int] = None + try: + async with httpx.AsyncClient(timeout=httpx.Timeout(30.0, connect=10.0, read=60.0, write=30.0, pool=10.0), follow_redirects=True) as client: + head = await client.head(url, headers={"Range": "bytes=0-0"}) + total_raw = head.headers.get("Content-Length") + if total_raw: + total_bytes = int(total_raw) + # Content-Range 响应时 total_bytes 在 Content-Range 头里 + cr = head.headers.get("Content-Range", "") + m = re.search(r"/(\d+)", cr) + if m: + total_bytes = int(m.group(1)) + except Exception: + pass + + prog = DownloadProgress( + url=url, + dest=str(dest_path), + total_bytes=total_bytes, + downloaded_bytes=resume_bytes, + ) + with self._dl_lock: + self._downloading[url] = prog + + try: + mode = "ab" if resume_bytes > 0 else "wb" + async with httpx.AsyncClient( + timeout=httpx.Timeout(300.0, connect=10.0, read=300.0, write=30.0, pool=10.0), + follow_redirects=True, + ) as client: + t0 = time.time() + last_bytes = resume_bytes + async with client.stream("GET", url, headers=headers) as resp: + if resp.status_code not in (200, 206): + raise RuntimeError(f"HTTP {resp.status_code}") + with open(dest_path, mode) as f: + async for chunk in resp.aiter_bytes(chunk_size=8192): + f.write(chunk) + prog.downloaded_bytes += len(chunk) + + # 速度 & ETA + elapsed = time.time() - t0 + if elapsed > 0.5: + speed_bps = (prog.downloaded_bytes - last_bytes) / elapsed + speed_str = _format_speed(speed_bps) + if prog.total_bytes and speed_bps > 0: + remain = prog.total_bytes - prog.downloaded_bytes + eta_s = remain / speed_bps + prog.eta = _format_eta(eta_s) + else: + prog.eta = "" + prog.speed = speed_str + last_bytes = prog.downloaded_bytes + t0 = time.time() + + if prog.total_bytes: + prog.progress_pct = min(prog.downloaded_bytes / prog.total_bytes * 100, 100) + except Exception as e: + prog.error = str(e) + finally: + prog.done = True + with self._dl_lock: + self._downloading[url] = prog + + return prog + + def get_download_progress(self, url: str) -> Optional[DownloadProgress]: + """查询下载进度。""" + with self._dl_lock: + return self._downloading.get(url) + + def list_downloads(self) -> list[DownloadProgress]: + """列出所有活跃下载。""" + with self._dl_lock: + return list(self._downloading.values()) + + +def _format_speed(bps: float) -> str: + if bps >= 1e9: + return f"{bps/1e9:.1f} GB/s" + if bps >= 1e6: + return f"{bps/1e6:.1f} MB/s" + if bps >= 1e3: + return f"{bps/1e3:.1f} KB/s" + return f"{bps:.0f} B/s" + + +def _format_eta(seconds: float) -> str: + if seconds < 60: + return f"{seconds:.0f}s" + if seconds < 3600: + return f"{seconds/60:.0f}m" + return f"{seconds/3600:.1f}h" + + +# ───────────────────────────────────────────────────────────────────────────── +# 全局单例 +# ───────────────────────────────────────────────────────────────────────────── +_lm: Optional[LlamaManager] = None + + +def get_llama_manager() -> LlamaManager: + global _lm + if _lm is None: + _lm = LlamaManager() + return _lm diff --git a/gateway/static/assets/CollaborationView-BIjnBJHk.js b/gateway/static/assets/CollaborationView-BIjnBJHk.js new file mode 100644 index 0000000..8154538 --- /dev/null +++ b/gateway/static/assets/CollaborationView-BIjnBJHk.js @@ -0,0 +1 @@ +import{A as e,C as t,D as n,E as r,F as i,I as ee,L as a,M as o,R as te,S as s,T as c,_ as ne,j as l,n as re,t as u,w as d,x as f,z as p}from"./index-BnlAnYlB.js";var ie={class:`collab-view`},ae={class:`collab-sidebar`},oe={class:`session-list`},se=[`onClick`],ce={class:`meta`},m={key:0,class:`stats`},h={class:`stat-grid`},g={class:`route-flow`},_={class:`collab-main`},v={key:0,class:`empty`},y={class:`section brief-section`},b={class:`brief-card`},x={class:`goal`},S={class:`tags`},C={key:0,class:`domain`},w={key:0,class:`constraints`},T={class:`section plan-section`},E={class:`plan-timeline`},D={class:`step-content`},O={class:`step-header`},k={class:`step-id`},A={class:`step-status`},j={class:`step-task`},M={key:0,class:`step-deps`},N={key:1,class:`step-artifact`},le={class:`section 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button[data-v-6692005c]:disabled{cursor:not-allowed;background:#9ca3af} diff --git a/gateway/static/favicon.svg b/gateway/static/favicon.svg new file mode 100644 index 0000000..6893eb1 --- /dev/null +++ b/gateway/static/favicon.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/gateway/static/icons.svg b/gateway/static/icons.svg new file mode 100644 index 0000000..e952219 --- /dev/null +++ b/gateway/static/icons.svg @@ -0,0 +1,24 @@ + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/gateway/static/index.html b/gateway/static/index.html index df4000d..1127e4a 100644 --- a/gateway/static/index.html +++ b/gateway/static/index.html @@ -1,224 +1,14 @@ - - - - - -端云协同 LLM 协作系统 - - - -
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⚙️ 端云协同 LLM 协作系统

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⚙️ 内置小模型（Worker）

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🧠 大模型（Architect / API）

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端云协同 LLM 协作系统 · v2 · 交流文本协议 · 北极星：token 下降 ≥80%
- - - \ No newline at end of file + + + + + + + webapp + + + + +
+ + diff --git a/router_system/architect.py b/router_system/architect.py index 750aa88..001ff46 100644 --- a/router_system/architect.py +++ b/router_system/architect.py @@ -59,7 +59,7 @@ class ArchitectClient: def __init__( self, model: str, - base_url: str = "https://api.deepseek.com/v1", + base_url: str = "https://api.deepseek.com", api_key: Optional[str] = None, temperature: float = 0.2, timeout_s: float = 60.0, @@ -220,8 +220,8 @@ def build_architect(cfg: Dict[str, Any], _env = get_env or os.environ.get key = cfg.get("api_key") or _env(cfg.get("api_key_env", "DEEPSEEK_API_KEY")) return ArchitectClient( - model=cfg.get("model", "deepseek-chat"), - base_url=cfg.get("base_url", "https://api.deepseek.com/v1"), + model=cfg.get("model", "deepseek-v4-flash"), + base_url=cfg.get("base_url", "https://api.deepseek.com"), api_key=key, temperature=float(cfg.get("temperature", 0.2)), timeout_s=float(cfg.get("timeout_s", 60)), diff --git a/router_system/classifier.py b/router_system/classifier.py index 2eb7437..1d10372 100644 --- a/router_system/classifier.py +++ b/router_system/classifier.py @@ -10,7 +10,7 @@ confidence = 1 - exp(-s)，保证 s=1 -> 0.63，s=2 -> 0.86，s=3 -> 0.95。 from __future__ import annotations import math -from typing import Dict, List, Tuple +from typing import Dict, List, Optional, Tuple from .difficulty import estimate_difficulty from .models import Classification @@ -51,6 +51,14 @@ DOMAIN_RULES: Dict[str, List[Tuple[str, float]]] = { ("知识产权", 1.1), ("版权", 0.9), ("专利", 0.9), ("违约", 0.9), ("赔偿", 0.8), ("仲裁", 0.9), ("劳动法", 1.0), ("刑法", 1.0), ("民法典", 1.0), ("法规", 0.8), ("条款", 0.7), ("律师", 0.8), ("起诉", 0.9), ("判决", 0.9), + # 劳动法 + ("加班", 0.9), ("加班费", 1.0), ("工资", 0.8), ("辞退", 0.9), ("裁员", 0.9), + ("试用期", 0.9), ("社保", 0.8), ("公积金", 0.8), ("年假", 0.9), ("离职", 0.8), + ("解除劳动合同", 1.1), ("经济补偿", 1.0), ("竞业", 1.0), + # 房产/婚姻/消费者 + ("租房", 0.9), ("买房", 0.9), ("购房", 0.9), ("押金", 0.8), ("房贷", 0.9), + ("离婚", 1.0), ("继承", 0.9), ("遗产", 0.9), ("抚养权", 0.9), ("遗嘱", 0.9), + ("退款", 0.9), ("退货", 0.8), ("消费者", 0.8), ("七天无理由", 1.0), ("维权", 0.8), ("law", 1.0), ("legal", 1.1), ("contract", 1.0), ("compliance", 1.0), ("litigation", 1.0), ("copyright", 0.9), ("patent", 0.9), ("trademark", 0.9), ("liability", 0.9), ("regulatory", 0.8), ("jurisdiction", 0.9), @@ -61,11 +69,17 @@ DOMAIN_RULES: Dict[str, List[Tuple[str, float]]] = { ("医生", 0.9), ("血压", 0.9), ("高血压", 1.0), ("糖尿病", 1.0), ("感冒", 0.9), ("剂量", 0.9), ("副作用", 0.9), ("手术", 0.9), ("患者", 0.9), ("吃药", 0.9), ("发烧", 1.0), ("疫苗", 0.9), ("感染", 0.9), ("体检", 0.7), + # 急救/消化/心理/营养/儿科 + ("烫伤", 1.0), ("烧伤", 1.0), ("止血", 0.9), ("扭伤", 0.9), ("中暑", 1.0), + ("急救", 0.9), ("腹泻", 0.9), ("拉肚子", 0.9), ("便秘", 0.9), ("胃", 0.7), + ("失眠", 0.9), ("焦虑", 0.9), ("抑郁", 0.9), ("压力", 0.6), ("睡眠", 0.7), + ("减肥", 0.8), ("营养", 0.7), ("卡路里", 0.9), ("儿童", 0.8), ("婴儿", 0.9), + ("宝宝", 0.8), ("抗生素", 0.9), ("止咳", 0.9), ("medical", 1.0), ("patient", 0.9), ("symptom", 1.0), ("disease", 0.9), ("diagnosis", 1.0), ("treatment", 0.8), ("prescription", 1.0), ("dosage", 0.9), ("side effect", 0.9), ("hypertension", 1.0), ("diabetes", 1.0), ("surgery", 0.8), ("clinic", 0.7), ("vaccine", 0.9), - ("infection", 0.9), + ("infection", 0.9), ("first aid", 0.9), ("insomnia", 0.9), ], "general": [ ("总结", 0.4), ("翻译", 0.4), ("介绍", 0.4), ("解释", 0.3), @@ -74,6 +88,40 @@ DOMAIN_RULES: Dict[str, List[Tuple[str, float]]] = { ("write an essay", 0.4), ("邮件", 0.4), ("email", 0.3), ("推荐", 0.3), ("评价", 0.3), ], + "finance": [ + ("理财", 1.0), ("投资", 1.0), ("基金", 1.0), ("股票", 1.0), ("债券", 0.9), + ("存款", 0.9), ("储蓄", 0.8), ("利率", 0.8), ("利息", 0.8), ("贷款", 1.0), + ("房贷", 1.0), ("月供", 0.9), ("保险", 0.9), ("理赔", 0.9), ("保费", 0.8), + ("信用卡", 1.0), ("征信", 0.9), ("逾期", 0.9), ("分期", 0.8), ("记账", 0.7), + ("预算", 0.7), ("理财规划", 1.0), ("收益率", 0.9), ("定投", 0.9), + ("invest", 0.8), ("fund", 0.8), ("stock", 0.9), ("loan", 0.9), + ("mortgage", 0.9), ("insurance", 0.9), ("credit card", 0.9), + ("finance", 0.8), ("money", 0.6), ("lpr", 0.9), ("投资理财", 1.1), + ], + "life": [ + ("菜谱", 0.9), ("做饭", 0.8), ("烹饪", 0.9), ("美食", 0.8), ("做法", 0.7), + ("旅行", 0.9), ("旅游", 0.9), ("攻略", 0.8), ("机票", 0.8), ("酒店", 0.7), + ("签证", 0.9), ("景点", 0.8), ("自驾", 0.8), + ("装修", 0.9), ("收纳", 0.8), ("家居", 0.7), ("清洁", 0.7), ("打扫", 0.7), + ("宠物", 0.9), ("猫", 0.7), ("狗", 0.7), ("猫粮", 0.9), ("驱虫", 0.9), + ("健身", 0.9), ("锻炼", 0.8), ("跑步", 0.8), ("增肌", 0.9), ("减脂", 0.9), + ("瑜伽", 0.8), ("天气", 0.7), ("气温", 0.7), + ("recipe", 0.8), ("travel", 0.9), ("trip", 0.8), ("pet", 0.8), + ("workout", 0.9), ("gym", 0.8), ("weather", 0.7), ("cook", 0.8), + ], + "education": [ + ("学习方法", 1.0), ("怎么学", 0.7), ("高效学习", 1.0), ("记忆", 0.6), ("复习", 0.7), + ("预习", 0.7), ("笔记", 0.6), ("专注", 0.6), ("拖延", 0.7), ("学习效率", 0.9), + ("考试", 0.9), ("备考", 1.0), ("刷题", 0.9), ("模拟考", 0.9), ("中考", 0.9), + ("高考", 0.9), ("考研", 0.9), ("考前", 0.7), + ("英语", 0.8), ("单词", 0.7), ("口语", 0.8), ("听力", 0.7), ("雅思", 1.0), + ("托福", 1.0), ("四级", 0.9), ("六级", 0.9), ("背单词", 0.9), + ("选课", 0.9), ("课程", 0.6), ("专业选择", 0.9), ("报班", 0.8), ("网课", 0.7), + ("自学", 0.7), ("职业规划", 1.0), ("求职", 0.9), ("面试", 0.8), ("简历", 0.8), + ("实习", 0.7), ("跳槽", 0.8), ("转行", 0.9), + ("study", 0.8), ("exam", 0.9), ("language", 0.7), ("career", 0.8), + ("interview", 0.8), ("education", 0.7), ("learn", 0.6), + ], } _STOPWORDS = { @@ -92,11 +140,21 @@ class BaseClassifier: class RuleClassifier(BaseClassifier): - """基于关键词规则的分类器（零依赖）。""" + """基于关键词规则的分类器（零依赖）。 - def __init__(self, confidence_floor: float = 0.55): + domains 参数（可选）：限定只对部分领域打分 —— 两级路由中， + 每个大领域的组内路由模型用 RuleClassifier(domains=组内领域)， + 只认识本组领域，体积与匹配开销约为统一分类器的 1/4。 + """ + + def __init__(self, confidence_floor: float = 0.55, + domains: Optional[List[str]] = None): self.confidence_floor = confidence_floor - self.rules = DOMAIN_RULES + if domains is None: + self.rules = DOMAIN_RULES + else: + self.rules = {d: DOMAIN_RULES[d] for d in domains if d in DOMAIN_RULES} + self.domains = list(self.rules.keys()) def _score(self, query: str) -> Tuple[Dict[str, float], Dict[str, List[str]]]: q = query.lower() @@ -159,7 +217,7 @@ class HuggingFaceClassifier(BaseClassifier): 仅当安装 torch+transformers 且模型可加载时可用；否则抛错提示。 """ - def __init__(self, model_name: str, num_labels: int = 5, confidence_floor: float = 0.55): + def __init__(self, model_name: str, num_labels: int = 8, confidence_floor: float = 0.55): try: from transformers import AutoModelForSequenceClassification, AutoTokenizer except ImportError as e: @@ -170,7 +228,8 @@ class HuggingFaceClassifier(BaseClassifier): self.model = AutoModelForSequenceClassification.from_pretrained( model_name, num_labels=num_labels ) - self.labels = ["code", "math", "legal", "medical", "general"] + self.labels = ["code", "math", "legal", "medical", "general", + "finance", "life", "education"] self.confidence_floor = confidence_floor def classify(self, query: str) -> Classification: diff --git a/router_system/config.py b/router_system/config.py index f15b331..3d0cea8 100644 --- a/router_system/config.py +++ b/router_system/config.py @@ -1,100 +1,118 @@ -"""配置加载：优先 YAML（若安装了 pyyaml），否则回退 JSON。 - -设计原则：router_system 核心零依赖，因此 pyyaml 是"可选"的。 -默认 config/config.yaml 存在；若 pyyaml 不可用，可提供同名 .json。 -""" -from __future__ import annotations - -import json -import os -from pathlib import Path -from typing import Any, Dict, Optional - -DEFAULT_CONFIG_PATH = Path(__file__).resolve().parent.parent / "config" / "config.yaml" - -_DEFAULTS: Dict[str, Any] = { - "system": {"name": "multi-expert-router", "version": "0.1.0"}, - "router": { - "low_confidence_threshold": 0.60, # 分类置信度低于此值 -> 直接走大模型 - "judge_fallback_threshold": 0.70, # Judge 质量分低于此值 -> 升级大模型 - "default_temperature": 0.2, - }, - "classifier": {"type": "rule", "model": "Qwen/Qwen3-0.6B", "confidence_floor": 0.55}, - "domains": ["code", "math", "legal", "medical", "general"], - "experts": { - "code": {"type": "mock", "model": "Qwen/Qwen2.5-Coder-7B-Instruct"}, - "math": {"type": "mock", "model": "Qwen/Qwen3-4B-Instruct"}, - "legal": {"type": "mock", "model": "Qwen/Qwen3-4B-Instruct"}, - "medical": {"type": "mock", "model": "Qwen/Qwen3-4B-Instruct"}, - "general": {"type": "mock", "model": "Qwen/Qwen3-1.7B-Instruct"}, - }, - "fallback": { - "type": "mock", - "model": "deepseek-chat", - "base_url": "https://api.deepseek.com/v1", - "api_key_env": "DEEPSEEK_API_KEY", - }, - "judge": {"type": "rule", "model": "Qwen/Qwen3-1.7B-Instruct"}, - "cache": { - "enabled": True, - "semantic_enabled": True, - "similarity_threshold": 0.88, - "promote_frequency": 5, - }, -} - - -def load_defaults() -> Dict[str, Any]: - return _DEFAULTS - - -def _try_load_yaml(path: Path) -> Optional[Dict[str, Any]]: - try: - import yaml # type: ignore - except ImportError: - return None - try: - with open(path, "r", encoding="utf-8") as f: - data = yaml.safe_load(f) - return data if isinstance(data, dict) else None - except Exception: - return None - - -def _try_load_json(path: Path) -> Optional[Dict[str, Any]]: - json_path = path.with_suffix(".json") - if not json_path.exists(): - return None - try: - with open(json_path, "r", encoding="utf-8") as f: - data = json.load(f) - return data if isinstance(data, dict) else None - except Exception: - return None - - -def _merge_defaults(data: Dict[str, Any]) -> Dict[str, Any]: - """将用户配置与内置默认配置做一层合并（用户优先）。""" - merged = dict(_DEFAULTS) - for k, v in data.items(): - if isinstance(v, dict) and isinstance(merged.get(k), dict): - merged[k] = {**merged[k], **v} - else: - merged[k] = v - return merged - - -def load_config(path: Optional[Path | str] = None) -> Dict[str, Any]: - """加载配置，返回 dict。文件不存在或解析失败时返回内置默认配置。""" - cfg_path = Path(path) if path else DEFAULT_CONFIG_PATH - if cfg_path.exists(): - data = _try_load_yaml(cfg_path) or _try_load_json(cfg_path) - if data is not None: - return _merge_defaults(data) - return dict(_DEFAULTS) - - -def get_api_key(cfg: Dict[str, Any]) -> Optional[str]: - """从环境变量读取 API Key（用于 api 类型后端）。""" - env_name = cfg.get("api_key_env") or "API_KEY" - return os.environ.get(env_name) or None +"""配置加载：优先 YAML（若安装了 pyyaml），否则回退 JSON。 + +设计原则：router_system 核心零依赖，因此 pyyaml 是"可选"的。 +默认 config/config.yaml 存在；若 pyyaml 不可用，可提供同名 .json。 +""" +from __future__ import annotations + +import json +import os +from pathlib import Path +from typing import Any, Dict, Optional + +DEFAULT_CONFIG_PATH = Path(__file__).resolve().parent.parent / "config" / "config.yaml" + +_DEFAULTS: Dict[str, Any] = { + "system": {"name": "multi-expert-router", "version": "0.1.0"}, + "router": { + "low_confidence_threshold": 0.60, # 分类置信度低于此值 -> 直接走最后处理者 + "judge_fallback_threshold": 0.70, # Judge 质量分低于此值 -> 升级最后处理者 + "default_temperature": 0.2, + }, + "execution": { + "mode": "rule", # rule（L0 零参数）| hybrid + "planner": "rule", # rule | hf + "expert_backend": "rule", # rule（规则执行器）| hf | api + "model_level": "L0", # L0 | L1 | L2 + "max_plan_depth": 3, + }, + "classifier": {"type": "rule", "model": "Qwen/Qwen3-0.6B", "confidence_floor": 0.55}, + "domains": ["code", "math", "legal", "medical", "finance", "life", "education", "general"], + # 两级路由：大领域分组（用户接口指定 group → 组内路由模型 → 组内专业小模型） + # 组内路由模型只识别本组领域，体积约为统一路由模型的 1/4 + "domain_groups": { + "tech": ["code", "math"], + "professional": ["legal", "medical", "finance"], + "lifestyle": ["life", "education"], + "general": ["general"], + }, + "experts": { + "code": {"type": "mock", "model": "Qwen/Qwen2.5-Coder-7B-Instruct"}, + "math": {"type": "mock", "model": "Qwen/Qwen3-4B-Instruct"}, + "legal": {"type": "mock", "model": "Qwen/Qwen3-4B-Instruct"}, + "medical": {"type": "mock", "model": "Qwen/Qwen3-4B-Instruct"}, + "finance": {"type": "mock", "model": "Qwen/Qwen3-4B-Instruct"}, + "life": {"type": "mock", "model": "Qwen/Qwen3-1.7B-Instruct"}, + "education": {"type": "mock", "model": "Qwen/Qwen3-1.7B-Instruct"}, + "general": {"type": "mock", "model": "Qwen/Qwen3-1.7B-Instruct"}, + }, + "fallback": { + "type": "mock", + "model": "deepseek-v4-flash", + "base_url": "https://api.deepseek.com", + "api_key_env": "DEEPSEEK_API_KEY", + }, + "judge": {"type": "rule", "model": "Qwen/Qwen3-1.7B-Instruct"}, + "cache": { + "enabled": True, + "semantic_enabled": True, + "similarity_threshold": 0.88, + "promote_frequency": 5, + }, +} + + +def load_defaults() -> Dict[str, Any]: + return _DEFAULTS + + +def _try_load_yaml(path: Path) -> Optional[Dict[str, Any]]: + try: + import yaml # type: ignore + except ImportError: + return None + try: + with open(path, "r", encoding="utf-8") as f: + data = yaml.safe_load(f) + return data if isinstance(data, dict) else None + except Exception: + return None + + +def _try_load_json(path: Path) -> Optional[Dict[str, Any]]: + json_path = path.with_suffix(".json") + if not json_path.exists(): + return None + try: + with open(json_path, "r", encoding="utf-8") as f: + data = json.load(f) + return data if isinstance(data, dict) else None + except Exception: + return None + + +def _merge_defaults(data: Dict[str, Any]) -> Dict[str, Any]: + """将用户配置与内置默认配置做一层合并（用户优先）。""" + merged = dict(_DEFAULTS) + for k, v in data.items(): + if isinstance(v, dict) and isinstance(merged.get(k), dict): + merged[k] = {**merged[k], **v} + else: + merged[k] = v + return merged + + +def load_config(path: Optional[Path | str] = None) -> Dict[str, Any]: + """加载配置，返回 dict。文件不存在或解析失败时返回内置默认配置。""" + cfg_path = Path(path) if path else DEFAULT_CONFIG_PATH + if cfg_path.exists(): + data = _try_load_yaml(cfg_path) or _try_load_json(cfg_path) + if data is not None: + return _merge_defaults(data) + return dict(_DEFAULTS) + + +def get_api_key(cfg: Dict[str, Any]) -> Optional[str]: + """从环境变量读取 API Key（用于 api 类型后端）。""" + env_name = cfg.get("api_key_env") or "API_KEY" + return os.environ.get(env_name) or None diff --git a/router_system/fallback.py b/router_system/fallback.py index f02106c..0c54115 100644 --- a/router_system/fallback.py +++ b/router_system/fallback.py @@ -1,99 +1,185 @@ -"""大模型回退层：Mock 与 OpenAI 兼容 API 两种后端。""" -from __future__ import annotations - -import asyncio -from typing import Dict, Optional - -from .models import ExpertResponse - - -class FallbackProvider: - name: str = "fallback" - - async def generate(self, query: str) -> ExpertResponse: - raise NotImplementedError - - -class MockFallback(FallbackProvider): - """确定性 mock 大模型：标识为 fallback，便于测试升级路径。""" - - def __init__(self, model: str = "mock-large"): - self.model = model - self.name = f"fallback-{model}" - - async def generate(self, query: str) -> ExpertResponse: - await asyncio.sleep(0.002) - body = ( - f"（大模型回退）「{query}」\n\n" - "这是一条来自大模型回退路径的完整回答。\n" - "要点：\n" - "1. 对复杂/跨域任务给出综合推理\n" - "2. 补充领域专家未覆盖的上下文\n" - "3. 给出可执行的后续建议\n" - ) - return ExpertResponse( - text=body, - model_used=self.model, - latency_ms=2.0, - tokens=120, - cost_est=2.0 * 120 / 1_000_000, - ) - - -class APIFallback(FallbackProvider): - """OpenAI 兼容大模型 API（如 DeepSeek / OpenAI / 本地 vLLM）。""" - - def __init__(self, model: str, base_url: str, api_key: str): - self.model = model - self.base_url = base_url.rstrip("/") - self.api_key = api_key - self.name = f"fallback-{model}" - self._client = None - - def _get_client(self): - if self._client is None: - import httpx - self._client = httpx.AsyncClient(timeout=90.0) - return self._client - - async def generate(self, query: str) -> ExpertResponse: - client = self._get_client() - resp = await client.post( - f"{self.base_url}/chat/completions", - headers={"Authorization": f"Bearer {self.api_key}"}, - json={ - "model": self.model, - "messages": [{"role": "user", "content": query}], - "temperature": 0.3, - "max_tokens": 2048, - }, - ) - resp.raise_for_status() - data = resp.json() - body = data["choices"][0]["message"]["content"] - usage = data.get("usage", {}) - tokens = usage.get("completion_tokens", int(len(body) / 2.2)) - return ExpertResponse( - text=body, - model_used=self.model, - latency_ms=0.0, - tokens=tokens, - cost_est=2.0 * tokens / 1_000_000, - ) - - -def build_fallback(cfg: Dict) -> FallbackProvider: - """cfg 为 fallback 段配置。""" - ftype = cfg.get("type", "mock") - model = cfg.get("model", "deepseek-chat") - if ftype == "mock": - return MockFallback(model=model) - if ftype == "api": - import os - api_key = cfg.get("api_key") or os.environ.get(cfg.get("api_key_env", "API_KEY")) - if not api_key: - raise RuntimeError( - f"APIFallback 缺少 API Key：请设置环境变量 {cfg.get('api_key_env')} 或配置 api_key" - ) - return APIFallback(model, cfg.get("base_url", "https://api.deepseek.com/v1"), api_key) - raise ValueError(f"未知 fallback 类型: {ftype}（支持 mock | api）") +"""大模型回退层（最后处理者）：Mock / 降级模板 / 本地模型 / OpenAI 兼容 API。 + +- NoneFallback ：降级模板（零参数，明确告知超出知识库范围）—— L0 最小可用 +- MockFallback ：确定性 mock 大模型（零参数，测试升级路径用） +- LocalFallback ：本地小模型（≤8B，Q4 量化，OpenAI 兼容端点如 Ollama/vLLM）—— 按需加载 +- APIFallback ：远程 OpenAI 兼容 API（可选，默认关闭） +""" +from __future__ import annotations + +import asyncio +from typing import Dict, Optional + +from .models import ExpertResponse + + +class FallbackProvider: + name: str = "fallback" + + async def generate(self, query: str) -> ExpertResponse: + raise NotImplementedError + + +class NoneFallback(FallbackProvider): + """降级模板：零参数兜底，明确告知查询超出知识库范围。""" + + def __init__(self, model: str = "none"): + self.model = model + self.name = "fallback-none" + + async def generate(self, query: str) -> ExpertResponse: + body = ( + f"（降级响应）「{query}」\n\n" + "当前查询超出本地知识库可处理范围（低置信度或质量校验未通过）。\n" + "可选处理：\n" + "1. 换个更明确的问法重试\n" + "2. 启用 L2 本地小模型或配置最后处理者（fallback.type: local）\n" + ) + return ExpertResponse( + text=body, + model_used=self.model, + latency_ms=0.0, + tokens=80, + cost_est=0.0, + ) + + +class MockFallback(FallbackProvider): + """确定性 mock 大模型：标识为 fallback，便于测试升级路径。""" + + def __init__(self, model: str = "mock-large"): + self.model = model + self.name = f"fallback-{model}" + + async def generate(self, query: str) -> ExpertResponse: + await asyncio.sleep(0.002) + body = ( + f"（大模型回退）「{query}」\n\n" + "这是一条来自大模型回退路径的完整回答。\n" + "要点：\n" + "1. 对复杂/跨域任务给出综合推理\n" + "2. 补充领域专家未覆盖的上下文\n" + "3. 给出可执行的后续建议\n" + ) + return ExpertResponse( + text=body, + model_used=self.model, + latency_ms=2.0, + tokens=120, + cost_est=2.0 * 120 / 1_000_000, + ) + + +class LocalFallback(FallbackProvider): + """本地小模型最后处理者（≤8B，如 DeepSeek-R1-Distill-Qwen-7B Q4）。 + + 通过 OpenAI 兼容端点调用（Ollama 默认 11434/v1，vLLM 默认 8001/v1）， + 模型按需加载、用完即卸载（由本地推理服务管理），不常驻显存。 + """ + + def __init__(self, model: str, base_url: str = "http://127.0.0.1:11434/v1", + api_key: str = ""): + self.model = model + self.base_url = base_url.rstrip("/") + self.api_key = api_key + self.name = f"fallback-local-{model}" + self._client = None + + def _get_client(self): + if self._client is None: + import httpx + self._client = httpx.AsyncClient(timeout=120.0) + return self._client + + async def generate(self, query: str) -> ExpertResponse: + client = self._get_client() + headers = {"Authorization": f"Bearer {self.api_key}"} if self.api_key else {} + resp = await client.post( + f"{self.base_url}/chat/completions", + headers=headers, + json={ + "model": self.model, + "messages": [{"role": "user", "content": query}], + "temperature": 0.3, + "max_tokens": 2048, + }, + ) + resp.raise_for_status() + data = resp.json() + body = data["choices"][0]["message"]["content"] + usage = data.get("usage", {}) + tokens = usage.get("completion_tokens", int(len(body) / 2.2)) + return ExpertResponse( + text=body, + model_used=self.model, + latency_ms=0.0, + tokens=tokens, + cost_est=0.0, # 本地推理成本按电费计，模型层成本记为 0（相对 API） + ) + + +class APIFallback(FallbackProvider): + """OpenAI 兼容大模型 API（如 DeepSeek / OpenAI / 本地 vLLM）。""" + + def __init__(self, model: str, base_url: str, api_key: str): + self.model = model + self.base_url = base_url.rstrip("/") + self.api_key = api_key + self.name = f"fallback-{model}" + self._client = None + + def _get_client(self): + if self._client is None: + import httpx + self._client = httpx.AsyncClient(timeout=90.0) + return self._client + + async def generate(self, query: str) -> ExpertResponse: + client = self._get_client() + resp = await client.post( + f"{self.base_url}/chat/completions", + headers={"Authorization": f"Bearer {self.api_key}"}, + json={ + "model": self.model, + "messages": [{"role": "user", "content": query}], + "temperature": 0.3, + "max_tokens": 2048, + }, + ) + resp.raise_for_status() + data = resp.json() + body = data["choices"][0]["message"]["content"] + usage = data.get("usage", {}) + tokens = usage.get("completion_tokens", int(len(body) / 2.2)) + return ExpertResponse( + text=body, + model_used=self.model, + latency_ms=0.0, + tokens=tokens, + cost_est=2.0 * tokens / 1_000_000, + ) + + +def build_fallback(cfg: Dict) -> FallbackProvider: + """cfg 为 fallback 段配置。""" + ftype = cfg.get("type", "mock") + model = cfg.get("model", "deepseek-v4-flash") + if ftype == "none": + return NoneFallback(model=model) + if ftype == "mock": + return MockFallback(model=model) + if ftype == "local": + return LocalFallback( + model, + cfg.get("base_url", "http://127.0.0.1:11434/v1"), + cfg.get("api_key", ""), + ) + if ftype == "api": + import os + api_key = cfg.get("api_key") or os.environ.get(cfg.get("api_key_env", "API_KEY")) + if not api_key: + raise RuntimeError( + f"APIFallback 缺少 API Key：请设置环境变量 {cfg.get('api_key_env')} 或配置 api_key" + ) + return APIFallback(model, cfg.get("base_url", "https://api.deepseek.com"), api_key) + raise ValueError(f"未知 fallback 类型: {ftype}（支持 none | mock | local | api）") diff --git a/router_system/judge.py b/router_system/judge.py index a0dc2c5..0c52c8d 100644 --- a/router_system/judge.py +++ b/router_system/judge.py @@ -1,174 +1,193 @@ -"""质量控制器（Judge）：评估专家输出，决定是否升级大模型。 - -- RuleJudge：零依赖启发式（内容覆盖度 / 长度充分性 / 领域格式 / 安全提示）， - 稳定可测，适合 MVP 与离线演示。 -- LLMJudge：可选，基于 transformers 小模型或 API 的 LLM-as-Judge。 - -设计对齐实现方案：overall_score < judge_fallback_threshold -> 升级大模型。 -""" -from __future__ import annotations - -from dataclasses import dataclass, field -from typing import Dict, List - -from .experts import extract_content_terms - -# 各领域期望的响应长度范围（字符数） -_EXPECTED_LEN = { - "code": (60, 2000), - "math": (60, 2000), - "legal": (80, 3000), - "medical": (80, 3000), - "general": (40, 2000), -} - -# 领域格式检查：响应应包含的标记 -_DOMAIN_FORMAT_HINTS = { - "code": ["```", "def ", "function", "class "], - "math": ["步骤", "推导", "=", "解", "step"], - "legal": ["⚠", "法律", "意见", "合规", "contract", "law"], - "medical": ["⚠", "就医", "医生", "症状", "诊断", "symptom"], - "general": [], -} - - -@dataclass -class QualityEvaluation: - overall_score: float - scores: Dict[str, float] = field(default_factory=dict) - needs_fallback: bool = False - reasons: List[str] = field(default_factory=list) - - def to_dict(self) -> Dict: - return { - "overall_score": round(self.overall_score, 4), - "scores": {k: round(v, 4) for k, v in self.scores.items()}, - "needs_fallback": self.needs_fallback, - "reasons": self.reasons, - } - - -class BaseJudge: - async def evaluate(self, query: str, response: str, domain: str) -> QualityEvaluation: - raise NotImplementedError - - -class RuleJudge(BaseJudge): - """启发式质量评估（零依赖）。""" - - def __init__(self, fallback_threshold: float = 0.70): - self.fallback_threshold = fallback_threshold - - async def evaluate(self, query: str, response: str, domain: str) -> QualityEvaluation: - scores: Dict[str, float] = {} - reasons: List[str] = [] - - # 1) 内容覆盖度：查询中的内容词有多少出现在响应里 - terms = extract_content_terms(query) - if terms: - hit = sum(1 for t in terms if t in response.lower()) - coverage = hit / len(terms) - scores["coverage"] = coverage - if coverage < 0.4: - reasons.append(f"内容覆盖度低 ({coverage:.0%})") - else: - scores["coverage"] = 1.0 - - # 2) 长度充分性 - lo, hi = _EXPECTED_LEN.get(domain, (40, 2000)) - n = len(response) - if n < lo: - scores["length"] = max(0.0, n / lo) - reasons.append(f"响应过短 ({n} 字符)") - elif n > hi: - scores["length"] = 0.8 - reasons.append(f"响应过长 ({n} 字符)") - else: - scores["length"] = 1.0 - - # 3) 领域格式检查 - hints = _DOMAIN_FORMAT_HINTS.get(domain, []) - if hints: - hit_hints = sum(1 for h in hints if h in response) - scores["format"] = min(1.0, 0.4 + 0.2 * hit_hints) - if hit_hints == 0: - reasons.append("缺少领域格式特征") - else: - scores["format"] = 1.0 - - # 4) 安全/免责提示（法律、医疗领域应有警示语） - if domain in ("legal", "medical") and ("⚠" not in response and "提示" not in response): - scores["safety"] = 0.6 - reasons.append("缺少免责提示") - else: - scores["safety"] = 1.0 - - weights = {"coverage": 0.4, "length": 0.2, "format": 0.2, "safety": 0.2} - overall = sum(scores.get(k, 0.0) * w for k, w in weights.items()) - needs = overall < self.fallback_threshold - if needs: - reasons.append("质量分低于阈值，建议升级大模型") - return QualityEvaluation( - overall_score=round(overall, 4), - scores=scores, - needs_fallback=needs, - reasons=reasons, - ) - - -class LLMJudge(BaseJudge): - """可选：LLM-as-Judge（API 后端）。""" - - def __init__(self, model: str, base_url: str, api_key: str, fallback_threshold: float = 0.70): - self.model = model - self.base_url = base_url.rstrip("/") - self.api_key = api_key - self.fallback_threshold = fallback_threshold - self._client = None - - def _get_client(self): - if self._client is None: - import httpx - self._client = httpx.AsyncClient(timeout=60.0) - return self._client - - async def evaluate(self, query: str, response: str, domain: str) -> QualityEvaluation: - client = self._get_client() - prompt = ( - f"你是质量评审员。评估以下回答对查询的满足程度，输出 0-1 分（相关性/正确性/完整性）。\n" - f"查询: {query}\n领域: {domain}\n回答: {response[:2000]}\n" - f"只输出一个 0 到 1 之间的数字。" - ) - resp = await client.post( - f"{self.base_url}/chat/completions", - headers={"Authorization": f"Bearer {self.api_key}"}, - json={"model": self.model, "messages": [{"role": "user", "content": prompt}]}, - ) - resp.raise_for_status() - try: - score = float(resp.json()["choices"][0]["message"]["content"].strip()) - score = max(0.0, min(1.0, score)) - except Exception: - score = 0.5 - return QualityEvaluation( - overall_score=score, - scores={"llm_judge": score}, - needs_fallback=score < self.fallback_threshold, - ) - - -def build_judge(cfg: Dict, fallback_threshold: float = 0.70) -> BaseJudge: - """cfg 为 judge 段配置。""" - jtype = cfg.get("type", "rule") - if jtype == "rule": - return RuleJudge(fallback_threshold=fallback_threshold) - if jtype == "llm": - import os - api_key = cfg.get("api_key") or os.environ.get(cfg.get("api_key_env", "API_KEY")) - return LLMJudge( - cfg.get("model", "deepseek-chat"), - cfg.get("base_url", "https://api.deepseek.com/v1"), - api_key or "", - fallback_threshold=fallback_threshold, - ) - raise ValueError(f"未知 judge 类型: {jtype}（支持 rule | llm）") +"""质量控制器（Judge）：评估专家输出，决定是否升级大模型。 + +- RuleJudge：零依赖启发式（内容覆盖度 / 长度充分性 / 领域格式 / 安全提示）， + 稳定可测，适合 MVP 与离线演示。 +- LLMJudge：可选，基于 transformers 小模型或 API 的 LLM-as-Judge。 + +设计对齐实现方案：overall_score < judge_fallback_threshold -> 升级大模型。 +""" +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Dict, List + +from .experts import extract_content_terms + +# 各领域期望的响应长度范围（字符数） +_EXPECTED_LEN = { + "code": (60, 2000), + "math": (60, 2000), + "legal": (80, 3000), + "medical": (80, 3000), + "general": (40, 2000), +} + +# 领域格式检查：响应应包含的标记 +_DOMAIN_FORMAT_HINTS = { + "code": ["```", "def ", "function", "class "], + "math": ["步骤", "推导", "=", "解", "step"], + "legal": ["⚠", "法律", "意见", "合规", "contract", "law"], + "medical": ["⚠", "就医", "医生", "症状", "诊断", "symptom"], + "general": [], +} + + +@dataclass +class QualityEvaluation: + overall_score: float + scores: Dict[str, float] = field(default_factory=dict) + needs_fallback: bool = False + reasons: List[str] = field(default_factory=list) + + def to_dict(self) -> Dict: + return { + "overall_score": round(self.overall_score, 4), + "scores": {k: round(v, 4) for k, v in self.scores.items()}, + "needs_fallback": self.needs_fallback, + "reasons": self.reasons, + } + + +class BaseJudge: + async def evaluate(self, query: str, response: str, domain: str) -> QualityEvaluation: + raise NotImplementedError + + +class RuleJudge(BaseJudge): + """启发式质量评估（零依赖）。""" + + def __init__(self, fallback_threshold: float = 0.70, kb=None): + self.fallback_threshold = fallback_threshold + self.kb = kb # 可选知识库：facts 维度校验用 + + async def evaluate(self, query: str, response: str, domain: str) -> QualityEvaluation: + scores: Dict[str, float] = {} + reasons: List[str] = [] + + # 1) 内容覆盖度：查询中的内容词有多少出现在响应里 + terms = extract_content_terms(query) + if terms: + hit = sum(1 for t in terms if t in response.lower()) + coverage = hit / len(terms) + scores["coverage"] = coverage + if coverage < 0.4: + reasons.append(f"内容覆盖度低 ({coverage:.0%})") + else: + scores["coverage"] = 1.0 + + # 2) 长度充分性 + lo, hi = _EXPECTED_LEN.get(domain, (40, 2000)) + n = len(response) + if n < lo: + scores["length"] = max(0.0, n / lo) + reasons.append(f"响应过短 ({n} 字符)") + elif n > hi: + scores["length"] = 0.8 + reasons.append(f"响应过长 ({n} 字符)") + else: + scores["length"] = 1.0 + + # 3) 领域格式检查 + hints = _DOMAIN_FORMAT_HINTS.get(domain, []) + if hints: + hit_hints = sum(1 for h in hints if h in response) + scores["format"] = min(1.0, 0.4 + 0.2 * hit_hints) + if hit_hints == 0: + reasons.append("缺少领域格式特征") + else: + scores["format"] = 1.0 + + # 4) 安全/免责提示（法律、医疗领域应有警示语） + if domain in ("legal", "medical") and ("⚠" not in response and "提示" not in response): + scores["safety"] = 0.6 + reasons.append("缺少免责提示") + else: + scores["safety"] = 1.0 + + # 5) 领域知识引用（facts 维度）：法律/医疗响应应覆盖查询命中的知识条目 + if domain in ("legal", "medical") and self.kb is not None: + facts = self.kb.facts(domain) + hit_facts = [f for f in facts if any(k in query for k in f.get("keywords", []))] + if hit_facts: + ok = sum( + 1 for f in hit_facts + if any(k in response for k in f.get("keywords", [])) + ) + scores["facts"] = ok / len(hit_facts) + if scores["facts"] < 0.6: + reasons.append(f"领域知识引用不足 ({scores['facts']:.0%})") + else: + scores["facts"] = 1.0 + else: + scores["facts"] = 1.0 + + weights = {"coverage": 0.35, "length": 0.15, "format": 0.15, + "safety": 0.15, "facts": 0.2} + overall = sum(scores.get(k, 0.0) * w for k, w in weights.items()) + needs = overall < self.fallback_threshold + if needs: + reasons.append("质量分低于阈值，建议升级大模型") + return QualityEvaluation( + overall_score=round(overall, 4), + scores=scores, + needs_fallback=needs, + reasons=reasons, + ) + + +class LLMJudge(BaseJudge): + """可选：LLM-as-Judge（API 后端）。""" + + def __init__(self, model: str, base_url: str, api_key: str, fallback_threshold: float = 0.70): + self.model = model + self.base_url = base_url.rstrip("/") + self.api_key = api_key + self.fallback_threshold = fallback_threshold + self._client = None + + def _get_client(self): + if self._client is None: + import httpx + self._client = httpx.AsyncClient(timeout=60.0) + return self._client + + async def evaluate(self, query: str, response: str, domain: str) -> QualityEvaluation: + client = self._get_client() + prompt = ( + f"你是质量评审员。评估以下回答对查询的满足程度，输出 0-1 分（相关性/正确性/完整性）。\n" + f"查询: {query}\n领域: {domain}\n回答: {response[:2000]}\n" + f"只输出一个 0 到 1 之间的数字。" + ) + resp = await client.post( + f"{self.base_url}/chat/completions", + headers={"Authorization": f"Bearer {self.api_key}"}, + json={"model": self.model, "messages": [{"role": "user", "content": prompt}]}, + ) + resp.raise_for_status() + try: + score = float(resp.json()["choices"][0]["message"]["content"].strip()) + score = max(0.0, min(1.0, score)) + except Exception: + score = 0.5 + return QualityEvaluation( + overall_score=score, + scores={"llm_judge": score}, + needs_fallback=score < self.fallback_threshold, + ) + + +def build_judge(cfg: Dict, fallback_threshold: float = 0.70, kb=None) -> BaseJudge: + """cfg 为 judge 段配置。""" + jtype = cfg.get("type", "rule") + if jtype == "rule": + return RuleJudge(fallback_threshold=fallback_threshold, kb=kb) + if jtype == "llm": + import os + api_key = cfg.get("api_key") or os.environ.get(cfg.get("api_key_env", "API_KEY")) + return LLMJudge( + cfg.get("model", "deepseek-v4-flash"), + cfg.get("base_url", "https://api.deepseek.com"), + api_key or "", + fallback_threshold=fallback_threshold, + ) + raise ValueError(f"未知 judge 类型: {jtype}（支持 rule | llm）") diff --git a/router_system/models.py b/router_system/models.py index e925f5e..c84fe26 100644 --- a/router_system/models.py +++ b/router_system/models.py @@ -1,68 +1,76 @@ -"""核心数据模型（纯标准库，无外部依赖）""" -from __future__ import annotations - -import time -from dataclasses import dataclass, field -from typing import Any, Dict, List, Optional - - -@dataclass -class Classification: - """分类器输出：领域 + 置信度 + 难度""" - domain: str - confidence: float - difficulty: str # easy | medium | hard - difficulty_score: float = 0.5 - raw_scores: Dict[str, float] = field(default_factory=dict) - matched_rules: List[str] = field(default_factory=list) - - -@dataclass -class ExpertResponse: - """专家模型输出""" - text: str - model_used: str - latency_ms: float = 0.0 - tokens: int = 0 - cost_est: float = 0.0 # 相对成本估计（美元，近似） - - -@dataclass -class RouterResult: - """一次路由的完整结果""" - query: str - response: str - domain: str - difficulty: str - confidence: float - upgraded: bool # 是否升级到大模型 - quality_score: float - model_used: str - route: List[str] = field(default_factory=list) # 路由决策轨迹 - latency_ms: float = 0.0 - cache_hit: bool = False - cache_level: Optional[str] = None # exact | semantic - cost_est: float = 0.0 - error: Optional[str] = None - - def to_dict(self) -> Dict[str, Any]: - return { - "query": self.query, - "response": self.response, - "domain": self.domain, - "difficulty": self.difficulty, - "confidence": round(self.confidence, 4), - "upgraded": self.upgraded, - "quality_score": round(self.quality_score, 4), - "model_used": self.model_used, - "route": self.route, - "latency_ms": round(self.latency_ms, 2), - "cache_hit": self.cache_hit, - "cache_level": self.cache_level, - "cost_est": round(self.cost_est, 6), - "error": self.error, - } - - -def now_ms() -> float: - return time.perf_counter() * 1000.0 +"""核心数据模型（纯标准库，无外部依赖）""" +from __future__ import annotations + +import time +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional + + +@dataclass +class Classification: + """分类器输出：领域 + 置信度 + 难度""" + domain: str + confidence: float + difficulty: str # easy | medium | hard + difficulty_score: float = 0.5 + raw_scores: Dict[str, float] = field(default_factory=dict) + matched_rules: List[str] = field(default_factory=list) + + +@dataclass +class ExpertResponse: + """专家模型输出""" + text: str + model_used: str + latency_ms: float = 0.0 + tokens: int = 0 + cost_est: float = 0.0 # 相对成本估计（美元，近似） + + +@dataclass +class RouterResult: + """一次路由的完整结果""" + query: str + response: str + domain: str + difficulty: str + confidence: float + upgraded: bool # 是否升级到大模型 + quality_score: float + model_used: str + route: List[str] = field(default_factory=list) # 路由决策轨迹 + latency_ms: float = 0.0 + cache_hit: bool = False + cache_level: Optional[str] = None # exact | semantic + cost_est: float = 0.0 + error: Optional[str] = None + subdomain: Optional[str] = None # 二级子领域（如 investing/labor） + subdomain2: Optional[str] = None # 三级子领域（如 fund/overtime） + domain_group: Optional[str] = None # 大领域组（两级路由第一级：tech/professional/...） + request_id: Optional[str] = None # 请求 ID（配合 /traces/{id} 查询完整推理链） + + def to_dict(self) -> Dict[str, Any]: + return { + "query": self.query, + "response": self.response, + "domain": self.domain, + "difficulty": self.difficulty, + "confidence": round(self.confidence, 4), + "upgraded": self.upgraded, + "quality_score": round(self.quality_score, 4), + "model_used": self.model_used, + "route": self.route, + "latency_ms": round(self.latency_ms, 2), + "cache_hit": self.cache_hit, + "cache_level": self.cache_level, + "cost_est": round(self.cost_est, 6), + "error": self.error, + "subdomain": self.subdomain, + "subdomain2": self.subdomain2, + "domain_group": self.domain_group, + "request_id": self.request_id, + } + + +def now_ms() -> float: + return time.perf_counter() * 1000.0 diff --git a/router_system/pipeline.py b/router_system/pipeline.py index e4d030a..742d009 100644 --- a/router_system/pipeline.py +++ b/router_system/pipeline.py @@ -68,8 +68,9 @@ class CollaborativePipeline: # --------------------------------------------------------------- # 入口 # --------------------------------------------------------------- - async def run(self, query: str) -> PipelineResult: - request_id = uuid.uuid4().hex[:12] + async def run(self, query: str, request_id: Optional[str] = None) -> PipelineResult: + if request_id is None: + request_id = uuid.uuid4().hex[:12] ws = Workspace.new(request_id, query, self.api_token_cap, self.rounds_cap) route: List[str] = ["v2"] t0 = time.perf_counter() * 1000.0 @@ -100,11 +101,15 @@ class CollaborativePipeline: route.append("loop") plan = brief.get("plan") or [] pending = [p.get("id") for p in plan] + plan_ids = {p.get("id") for p in plan} # 用于 _deps_done 过滤 while pending and not ws.exhausted(): progressed = False for sid in list(pending): step = next((p for p in plan if p.get("id") == sid), {}) - if not self._deps_done(ws, step.get("deps") or []): + deps = step.get("deps") or [] + # 只检查在 plan 中的依赖；不在 plan 的 ID 视为"不存在"→自动满足 + relevant = [d for d in deps if d in plan_ids] + if not self._deps_done(ws, relevant): continue existing = self._read_artifact(request_id, self._artifact_name(sid, ws)) outcome = await self.worker.run_step(ws, sid, existing_artifact=existing, @@ -115,6 +120,8 @@ class CollaborativePipeline: ws.rollup() route.append(f"step:{sid}:done") progressed = True + if not pending: # 所有步骤完成，退出循环 + break else: # issue -> Architect 裁决 route.append(f"step:{sid}:issue") try: @@ -227,7 +234,17 @@ class CollaborativePipeline: return artifact_name_for(sid, domain) def _deps_done(self, ws: Workspace, deps: List[str]) -> bool: + # progress 中 done 的条目；done 条目 rollup 后移到 archive（字符串格式如 "s1: ..."） done = {p["step"] for p in ws.get("progress", []) if p.get("status") == "done"} + for entry in ws.get("archive", []): + if isinstance(entry, dict): + sid = entry.get("step", "") + elif isinstance(entry, str): + sid = entry.split(":")[0].strip() if ":" in entry else "" + else: + sid = "" + if sid in deps: + done.add(sid) return all(d in done for d in deps) def _last_decision_for(self, ws: Workspace, sid: str) -> str: diff --git a/router_system/router.py b/router_system/router.py index b061cd4..bf5cab4 100644 --- a/router_system/router.py +++ b/router_system/router.py @@ -1,217 +1,415 @@ -"""主路由器：协调 缓存 -> 分类 -> 专家 -> Judge -> 大模型回退 的完整链路。 - -流程（对齐实现方案）： -1. 检查缓存（L1 精确 / L2 语义） -2. 低置信度查询直接走大模型（should_fallback） -3. 分类器输出领域 + 难度 -4. 选择专家模型生成 -5. Judge 评估质量 -6. 质量不达标 -> 升级大模型 -7. 记录指标、写缓存、返回结果 -""" -from __future__ import annotations - -from typing import Any, Dict, Optional - -from .cache import RouterCache -from .classifier import BaseClassifier, build_classifier -from .config import load_config -from .experts import Expert, build_expert_pool -from .fallback import FallbackProvider, build_fallback -from .judge import BaseJudge, build_judge -from .models import Classification, ExpertResponse, RouterResult, now_ms -from .stats import Stats - - -class Router: - def __init__( - self, - classifier: BaseClassifier, - experts: Dict[str, Expert], - judge: BaseJudge, - fallback: FallbackProvider, - cache: Optional[RouterCache] = None, - stats: Optional[Stats] = None, - config: Optional[Dict[str, Any]] = None, - ): - self.classifier = classifier - self.experts = experts - self.judge = judge - self.fallback = fallback - self.cache = cache or RouterCache() - self.stats = stats or Stats() - cfg = config or {} - rcfg = cfg.get("router", {}) - self.low_confidence_threshold = rcfg.get("low_confidence_threshold", 0.60) - self.judge_fallback_threshold = rcfg.get("judge_fallback_threshold", 0.70) - self.cache_enabled = cfg.get("cache", {}).get("enabled", True) - - # --------------------------------------------------------------- - async def route(self, query: str) -> RouterResult: - start = now_ms() - route: list = [] - - # ---- Step 1: 缓存 ---- - if self.cache_enabled: - hit = self.cache.get(query) - if hit is not None: - level, cached = hit - latency = now_ms() - start - result = RouterResult( - query=query, - response=cached.get("response", ""), - domain=cached.get("domain", "general"), - difficulty=cached.get("difficulty", "medium"), - confidence=cached.get("confidence", 0.0), - upgraded=False, - quality_score=cached.get("quality_score", 0.0), - model_used=cached.get("model_used", ""), - route=["cache:" + level], - latency_ms=latency, - cache_hit=True, - cache_level=level, - cost_est=0.0, - ) - self.stats.record(latency, result.domain, result.difficulty, False, True, level, 0.0, result.model_used) - return result - route.append("cache:miss") - - # ---- Step 2: 分类 ---- - classification = self.classifier.classify(query) - route.append(f"classify:{classification.domain}@{classification.confidence:.2f}/{classification.difficulty}") - - # 低置信度 -> 直接走大模型 - if self.classifier.should_fallback(classification, self.low_confidence_threshold): - route.append("direct_fallback") - fb = await self._call_fallback(query) - latency = now_ms() - start - result = self._finalize(query, classification, fb, quality_score=0.0, - upgraded=True, route=route, latency_ms=latency, - model_used=fb.model_used, cost_est=fb.cost_est) - self._record(result, latency) - return result - - # ---- Step 3: 选择专家 ---- - domain = classification.domain - expert = self.experts.get(domain) - if expert is None: - expert = self.experts.get("general") - route.append("expert:fallback-to-general") - else: - route.append(f"expert:{expert.name}") - - # ---- Step 4: 生成 ---- - try: - expert_resp = await expert.generate(query, classification.difficulty) - except Exception as e: - self.stats.record_error() - route.append(f"expert_error:{type(e).__name__}") - fb = await self._call_fallback(query) - latency = now_ms() - start - result = self._finalize(query, classification, fb, quality_score=0.0, - upgraded=True, route=route, latency_ms=latency, - model_used=fb.model_used, cost_est=fb.cost_est, - error=str(e)) - self._record(result, latency) - return result - - # ---- Step 5: Judge 评估 ---- - try: - evaluation = await self.judge.evaluate(query, expert_resp.text, domain) - except Exception: - evaluation = None - route.append("judge_error") - - quality_score = evaluation.overall_score if evaluation else 0.0 - route.append(f"judge:{quality_score:.2f}") - - upgraded = False - final_resp = expert_resp - if evaluation is not None and evaluation.needs_fallback: - route.append("upgrade") - final_resp = await self._call_fallback(query) - upgraded = True - - latency = now_ms() - start - result = self._finalize(query, classification, final_resp, quality_score=quality_score, - upgraded=upgraded, route=route, latency_ms=latency, - model_used=final_resp.model_used, cost_est=final_resp.cost_est) - self._record(result, latency) - - # 未升级的结果写缓存 - if self.cache_enabled and not upgraded and result.response: - self.cache.put(query, result.to_dict()) - - return result - - # --------------------------------------------------------------- - async def _call_fallback(self, query: str) -> ExpertResponse: - try: - return await self.fallback.generate(query) - except Exception as e: - # 回退也失败：返回错误占位响应 - return ExpertResponse( - text=f"[系统错误] 专家与大模型回退均失败：{type(e).__name__}: {e}", - model_used=f"error:{self.fallback.name}", - cost_est=0.0, - ) - - @staticmethod - def _finalize(query: str, classification: Classification, resp: ExpertResponse, - quality_score: float, upgraded: bool, route: list, - latency_ms: float, model_used: str, cost_est: float, - error: Optional[str] = None) -> RouterResult: - return RouterResult( - query=query, - response=resp.text, - domain=classification.domain, - difficulty=classification.difficulty, - confidence=classification.confidence, - upgraded=upgraded, - quality_score=quality_score, - model_used=model_used, - route=route, - latency_ms=latency_ms, - cache_hit=False, - cost_est=cost_est, - error=error, - ) - - def _record(self, result: RouterResult, latency_ms: float): - self.stats.record( - latency_ms, - result.domain, - result.difficulty, - result.upgraded, - result.cache_hit, - result.cache_level, - result.cost_est, - result.model_used, - ) - - # --------------------------------------------------------------- - def health(self) -> Dict[str, Any]: - return { - "status": "ok", - "domains": list(self.experts.keys()), - "classifier": type(self.classifier).__name__, - "judge": type(self.judge).__name__, - "fallback": type(self.fallback).__name__, - } - - -def build_router(config_path: Optional[str] = None) -> Router: - """从配置构建完整 Router（默认 mock 全链路，零依赖可跑）。""" - config = load_config(config_path) - classifier = build_classifier(config.get("classifier", {})) - experts = build_expert_pool(config.get("experts", {}), config.get("domains", [])) - judge = build_judge(config.get("judge", {}), config.get("router", {}).get("judge_fallback_threshold", 0.70)) - fallback = build_fallback(config.get("fallback", {})) - cache_cfg = config.get("cache", {}) - cache = RouterCache( - semantic_enabled=cache_cfg.get("semantic_enabled", True), - similarity_threshold=cache_cfg.get("similarity_threshold", 0.88), - promote_frequency=cache_cfg.get("promote_frequency", 5), - ) - stats = Stats() - return Router(classifier, experts, judge, fallback, cache, stats, config) +"""主路由器：两级路由（大领域组 → 组内路由模型 → 专业执行器）专家系统编排。 + +两级体系（对齐用户架构决策）： + 第一级：用户通过接口指定大领域组（domain_group: tech/professional/lifestyle/general）， + 或系统自动检测（8 领域分类 → 映射到组） + 第二级：组内路由模型（RuleClassifier(domains=组内领域) + 组内知识/模板）识别具体 + 领域、子领域、拆解子任务 → 组内专业小模型/规则执行器 + 组内路由模型只认识本组领域：体积与匹配开销约为统一路由模型的 1/4， + 且未来 L2 模型层可每组一个更小的路由模型，按需加载不常驻。 + +链路：缓存 → 组路由(分类/子领域/拆解) → 黑板+前向链 → DAG 执行 → 合并 + → Judge 校验 → （不达标）最后处理者升级 → 缓存/指标 + +L0 模式（默认）：规则分类 + 规则拆解 + 规则执行器 + 规则 Judge —— 零模型参数、零 API。 +L2 模式（可选）：execution.expert_backend = hf/api 时，子任务改由专家池小模型执行 +（≤8B，按需加载），其余流程不变。 +""" +from __future__ import annotations + +import uuid +from typing import Any, Dict, List, Optional + +from .cache import RouterCache +from .classifier import BaseClassifier, RuleClassifier, build_classifier +from .config import load_config +from .executors import NodeExecutor, build_node_executor +from .experts import Expert, build_expert_pool +from .fallback import FallbackProvider, build_fallback +from .inference import InferenceEngine +from .judge import BaseJudge, build_judge +from .knowledge import KnowledgeBase +from .memory import TaskGraph, TaskNode, WorkingMemory +from .models import Classification, ExpertResponse, RouterResult, now_ms +from .planner import Planner +from .stats import Stats +from .trace import TraceStore + + +class Router: + def __init__( + self, + classifier: BaseClassifier, + experts: Dict[str, Expert], + judge: BaseJudge, + fallback: FallbackProvider, + cache: Optional[RouterCache] = None, + stats: Optional[Stats] = None, + config: Optional[Dict[str, Any]] = None, + kb: Optional[KnowledgeBase] = None, + planner: Optional[Planner] = None, + ): + self.classifier = classifier + self.experts = experts + self.judge = judge + self.fallback = fallback + self.cache = cache or RouterCache() + self.stats = stats or Stats() + cfg = config or {} + rcfg = cfg.get("router", {}) + self.low_confidence_threshold = rcfg.get("low_confidence_threshold", 0.60) + self.judge_fallback_threshold = rcfg.get("judge_fallback_threshold", 0.70) + self.cache_enabled = cfg.get("cache", {}).get("enabled", True) + # ---- 专家系统内核 ---- + self.kb = kb or KnowledgeBase() + self.planner = planner or Planner(self.kb) + self.inference = InferenceEngine(self.kb) + ecfg = cfg.get("execution", {}) + self.expert_backend = ecfg.get("expert_backend", "rule") # rule | hf | api + # 子任务执行后端（T1 抽象：NodeExecutor 工厂，新增后端无需改 Router） + self.node_executor: NodeExecutor = build_node_executor( + self.expert_backend, kb=self.kb, experts=experts) + # 推理链轨迹存储（T3：可解释性产品化） + self.trace_store = TraceStore() + # ---- 两级路由：大领域分组 + 组内路由模型（更小更专） ---- + self.domain_groups: Dict[str, List[str]] = cfg.get("domain_groups", {}) or {} + if not self.domain_groups: + # 兜底：未配置时按单组（全部领域）处理，行为退化为一级路由 + self.domain_groups = {"all": list(self.experts.keys())} + self._group_of_domain: Dict[str, str] = {} + for g, domains in self.domain_groups.items(): + for d in domains: + self._group_of_domain[d] = g + # 组内路由模型：每组一个轻量分类器（只认识组内领域） + self._group_classifiers: Dict[str, RuleClassifier] = { + g: RuleClassifier(domains=domains) + for g, domains in self.domain_groups.items() + } + + # --------------------------------------------------------------- + async def route(self, query: str, domain_group: Optional[str] = None) -> RouterResult: + """两级路由入口。 + + domain_group 指定时：跳过 8 领域统一分类器，直接用组内路由模型 + （RuleClassifier(domains=组内领域)）识别组内领域 —— 更小更专。 + 未指定时：统一分类器识别领域 → 自动映射到大领域组（向后兼容）。 + """ + start = now_ms() + route: list = [] + request_id = uuid.uuid4().hex[:12] + + # ---- Step 1: 缓存 ---- + if self.cache_enabled: + hit = self.cache.get(query) + if hit is not None: + level, cached = hit + latency = now_ms() - start + result = RouterResult( + query=query, + response=cached.get("response", ""), + domain=cached.get("domain", "general"), + difficulty=cached.get("difficulty", "medium"), + confidence=cached.get("confidence", 0.0), + upgraded=False, + quality_score=cached.get("quality_score", 0.0), + model_used=cached.get("model_used", ""), + route=["cache:" + level], + latency_ms=latency, + cache_hit=True, + cache_level=level, + cost_est=0.0, + subdomain=cached.get("subdomain"), + subdomain2=cached.get("subdomain2"), + domain_group=cached.get("domain_group"), + request_id=request_id, + ) + self._store_trace( + request_id=request_id, query=query, group=cached.get("domain_group"), + domain=result.domain, difficulty=result.difficulty, + confidence=result.confidence, subdomain=result.subdomain, + subdomain2=result.subdomain2, route=route, quality=result.quality_score, + upgraded=False, model=result.model_used, latency=latency, + cache_hit=True, cache_level=level, + ) + self.stats.record(latency, result.domain, result.difficulty, False, True, level, 0.0, result.model_used) + return result + route.append("cache:miss") + + # ---- Step 2: 组路由（两级第一级）→ 组内分类（两级第二级） ---- + classifier = self.classifier + group = domain_group + if group is not None: + # 用户指定大领域：校验 + 使用组内路由模型 + if group not in self.domain_groups: + raise ValueError( + f"未知大领域组: {group}（可用: {sorted(self.domain_groups)}）" + ) + classifier = self._group_classifiers[group] + route.append(f"group:{group}@explicit") + classification = classifier.classify(query) + if group is None: + # 自动检测：8 领域分类 → 映射大领域组 + group = self._group_of_domain.get(classification.domain, "general") + route.append(f"group:{group}@auto") + route.append(f"classify:{classification.domain}@{classification.confidence:.2f}/{classification.difficulty}") + subdomain, subdomain2 = self._detect_subdomain(query, classification.domain) + if subdomain: + route.append(f"subdomain:{subdomain}") + if subdomain2: + route.append(f"subdomain2:{subdomain2}") + + # ---- Step 3: 低置信度 -> 直接走最后处理者 ---- + if classifier.should_fallback(classification, self.low_confidence_threshold): + route.append("direct_fallback") + fb = await self._call_fallback(query) + latency = now_ms() - start + result = self._finalize(query, classification, fb, quality_score=0.0, + upgraded=True, route=route, latency_ms=latency, + model_used=fb.model_used, cost_est=fb.cost_est, + subdomain=subdomain, subdomain2=subdomain2, + domain_group=group) + result.request_id = request_id + self._store_trace( + request_id=request_id, query=query, group=group, + domain=result.domain, difficulty=result.difficulty, + confidence=result.confidence, subdomain=subdomain, + subdomain2=subdomain2, route=route, quality=0.0, + upgraded=True, model=fb.model_used, latency=latency, + ) + self._record(result, latency) + return result + + # ---- Step 4: Planner 任务拆解（DAG） ---- + graph = self.planner.plan(query, classification) + route.extend(self.planner.explain_plan(graph)) + + # ---- Step 5: 黑板初始化 + 前向链（规则轨迹） ---- + memory = WorkingMemory() + self.inference.initialize( + query, classification.domain, classification.difficulty, + classification.confidence, memory, + ) + fired = self.inference.run(query, classification.domain, memory) + if fired: + route.append(f"rules:{','.join(fired[:5])}") + + # ---- Step 6: DAG 顺序执行（拓扑序） ---- + order = graph.topo_order() + last_model = f"rule:{classification.domain}" + for node in order: + last_model = await self._execute_node(graph, node, classification, memory, route) or last_model + + # ---- Step 7: 黑板合并（节点输出 + 推理机规则产出） ---- + response = memory.merge([n.id for n in order]) + # 追加推理机规则产出的部分解（带 output 的知识规则，如 git/docker/常识条目） + node_ids = {n.id for n in order} + extra_sections = [sid for sid in memory.sections if sid not in node_ids] + extras = [memory.section(s) for s in extra_sections if memory.section(s)] + if extras: + extra_text = "\n\n".join(extras) + response = (response + "\n\n" + extra_text) if response.strip() else extra_text + if not response.strip(): + response = "（规则执行器）未能生成有效回答：任务均未产出内容。" + route.append("merge:empty") + + # ---- Step 8: Judge 校验 ---- + try: + evaluation = await self.judge.evaluate(query, response, classification.domain) + except Exception: + evaluation = None + route.append("judge_error") + quality_score = evaluation.overall_score if evaluation else 0.0 + route.append(f"judge:{quality_score:.2f}") + + upgraded = False + if evaluation is not None and evaluation.needs_fallback: + route.append("upgrade") + fb = await self._call_fallback(query) + response = fb.text + last_model = fb.model_used + upgraded = True + + latency = now_ms() - start + result = self._finalize(query, classification, ExpertResponse( + text=response, model_used=last_model, latency_ms=latency, + tokens=max(8, int(len(response) / 2.2)), cost_est=0.0, + ), quality_score=quality_score, upgraded=upgraded, route=route, + latency_ms=latency, model_used=last_model, cost_est=0.0, + subdomain=subdomain, subdomain2=subdomain2, domain_group=group) + result.request_id = request_id + self._store_trace( + request_id=request_id, query=query, group=group, + domain=result.domain, difficulty=result.difficulty, + confidence=result.confidence, subdomain=subdomain, + subdomain2=subdomain2, route=route, quality=quality_score, + upgraded=upgraded, model=last_model, latency=latency, + ) + self._record(result, latency) + + # 未升级的结果写缓存 + if self.cache_enabled and not upgraded and result.response: + self.cache.put(query, result.to_dict()) + + return result + + # --------------------------------------------------------------- + def _store_trace(self, request_id: str, query: str, group: Optional[str], + domain: str, difficulty: str, confidence: float, + subdomain: Optional[str], subdomain2: Optional[str], + route: list, quality: float, upgraded: bool, + model: str, latency: float, + cache_hit: bool = False, cache_level: Optional[str] = None) -> None: + """记录完整推理链到轨迹存储（T3：可解释性产品化）。""" + self.trace_store.put(request_id, { + "request_id": request_id, + "query": query, + "domain_group": group, + "domain": domain, + "difficulty": difficulty, + "confidence": round(confidence, 4), + "subdomain": subdomain, + "subdomain2": subdomain2, + "route": list(route), + "quality_score": round(quality, 4), + "upgraded": upgraded, + "model_used": model, + "latency_ms": round(latency, 2), + "cache_hit": cache_hit, + "cache_level": cache_level, + }) + + # --------------------------------------------------------------- + def _detect_subdomain(self, query: str, domain: str) -> tuple: + """子领域识别：返回 (二级 subdomain, 三级 subdomain2)。 + + 二级取领域内最高优先级带 subdomain 的命中规则； + 三级取最高优先级带 subdomain2 的命中规则（可与二级来自不同规则）。 + """ + hits = self.kb.match(query, domain=domain) + sub = None + sub2 = None + for h in hits: + if sub is None and h.subdomain: + sub = h.subdomain + if sub2 is None and h.subdomain2: + sub2 = h.subdomain2 + if sub is not None and sub2 is not None: + break + return sub, sub2 + + # --------------------------------------------------------------- + async def _execute_node(self, graph: TaskGraph, node: TaskNode, + classification: Classification, memory: WorkingMemory, + route: list) -> Optional[str]: + """执行一个子任务节点；返回使用的 model_used（失败返回 None）。""" + # 依赖检查：依赖失败/跳过 → 本节点跳过 + for dep_id in node.deps: + dep = graph.get(dep_id) + if dep is not None and dep.status in ("failed", "skipped"): + node.status = "skipped" + route.append(f"{node.id}:{node.kind}:skip") + return None + node.status = "running" + try: + # NodeExecutor 后端执行（rule 零参数 / model 专家池 ≤8B） + resp = await self.node_executor.execute( + node, classification.domain, classification.difficulty, memory) + node.output = resp.text + node.status = "done" + memory.write_section(node.id, resp.text) + route.append(f"{node.id}:{node.kind}") + return resp.model_used + except Exception as e: + node.status = "failed" + node.error = str(e) + self.stats.record_error() + route.append(f"{node.id}:{node.kind}:error:{type(e).__name__}") + return None + + # --------------------------------------------------------------- + async def _call_fallback(self, query: str) -> ExpertResponse: + try: + return await self.fallback.generate(query) + except Exception as e: + # 回退也失败：返回错误占位响应 + return ExpertResponse( + text=f"[系统错误] 专家与最后处理者均失败：{type(e).__name__}: {e}", + model_used=f"error:{self.fallback.name}", + cost_est=0.0, + ) + + @staticmethod + def _finalize(query: str, classification: Classification, resp: ExpertResponse, + quality_score: float, upgraded: bool, route: list, + latency_ms: float, model_used: str, cost_est: float, + error: Optional[str] = None, + subdomain: Optional[str] = None, + subdomain2: Optional[str] = None, + domain_group: Optional[str] = None) -> RouterResult: + return RouterResult( + query=query, + response=resp.text, + domain=classification.domain, + difficulty=classification.difficulty, + confidence=classification.confidence, + upgraded=upgraded, + quality_score=quality_score, + model_used=model_used, + route=route, + latency_ms=latency_ms, + cache_hit=False, + cost_est=cost_est, + error=error, + subdomain=subdomain, + subdomain2=subdomain2, + domain_group=domain_group, + ) + + def _record(self, result: RouterResult, latency_ms: float): + self.stats.record( + latency_ms, + result.domain, + result.difficulty, + result.upgraded, + result.cache_hit, + result.cache_level, + result.cost_est, + result.model_used, + ) + + # --------------------------------------------------------------- + def health(self) -> Dict[str, Any]: + return { + "status": "ok", + "domains": list(self.experts.keys()), + "domain_groups": self.domain_groups, + "classifier": type(self.classifier).__name__, + "judge": type(self.judge).__name__, + "fallback": type(self.fallback).__name__, + "planner": type(self.planner).__name__, + "execution_mode": self.expert_backend, + "rules": self.kb.rules_count(), + } + + +def build_router(config_path: Optional[str] = None) -> Router: + """从配置构建完整 Router（默认 L0 专家系统模式：零参数可跑）。""" + config = load_config(config_path) + kb = KnowledgeBase() + classifier = build_classifier(config.get("classifier", {})) + experts = build_expert_pool(config.get("experts", {}), config.get("domains", [])) + judge = build_judge(config.get("judge", {}), + config.get("router", {}).get("judge_fallback_threshold", 0.70), + kb=kb) + fallback = build_fallback(config.get("fallback", {})) + cache_cfg = config.get("cache", {}) + cache = RouterCache( + semantic_enabled=cache_cfg.get("semantic_enabled", True), + similarity_threshold=cache_cfg.get("similarity_threshold", 0.88), + promote_frequency=cache_cfg.get("promote_frequency", 5), + ) + ecfg = config.get("execution", {}) + planner = Planner(kb, max_depth=ecfg.get("max_plan_depth", 3)) + stats = Stats() + return Router(classifier, experts, judge, fallback, cache, stats, config, + kb=kb, planner=planner) diff --git a/router_system/workspace.py b/router_system/workspace.py index 6514324..83f93f0 100644 --- a/router_system/workspace.py +++ b/router_system/workspace.py @@ -39,7 +39,7 @@ LIMITS = { # 允许的领域标签（4.2 brief.tags；仅用于安全标记与验证接地，不做路由 D3） ALLOWED_TAGS = {"code", "math", "legal", "medical", "finance", - "life", "education", "general", "safety"} + "life", "education", "general", "safety", "science"} STATUS_FLOW = { "draft": {"in_progress"}, diff --git a/scripts/demo.py b/scripts/demo.py index 796f560..236a9b8 100644 --- a/scripts/demo.py +++ b/scripts/demo.py @@ -1,65 +1,75 @@ -"""CLI 演示：构建 mock 全链路路由系统，跑一组样例查询并打印结果。 - -用法： - python scripts/demo.py [--query "自定义查询"] [--batch] -""" -from __future__ import annotations - -import argparse -import asyncio -import sys -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) - -from router_system.router import build_router - -SAMPLE_QUERIES = [ - "用 Python 写一个快速排序函数，并解释时间复杂度", - "求解方程 x^2 - 5x + 6 = 0", - "劳动合同里约定离职后两年内不得从事同行业，是否有效？", - "高血压患者日常饮食需要注意什么？", - "给我总结一下深度学习中注意力机制的优缺点", - "为什么天空是蓝色的？", - "帮我调试这段代码：def f(x): return x + 1 报 TypeError", - "求 ∫ x^2 dx 从 0 到 1 的定积分是多少？", -] - - -async def run_demo(router, queries, verbose: bool = False): - for q in queries: - r = await router.route(q) - print("=" * 72) - print(f"Q: {q}") - print(f" domain={r.domain} difficulty={r.difficulty} conf={r.confidence:.2f} " - f"upgraded={r.upgraded} quality={r.quality_score:.2f} model={r.model_used} " - f"latency={r.latency_ms:.1f}ms cache={r.cache_hit}({r.cache_level}) cost=${r.cost_est:.6f}") - print(f" route: {' -> '.join(r.route)}") - if verbose: - print(f" --- response ---\n{r.response[:400]}") - - -async def main(): - parser = argparse.ArgumentParser(description="多专家路由系统 CLI 演示") - parser.add_argument("--query", type=str, default=None, help="单条查询（覆盖默认样例）") - parser.add_argument("--batch", action="store_true", help="批量模式（打印全部响应）") - parser.add_argument("--verbose", action="store_true", help="打印响应正文") - args = parser.parse_args() - - router = build_router() - print("系统组件：", router.health()) - print() - - if args.query: - await run_demo(router, [args.query], verbose=True) - else: - await run_demo(router, SAMPLE_QUERIES, verbose=args.verbose) - - print() - print("=" * 72) - print("运行指标:", router.stats.summary()) - print("缓存统计:", router.cache.stats()) - - -if __name__ == "__main__": - asyncio.run(main()) +"""CLI 演示：构建专家系统内核路由（默认 L0 零参数模式），跑样例查询并打印推理链。 + +用法： + python scripts/demo.py [--query "自定义查询"] [--batch] [--trace] [--verbose] +--trace 打印完整推理链（分类 → 拆解 DAG → 规则轨迹 → 子任务执行 → Judge） +""" +from __future__ import annotations + +import argparse +import asyncio +import sys +from pathlib import Path + +if hasattr(sys.stdout, "reconfigure"): + sys.stdout.reconfigure(encoding="utf-8") + sys.stderr.reconfigure(encoding="utf-8") + +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) + +from router_system.router import build_router + +SAMPLE_QUERIES = [ + "用 Python 写一个快速排序函数，并解释时间复杂度", + "求解方程 x^2 - 5x + 6 = 0", + "劳动合同里约定离职后两年内不得从事同行业，是否有效？", + "高血压患者日常饮食需要注意什么？", + "给我总结一下深度学习中注意力机制的优缺点", + "为什么天空是蓝色的？", + "帮我调试这段代码：def f(x): return x + 1 报 TypeError", + "求 ∫ x^2 dx 从 0 到 1 的定积分是多少？", +] + + +async def run_demo(router, queries, verbose: bool = False, trace: bool = False): + for q in queries: + r = await router.route(q) + print("=" * 72) + print(f"Q: {q}") + print(f" domain={r.domain} difficulty={r.difficulty} conf={r.confidence:.2f} " + f"upgraded={r.upgraded} quality={r.quality_score:.2f} model={r.model_used} " + f"latency={r.latency_ms:.1f}ms cache={r.cache_hit}({r.cache_level}) cost=${r.cost_est:.6f}") + print(f" route: {' -> '.join(r.route)}") + if trace: + # 拆解轨迹从 route 中展开为更可读的形式 + plan_steps = [s for s in r.route if s.startswith("plan:") or ":" in s] + print(" 推理链: " + " -> ".join(r.route)) + if verbose: + print(f" --- response ---\n{r.response[:600]}") + + +async def main(): + parser = argparse.ArgumentParser(description="多专家路由系统（专家系统内核）CLI 演示") + parser.add_argument("--query", type=str, default=None, help="单条查询（覆盖默认样例）") + parser.add_argument("--batch", action="store_true", help="批量模式（打印全部响应）") + parser.add_argument("--verbose", action="store_true", help="打印响应正文") + parser.add_argument("--trace", action="store_true", help="打印完整推理链") + args = parser.parse_args() + + router = build_router() + print("系统组件：", router.health()) + print() + + if args.query: + await run_demo(router, [args.query], verbose=True, trace=True) + else: + await run_demo(router, SAMPLE_QUERIES, verbose=args.verbose, trace=args.trace) + + print() + print("=" * 72) + print("运行指标:", router.stats.summary()) + print("缓存统计:", router.cache.stats()) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/scripts/eval.py b/scripts/eval.py index 6f27f23..3dc3443 100644 --- a/scripts/eval.py +++ b/scripts/eval.py @@ -1,80 +1,121 @@ -"""迷你评估：对带标注的样例查询评估分类准确率、升级率、成本。 - -用法： - python scripts/eval.py [--repeat 2] [--config path] ---repeat 用于把样例跑 N 遍，验证语义缓存命中与降本效果。 -""" -from __future__ import annotations - -import argparse -import asyncio -import sys -from collections import Counter -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) - -from router_system.router import build_router - -# (query, 期望领域) -BENCH = [ - ("用 Python 实现二分查找", "code"), - ("这段 JavaScript 为什么报错：undefined is not a function", "code"), - ("帮我优化这个 SQL 查询的索引", "code"), - ("求解一元二次方程 ax^2+bx+c=0 的求根公式", "math"), - ("证明勾股定理", "math"), - ("计算 3x + 5 = 20，x 等于多少", "math"), - ("劳动合同到期不续签，公司需要支付经济补偿吗", "legal"), - ("在合同中约定违约金上限 30%，是否合规", "legal"), - ("专利申请的流程和费用大概是多少", "legal"), - ("高血压患者可以吃哪些降压药，副作用是什么", "medical"), - ("感冒发烧 38.5 度，需要吃退烧药吗", "medical"), - ("糖尿病患者的日常饮食建议", "medical"), - ("介绍一下 Transformer 架构", "general"), - ("写一封请假邮件", "general"), - ("为什么天空是蓝色的", "general"), -] - - -async def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--repeat", type=int, default=2, help="重复轮数（验证缓存）") - parser.add_argument("--config", type=str, default=None) - args = parser.parse_args() - - router = build_router(args.config) - correct = Counter() - total = 0 - upgraded = 0 - cache_hits = 0 - - for round_i in range(args.repeat): - for q, expected in BENCH: - r = await router.route(q) - total += 1 - if r.domain == expected: - correct["total"] += 1 - else: - correct[f"misclass->{r.domain}"] += 1 - if r.upgraded: - upgraded += 1 - if r.cache_hit: - cache_hits += 1 - - acc = correct["total"] / total - print(f"样例数: {len(BENCH)} x {args.repeat} 轮 = {total} 次请求") - print(f"分类准确率: {acc:.1%} ({correct['total']}/{total})") - print(f"升级率: {upgraded/total:.1%} ({upgraded}/{total})") - print(f"缓存命中率: {cache_hits/total:.1%} ({cache_hits}/{total})") - print() - print("运行指标:", router.stats.summary()) - print("缓存统计:", router.cache.stats()) - print() - if acc < 0.8: - print("⚠️ 准确率低于 80%，请检查分类规则。") - else: - print("✅ 分类准确率达标（≥80%）。") - - -if __name__ == "__main__": - asyncio.run(main()) +"""迷你评估：对带标注的样例查询评估分类准确率、升级率、成本。 + +用法： + python scripts/eval.py [--repeat 2] [--config path] +--repeat 用于把样例跑 N 遍，验证语义缓存命中与降本效果。 +""" +from __future__ import annotations + +import argparse +import asyncio +import sys +from collections import Counter +from pathlib import Path + +# 兼容 GBK 控制台（中文 Windows 默认编码），避免打印 ✅/⚠️ 时 UnicodeEncodeError +if hasattr(sys.stdout, "reconfigure"): + sys.stdout.reconfigure(encoding="utf-8") + sys.stderr.reconfigure(encoding="utf-8") + +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) + +from router_system.router import build_router + +# (query, 期望领域, 期望大领域组, 期望子领域) +# 覆盖 8 领域 × 3 条 = 24 条 + 两级路由/三级子领域指标 +BENCH = [ + # ---- tech：code / math ---- + ("用 Python 实现二分查找", "code", "tech", "algorithm"), + ("这段 JavaScript 为什么报错：undefined is not a function", "code", "tech", "debugging"), + ("帮我优化这个 SQL 查询的索引", "code", "tech", "database"), + ("求解一元二次方程 ax^2+bx+c=0 的求根公式", "math", "tech", "algebra"), + ("证明勾股定理", "math", "tech", "geometry"), + ("计算 3x + 5 = 20，x 等于多少", "math", "tech", "algebra"), + # ---- professional：legal / medical / finance ---- + ("劳动合同到期不续签，公司需要支付经济补偿吗", "legal", "professional", "labor"), + ("在合同中约定违约金上限 30%，是否合规", "legal", "professional", "contract"), + ("加班费怎么计算", "legal", "professional", "labor"), + ("高血压患者可以吃哪些降压药，副作用是什么", "medical", "professional", "medication"), + ("感冒发烧 38.5 度，需要吃退烧药吗", "medical", "professional", "medication"), + ("烫伤后怎么处理", "medical", "professional", "firstaid"), + ("基金定投的收益率怎么计算", "finance", "professional", "investing"), + ("信用卡逾期了怎么办", "finance", "professional", "credit"), + ("房贷利率是 LPR 加多少", "finance", "professional", "loan"), + # ---- lifestyle：life / education ---- + ("日本旅行攻略", "life", "lifestyle", "travel"), + ("健身增肌计划怎么安排", "life", "lifestyle", "fitness"), + ("家常菜谱推荐", "life", "lifestyle", "food"), + ("考研英语怎么备考", "education", "lifestyle", "exam"), + ("高效学习方法", "education", "lifestyle", "study"), + ("面试技巧有哪些", "education", "lifestyle", "career"), + # ---- general ---- + ("介绍一下 Transformer 架构", "general", "general", "explain"), + ("写一封请假邮件", "general", "general", "writing"), + ("为什么天空是蓝色的", "general", "general", "explain"), +] + + +async def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--repeat", type=int, default=2, help="重复轮数（验证缓存）") + parser.add_argument("--config", type=str, default=None) + parser.add_argument("--group", type=str, default=None, + help="指定大领域组测试两级路由（如 tech）；默认自动检测") + args = parser.parse_args() + + router = build_router(args.config) + # 指定组时只评测组内样例（组路由只认识本组领域） + bench = BENCH + if args.group is not None: + bench = [b for b in BENCH if b[2] == args.group] + if not bench: + print(f"组 {args.group} 无评测样例，可用组: {sorted({b[2] for b in BENCH})}") + return + correct = Counter() + group_correct = 0 + subdomain_correct = 0 + total = 0 + upgraded = 0 + cache_hits = 0 + decomposed = 0 # 被 Planner 拆解为多子任务的请求数 + + for round_i in range(args.repeat): + for q, expected, expected_group, expected_sub in bench: + r = await router.route(q, domain_group=args.group) + total += 1 + if r.domain == expected: + correct["total"] += 1 + else: + correct[f"misclass->{r.domain}"] += 1 + if args.group is None and r.domain_group == expected_group: + group_correct += 1 + if r.subdomain == expected_sub: + subdomain_correct += 1 + if r.upgraded: + upgraded += 1 + if r.cache_hit: + cache_hits += 1 + if any("plan:multi" in s for s in r.route): + decomposed += 1 + + acc = correct["total"] / total + print(f"样例数: {len(bench)} x {args.repeat} 轮 = {total} 次请求") + print(f"分类准确率: {acc:.1%} ({correct['total']}/{total})") + if args.group is None: + print(f"大领域组识别准确率: {group_correct/total:.1%} ({group_correct}/{total})") + print(f"子领域识别准确率: {subdomain_correct/total:.1%} ({subdomain_correct}/{total})") + print(f"升级率: {upgraded/total:.1%} ({upgraded}/{total})") + print(f"缓存命中率: {cache_hits/total:.1%} ({cache_hits}/{total})") + print(f"任务拆解率: {decomposed/total:.1%} ({decomposed}/{total})") + print() + print("运行指标:", router.stats.summary()) + print("缓存统计:", router.cache.stats()) + print() + if acc < 0.8: + print("⚠️ 准确率低于 80%，请检查分类规则。") + else: + print("✅ 分类准确率达标（≥80%）。") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/scripts/serve.py b/scripts/serve.py index 76445d8..d9ff253 100644 --- a/scripts/serve.py +++ b/scripts/serve.py @@ -1,4 +1,4 @@ -"""启动路由网关服务（后台、无窗口）。 +"""启动路由网关服务（后台、无窗口）。 用法： python scripts/serve.py [--port 8000] [--stop] @@ -41,11 +41,16 @@ def stop(): return pid = int(PID_FILE.read_text().strip()) try: - import signal - os.kill(pid, signal.SIGTERM) - print(f"已发送终止信号 pid={pid}") - except ProcessLookupError: - print(f"进程 {pid} 不存在，清理 pid 文件。") + # Windows 下 SIGTERM 对 detached 进程不可靠，改用 taskkill 强制结束进程树 + subprocess.run( + ["taskkill", "/PID", str(pid), "/T", "/F"], + capture_output=True, text=True, timeout=15, + ) + print(f"已终止服务 pid={pid}") + except subprocess.TimeoutExpired: + print(f"终止超时 pid={pid}，请手动结束进程。") + except Exception as e: + print(f"终止失败（进程可能不存在）: {e}") PID_FILE.unlink(missing_ok=True) diff --git a/tests/test_cache.py b/tests/test_cache.py index 29bb74e..329ebed 100644 --- a/tests/test_cache.py +++ b/tests/test_cache.py @@ -13,9 +13,9 @@ def test_exact_hit(): def test_semantic_hit(): c = RouterCache(semantic_enabled=True, similarity_threshold=0.5) - c.put("?python?????", {"response": "code", "domain": "code"}) - # ?????????? L2 - hit = c.get("?python????????") + c.put("用python写一个快速排序", {"response": "code", "domain": "code"}) + # 相似改写查询命中 L2 语义缓存 + hit = c.get("用python写一个快速排序算法") assert hit is not None assert hit[0] == "semantic" @@ -24,7 +24,7 @@ def test_promote_to_exact(): c = RouterCache(promote_frequency=3) result = {"response": "x", "domain": "general"} c.put("query", result) - # ?????? 3 ? ? ??????? + # 语义命中 3 次后提升为精确缓存 for _ in range(3): hit = c.get("query") assert hit is not None diff --git a/tests/test_classifier.py b/tests/test_classifier.py index d882199..cfb6b84 100644 --- a/tests/test_classifier.py +++ b/tests/test_classifier.py @@ -1,44 +1,44 @@ -"""分类器单元测试。""" -from router_system.classifier import RuleClassifier - - -def test_code_classification(): - clf = RuleClassifier() - r = clf.classify("用 Python 写一个快速排序函数") - assert r.domain == "code" - assert r.confidence > 0.7 - - -def test_math_classification(): - clf = RuleClassifier() - r = clf.classify("求解方程 x^2 - 5x + 6 = 0") - assert r.domain == "math" - assert r.confidence > 0.7 - - -def test_legal_classification(): - clf = RuleClassifier() - r = clf.classify("劳动合同到期不续签需要支付经济补偿吗") - assert r.domain == "legal" - - -def test_medical_classification(): - clf = RuleClassifier() - r = clf.classify("高血压患者日常饮食需要注意什么") - assert r.domain == "medical" - - -def test_general_low_confidence(): - clf = RuleClassifier() - r = clf.classify("今天天气怎么样") - # 未命中任何领域 -> 低置信度，触发 should_fallback - assert r.domain == "general" - assert clf.should_fallback(r, 0.6) is True - - -def test_difficulty_estimation(): - clf = RuleClassifier() - easy = clf.classify("1 + 1 = ?") - hard = clf.classify("证明费马大定理并推导其推论，给出详细步骤") - assert hard.difficulty in ("medium", "hard") - assert easy.difficulty == "easy" +"""分类器单元测试。""" +from router_system.classifier import RuleClassifier + + +def test_code_classification(): + clf = RuleClassifier() + r = clf.classify("用 Python 写一个快速排序函数") + assert r.domain == "code" + assert r.confidence > 0.7 + + +def test_math_classification(): + clf = RuleClassifier() + r = clf.classify("求解方程 x^2 - 5x + 6 = 0") + assert r.domain == "math" + assert r.confidence > 0.7 + + +def test_legal_classification(): + clf = RuleClassifier() + r = clf.classify("劳动合同到期不续签需要支付经济补偿吗") + assert r.domain == "legal" + + +def test_medical_classification(): + clf = RuleClassifier() + r = clf.classify("高血压患者日常饮食需要注意什么") + assert r.domain == "medical" + + +def test_general_low_confidence(): + clf = RuleClassifier() + r = clf.classify("你好呀") + # 未命中任何领域 -> general，低置信度，触发 should_fallback + assert r.domain == "general" + assert clf.should_fallback(r, 0.6) is True + + +def test_difficulty_estimation(): + clf = RuleClassifier() + easy = clf.classify("1 + 1 = ?") + hard = clf.classify("证明费马大定理并推导其推论，给出详细步骤") + assert hard.difficulty in ("medium", "hard") + assert easy.difficulty == "easy" diff --git a/tests/test_gateway.py b/tests/test_gateway.py index 348c6a8..1022d45 100644 --- a/tests/test_gateway.py +++ b/tests/test_gateway.py @@ -41,14 +41,30 @@ def test_chat_legacy(client): def test_chat_v2(v2_client): + # POST /chat 立即返回 request_id（异步协议） resp = v2_client.post("/chat", json={"query": "请介绍快速排序算法"}) assert resp.status_code == 200 data = resp.json() - assert data["response"] - assert data["request_id"] - assert "fast_path" in data - assert "route" in data - assert data["status"] in ("fast_path", "done", "escalated", "failed") + assert "request_id" in data + assert data["status"] == "pending" + + # 轮询 /runs/{id}/status 直到完成 + import time + for _ in range(50): # 最多 5s + time.sleep(0.1) + status_resp = v2_client.get(f"/runs/{data['request_id']}/status") + assert status_resp.status_code == 200 + s = status_resp.json() + if s["status"] in ("done", "failed"): + break + + assert s["status"] == "done", f"期望 done，实际 {s['status']}，error={s.get('error')}" + assert s["response"] + assert "pipeline_status" in s + assert s["pipeline_status"] in ("fast_path", "done", "escalated") + # fast_path 不写 workspace.json，所以 workspace_path 可能为 None + if s["pipeline_status"] != "fast_path": + assert s["workspace_path"] is not None def test_chat_empty_query(client): @@ -93,8 +109,9 @@ def test_web_ui_served(client): resp = client.get("/") assert resp.status_code == 200 assert resp.headers["content-type"].startswith("text/html") - assert "端云协同" in resp.text - assert "发送" in resp.text + # Vue SPA：由 Vite 生成，特征是
和 /static/assets/ 引用 + assert '
' in resp.text + assert '/static/assets/' in resp.text def test_review_flow(client): diff --git a/tests/test_router.py b/tests/test_router.py index 341b964..b06c5d3 100644 --- a/tests/test_router.py +++ b/tests/test_router.py @@ -1,56 +1,56 @@ -"""路由主流程单元测试。""" -import pytest - -from router_system.router import build_router - - -@pytest.mark.asyncio -async def test_normal_flow_code(router): - r = await router.route("用 Python 写一个快速排序函数") - assert r.domain == "code" - assert r.response - assert r.model_used - assert r.latency_ms >= 0 - assert "expert" in r.route[1] or "classify" in r.route[1] - - -@pytest.mark.asyncio -async def test_low_confidence_direct_fallback(router): - r = await router.route("今天天气怎么样") - assert r.upgraded is True - assert "direct_fallback" in r.route - assert r.model_used == router.fallback.model - - -@pytest.mark.asyncio -async def test_cache_second_round(router): - q = "用 Python 写一个快速排序函数" - r1 = await router.route(q) - assert r1.cache_hit is False - r2 = await router.route(q) - assert r2.cache_hit is True - assert r2.cache_level in ("exact", "semantic") - assert r2.response == r1.response - - -@pytest.mark.asyncio -async def test_judge_route_present(router): - r = await router.route("高血压患者日常饮食需要注意什么") - assert any(step.startswith("judge:") for step in r.route) - - -@pytest.mark.asyncio -async def test_stats_recorded(router): - await router.route("写一个 python 函数") - await router.route("写一个 python 函数") - s = router.stats.summary() - assert s["total_requests"] == 2 - assert s["domain_distribution"]["code"] == 2 - assert s["cache_hit_rate"] > 0 - - -def test_health(router): - h = router.health() - assert h["status"] == "ok" - assert "code" in h["domains"] - assert "math" in h["domains"] +"""路由主流程单元测试。""" +import pytest + +from router_system.router import build_router + + +@pytest.mark.asyncio +async def test_normal_flow_code(router): + r = await router.route("用 Python 写一个快速排序函数") + assert r.domain == "code" + assert r.response + assert r.model_used + assert r.latency_ms >= 0 + assert any("classify:" in s for s in r.route) + + +@pytest.mark.asyncio +async def test_low_confidence_direct_fallback(router): + r = await router.route("今天天气怎么样") + assert r.upgraded is True + assert "direct_fallback" in r.route + assert r.model_used == router.fallback.model + + +@pytest.mark.asyncio +async def test_cache_second_round(router): + q = "用 Python 写一个快速排序函数" + r1 = await router.route(q) + assert r1.cache_hit is False + r2 = await router.route(q) + assert r2.cache_hit is True + assert r2.cache_level in ("exact", "semantic") + assert r2.response == r1.response + + +@pytest.mark.asyncio +async def test_judge_route_present(router): + r = await router.route("高血压患者日常饮食需要注意什么") + assert any(step.startswith("judge:") for step in r.route) + + +@pytest.mark.asyncio +async def test_stats_recorded(router): + await router.route("写一个 python 函数") + await router.route("写一个 python 函数") + s = router.stats.summary() + assert s["total_requests"] == 2 + assert s["domain_distribution"]["code"] == 2 + assert s["cache_hit_rate"] > 0 + + +def test_health(router): + h = router.health() + assert h["status"] == "ok" + assert "code" in h["domains"] + assert "math" in h["domains"] diff --git a/webapp/.gitignore b/webapp/.gitignore new file mode 100644 index 0000000..a547bf3 --- /dev/null +++ b/webapp/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/webapp/README.md b/webapp/README.md new file mode 100644 index 0000000..33895ab --- /dev/null +++ b/webapp/README.md @@ -0,0 +1,5 @@ +# Vue 3 + TypeScript + Vite + +This template should help get you started developing with Vue 3 and TypeScript in Vite. 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0000000..6893eb1 --- /dev/null +++ b/webapp/public/favicon.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/webapp/public/icons.svg b/webapp/public/icons.svg new file mode 100644 index 0000000..e952219 --- /dev/null +++ b/webapp/public/icons.svg @@ -0,0 +1,24 @@ + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/webapp/src/App.vue b/webapp/src/App.vue new file mode 100644 index 0000000..1b2656e --- /dev/null +++ b/webapp/src/App.vue @@ -0,0 +1,86 @@ + + + + + diff --git a/webapp/src/api/index.ts b/webapp/src/api/index.ts new file mode 100644 index 0000000..8f1f02d --- /dev/null +++ b/webapp/src/api/index.ts @@ -0,0 +1,262 @@ +import axios from 'axios' +import type { + ChatInitResponse, + RunStatus, + SSEEvent, + ReviewItem, + Metrics, +} from '@/types' + +const http = axios.create({ timeout: 10_000 }) + +// ── Chat ───────────────────────────────────────────────────────────────────── + +/** POST /chat：异步提交，立即返回 request_id */ +export async function chat(query: string, domain_group?: string) { + const { data } = await http.post('/chat', { query, domain_group }) + return data +} + +/** GET /runs/{id}/status：查询任务状态 */ +export async function getRunStatus(requestId: string) { + const { data } = await http.get(`/runs/${requestId}/status`) + return data +} + +/** GET /runs/{id}/workspace：获取交流文本全文 */ +export async function getWorkspace(requestId: string) { + const { data } = await http.get(`/runs/${requestId}/workspace`) + return data +} + +/** SSE /runs/{id}/stream：订阅实时协作事件 */ +export function watchRun(requestId: string) { + const es = new EventSource(`/runs/${requestId}/stream`) + return { + /** 监听器会在组件卸载时自动断开，调用者只需提供 onXxx 回调 */ + subscribe(opts: { + onWorkspace: (ws: import('@/types').Workspace) => void + onStatus: (s: string) => void + onError: (detail: string) => void + onDone: () => void + }) { + es.addEventListener('message', (ev) => { + const d: SSEEvent = JSON.parse(ev.data) + if (d.type === 'workspace') opts.onWorkspace(d.workspace) + else if (d.type === 'status') { + opts.onStatus(d.value) + if (d.value === 'done' || d.value === 'failed') opts.onDone() + } + else if (d.type === 'error') opts.onError(d.detail) + }) + es.addEventListener('error', () => { + // EventSource 自己会重连，这里只记录 + }) + }, + close() { es.close() }, + } +} + +// ── 人工检验 ───────────────────────────────────────────────────────────────── + +export async function listReviews(status?: string) { + const params = status ? { status } : {} + const { data } = await http.get('/review/queue', { params }) + return data +} + +export async function submitReview(reviewId: number, verdict: string, correction?: string) { + const { data } = await http.post(`/review/${reviewId}`, null, { + params: { verdict, correction }, + }) + return data +} + +// ── 指标 ───────────────────────────────────────────────────────────────────── + +export async function getMetrics() { + const { data } = await http.get('/api/metrics') + return data +} + +// ── 模型设置 ───────────────────────────────────────────────────────────────── + +export interface ModelSettings { + worker: { + backend: string + model: string + base_url: string + port: number + temperature: number + max_fix_attempts: number + code_timeout_s: number + } + architect: { + model: string + base_url: string + api_key: string + } + pipeline: { + fast_path: boolean + rounds_cap: number + api_token_cap: number + breach_policy: string + } +} + +/** GET /config：读取当前模型设置 */ +export async function getConfig() { + const { data } = await http.get('/config') + return data +} + +/** PUT /config：更新模型设置（部分更新） */ +export async function updateConfig(patch: Partial) { + const { data } = await http.put('/config', patch) + return data +} + +/** POST /config/reset：恢复默认设置 */ +export async function resetConfig() { + const { data } = await http.post('/config/reset') + return data +} + +// ── 模型发现 & 连接验证 ─────────────────────────────────────────────────────── + +export interface ModelInfo { + id: string + name: string +} + +/** + * GET /config/models?backend=llama_server&base_url=...&api_key=... + * 返回 { models: ModelInfo[] } 或 { error: string } + */ +export async function listModels( + backend: string, + base_url: string, + api_key: string, +) { + const params: Record = { backend } + if (base_url) params.base_url = base_url + if (api_key) params.api_key = api_key + const { data } = await http.get<{ models?: ModelInfo[]; error?: string }>( + '/config/models', { params }, + ) + return data +} + +/** + * GET /config/ping?backend=...&base_url=...&api_key=... + * 返回 { ok: boolean, detail?: string } + */ +export async function pingBackend( + backend: string, + base_url: string, + api_key: string, +) { + const params: Record = { backend } + if (base_url) params.base_url = base_url + if (api_key) params.api_key = api_key + const { data } = await http.get<{ ok: boolean; detail?: string; status_code?: number }>( + '/config/ping', { params }, + ) + return data +} + +// ── llama-server 内置管理 ───────────────────────────────────────────────────── + +export interface LlamaStatus { + running: boolean + pid?: number | null + model?: string | null + port?: number | null + base_url?: string | null + started_at?: number | null + error?: string | null +} + +export interface LocalModel { + id: string + name: string + size_mb: number + path: string +} + +export interface DownloadProgress { + url: string + dest: string + downloaded_bytes: number + total_bytes?: number | null + progress_pct: number + speed: string + eta: string + done: boolean + error?: string | null +} + +/** GET /llama/status */ +export async function getLlamaStatus() { + const { data } = await http.get('/llama/status') + return data +} + +/** GET /llama/models */ +export async function listLocalModels() { + const { data } = await http.get<{ models: LocalModel[] }>('/llama/models') + return data.models +} + +/** POST /llama/start */ +export async function startLlama(model: string, port = 8901, ngl = 99, ctx = 4096) { + const { data } = await http.post('/llama/start', null, { + params: { model, port, ngl, ctx }, + }) + return data +} + +/** POST /llama/stop */ +export async function stopLlama() { + const { data } = await http.post<{ running: boolean }>('/llama/stop') + return data +} + +/** POST /llama/download */ +export async function downloadModel(url: string, dest?: string) { + const params: Record = { url } + if (dest) params.dest = dest + const { data } = await http.post('/llama/download', null, { params }) + return data +} + +/** SSE /llama/download/stream?url=... */ +export function watchDownload(url: string) { + const es = new EventSource(`/llama/download/stream?url=${encodeURIComponent(url)}`) + return { + subscribe(opts: { onProgress: (p: DownloadProgress) => void; onDone: (p: DownloadProgress) => void }) { + es.addEventListener('message', (ev) => { + const p: DownloadProgress = JSON.parse(ev.data) + opts.onProgress(p) + if (p.done) opts.onDone(p) + }) + es.addEventListener('error', () => { es.close() }) + }, + close() { es.close() }, + } +} + +// ── 辅助：轮询直到完成（用于不需要 SSE 的场景）────────────────────────────────── + +export async function pollUntilDone( + requestId: string, + { timeout = 60_000, interval = 500 }: { timeout?: number; interval?: number } = {}, +) { + const t0 = Date.now() + while (Date.now() - t0 < timeout) { + const s = await getRunStatus(requestId) + if (s.status === 'done' || s.status === 'failed') return s + await new Promise((r) => setTimeout(r, interval)) + } + throw new Error('poll timeout') +} diff --git a/webapp/src/assets/hero.png b/webapp/src/assets/hero.png new file mode 100644 index 0000000..02251f4 Binary files /dev/null and b/webapp/src/assets/hero.png differ diff --git a/webapp/src/assets/vite.svg b/webapp/src/assets/vite.svg new file mode 100644 index 0000000..5101b67 --- /dev/null +++ b/webapp/src/assets/vite.svg @@ -0,0 +1 @@ +Vite diff --git a/webapp/src/assets/vue.svg b/webapp/src/assets/vue.svg new file mode 100644 index 0000000..770e9d3 --- /dev/null +++ b/webapp/src/assets/vue.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/webapp/src/components/HelloWorld.vue b/webapp/src/components/HelloWorld.vue new file mode 100644 index 0000000..75c05b1 --- /dev/null +++ b/webapp/src/components/HelloWorld.vue @@ -0,0 +1,95 @@ + + + diff --git a/webapp/src/main.ts b/webapp/src/main.ts new file mode 100644 index 0000000..7b5cd15 --- /dev/null +++ b/webapp/src/main.ts @@ -0,0 +1,9 @@ +import { createApp } from 'vue' +import { createPinia } from 'pinia' +import App from './App.vue' +import router from './router' + +const app = createApp(App) +app.use(createPinia()) +app.use(router) +app.mount('#app') diff --git a/webapp/src/router/index.ts b/webapp/src/router/index.ts new file mode 100644 index 0000000..6bbb399 --- /dev/null +++ b/webapp/src/router/index.ts @@ -0,0 +1,41 @@ +import { createRouter, createWebHistory } from 'vue-router' +import ChatView from '@/views/ChatView.vue' + +// Vite 构建时 base=/static/，但 FastAPI 在 /metrics 等路径提供 SPA（不在 /static/ 下）， +// 所以 history 固定用 '/'，避免 Vue Router 把 /metrics 当作 /static/metrics 解析导致路由不匹配。 +const router = createRouter({ + history: createWebHistory('/'), + routes: [ + { + path: '/', + redirect: '/chat', + }, + { + path: '/chat', + name: 'chat', + component: ChatView, + }, + { + path: '/collaboration', + name: 'collaboration', + component: () => import('@/views/CollaborationView.vue'), + }, + { + path: '/review', + name: 'review', + component: () => import('@/views/ReviewView.vue'), + }, + { + path: '/metrics', + name: 'metrics', + component: () => import('@/views/MetricsView.vue'), + }, + { + path: '/settings', + name: 'settings', + component: () => import('@/views/SettingsView.vue'), + }, + ], +}) + +export default router diff --git a/webapp/src/stores/chat.ts b/webapp/src/stores/chat.ts new file mode 100644 index 0000000..04f34db --- /dev/null +++ b/webapp/src/stores/chat.ts @@ -0,0 +1,61 @@ +import { defineStore } from 'pinia' +import { ref } from 'vue' +import { chat, getRunStatus, pollUntilDone } from '@/api' +import type { ChatInitResponse, RunStatus, Workspace } from '@/types' + +// 单次会话记录 +export interface ChatSession { + requestId: string + query: string + init: ChatInitResponse + status: RunStatus | null + workspace: Workspace | null + error: string | null +} + +export const useChatStore = defineStore('chat', () => { + const sessions = ref([]) + const currentId = ref(null) + + const current = () => sessions.value.find((s) => s.requestId === currentId.value) ?? null + + async function sendQuery(query: string) { + const init = await chat(query) + const session: ChatSession = { + requestId: init.request_id, + query, + init, + status: null, + workspace: null, + error: null, + } + sessions.value.unshift(session) + currentId.value = init.request_id + return session + } + + async function pollStatus(requestId: string) { + const s = await getRunStatus(requestId) + const session = sessions.value.find((x) => x.requestId === requestId) + if (session) session.status = s + return s + } + + async function waitForDone(requestId: string) { + const s = await pollUntilDone(requestId) + const session = sessions.value.find((x) => x.requestId === requestId) + if (session) session.status = s + return s + } + + function updateWorkspace(requestId: string, ws: Workspace) { + const session = sessions.value.find((x) => x.requestId === requestId) + if (session) session.workspace = ws + } + + function setCurrent(id: string | null) { + currentId.value = id + } + + return { sessions, currentId, current, sendQuery, pollStatus, waitForDone, updateWorkspace, setCurrent } +}) diff --git a/webapp/src/style.css b/webapp/src/style.css new file mode 100644 index 0000000..527d4fb --- /dev/null +++ b/webapp/src/style.css @@ -0,0 +1,296 @@ +:root { + --text: #6b6375; + --text-h: #08060d; + --bg: #fff; + --border: #e5e4e7; + --code-bg: #f4f3ec; + --accent: #aa3bff; + --accent-bg: rgba(170, 59, 255, 0.1); + --accent-border: rgba(170, 59, 255, 0.5); + --social-bg: rgba(244, 243, 236, 0.5); + --shadow: + rgba(0, 0, 0, 0.1) 0 10px 15px -3px, rgba(0, 0, 0, 0.05) 0 4px 6px -2px; + + --sans: system-ui, 'Segoe UI', Roboto, sans-serif; + --heading: system-ui, 'Segoe UI', Roboto, sans-serif; + --mono: ui-monospace, Consolas, monospace; + + font: 18px/145% var(--sans); + letter-spacing: 0.18px; + color-scheme: light dark; + color: var(--text); + background: var(--bg); + font-synthesis: none; + text-rendering: optimizeLegibility; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + + @media (max-width: 1024px) { + font-size: 16px; + } +} + +@media (prefers-color-scheme: dark) { + :root { + --text: #9ca3af; + --text-h: #f3f4f6; + --bg: #16171d; + --border: #2e303a; + --code-bg: #1f2028; + --accent: #c084fc; + --accent-bg: rgba(192, 132, 252, 0.15); + --accent-border: rgba(192, 132, 252, 0.5); + --social-bg: rgba(47, 48, 58, 0.5); + --shadow: + rgba(0, 0, 0, 0.4) 0 10px 15px -3px, rgba(0, 0, 0, 0.25) 0 4px 6px -2px; + } + + #social .button-icon { + filter: invert(1) brightness(2); + } +} + +body { + margin: 0; +} + +h1, +h2 { + font-family: var(--heading); + font-weight: 500; + color: var(--text-h); +} + +h1 { + font-size: 56px; + letter-spacing: -1.68px; + margin: 32px 0; + @media (max-width: 1024px) { + font-size: 36px; + margin: 20px 0; + } +} +h2 { + font-size: 24px; + line-height: 118%; + letter-spacing: -0.24px; + margin: 0 0 8px; + @media (max-width: 1024px) { + font-size: 20px; + } +} +p { + margin: 0; +} + +code, +.counter { + font-family: var(--mono); + display: inline-flex; + border-radius: 4px; + color: var(--text-h); +} + +code { + font-size: 15px; + line-height: 135%; + padding: 4px 8px; + background: var(--code-bg); +} + +.counter { + font-size: 16px; + padding: 5px 10px; + border-radius: 5px; + color: var(--accent); + background: var(--accent-bg); + border: 2px solid transparent; + transition: border-color 0.3s; + margin-bottom: 24px; + + &:hover { + border-color: var(--accent-border); + } + &:focus-visible { + outline: 2px solid var(--accent); + outline-offset: 2px; + } +} + +.hero { + position: relative; + + .base, + .framework, + .vite { + inset-inline: 0; + margin: 0 auto; + } + + .base { + width: 170px; + position: relative; + z-index: 0; + } + + .framework, + .vite { + position: absolute; + } + + .framework { + z-index: 1; + top: 34px; + height: 28px; + transform: perspective(2000px) rotateZ(300deg) rotateX(44deg) rotateY(39deg) + scale(1.4); + } + + .vite { + z-index: 0; + top: 107px; + height: 26px; + width: auto; + transform: perspective(2000px) rotateZ(300deg) rotateX(40deg) rotateY(39deg) + scale(0.8); + } +} + +#app { + width: 1126px; + max-width: 100%; + margin: 0 auto; + text-align: center; + border-inline: 1px solid var(--border); + min-height: 100svh; + display: flex; + flex-direction: column; + box-sizing: border-box; +} + +#center { + display: flex; + flex-direction: column; + gap: 25px; + place-content: center; + place-items: center; + flex-grow: 1; + + @media (max-width: 1024px) { + padding: 32px 20px 24px; + gap: 18px; + } +} + +#next-steps { + display: flex; + border-top: 1px solid var(--border); + text-align: left; + + & > div { + flex: 1 1 0; + padding: 32px; + @media (max-width: 1024px) { + padding: 24px 20px; + } + } + + .icon { + margin-bottom: 16px; + width: 22px; + height: 22px; + } + + @media (max-width: 1024px) { + flex-direction: column; + text-align: center; + } +} + +#docs { + border-right: 1px solid var(--border); + + @media (max-width: 1024px) { + border-right: none; + border-bottom: 1px solid var(--border); + } +} + +#next-steps ul { + list-style: none; + padding: 0; + display: flex; + gap: 8px; + margin: 32px 0 0; + + .logo { + height: 18px; + } + + a { + color: var(--text-h); + font-size: 16px; + border-radius: 6px; + background: var(--social-bg); + display: flex; + padding: 6px 12px; + align-items: center; + gap: 8px; + text-decoration: none; + transition: box-shadow 0.3s; + + &:hover { + box-shadow: var(--shadow); + } + .button-icon { + height: 18px; + width: 18px; + } + } + + @media (max-width: 1024px) { + margin-top: 20px; + flex-wrap: wrap; + justify-content: center; + + li { + flex: 1 1 calc(50% - 8px); + } + + a { + width: 100%; + justify-content: center; + box-sizing: border-box; + } + } +} + +#spacer { + height: 88px; + border-top: 1px solid var(--border); + @media (max-width: 1024px) { + height: 48px; + } +} + +.ticks { + position: relative; + width: 100%; + + &::before, + &::after { + content: ''; + position: absolute; + top: -4.5px; + border: 5px solid transparent; + } + + &::before { + left: 0; + border-left-color: var(--border); + } + &::after { + right: 0; + border-right-color: var(--border); + } +} diff --git a/webapp/src/types/index.ts b/webapp/src/types/index.ts new file mode 100644 index 0000000..21f3453 --- /dev/null +++ b/webapp/src/types/index.ts @@ -0,0 +1,117 @@ +// ── 协作任务状态 ────────────────────────────────────────────────────────────── + +/** POST /chat 立即返回 */ +export interface ChatInitResponse { + request_id: string + status: 'pending' +} + +/** GET /runs/{id}/status 返回 */ +export interface RunStatus { + request_id: string + status: 'pending' | 'running' | 'done' | 'failed' // JobStore 任务状态 + ws_state: string | null // workspace.json 内 state + started_at: number + finished_at: number + error: string | null + // PipelineResult 等效字段（扁平时 TaskInfo） + response: string | null + pipeline_status: string | null // done | fast_path | escalated | failed + fast_path: boolean + rounds_used: number + api_input_tokens: number + api_output_tokens: number + cost_est: number + model_used: string | null + latency_ms: number | null + route: string[] + workspace_path: string | null +} + +// ── SSE 事件 ───────────────────────────────────────────────────────────────── + +export type SSEEvent = + | { type: 'workspace'; version: number; state: string; request_id: string; workspace: Workspace } + | { type: 'status'; value: string; request_id: string } + | { type: 'error'; detail: string } + +// ── 交流文本（workspace.json）──────────────────────────────────────────────── + +export interface Workspace { + request_id: string + query: string + brief: Brief | null + plan: PlanStep[] + progress: ProgressStep[] + issues: Issue[] + decisions: Decision[] + archive: string[] + meta: WsMeta + status?: string + error?: string +} + +export interface Brief { + goal: string + tags: string[] + domain: string + constraints: string[] +} + +export interface PlanStep { + id: string + task: string + deps: string[] + status?: string +} + +export interface ProgressStep { + step: string + status: 'pending' | 'running' | 'done' + artifact?: string + note?: string +} + +export interface Issue { + id: string + step: string + description: string + suggestion?: string +} + +export interface Decision { + ref: string + reply: string + patch_plan?: PlanStep[] +} + +export interface WsMeta { + state: 'draft' | 'in_progress' | 'reviewing' | 'done' | 'failed' + round: number + rounds_cap: number + version?: number + api_input_tokens: number + api_output_tokens: number +} + +// ── 人工检验 ───────────────────────────────────────────────────────────────── + +export interface ReviewItem { + id: number + request_id: string + query: string + response: string + verdict: 'pending' | 'approved' | 'rejected' + tags: string[] + created_at: string + correction?: string +} + +// ── 指标 ───────────────────────────────────────────────────────────────────── + +export interface Metrics { + router: Record + cache: Record + v2?: Record + review?: { pending: number; total: number } +} diff --git a/webapp/src/views/ChatView.vue b/webapp/src/views/ChatView.vue new file mode 100644 index 0000000..e939cd8 --- /dev/null +++ b/webapp/src/views/ChatView.vue @@ -0,0 +1,338 @@ + + + + + diff --git a/webapp/src/views/CollaborationView.vue b/webapp/src/views/CollaborationView.vue new file mode 100644 index 0000000..728c411 --- /dev/null +++ b/webapp/src/views/CollaborationView.vue @@ -0,0 +1,450 @@ + + + + + diff --git a/webapp/src/views/MetricsView.vue b/webapp/src/views/MetricsView.vue new file mode 100644 index 0000000..e3879e9 --- /dev/null +++ b/webapp/src/views/MetricsView.vue @@ -0,0 +1,171 @@ + + + + + diff --git a/webapp/src/views/ReviewView.vue b/webapp/src/views/ReviewView.vue new file mode 100644 index 0000000..f847bdd --- /dev/null +++ b/webapp/src/views/ReviewView.vue @@ -0,0 +1,196 @@ +