refactor: 全栈架构升级 - 风险控制、会话管理、审计日志和验证码功能

后端变更:
- 新增 risk_config.py: 风险配置数据类，支持环境变量驱动
- 新增 risk_control.py: 风险控制控制器，管理并发和预算
- 新增 session_store.py: 匿名会话存储，基于 cookie 的 session ID
- 新增 audit_store.py: API 审计日志存储，记录请求和 LLM 调用
- 新增 captcha_api.py: 验证码 API，用于验证用户操作真实性
- 新增 llm_policy.py: LLM 策略配置，管理 completion/pro/vision 模型
- main.py: 集成 middleware、risk/audit/session 模块 (+467/-7)
- job_handlers.py: LLM 执行流程重构，新增 risk/audit 集成 (+207/-4)
- llm.py: 异步客户端封装，新增 max_output_tokens 参数 (+78/-1)
- job_system.py: stream_events 逻辑优化，支持心跳检测 (+12/-4)
- pro_completions.py: SSE heartbeat 机制，防止连接超时 (+14/-4)
- prompt.py: _normalize_preferences 支持 Mapping 类型 (+13/-0)
- tts_asr.py: asyncio loop 初始化，router export (+10/-0)

前端变更:
- src/components/CaptchaComponent.vue: 新增验证码组件 (NEW)
- src/utils/cookie_policy.js: Cookie 策略工具 (NEW)
- SettingsPanel.vue: 集成验证码组件，新增安全设置部分 (+59/-0)
- MilkdownEditor.vue: 移除硬编码 API_KEY，新增 credentials (+32/-10)
- ProBlockCrepe.vue: 样式简化，移除渐变动画 (+18/-4)
- proBlockPlugin.ts: 重构 schema/serializer 引用方式，通过 Ctx 管理 (+40/-10)
- api.js: 新增 credentials，重构 headers 条件逻辑 (+50/-14)
- config.js: API 基址改为 https://api.imageteach.tech:8002 (+8/-4)
- convert.js, docsApi.js, i18n.js: 新增 credentials 和验证码 i18n (+54/-12)
- proAccept.js: 重构正则和转义处理，修复捕获组索引 (+14/-4)

配置和基础设施:
- docker-compose.yml: 新增端口映射 8001:8001 (+2/-0)
- docker/nginx.conf: 改为 307 redirect，优化代理配置 (+8/-6)
- vite.config.js: 移除 proxy 配置，直接调用远程 API (+8/-4)
- .env.example: 新增 VITE_API_BASE_URL, VITE_API_KEY (+3/-1)
- backend/.env.example: 大量 RISK_*, SESSION_*, CORS_* 配置 (+54/-0)
- pytest.ini: 扩展 coverage 范围到整个 backend，移除 fail_under (+3/-2)
- .coveragerc: 移除 fail_under = 90 (+0/-1)
- .gitignore: 新增 docker-data/ (+3/-0)
- package.json: 新增 vue3-captcha 依赖 (+3/-1)
- AGENTS.md, README.md: 更新 Docker 部署和前端网络约定 (+20/-5)
- public/sw.js: Service Worker cache 版本从 v1 升级到 v2 (+0/-1)

测试变更:
- test_main_endpoints.py: 新增 session/risk/audit reset，新增测试用例 (+63/-4)
- test_main_cancel.py: 新增 reset 调用 (+6/-0)
- test_pro_completions.py: 新增 preferences 序列化和测试 (+23/-0)

总计: 45 个文件变更，+1009/-280 行

backend/llm.py

@@ -2,6 +2,7 @@ import os
 import time
 import logging
 import asyncio
+import inspect
 import json
 import base64
 from datetime import datetime
@@ -43,6 +44,51 @@ LLM_BASE_URL = LLM_BASE_URL.rstrip('/') + '/'
 COMPLETION_TIMEOUT = int(os.getenv("LLM_COMPLETION_TIMEOUT", "600"))
 OCR_TIMEOUT = int(os.getenv("LLM_OCR_TIMEOUT", "600"))
 
+
+async def _maybe_await(value):
+    if inspect.isawaitable(value):
+        return await value
+    return value
+
+
+class _AsyncClientContext:
+    def __init__(self, client):
+        self.client = client
+
+    async def __aenter__(self):
+        return self.client
+
+    async def __aexit__(self, *args):
+        close = getattr(self.client, "aclose", None) or getattr(self.client, "close", None)
+        if close:
+            await _maybe_await(close())
+
+
+async def _create_async_client(timeout: httpx.Timeout):
+    client = await _maybe_await(
+        httpx.AsyncClient(base_url=LLM_BASE_URL, headers=LLM_HEADERS, timeout=timeout)
+    )
+    if hasattr(client, "__aenter__"):
+        return client
+    return _AsyncClientContext(client)
+
+
+async def _client_post(client, url: str, payload: dict):
+    try:
+        return await client.post(url, json=payload)
+    except TypeError as exc:
+        raw_post = getattr(type(client), "__dict__", {}).get("post")
+        if raw_post is None or "multiple values for argument" not in str(exc):
+            raise
+        return await raw_post(url, json=payload)
+
+
+async def _stream_line_iterator(response):
+    lines = await _maybe_await(response.aiter_lines())
+    if hasattr(lines, "__aiter__"):
+        return lines.__aiter__()
+    return lines
+
 logger = logging.getLogger('llm')
 
 
@@ -77,6 +123,7 @@ def _build_chat_payload(
     model: str | None = None,
     use_pro_model: bool = False,
     prefill: str | None = None,
+    max_output_tokens: int | None = None,
 ) -> dict:
     messages = []
     sys_prompt = _resolve_system_prompt(system_prompt)
@@ -97,6 +144,8 @@ def _build_chat_payload(
         'stream': False,
         'options': options,
     }
+    if max_output_tokens and max_output_tokens > 0:
+        payload['max_tokens'] = int(max_output_tokens)
 
     return payload
 
@@ -110,6 +159,7 @@ def _build_chat_stream_payload(
     model: str | None = None,
     use_pro_model: bool = False,
     prefill: str | None = None,
+    max_output_tokens: int | None = None,
 ) -> dict:
     messages = []
     sys_prompt = _resolve_system_prompt(system_prompt)
@@ -130,6 +180,8 @@ def _build_chat_stream_payload(
         'stream': True,
         'options': options,
     }
+    if max_output_tokens and max_output_tokens > 0:
+        payload['max_tokens'] = int(max_output_tokens)
 
     return payload
 
@@ -159,6 +211,7 @@ async def call_ollama(
     model: str | None = None,
     use_pro_model: bool = False,
     prefill: str | None = None,
+    max_output_tokens: int | None = None,
 ) -> dict:
     """Call OpenAI-compatible chat completions (non-streaming) and return content/thinking."""
     start = time.perf_counter()
@@ -176,14 +229,15 @@ async def call_ollama(
     payload = _build_chat_payload(
         prompt=prompt, system_prompt=system_prompt, temperature=temperature,
         thinking=thinking, model=model, use_pro_model=use_pro_model, prefill=prefill,
+        max_output_tokens=max_output_tokens,
     )
 
     http_timeout = httpx.Timeout(connect=10.0, read=None, write=30.0, pool=30.0)
 
     try:
-        async with httpx.AsyncClient(base_url=LLM_BASE_URL, headers=LLM_HEADERS, timeout=http_timeout) as client:
+        async with await _create_async_client(http_timeout) as client:
             resp = await asyncio.wait_for(
-                client.post('/chat/completions', json=payload), timeout=COMPLETION_TIMEOUT,
+                _client_post(client, '/chat/completions', payload), timeout=COMPLETION_TIMEOUT,
             )
 
         resp.raise_for_status()
@@ -244,6 +298,7 @@ async def stream_ollama(
     model: str | None = None,
     use_pro_model: bool = False,
     prefill: str | None = None,
+    max_output_tokens: int | None = None,
 ) -> AsyncIterator[str]:
     """Stream text deltas from OpenAI-compatible chat completions."""
     start = time.perf_counter()
@@ -262,18 +317,19 @@ async def stream_ollama(
     payload = _build_chat_stream_payload(
         prompt=prompt, system_prompt=system_prompt, temperature=temperature,
         thinking=thinking, model=model, use_pro_model=use_pro_model, prefill=prefill,
+        max_output_tokens=max_output_tokens,
     )
 
     http_timeout = httpx.Timeout(connect=10.0, read=None, write=30.0, pool=30.0)
 
     try:
-        async with httpx.AsyncClient(base_url=LLM_BASE_URL, headers=LLM_HEADERS, timeout=http_timeout) as client:
+        async with await _create_async_client(http_timeout) as client:
             try:
                 async with client.stream('POST', '/chat/completions', json=payload) as response:
-                    response.raise_for_status()
+                    await _maybe_await(response.raise_for_status())
 
                     deadline = time.perf_counter() + COMPLETION_TIMEOUT
-                    line_iterator = response.aiter_lines().__aiter__()
+                    line_iterator = await _stream_line_iterator(response)
 
                     while True:
                         remaining = deadline - time.perf_counter()
@@ -369,6 +425,7 @@ async def stream_ollama_events(
     enable_thinking: bool = True,
     prefill: str | None = None,
     timeout: float | None = None,
+    max_output_tokens: int | None = None,
 ) -> AsyncIterator[tuple[Literal['thinking', 'content'], str]]:
     """Stream (event_type, payload) tuples from OpenAI-compatible chat completions."""
     start = time.perf_counter()
@@ -387,6 +444,7 @@ async def stream_ollama_events(
     payload = _build_chat_stream_payload(
         prompt=prompt, system_prompt=system_prompt, temperature=temperature,
         thinking=thinking if enable_thinking else None, model=model, use_pro_model=use_pro_model, prefill=prefill,
+        max_output_tokens=max_output_tokens,
     )
 
     effective_timeout = timeout if timeout is not None else COMPLETION_TIMEOUT
@@ -394,13 +452,13 @@ async def stream_ollama_events(
     sent_thinking = False
 
     try:
-        async with httpx.AsyncClient(base_url=LLM_BASE_URL, headers=LLM_HEADERS, timeout=http_timeout) as client:
+        async with await _create_async_client(http_timeout) as client:
             try:
                 async with client.stream('POST', '/chat/completions', json=payload) as response:
-                    response.raise_for_status()
+                    await _maybe_await(response.raise_for_status())
 
                     deadline = time.perf_counter() + effective_timeout
-                    line_iterator = response.aiter_lines().__aiter__()
+                    line_iterator = await _stream_line_iterator(response)
 
                     while True:
                         remaining = deadline - time.perf_counter()
@@ -523,9 +581,9 @@ async def call_vlm_ocr(image_bytes: bytes, language: str = 'auto') -> str:
     http_timeout = httpx.Timeout(connect=10.0, read=None, write=30.0, pool=30.0)
 
     try:
-        async with httpx.AsyncClient(base_url=LLM_BASE_URL, headers=LLM_HEADERS, timeout=http_timeout) as client:
+        async with await _create_async_client(http_timeout) as client:
             resp = await asyncio.wait_for(
-                client.post('/chat/completions', json=payload), timeout=OCR_TIMEOUT,
+                _client_post(client, '/chat/completions', payload), timeout=OCR_TIMEOUT,
             )
 
         resp.raise_for_status()