feat: add Lingma OpenAI-compatible gateway service

.env.example

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+HOST=0.0.0.0
+PORT=8317
+API_KEYS=sk-DXKFFEL0A1bN8Teqz
+
+LINGMA_BIN=/app/bin/Lingma
+LINGMA_WORK_DIR=/root/.lingma/vscode/sharedClientCache
+LINGMA_SOCKET_PORT=36510
+LINGMA_STARTUP_TIMEOUT=40
+LINGMA_RPC_TIMEOUT=30
+
+DEFAULT_MODEL=org_auto
+DEFAULT_ASK_MODE=chat
+
+DEDICATED_DOMAIN_URL=
+
+AUTO_LOGIN_ENABLED=true
+AUTO_LOGIN_HEADLESS=true
+AUTO_LOGIN_TIMEOUT=180
+AUTO_LOGIN_MAX_RETRY=2
+
+LINGMA_USERNAME=
+LINGMA_PASSWORD=

.gitignore

@@ -0,0 +1,4 @@
+.env
+__pycache__/
+*.pyc
+bin/

Dockerfile

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+FROM mcr.microsoft.com/playwright/python:v1.52.0-jammy
+
+WORKDIR /app
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1
+
+COPY requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir -r /app/requirements.txt
+
+COPY app /app/app
+
+EXPOSE 8317
+
+CMD ["sh", "-c", "uvicorn app.main:app --host ${HOST:-0.0.0.0} --port ${PORT:-8317}"]

README.md

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+# Lingma OpenAI Gateway
+
+把本地 Lingma 能力封装为 OpenAI 兼容接口，支持：
+
+- `GET /v1/models`
+- `POST /v1/chat/completions`
+- `stream=true`（SSE）
+- Bearer API Key 鉴权
+
+## 1. 准备目录
+
+```bash
+mkdir -p bin
+cp ../Lingma bin/Lingma
+chmod +x bin/Lingma
+```
+
+## 2. 配置环境变量
+
+```bash
+cp .env.example .env
+```
+
+至少修改：
+
+- `API_KEYS`
+- `LINGMA_USERNAME`
+- `LINGMA_PASSWORD`
+
+如果你的 Lingma 路径不同，修改：
+
+- `LINGMA_BIN`
+
+可选（企业专属域）：
+
+- `DEDICATED_DOMAIN_URL`
+
+## 3. Docker 运行
+
+```bash
+docker compose up -d --build
+```
+
+查看日志：
+
+```bash
+docker compose logs -f
+```
+
+## 4. 调用示例
+
+### 模型列表
+
+```bash
+curl -s http://127.0.0.1:8317/v1/models \
+  -H "Authorization: Bearer sk-DXKFFEL0A1bN8Teqz"
+```
+
+说明：
+
+- `id` 保持 Lingma 原始模型 key（兼容 OpenAI 客户端）
+- `name` 提供可读名称（如 `qwen3.6-plus`）
+- 调用 `/v1/chat/completions` 时，`model` 既可传 `id`，也可直接传 `name`
+
+### 非流式聊天
+
+```bash
+curl -s http://127.0.0.1:8317/v1/chat/completions \
+  -H "Authorization: Bearer sk-DXKFFEL0A1bN8Teqz" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "dashscope_qmodel",
+    "messages": [
+      {"role": "user", "content": "写一个 python hello world"}
+    ]
+  }'
+```
+
+### 流式聊天
+
+```bash
+curl -N http://127.0.0.1:8317/v1/chat/completions \
+  -H "Authorization: Bearer sk-DXKFFEL0A1bN8Teqz" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "dashscope_qmodel",
+    "stream": true,
+    "messages": [
+      {"role": "user", "content": "介绍一下你自己"}
+    ]
+  }'
+```
+
+## 5. 统计与监控
+
+支持调用次数与 token（估算值）统计：
+
+- `GET /internal/stats`（需 Bearer）
+- `GET /metrics`（Prometheus 文本格式）
+
+示例：
+
+```bash
+curl -s http://127.0.0.1:8317/internal/stats \
+  -H "Authorization: Bearer sk-xxx"
+```
+
+```bash
+curl -s http://127.0.0.1:8317/metrics
+```
+
+说明：
+
+- `usage.prompt_tokens/completion_tokens` 为估算值（按字节近似换算）。
+- 非流式响应里会附带 `usage` 字段。
+
+## 6. 容器内自动登录
+
+已内置自动登录能力（Playwright + Chromium）。
+
+你可以主动触发：
+
+```bash
+curl -s -X POST http://127.0.0.1:8317/internal/auto-login/start \
+  -H "Authorization: Bearer sk-DXKFFEL0A1bN8Teqz"
+```
+
+查看状态：
+
+```bash
+curl -s http://127.0.0.1:8317/internal/auto-login/status \
+  -H "Authorization: Bearer sk-DXKFFEL0A1bN8Teqz"
+```
+
+说明：
+
+- 若未登录，`/v1/models` 与 `/v1/chat/completions` 也会尝试自动登录。
+- 账号密码来自 `.env`（`LINGMA_USERNAME` / `LINGMA_PASSWORD`）。
+- 建议仅在受控环境使用，并妥善保护 `.env`。
+
+## 7. agent 模式
+
+在 v1 中，若 `model` 传 `agent` 或 `lingma-agent`，会走 agent 模式。
+
+```bash
+curl -s http://127.0.0.1:8317/v1/chat/completions \
+  -H "Authorization: Bearer sk-xxx" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "agent",
+    "messages": [
+      {"role": "user", "content": "分析这个项目目录结构"}
+    ]
+  }'
+```

app/auth.py

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+from __future__ import annotations
+
+from fastapi import HTTPException, Request, status
+
+
+def require_bearer(request: Request, api_keys: list[str]) -> None:
+    if not api_keys:
+        return
+
+    auth = request.headers.get("authorization", "")
+    if not auth.startswith("Bearer "):
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail={
+                "error": {
+                    "message": "Missing or invalid Authorization header",
+                    "type": "invalid_request_error",
+                    "code": "invalid_api_key",
+                }
+            },
+        )
+
+    token = auth[len("Bearer ") :].strip()
+    if token not in api_keys:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail={
+                "error": {
+                    "message": "Invalid API key",
+                    "type": "invalid_request_error",
+                    "code": "invalid_api_key",
+                }
+            },
+        )

app/auto_login.py

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+from __future__ import annotations
+
+import asyncio
+import contextlib
+import time
+
+from playwright.async_api import TimeoutError as PlaywrightTimeoutError
+from playwright.async_api import async_playwright
+
+
+class AutoLoginManager:
+    def __init__(
+        self,
+        username: str,
+        password: str,
+        headless: bool,
+        timeout_sec: int,
+        max_retry: int,
+    ):
+        self.username = username
+        self.password = password
+        self.headless = headless
+        self.timeout_sec = timeout_sec
+        self.max_retry = max_retry
+        self._lock = asyncio.Lock()
+        self._task: asyncio.Task | None = None
+        self._state = "idle"
+        self._last_error = ""
+        self._last_started_at = 0.0
+        self._last_finished_at = 0.0
+
+    @property
+    def state(self) -> str:
+        return self._state
+
+    def status(self) -> dict:
+        return {
+            "state": self._state,
+            "last_error": self._last_error,
+            "last_started_at": self._last_started_at,
+            "last_finished_at": self._last_finished_at,
+            "running": self._task is not None and not self._task.done(),
+        }
+
+    async def ensure_started(self, login_url: str):
+        async with self._lock:
+            if self._task and not self._task.done():
+                return False
+            self._task = asyncio.create_task(self._run(login_url))
+            return True
+
+    async def wait_done(self, timeout: float):
+        if not self._task:
+            return
+        await asyncio.wait_for(asyncio.shield(self._task), timeout=timeout)
+
+    async def _run(self, login_url: str):
+        self._state = "running"
+        self._last_error = ""
+        self._last_started_at = time.time()
+
+        try:
+            for attempt in range(1, self.max_retry + 1):
+                try:
+                    await self._auto_login_once(login_url)
+                    self._state = "success"
+                    self._last_finished_at = time.time()
+                    return
+                except Exception as exc:
+                    self._last_error = f"attempt {attempt}: {exc}"
+                    if attempt >= self.max_retry:
+                        raise
+                    await asyncio.sleep(1.5)
+        except Exception:
+            self._state = "failed"
+            self._last_finished_at = time.time()
+
+    async def _auto_login_once(self, login_url: str):
+        if not self.username or not self.password:
+            raise RuntimeError("LINGMA_USERNAME/LINGMA_PASSWORD not configured")
+
+        deadline = time.time() + self.timeout_sec
+
+        async with async_playwright() as p:
+            browser = await p.chromium.launch(headless=self.headless)
+            context = await browser.new_context(ignore_https_errors=True)
+            page = await context.new_page()
+            try:
+                await page.goto(login_url, wait_until="domcontentloaded", timeout=30000)
+
+                # Try common login selectors.
+                await self._fill_if_visible(page, [
+                    'input[type="email"]',
+                    'input[name="loginId"]',
+                    'input[name="username"]',
+                    'input[name="account"]',
+                    'input[placeholder*="账号"]',
+                    'input[placeholder*="邮箱"]',
+                ], self.username)
+
+                await self._fill_if_visible(page, [
+                    'input[type="password"]',
+                    'input[name="password"]',
+                    'input[placeholder*="密码"]',
+                ], self.password)
+
+                await self._click_if_visible(page, [
+                    'button:has-text("登录")',
+                    'button:has-text("登 录")',
+                    'button:has-text("Login")',
+                    'button[type="submit"]',
+                ])
+
+                # Wait for redirect / callback activity.
+                while time.time() < deadline:
+                    url = page.url
+                    if "lingma" in url and ("callback" in url or "tokenString=" in url):
+                        break
+                    await asyncio.sleep(1.0)
+
+            except PlaywrightTimeoutError as exc:
+                raise RuntimeError(f"playwright timeout: {exc}") from exc
+            finally:
+                with contextlib.suppress(Exception):
+                    await context.close()
+                with contextlib.suppress(Exception):
+                    await browser.close()
+
+    async def _fill_if_visible(self, page, selectors: list[str], value: str):
+        for sel in selectors:
+            locator = page.locator(sel).first
+            try:
+                if await locator.is_visible(timeout=1500):
+                    await locator.fill(value)
+                    return True
+            except Exception:
+                continue
+        return False
+
+    async def _click_if_visible(self, page, selectors: list[str]):
+        for sel in selectors:
+            locator = page.locator(sel).first
+            try:
+                if await locator.is_visible(timeout=1500):
+                    await locator.click()
+                    return True
+            except Exception:
+                continue
+        return False

app/config.py

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+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+from pathlib import Path
+
+
+@dataclass
+class Settings:
+    host: str
+    port: int
+    api_keys: list[str]
+    lingma_bin: str
+    lingma_work_dir: str
+    lingma_socket_port: int
+    lingma_startup_timeout: int
+    lingma_rpc_timeout: int
+    default_model: str
+    default_ask_mode: str
+    dedicated_domain_url: str
+    auto_login_enabled: bool
+    auto_login_headless: bool
+    auto_login_timeout: int
+    auto_login_max_retry: int
+    lingma_username: str
+    lingma_password: str
+
+
+def load_settings() -> Settings:
+    keys_raw = os.getenv("API_KEYS", "")
+    api_keys = [k.strip() for k in keys_raw.split(",") if k.strip()]
+    work_dir = os.getenv(
+        "LINGMA_WORK_DIR",
+        str(Path.home() / ".lingma" / "vscode" / "sharedClientCache"),
+    )
+    return Settings(
+        host=os.getenv("HOST", "0.0.0.0"),
+        port=int(os.getenv("PORT", "8317")),
+        api_keys=api_keys,
+        lingma_bin=os.getenv("LINGMA_BIN", "/app/bin/Lingma"),
+        lingma_work_dir=work_dir,
+        lingma_socket_port=int(os.getenv("LINGMA_SOCKET_PORT", "36510")),
+        lingma_startup_timeout=int(os.getenv("LINGMA_STARTUP_TIMEOUT", "40")),
+        lingma_rpc_timeout=int(os.getenv("LINGMA_RPC_TIMEOUT", "30")),
+        default_model=os.getenv("DEFAULT_MODEL", "org_auto"),
+        default_ask_mode=os.getenv("DEFAULT_ASK_MODE", "chat"),
+        dedicated_domain_url=os.getenv("DEDICATED_DOMAIN_URL", "").strip(),
+        auto_login_enabled=os.getenv("AUTO_LOGIN_ENABLED", "true").lower() in {"1", "true", "yes", "on"},
+        auto_login_headless=os.getenv("AUTO_LOGIN_HEADLESS", "true").lower() in {"1", "true", "yes", "on"},
+        auto_login_timeout=int(os.getenv("AUTO_LOGIN_TIMEOUT", "180")),
+        auto_login_max_retry=int(os.getenv("AUTO_LOGIN_MAX_RETRY", "2")),
+        lingma_username=os.getenv("LINGMA_USERNAME", "").strip(),
+        lingma_password=os.getenv("LINGMA_PASSWORD", "").strip(),
+    )

app/lingma_client.py

@@ -0,0 +1,386 @@
+from __future__ import annotations
+
+import asyncio
+import contextlib
+import json
+import os
+import socket
+import subprocess
+import time
+import uuid
+from pathlib import Path
+from typing import AsyncIterator
+
+import websockets
+
+
+def _is_port_open(host: str, port: int, timeout_sec: float = 0.5) -> bool:
+    try:
+        with socket.create_connection((host, port), timeout=timeout_sec):
+            return True
+    except OSError:
+        return False
+
+
+def _read_info_file(info_path: Path):
+    if not info_path.exists():
+        return None, None
+    txt = info_path.read_text(encoding="utf-8", errors="ignore").strip()
+    if not txt:
+        return None, None
+    lines = txt.splitlines()
+    if len(lines) < 2:
+        return None, None
+    try:
+        return int(lines[0].strip()), int(lines[1].strip())
+    except ValueError:
+        return None, None
+
+
+def _wait_info_any(info_paths: list[Path], timeout_sec: int):
+    start = time.time()
+    while time.time() - start < timeout_sec:
+        for p in info_paths:
+            port, pid = _read_info_file(p)
+            if port and pid:
+                return port, pid, p
+        time.sleep(0.2)
+    raise TimeoutError(".info not ready")
+
+
+def _encode_lsp_frame(payload_obj: dict) -> bytes:
+    body = json.dumps(payload_obj, ensure_ascii=False).encode("utf-8")
+    header = f"Content-Length: {len(body)}\r\n\r\n".encode("ascii")
+    return header + body
+
+
+def _parse_lsp_frames(buf: bytes):
+    frames = []
+    while True:
+        header_end = buf.find(b"\r\n\r\n")
+        if header_end < 0:
+            break
+        header = buf[:header_end]
+        body_start = header_end + 4
+        content_length = None
+        for line in header.split(b"\r\n"):
+            if line.lower().startswith(b"content-length:"):
+                content_length = int(line.split(b":", 1)[1].strip())
+                break
+        if content_length is None:
+            buf = buf[body_start:]
+            continue
+        if len(buf) < body_start + content_length:
+            break
+        body = buf[body_start : body_start + content_length]
+        frames.append(body.decode("utf-8", errors="ignore"))
+        buf = buf[body_start + content_length :]
+    return frames, buf
+
+
+class LspWsRpcClient:
+    def __init__(self, ws):
+        self.ws = ws
+        self._id = 1
+        self._pending: dict[int, asyncio.Future] = {}
+        self._send_lock = asyncio.Lock()
+        self._reader_task = None
+        self._rx_buffer = b""
+        self._chat_streams: dict[str, dict] = {}
+
+    async def start(self):
+        self._reader_task = asyncio.create_task(self._reader_loop())
+
+    async def close(self):
+        if self._reader_task:
+            self._reader_task.cancel()
+            with contextlib.suppress(Exception):
+                await self._reader_task
+
+    async def _send(self, payload: dict):
+        async with self._send_lock:
+            await self.ws.send(_encode_lsp_frame(payload))
+
+    async def _reader_loop(self):
+        try:
+            while True:
+                raw = await self.ws.recv()
+                chunk = raw if isinstance(raw, bytes) else raw.encode("utf-8", errors="ignore")
+                self._rx_buffer += chunk
+                bodies, self._rx_buffer = _parse_lsp_frames(self._rx_buffer)
+                for body in bodies:
+                    try:
+                        msg = json.loads(body)
+                    except Exception:
+                        continue
+
+                    if "method" in msg and "result" not in msg and "error" not in msg:
+                        await self._handle_server_message(msg)
+                        continue
+
+                    rid = msg.get("id")
+                    if rid is None:
+                        continue
+                    fut = self._pending.pop(rid, None)
+                    if fut and not fut.done():
+                        fut.set_result(msg)
+        except asyncio.CancelledError:
+            pass
+        except Exception as exc:
+            for fut in self._pending.values():
+                if not fut.done():
+                    fut.set_exception(exc)
+            self._pending.clear()
+
+    async def _handle_server_message(self, msg: dict):
+        method = msg.get("method")
+        params = msg.get("params") or {}
+
+        if method == "chat/answer":
+            req_id = params.get("requestId")
+            stream = self._chat_streams.get(req_id)
+            if stream is not None:
+                text = params.get("text") or params.get("content") or ""
+                if text:
+                    stream["parts"].append(text)
+                    if stream["first_chunk_at"] is None:
+                        stream["first_chunk_at"] = time.monotonic()
+                    stream["chunks"].put_nowait(text)
+
+        if method == "chat/finish":
+            req_id = params.get("requestId")
+            stream = self._chat_streams.get(req_id)
+            if stream is not None and not stream["done"].is_set():
+                stream["finish"] = params
+                stream["finish_at"] = time.monotonic()
+                stream["done"].set()
+                stream["chunks"].put_nowait(None)
+
+        if "id" in msg:
+            await self._send({"jsonrpc": "2.0", "id": msg.get("id"), "result": {}})
+
+    async def request(self, method, params=None, timeout=20):
+        rid = self._id
+        self._id += 1
+        payload = {"jsonrpc": "2.0", "id": rid, "method": method, "params": params or {}}
+        fut = asyncio.get_running_loop().create_future()
+        self._pending[rid] = fut
+        await self._send(payload)
+        try:
+            msg = await asyncio.wait_for(fut, timeout=timeout)
+        except TimeoutError:
+            self._pending.pop(rid, None)
+            raise TimeoutError(f"RPC timeout: {method}")
+        if "error" in msg:
+            raise RuntimeError(f"RPC {method} error: {msg['error']}")
+        return msg.get("result")
+
+    async def notify(self, method, params=None):
+        await self._send({"jsonrpc": "2.0", "method": method, "params": params or {}})
+
+    def create_stream(self, request_id: str):
+        self._chat_streams[request_id] = {
+            "parts": [],
+            "chunks": asyncio.Queue(),
+            "done": asyncio.Event(),
+            "finish": None,
+            "started_at": time.monotonic(),
+            "first_chunk_at": None,
+            "finish_at": None,
+        }
+
+    async def consume_stream(self, request_id: str, timeout: float) -> AsyncIterator[str]:
+        stream = self._chat_streams[request_id]
+        start = time.monotonic()
+        while True:
+            remain = timeout - (time.monotonic() - start)
+            if remain <= 0:
+                raise TimeoutError("chat stream timeout")
+            chunk = await asyncio.wait_for(stream["chunks"].get(), timeout=remain)
+            if chunk is None:
+                break
+            yield chunk
+
+    def get_stream_result(self, request_id: str) -> dict:
+        stream = self._chat_streams.get(request_id) or {}
+        first_ms = None
+        total_ms = None
+        if stream.get("first_chunk_at") is not None:
+            first_ms = int((stream["first_chunk_at"] - stream["started_at"]) * 1000)
+        if stream.get("finish_at") is not None:
+            total_ms = int((stream["finish_at"] - stream["started_at"]) * 1000)
+        return {
+            "text": "".join(stream.get("parts") or []),
+            "finish": stream.get("finish") or {},
+            "firstTokenLatencyMs": first_ms,
+            "totalLatencyMs": total_ms,
+        }
+
+
+class LingmaGatewayClient:
+    def __init__(
+        self,
+        lingma_bin: str,
+        work_dir: str,
+        socket_port: int,
+        startup_timeout: int,
+        rpc_timeout: int,
+        default_model: str,
+        default_ask_mode: str,
+    ):
+        self.lingma_bin = Path(lingma_bin)
+        self.work_dir = Path(work_dir)
+        self.socket_port = socket_port
+        self.startup_timeout = startup_timeout
+        self.rpc_timeout = rpc_timeout
+        self.default_model = default_model
+        self.default_ask_mode = default_ask_mode
+        self._rpc: LspWsRpcClient | None = None
+        self._ws = None
+
+    async def start(self):
+        if not self.lingma_bin.exists():
+            raise FileNotFoundError(f"Lingma not found: {self.lingma_bin}")
+        if not _is_port_open("127.0.0.1", self.socket_port):
+            self.work_dir.mkdir(parents=True, exist_ok=True)
+            # Remove stale info files from host-mounted workspace before boot.
+            for p in [self.work_dir / ".info", Path.home() / ".lingma" / ".info"]:
+                with contextlib.suppress(Exception):
+                    if p.exists():
+                        p.unlink()
+            subprocess.Popen(
+                [str(self.lingma_bin), "start", "--workDir", str(self.work_dir)],
+                cwd=str(self.lingma_bin.parent),
+                stdout=subprocess.DEVNULL,
+                stderr=subprocess.DEVNULL,
+                start_new_session=True,
+            )
+            info, _, _ = _wait_info_any(
+                [self.work_dir / ".info", Path.home() / ".lingma" / ".info"],
+                timeout_sec=self.startup_timeout,
+            )
+            self.socket_port = info
+
+        # Wait for socket to actually become connectable.
+        deadline = time.time() + self.startup_timeout
+        while time.time() < deadline:
+            if _is_port_open("127.0.0.1", self.socket_port, timeout_sec=0.3):
+                break
+            await asyncio.sleep(0.2)
+        else:
+            raise TimeoutError(f"Lingma socket not open on port {self.socket_port}")
+
+        ws_url = f"ws://127.0.0.1:{self.socket_port}"
+        self._ws = await websockets.connect(ws_url, max_size=10 * 1024 * 1024)
+        self._rpc = LspWsRpcClient(self._ws)
+        await self._rpc.start()
+        await self._rpc.request(
+            "initialize",
+            {
+                "processId": os.getpid(),
+                "clientInfo": {"name": "lingma-openai-gateway", "version": "0.1.0"},
+                "capabilities": {},
+                "workspaceFolders": [],
+                "rootUri": None,
+            },
+            timeout=self.rpc_timeout,
+        )
+        await self._rpc.notify("initialized", {})
+
+    async def close(self):
+        if self._rpc:
+            await self._rpc.close()
+        if self._ws:
+            await self._ws.close()
+
+    @property
+    def rpc(self) -> LspWsRpcClient:
+        if self._rpc is None:
+            raise RuntimeError("Lingma RPC not initialized")
+        return self._rpc
+
+    async def auth_status(self):
+        return await self.rpc.request("auth/status", {}, timeout=self.rpc_timeout)
+
+    async def query_models(self):
+        return await self.rpc.request("config/queryModels", {}, timeout=self.rpc_timeout)
+
+    async def get_endpoint(self):
+        return await self.rpc.request("config/getEndpoint", {}, timeout=self.rpc_timeout)
+
+    async def update_endpoint(self, endpoint: str):
+        return await self.rpc.request("config/updateEndpoint", {"endpoint": endpoint}, timeout=self.rpc_timeout)
+
+    async def generate_login_url(self):
+        result = await self.rpc.request("login/generateUrl", {}, timeout=self.rpc_timeout)
+        if isinstance(result, str):
+            return result, {"raw": result}
+        if isinstance(result, dict):
+            for key in ("loginUrl", "url", "login_url"):
+                if isinstance(result.get(key), str):
+                    return result[key], result
+            return "", result
+        return "", {"raw": result}
+
+    def _build_payload(self, prompt: str, model_key: str, ask_mode: str, session_id: str, request_id: str):
+        session_type = "developer" if ask_mode == "agent" else "chat"
+        return {
+            "requestId": request_id,
+            "sessionId": session_id,
+            "sessionType": session_type,
+            "chatTask": "FREE_INPUT",
+            "mode": ask_mode,
+            "stream": True,
+            "source": 1,
+            "isReply": False,
+            "taskDefinitionType": "system",
+            "content": prompt,
+            "text": prompt,
+            "message": prompt,
+            "questionText": prompt,
+            "extra": {
+                "modelConfig": {"key": model_key},
+                "workspacePath": str(Path.cwd()),
+            },
+            "pluginPayloadConfig": {
+                "isEnableAskAgent": ask_mode == "agent",
+                "isEnableAutoMemory": True,
+            },
+            "chatContext": {
+                "text": prompt,
+                "features": [],
+                "preferredLanguage": "zh-CN",
+                "localeLang": "zh-CN",
+            },
+        }
+
+    async def chat_complete(self, prompt: str, model_key: str, ask_mode: str) -> dict:
+        request_id = str(uuid.uuid4())
+        session_id = str(uuid.uuid4())
+        payload = self._build_payload(prompt, model_key, ask_mode, session_id, request_id)
+        self.rpc.create_stream(request_id)
+        try:
+            await self.rpc.request("chat/ask", payload, timeout=self.rpc_timeout)
+        except (TimeoutError, asyncio.TimeoutError):
+            pass
+        async for _ in self.rpc.consume_stream(request_id, timeout=max(20.0, self.rpc_timeout + 20.0)):
+            pass
+        result = self.rpc.get_stream_result(request_id)
+        finish = result.get("finish") or {}
+        result["requestId"] = request_id
+        result["sessionId"] = finish.get("sessionId") or session_id
+        result["model"] = model_key
+        result["mode"] = ask_mode
+        return result
+
+    async def chat_stream(self, prompt: str, model_key: str, ask_mode: str) -> AsyncIterator[str]:
+        request_id = str(uuid.uuid4())
+        session_id = str(uuid.uuid4())
+        payload = self._build_payload(prompt, model_key, ask_mode, session_id, request_id)
+        self.rpc.create_stream(request_id)
+        try:
+            await self.rpc.request("chat/ask", payload, timeout=self.rpc_timeout)
+        except (TimeoutError, asyncio.TimeoutError):
+            pass
+        async for chunk in self.rpc.consume_stream(request_id, timeout=max(20.0, self.rpc_timeout + 40.0)):
+            yield chunk

app/main.py

@@ -0,0 +1,283 @@
+from __future__ import annotations
+
+import json
+import time
+import uuid
+
+from fastapi import Depends, FastAPI, HTTPException, Request
+from fastapi.responses import JSONResponse, StreamingResponse
+
+from .auto_login import AutoLoginManager
+from .auth import require_bearer
+from .config import Settings, load_settings
+from .lingma_client import LingmaGatewayClient
+from .model_map import build_model_name_map, flatten_model_keys, resolve_model
+from .openai_schema import (
+    ChatCompletionChoice,
+    ChatCompletionResponse,
+    ChatCompletionsRequest,
+    ModelData,
+    ModelsResponse,
+)
+from .stats import StatsCollector, estimate_tokens
+
+
+app = FastAPI(title="Lingma OpenAI Gateway", version="0.1.0")
+settings: Settings = load_settings()
+lingma: LingmaGatewayClient | None = None
+auto_login: AutoLoginManager | None = None
+stats_collector = StatsCollector()
+
+
+def auth_guard(request: Request):
+    require_bearer(request, settings.api_keys)
+
+
+@app.on_event("startup")
+async def on_startup():
+    global lingma, auto_login
+    lingma = LingmaGatewayClient(
+        lingma_bin=settings.lingma_bin,
+        work_dir=settings.lingma_work_dir,
+        socket_port=settings.lingma_socket_port,
+        startup_timeout=settings.lingma_startup_timeout,
+        rpc_timeout=settings.lingma_rpc_timeout,
+        default_model=settings.default_model,
+        default_ask_mode=settings.default_ask_mode,
+    )
+    await lingma.start()
+    auto_login = AutoLoginManager(
+        username=settings.lingma_username,
+        password=settings.lingma_password,
+        headless=settings.auto_login_headless,
+        timeout_sec=settings.auto_login_timeout,
+        max_retry=settings.auto_login_max_retry,
+    )
+
+
+@app.on_event("shutdown")
+async def on_shutdown():
+    if lingma:
+        await lingma.close()
+
+
+@app.get("/healthz")
+async def healthz():
+    return {"ok": True, "time": int(time.time())}
+
+
+async def _ensure_logged_in_or_auto_login() -> dict:
+    assert lingma is not None
+    status = await lingma.auth_status()
+    if status and status.get("id"):
+        return status
+
+    if not settings.auto_login_enabled:
+        raise HTTPException(status_code=401, detail={"error": {"message": "Lingma not logged in"}})
+
+    if settings.dedicated_domain_url:
+        current = await lingma.get_endpoint()
+        current_ep = (current or {}).get("endpoint", "") if isinstance(current, dict) else ""
+        if current_ep != settings.dedicated_domain_url:
+            await lingma.update_endpoint(settings.dedicated_domain_url)
+
+    login_url, login_raw = await lingma.generate_login_url()
+    if not login_url:
+        raise HTTPException(
+            status_code=500,
+            detail={"error": {"message": f"generate login url failed: {login_raw}"}},
+        )
+
+    assert auto_login is not None
+    await auto_login.ensure_started(login_url)
+    try:
+        await auto_login.wait_done(timeout=settings.auto_login_timeout + 20)
+    except Exception:
+        pass
+
+    status = await lingma.auth_status()
+    if status and status.get("id"):
+        return status
+
+    raise HTTPException(
+        status_code=401,
+        detail={"error": {"message": "Lingma auto login failed", "auto_login": auto_login.status()}},
+    )
+
+
+@app.get("/v1/models", dependencies=[Depends(auth_guard)])
+async def v1_models():
+    assert lingma is not None
+    await _ensure_logged_in_or_auto_login()
+    await stats_collector.inc_models()
+    models = await lingma.query_models()
+    keys = flatten_model_keys(models)
+    name_map = build_model_name_map(models)
+    resp = ModelsResponse(data=[ModelData(id=k, name=name_map.get(k)) for k in keys])
+    return JSONResponse(content=resp.model_dump())
+
+
+def _messages_to_prompt(messages: list[dict]) -> str:
+    parts = []
+    for m in messages:
+        role = m.get("role", "user")
+        content = m.get("content", "")
+        parts.append(f"[{role}] {content}")
+    return "\n".join(parts).strip()
+
+
+@app.post("/v1/chat/completions", dependencies=[Depends(auth_guard)])
+async def v1_chat_completions(req: ChatCompletionsRequest):
+    assert lingma is not None
+    await _ensure_logged_in_or_auto_login()
+
+    models = await lingma.query_models()
+    available = flatten_model_keys(models)
+    name_map = build_model_name_map(models)
+    model = resolve_model(req.model, available, settings.default_model, name_map)
+
+    ask_mode = settings.default_ask_mode
+    if req.model.lower() in {"lingma-agent", "agent"}:
+        ask_mode = "agent"
+
+    prompt = _messages_to_prompt([m.model_dump() for m in req.messages])
+    if not prompt:
+        raise HTTPException(status_code=400, detail={"error": {"message": "messages is empty"}})
+    prompt_tokens = estimate_tokens(prompt)
+
+    if req.stream:
+        created = int(time.time())
+        completion_id = f"chatcmpl-{uuid.uuid4().hex}"
+        completion_tokens_holder = {"n": 0}
+
+        async def event_stream():
+            success = False
+            try:
+                async for chunk in lingma.chat_stream(prompt, model, ask_mode):
+                    completion_tokens_holder["n"] += estimate_tokens(chunk)
+                    payload = {
+                        "id": completion_id,
+                        "object": "chat.completion.chunk",
+                        "created": created,
+                        "model": model,
+                        "choices": [
+                            {
+                                "index": 0,
+                                "delta": {"content": chunk},
+                                "finish_reason": None,
+                            }
+                        ],
+                    }
+                    yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
+
+                done_payload = {
+                    "id": completion_id,
+                    "object": "chat.completion.chunk",
+                    "created": created,
+                    "model": model,
+                    "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
+                }
+                yield f"data: {json.dumps(done_payload, ensure_ascii=False)}\n\n"
+                yield "data: [DONE]\n\n"
+                success = True
+            finally:
+                await stats_collector.record_chat(
+                    stream=True,
+                    success=success,
+                    prompt_tokens=prompt_tokens,
+                    completion_tokens=completion_tokens_holder["n"],
+                )
+
+        return StreamingResponse(event_stream(), media_type="text/event-stream")
+
+    try:
+        result = await lingma.chat_complete(prompt, model, ask_mode)
+    except Exception:
+        await stats_collector.record_chat(
+            stream=False,
+            success=False,
+            prompt_tokens=prompt_tokens,
+            completion_tokens=0,
+        )
+        raise
+
+    completion_tokens = estimate_tokens(result.get("text") or "")
+    await stats_collector.record_chat(
+        stream=False,
+        success=True,
+        prompt_tokens=prompt_tokens,
+        completion_tokens=completion_tokens,
+    )
+    response = ChatCompletionResponse(
+        id=f"chatcmpl-{uuid.uuid4().hex}",
+        created=int(time.time()),
+        model=model,
+        choices=[
+            ChatCompletionChoice(
+                index=0,
+                finish_reason="stop",
+                message={"role": "assistant", "content": result.get("text") or ""},
+            )
+        ],
+    )
+    data = response.model_dump()
+    data["latency"] = {
+        "first_token_ms": result.get("firstTokenLatencyMs"),
+        "total_ms": result.get("totalLatencyMs"),
+    }
+    data["usage"] = {
+        "prompt_tokens": prompt_tokens,
+        "completion_tokens": completion_tokens,
+        "total_tokens": prompt_tokens + completion_tokens,
+    }
+    return JSONResponse(content=data)
+
+
+@app.post("/internal/auto-login/start", dependencies=[Depends(auth_guard)])
+async def internal_auto_login_start():
+    assert lingma is not None
+    assert auto_login is not None
+
+    status = await lingma.auth_status()
+    if status and status.get("id"):
+        return {"ok": True, "state": "already_logged_in", "auth": status}
+
+    if settings.dedicated_domain_url:
+        current = await lingma.get_endpoint()
+        current_ep = (current or {}).get("endpoint", "") if isinstance(current, dict) else ""
+        if current_ep != settings.dedicated_domain_url:
+            await lingma.update_endpoint(settings.dedicated_domain_url)
+
+    login_url, login_raw = await lingma.generate_login_url()
+    if not login_url:
+        raise HTTPException(status_code=500, detail={"error": {"message": "generate login url failed", "raw": login_raw}})
+
+    started = await auto_login.ensure_started(login_url)
+    return {
+        "ok": True,
+        "state": "running" if started else "already_running",
+        "loginUrl": login_url,
+        "auto_login": auto_login.status(),
+    }
+
+
+@app.get("/internal/auto-login/status", dependencies=[Depends(auth_guard)])
+async def internal_auto_login_status():
+    assert auto_login is not None
+    assert lingma is not None
+    return {
+        "ok": True,
+        "auto_login": auto_login.status(),
+        "auth": await lingma.auth_status(),
+    }
+
+
+@app.get("/internal/stats", dependencies=[Depends(auth_guard)])
+async def internal_stats():
+    return {"ok": True, "stats": await stats_collector.snapshot()}
+
+
+@app.get("/metrics")
+async def metrics():
+    text = await stats_collector.prometheus_text()
+    return StreamingResponse(iter([text]), media_type="text/plain; version=0.0.4")

app/model_map.py

@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+
+DEFAULT_MODEL_NAME_MAP = {
+    "org_auto": "Auto",
+    "dashscope_qmodel": "qwen3.6-plus",
+    "dashscope_qwen3_coder": "qwen3-coder",
+    "dashscope_qwen_plus_20250428_thinking": "qwen3-thinking",
+    "dashscope_qwen_max_latest": "qwen3-max",
+}
+
+
+def build_model_name_map(models: dict) -> dict[str, str]:
+    name_map = dict(DEFAULT_MODEL_NAME_MAP)
+    if not isinstance(models, dict):
+        return name_map
+    for group in ("chat", "assistant", "developer", "inline"):
+        items = models.get(group) or []
+        if not isinstance(items, list):
+            continue
+        for item in items:
+            if not isinstance(item, dict):
+                continue
+            key = item.get("key")
+            if not isinstance(key, str) or not key:
+                continue
+            display_name = item.get("displayName") or item.get("name")
+            if isinstance(display_name, str) and display_name.strip():
+                name_map[key] = display_name.strip()
+    return name_map
+
+
+def reverse_name_map(name_map: dict[str, str]) -> dict[str, str]:
+    rev: dict[str, str] = {}
+    for key, name in name_map.items():
+        if not isinstance(name, str) or not name:
+            continue
+        rev[name] = key
+        rev[name.lower()] = key
+    return rev
+
+
+def flatten_model_keys(models: dict) -> list[str]:
+    keys: list[str] = []
+    if not isinstance(models, dict):
+        return keys
+    for group in ("chat", "assistant", "developer", "inline"):
+        items = models.get(group) or []
+        if not isinstance(items, list):
+            continue
+        for item in items:
+            if not isinstance(item, dict):
+                continue
+            key = item.get("key")
+            if isinstance(key, str) and key and key not in keys:
+                keys.append(key)
+    return keys
+
+
+def resolve_model(
+    request_model: str,
+    available_keys: list[str],
+    default_model: str,
+    model_name_map: dict[str, str] | None = None,
+) -> str:
+    model_name_map = model_name_map or {}
+    rev_map = reverse_name_map(model_name_map)
+
+    if request_model in available_keys:
+        return request_model
+    if request_model in rev_map and rev_map[request_model] in available_keys:
+        return rev_map[request_model]
+    if request_model.lower() in rev_map and rev_map[request_model.lower()] in available_keys:
+        return rev_map[request_model.lower()]
+    if request_model in {"gpt-4o-mini", "gpt-4o", "gpt-4.1", "gpt-3.5-turbo"}:
+        if default_model in available_keys:
+            return default_model
+        if available_keys:
+            return available_keys[0]
+    if default_model in available_keys:
+        return default_model
+    if available_keys:
+        return available_keys[0]
+    return request_model or default_model

app/openai_schema.py

@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class ChatMessage(BaseModel):
+    role: Literal["system", "user", "assistant", "tool"]
+    content: str
+
+
+class ChatCompletionsRequest(BaseModel):
+    model: str
+    messages: list[ChatMessage]
+    stream: bool = False
+    temperature: float | None = None
+    top_p: float | None = None
+
+
+class ModelData(BaseModel):
+    id: str
+    name: str | None = None
+    object: str = "model"
+    created: int = 0
+    owned_by: str = "lingma"
+
+
+class ModelsResponse(BaseModel):
+    object: str = "list"
+    data: list[ModelData]
+
+
+class ChatCompletionChoice(BaseModel):
+    index: int = 0
+    finish_reason: str | None = "stop"
+    message: dict = Field(default_factory=dict)
+
+
+class ChatCompletionResponse(BaseModel):
+    id: str
+    object: str = "chat.completion"
+    created: int
+    model: str
+    choices: list[ChatCompletionChoice]

app/stats.py

@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+import asyncio
+import time
+
+
+def estimate_tokens(text: str) -> int:
+    if not text:
+        return 0
+    # Heuristic only: roughly 1 token ~= 4 bytes.
+    return max(1, int(len(text.encode("utf-8")) / 4))
+
+
+class StatsCollector:
+    def __init__(self):
+        self._lock = asyncio.Lock()
+        self.started_at = int(time.time())
+        self.models_requests_total = 0
+        self.chat_requests_total = 0
+        self.chat_requests_success = 0
+        self.chat_requests_error = 0
+        self.chat_stream_requests = 0
+        self.chat_non_stream_requests = 0
+        self.prompt_tokens_estimated_total = 0
+        self.completion_tokens_estimated_total = 0
+
+    async def inc_models(self):
+        async with self._lock:
+            self.models_requests_total += 1
+
+    async def record_chat(self, *, stream: bool, success: bool, prompt_tokens: int, completion_tokens: int):
+        async with self._lock:
+            self.chat_requests_total += 1
+            if stream:
+                self.chat_stream_requests += 1
+            else:
+                self.chat_non_stream_requests += 1
+
+            if success:
+                self.chat_requests_success += 1
+            else:
+                self.chat_requests_error += 1
+
+            self.prompt_tokens_estimated_total += max(0, int(prompt_tokens))
+            self.completion_tokens_estimated_total += max(0, int(completion_tokens))
+
+    async def snapshot(self) -> dict:
+        async with self._lock:
+            total_tokens = self.prompt_tokens_estimated_total + self.completion_tokens_estimated_total
+            return {
+                "started_at": self.started_at,
+                "models_requests_total": self.models_requests_total,
+                "chat_requests_total": self.chat_requests_total,
+                "chat_requests_success": self.chat_requests_success,
+                "chat_requests_error": self.chat_requests_error,
+                "chat_stream_requests": self.chat_stream_requests,
+                "chat_non_stream_requests": self.chat_non_stream_requests,
+                "prompt_tokens_estimated_total": self.prompt_tokens_estimated_total,
+                "completion_tokens_estimated_total": self.completion_tokens_estimated_total,
+                "total_tokens_estimated": total_tokens,
+            }
+
+    async def prometheus_text(self) -> str:
+        s = await self.snapshot()
+        lines = [
+            "# TYPE gateway_models_requests_total counter",
+            f"gateway_models_requests_total {s['models_requests_total']}",
+            "# TYPE gateway_chat_requests_total counter",
+            f"gateway_chat_requests_total {s['chat_requests_total']}",
+            "# TYPE gateway_chat_requests_success counter",
+            f"gateway_chat_requests_success {s['chat_requests_success']}",
+            "# TYPE gateway_chat_requests_error counter",
+            f"gateway_chat_requests_error {s['chat_requests_error']}",
+            "# TYPE gateway_chat_stream_requests counter",
+            f"gateway_chat_stream_requests {s['chat_stream_requests']}",
+            "# TYPE gateway_chat_non_stream_requests counter",
+            f"gateway_chat_non_stream_requests {s['chat_non_stream_requests']}",
+            "# TYPE gateway_prompt_tokens_estimated_total counter",
+            f"gateway_prompt_tokens_estimated_total {s['prompt_tokens_estimated_total']}",
+            "# TYPE gateway_completion_tokens_estimated_total counter",
+            f"gateway_completion_tokens_estimated_total {s['completion_tokens_estimated_total']}",
+            "# TYPE gateway_total_tokens_estimated counter",
+            f"gateway_total_tokens_estimated {s['total_tokens_estimated']}",
+        ]
+        return "\n".join(lines) + "\n"

docker-compose.yml

@@ -0,0 +1,13 @@
+
+services:
+  lingma-openai-gateway:
+    build: .
+    container_name: lingma-openai-gateway
+    env_file:
+      - .env
+    ports:
+      - "${PORT:-8317}:${PORT:-8317}"
+    volumes:
+      - ./bin:/app/bin:ro
+      - /root/.lingma:/root/.lingma
+    restart: unless-stopped

requirements.txt

@@ -0,0 +1,5 @@
+fastapi==0.115.0
+uvicorn[standard]==0.30.6
+websockets==13.1
+pydantic==2.9.2
+playwright==1.52.0