fix: rebuild app image when base image changes during deployment

- Track BASE_REBUILT flag to detect base image rebuilds
- Force app image rebuild when base image is rebuilt
- Prevents stale app images built with outdated base images
- Ensures consistent deployment when base dependencies change

Dockerfile.base

@@ -70,6 +70,8 @@ RUN printf '%s\n' \
 '        proxy_cache off;' \
 '        proxy_request_buffering off;' \
 '        chunked_transfer_encoding on;' \
+'        gzip off;' \
+'        add_header X-Accel-Buffering no;' \
 '        proxy_connect_timeout 600s;' \
 '        proxy_send_timeout 600s;' \
 '        proxy_read_timeout 600s;' \

Dockerfile.base.local

@@ -74,6 +74,8 @@ RUN printf '%s\n' \
 '        proxy_cache off;' \
 '        proxy_request_buffering off;' \
 '        chunked_transfer_encoding on;' \
+'        gzip off;' \
+'        add_header X-Accel-Buffering no;' \
 '        proxy_connect_timeout 600s;' \
 '        proxy_send_timeout 600s;' \
 '        proxy_read_timeout 600s;' \

deploy.sh

@@ -21,9 +21,9 @@ HASH_FILE=".deps-hash"
 CODE_HASH_FILE=".code-hash"
 MIGRATION_HASH_FILE=".migration-hash"
 
-# 计算依赖文件的哈希值
+# 计算依赖文件的哈希值（包含 Dockerfile.base.local）
 calc_deps_hash() {
-    cat pyproject.toml frontend/package.json frontend/package-lock.json 2>/dev/null | md5sum | cut -d' ' -f1
+    cat pyproject.toml frontend/package.json frontend/package-lock.json Dockerfile.base.local 2>/dev/null | md5sum | cut -d' ' -f1
 }
 
 # 计算代码文件的哈希值
@@ -162,25 +162,32 @@ git pull
 
 # 标记是否需要重启
 NEED_RESTART=false
+BASE_REBUILT=false
 
 # 检查基础镜像是否存在，或依赖是否变化
 if ! docker image inspect aether-base:latest >/dev/null 2>&1; then
     echo ">>> Base image not found, building..."
     build_base
+    BASE_REBUILT=true
     NEED_RESTART=true
 elif check_deps_changed; then
     echo ">>> Dependencies changed, rebuilding base image..."
     build_base
+    BASE_REBUILT=true
     NEED_RESTART=true
 else
     echo ">>> Dependencies unchanged."
 fi
 
-# 检查代码是否变化
+# 检查代码是否变化，或者 base 重建了（app 依赖 base）
 if ! docker image inspect aether-app:latest >/dev/null 2>&1; then
     echo ">>> App image not found, building..."
     build_app
     NEED_RESTART=true
+elif [ "$BASE_REBUILT" = true ]; then
+    echo ">>> Base image rebuilt, rebuilding app image..."
+    build_app
+    NEED_RESTART=true
 elif check_code_changed; then
     echo ">>> Code changed, rebuilding app image..."
     build_app

frontend/src/views/admin/SystemSettings.vue

@@ -464,6 +464,78 @@
           </div>
         </div>
       </CardSection>
+
+      <!-- 流式输出配置 -->
+      <CardSection
+        title="流式输出"
+        description="配置流式响应的输出效果"
+      >
+        <div class="grid grid-cols-1 md:grid-cols-2 gap-6">
+          <div class="md:col-span-2">
+            <div class="flex items-center space-x-2">
+              <Checkbox
+                id="stream-smoothing-enabled"
+                v-model:checked="systemConfig.stream_smoothing_enabled"
+              />
+              <div>
+                <Label
+                  for="stream-smoothing-enabled"
+                  class="cursor-pointer"
+                >
+                  启用平滑输出
+                </Label>
+                <p class="text-xs text-muted-foreground">
+                  将上游返回的大块内容拆分成小块，模拟打字效果
+                </p>
+              </div>
+            </div>
+          </div>
+
+          <div>
+            <Label
+              for="stream-smoothing-chunk-size"
+              class="block text-sm font-medium"
+            >
+              每块字符数
+            </Label>
+            <Input
+              id="stream-smoothing-chunk-size"
+              v-model.number="systemConfig.stream_smoothing_chunk_size"
+              type="number"
+              min="1"
+              max="100"
+              placeholder="20"
+              class="mt-1"
+              :disabled="!systemConfig.stream_smoothing_enabled"
+            />
+            <p class="mt-1 text-xs text-muted-foreground">
+              每次输出的字符数量（1-100）
+            </p>
+          </div>
+
+          <div>
+            <Label
+              for="stream-smoothing-delay-ms"
+              class="block text-sm font-medium"
+            >
+              输出间隔 (毫秒)
+            </Label>
+            <Input
+              id="stream-smoothing-delay-ms"
+              v-model.number="systemConfig.stream_smoothing_delay_ms"
+              type="number"
+              min="1"
+              max="100"
+              placeholder="8"
+              class="mt-1"
+              :disabled="!systemConfig.stream_smoothing_enabled"
+            />
+            <p class="mt-1 text-xs text-muted-foreground">
+              每块之间的延迟毫秒数（1-100）
+            </p>
+          </div>
+        </div>
+      </CardSection>
     </div>
 
     <!-- 导入配置对话框 -->
@@ -812,6 +884,10 @@ interface SystemConfig {
   log_retention_days: number
   cleanup_batch_size: number
   audit_log_retention_days: number
+  // 流式输出
+  stream_smoothing_enabled: boolean
+  stream_smoothing_chunk_size: number
+  stream_smoothing_delay_ms: number
 }
 
 const loading = ref(false)
@@ -861,6 +937,10 @@ const systemConfig = ref<SystemConfig>({
   log_retention_days: 365,
   cleanup_batch_size: 1000,
   audit_log_retention_days: 30,
+  // 流式输出
+  stream_smoothing_enabled: false,
+  stream_smoothing_chunk_size: 20,
+  stream_smoothing_delay_ms: 8,
 })
 
 // 计算属性：KB 和 字节 之间的转换
@@ -917,6 +997,10 @@ async function loadSystemConfig() {
       'log_retention_days',
       'cleanup_batch_size',
       'audit_log_retention_days',
+      // 流式输出
+      'stream_smoothing_enabled',
+      'stream_smoothing_chunk_size',
+      'stream_smoothing_delay_ms',
     ]
 
     for (const key of configs) {
@@ -1024,6 +1108,22 @@ async function saveSystemConfig() {
         value: systemConfig.value.audit_log_retention_days,
         description: '审计日志保留天数'
       },
+      // 流式输出
+      {
+        key: 'stream_smoothing_enabled',
+        value: systemConfig.value.stream_smoothing_enabled,
+        description: '是否启用流式平滑输出'
+      },
+      {
+        key: 'stream_smoothing_chunk_size',
+        value: systemConfig.value.stream_smoothing_chunk_size,
+        description: '流式平滑输出每个小块的字符数'
+      },
+      {
+        key: 'stream_smoothing_delay_ms',
+        value: systemConfig.value.stream_smoothing_delay_ms,
+        description: '流式平滑输出每个小块之间的延迟毫秒数'
+      },
     ]
 
     const promises = configItems.map(item =>

src/api/admin/providers/models.py

@@ -9,6 +9,7 @@ from fastapi import APIRouter, Depends, Request
 from sqlalchemy.orm import Session, joinedload
 
 from src.api.base.admin_adapter import AdminApiAdapter
+from src.api.base.models_service import invalidate_models_list_cache
 from src.api.base.pipeline import ApiRequestPipeline
 from src.core.exceptions import InvalidRequestException, NotFoundException
 from src.core.logger import logger
@@ -419,4 +420,8 @@ class AdminBatchAssignModelsToProviderAdapter(AdminApiAdapter):
             f"Batch assigned {len(success)} GlobalModels to provider {provider.name} by {context.user.username}"
         )
 
+        # 清除 /v1/models 列表缓存
+        if success:
+            await invalidate_models_list_cache()
+
         return BatchAssignModelsToProviderResponse(success=success, errors=errors)

src/api/base/models_service.py

@@ -55,6 +55,23 @@ async def _set_cached_models(api_formats: list[str], models: list["ModelInfo"])
         logger.warning(f"[ModelsService] 缓存写入失败: {e}")
 
 
+async def invalidate_models_list_cache() -> None:
+    """
+    清除所有 /v1/models 列表缓存
+
+    在模型创建、更新、删除时调用，确保模型列表实时更新
+    """
+    # 清除所有格式的缓存
+    all_formats = ["CLAUDE", "OPENAI", "GEMINI"]
+    for fmt in all_formats:
+        cache_key = f"{_CACHE_KEY_PREFIX}:{fmt}"
+        try:
+            await CacheService.delete(cache_key)
+            logger.debug(f"[ModelsService] 已清除缓存: {cache_key}")
+        except Exception as e:
+            logger.warning(f"[ModelsService] 清除缓存失败 {cache_key}: {e}")
+
+
 @dataclass
 class ModelInfo:
     """统一的模型信息结构"""

src/api/handlers/base/cli_handler_base.py

@@ -1114,8 +1114,10 @@ class CliMessageHandlerBase(BaseMessageHandler):
             async for chunk in stream_generator:
                 yield chunk
         except asyncio.CancelledError:
-            ctx.status_code = 499
+            # 如果响应已完成，不标记为失败
-            ctx.error_message = "Client disconnected"
+            if not ctx.has_completion:
+                ctx.status_code = 499
+                ctx.error_message = "Client disconnected"
             raise
         except httpx.TimeoutException as e:
             ctx.status_code = 504

src/api/handlers/base/content_extractors.py

@@ -0,0 +1,274 @@
+"""
+流式内容提取器 - 策略模式实现
+
+为不同 API 格式（OpenAI、Claude、Gemini）提供内容提取和 chunk 构造的抽象。
+StreamSmoother 使用这些提取器来处理不同格式的 SSE 事件。
+"""
+
+import copy
+import json
+from abc import ABC, abstractmethod
+from typing import Optional
+
+
+class ContentExtractor(ABC):
+    """
+    流式内容提取器抽象基类
+
+    定义从 SSE 事件中提取文本内容和构造新 chunk 的接口。
+    每种 API 格式（OpenAI、Claude、Gemini）需要实现自己的提取器。
+    """
+
+    @abstractmethod
+    def extract_content(self, data: dict) -> Optional[str]:
+        """
+        从 SSE 数据中提取可拆分的文本内容
+
+        Args:
+            data: 解析后的 JSON 数据
+
+        Returns:
+            提取的文本内容，如果无法提取则返回 None
+        """
+        pass
+
+    @abstractmethod
+    def create_chunk(
+        self,
+        original_data: dict,
+        new_content: str,
+        event_type: str = "",
+        is_first: bool = False,
+    ) -> bytes:
+        """
+        使用新内容构造 SSE chunk
+
+        Args:
+            original_data: 原始 JSON 数据
+            new_content: 新的文本内容
+            event_type: SSE 事件类型（某些格式需要）
+            is_first: 是否是第一个 chunk（用于保留 role 等字段）
+
+        Returns:
+            编码后的 SSE 字节数据
+        """
+        pass
+
+
+class OpenAIContentExtractor(ContentExtractor):
+    """
+    OpenAI 格式内容提取器
+
+    处理 OpenAI Chat Completions API 的流式响应格式：
+    - 数据结构: choices[0].delta.content
+    - 只在 delta 仅包含 role/content 时允许拆分，避免破坏 tool_calls 等结构
+    """
+
+    def extract_content(self, data: dict) -> Optional[str]:
+        if not isinstance(data, dict):
+            return None
+
+        choices = data.get("choices")
+        if not isinstance(choices, list) or len(choices) != 1:
+            return None
+
+        first_choice = choices[0]
+        if not isinstance(first_choice, dict):
+            return None
+
+        delta = first_choice.get("delta")
+        if not isinstance(delta, dict):
+            return None
+
+        content = delta.get("content")
+        if not isinstance(content, str):
+            return None
+
+        # 只有 delta 仅包含 role/content 时才允许拆分
+        # 避免破坏 tool_calls、function_call 等复杂结构
+        allowed_keys = {"role", "content"}
+        if not all(key in allowed_keys for key in delta.keys()):
+            return None
+
+        return content
+
+    def create_chunk(
+        self,
+        original_data: dict,
+        new_content: str,
+        event_type: str = "",
+        is_first: bool = False,
+    ) -> bytes:
+        new_data = original_data.copy()
+
+        if "choices" in new_data and new_data["choices"]:
+            new_choices = []
+            for choice in new_data["choices"]:
+                new_choice = choice.copy()
+                if "delta" in new_choice:
+                    new_delta = {}
+                    # 只有第一个 chunk 保留 role
+                    if is_first and "role" in new_choice["delta"]:
+                        new_delta["role"] = new_choice["delta"]["role"]
+                    new_delta["content"] = new_content
+                    new_choice["delta"] = new_delta
+                new_choices.append(new_choice)
+            new_data["choices"] = new_choices
+
+        return f"data: {json.dumps(new_data, ensure_ascii=False)}\n\n".encode("utf-8")
+
+
+class ClaudeContentExtractor(ContentExtractor):
+    """
+    Claude 格式内容提取器
+
+    处理 Claude Messages API 的流式响应格式：
+    - 事件类型: content_block_delta
+    - 数据结构: delta.type=text_delta, delta.text
+    """
+
+    def extract_content(self, data: dict) -> Optional[str]:
+        if not isinstance(data, dict):
+            return None
+
+        # 检查事件类型
+        if data.get("type") != "content_block_delta":
+            return None
+
+        delta = data.get("delta", {})
+        if not isinstance(delta, dict):
+            return None
+
+        # 检查 delta 类型
+        if delta.get("type") != "text_delta":
+            return None
+
+        text = delta.get("text")
+        if not isinstance(text, str):
+            return None
+
+        return text
+
+    def create_chunk(
+        self,
+        original_data: dict,
+        new_content: str,
+        event_type: str = "",
+        is_first: bool = False,
+    ) -> bytes:
+        new_data = original_data.copy()
+
+        if "delta" in new_data:
+            new_delta = new_data["delta"].copy()
+            new_delta["text"] = new_content
+            new_data["delta"] = new_delta
+
+        # Claude 格式需要 event: 前缀
+        event_name = event_type or "content_block_delta"
+        return f"event: {event_name}\ndata: {json.dumps(new_data, ensure_ascii=False)}\n\n".encode(
+            "utf-8"
+        )
+
+
+class GeminiContentExtractor(ContentExtractor):
+    """
+    Gemini 格式内容提取器
+
+    处理 Gemini API 的流式响应格式：
+    - 数据结构: candidates[0].content.parts[0].text
+    - 只有纯文本块才拆分
+    """
+
+    def extract_content(self, data: dict) -> Optional[str]:
+        if not isinstance(data, dict):
+            return None
+
+        candidates = data.get("candidates")
+        if not isinstance(candidates, list) or len(candidates) != 1:
+            return None
+
+        first_candidate = candidates[0]
+        if not isinstance(first_candidate, dict):
+            return None
+
+        content = first_candidate.get("content", {})
+        if not isinstance(content, dict):
+            return None
+
+        parts = content.get("parts", [])
+        if not isinstance(parts, list) or len(parts) != 1:
+            return None
+
+        first_part = parts[0]
+        if not isinstance(first_part, dict):
+            return None
+
+        text = first_part.get("text")
+        # 只有纯文本块（只有 text 字段）才拆分
+        if not isinstance(text, str) or len(first_part) != 1:
+            return None
+
+        return text
+
+    def create_chunk(
+        self,
+        original_data: dict,
+        new_content: str,
+        event_type: str = "",
+        is_first: bool = False,
+    ) -> bytes:
+        new_data = copy.deepcopy(original_data)
+
+        if "candidates" in new_data and new_data["candidates"]:
+            first_candidate = new_data["candidates"][0]
+            if "content" in first_candidate:
+                content = first_candidate["content"]
+                if "parts" in content and content["parts"]:
+                    content["parts"][0]["text"] = new_content
+
+        return f"data: {json.dumps(new_data, ensure_ascii=False)}\n\n".encode("utf-8")
+
+
+# 提取器注册表
+_EXTRACTORS: dict[str, type[ContentExtractor]] = {
+    "openai": OpenAIContentExtractor,
+    "claude": ClaudeContentExtractor,
+    "gemini": GeminiContentExtractor,
+}
+
+
+def get_extractor(format_name: str) -> Optional[ContentExtractor]:
+    """
+    根据格式名获取对应的内容提取器实例
+
+    Args:
+        format_name: 格式名称（openai, claude, gemini）
+
+    Returns:
+        对应的提取器实例，如果格式不支持则返回 None
+    """
+    extractor_class = _EXTRACTORS.get(format_name.lower())
+    if extractor_class:
+        return extractor_class()
+    return None
+
+
+def register_extractor(format_name: str, extractor_class: type[ContentExtractor]) -> None:
+    """
+    注册新的内容提取器
+
+    Args:
+        format_name: 格式名称
+        extractor_class: 提取器类
+    """
+    _EXTRACTORS[format_name.lower()] = extractor_class
+
+
+def get_extractor_formats() -> list[str]:
+    """
+    获取所有已注册的格式名称列表
+
+    Returns:
+        格式名称列表
+    """
+    return list(_EXTRACTORS.keys())

src/api/handlers/base/stream_processor.py

@@ -6,16 +6,22 @@
 2. 响应流生成
 3. 预读和嵌套错误检测
 4. 客户端断开检测
+5. 流式平滑输出
 """
 
 import asyncio
 import codecs
 import json
-import time
+from dataclasses import dataclass
 from typing import Any, AsyncGenerator, Callable, Optional
 
 import httpx
 
+from src.api.handlers.base.content_extractors import (
+    ContentExtractor,
+    get_extractor,
+    get_extractor_formats,
+)
 from src.api.handlers.base.parsers import get_parser_for_format
 from src.api.handlers.base.response_parser import ResponseParser
 from src.api.handlers.base.stream_context import StreamContext
@@ -25,11 +31,20 @@ from src.models.database import Provider, ProviderEndpoint
 from src.utils.sse_parser import SSEEventParser
 
 
+@dataclass
+class StreamSmoothingConfig:
+    """流式平滑输出配置"""
+
+    enabled: bool = False
+    chunk_size: int = 20
+    delay_ms: int = 8
+
+
 class StreamProcessor:
     """
     流式响应处理器
 
-    负责处理 SSE 流的解析、错误检测和响应生成。
+    负责处理 SSE 流的解析、错误检测、响应生成和平滑输出。
     从 ChatHandlerBase 中提取，使其职责更加单一。
     """
 
@@ -40,6 +55,7 @@ class StreamProcessor:
         on_streaming_start: Optional[Callable[[], None]] = None,
         *,
         collect_text: bool = False,
+        smoothing_config: Optional[StreamSmoothingConfig] = None,
     ):
         """
         初始化流处理器
@@ -48,11 +64,17 @@ class StreamProcessor:
             request_id: 请求 ID（用于日志）
             default_parser: 默认响应解析器
             on_streaming_start: 流开始时的回调（用于更新状态）
+            collect_text: 是否收集文本内容
+            smoothing_config: 流式平滑输出配置
         """
         self.request_id = request_id
         self.default_parser = default_parser
         self.on_streaming_start = on_streaming_start
         self.collect_text = collect_text
+        self.smoothing_config = smoothing_config or StreamSmoothingConfig()
+
+        # 内容提取器缓存
+        self._extractors: dict[str, ContentExtractor] = {}
 
     def get_parser_for_provider(self, ctx: StreamContext) -> ResponseParser:
         """
@@ -127,6 +149,13 @@ class StreamProcessor:
         if event_type in ("response.completed", "message_stop"):
             ctx.has_completion = True
 
+        # 检查 OpenAI 格式的 finish_reason
+        choices = data.get("choices", [])
+        if choices and isinstance(choices, list) and len(choices) > 0:
+            finish_reason = choices[0].get("finish_reason")
+            if finish_reason is not None:
+                ctx.has_completion = True
+
     async def prefetch_and_check_error(
         self,
         byte_iterator: Any,
@@ -369,7 +398,7 @@ class StreamProcessor:
             sse_parser: SSE 解析器
             line: 原始行数据
         """
-        # SSEEventParser 以“去掉换行符”的单行文本作为输入；这里统一剔除 CR/LF，
+        # SSEEventParser 以"去掉换行符"的单行文本作为输入；这里统一剔除 CR/LF，
         # 避免把空行误判成 "\n" 并导致事件边界解析错误。
         normalized_line = line.rstrip("\r\n")
         events = sse_parser.feed_line(normalized_line)
@@ -400,32 +429,201 @@ class StreamProcessor:
             响应数据块
         """
         try:
-            # 断连检查频率：每次 await 都会引入调度开销，过于频繁会让流式"发一段停一段"
+            # 使用后台任务检查断连，完全不阻塞流式传输
-            # 这里按时间间隔节流，兼顾及时停止上游读取与吞吐平滑性。
+            disconnected = False
-            next_disconnect_check_at = 0.0
-            disconnect_check_interval_s = 0.25
 
-            async for chunk in stream_generator:
+            async def check_disconnect_background() -> None:
-                now = time.monotonic()
+                nonlocal disconnected
-                if now >= next_disconnect_check_at:
+                while not disconnected and not ctx.has_completion:
-                    next_disconnect_check_at = now + disconnect_check_interval_s
+                    await asyncio.sleep(0.5)
                     if await is_disconnected():
-                        logger.warning(f"ID:{self.request_id} | Client disconnected")
+                        disconnected = True
-                        ctx.status_code = 499  # Client Closed Request
-                        ctx.error_message = "client_disconnected"
-
                         break
-                yield chunk
-        except asyncio.CancelledError:
-            ctx.status_code = 499
-            ctx.error_message = "client_disconnected"
 
+            # 启动后台检查任务
+            check_task = asyncio.create_task(check_disconnect_background())
+
+            try:
+                async for chunk in stream_generator:
+                    if disconnected:
+                        # 如果响应已完成，客户端断开不算失败
+                        if ctx.has_completion:
+                            logger.info(
+                                f"ID:{self.request_id} | Client disconnected after completion"
+                            )
+                        else:
+                            logger.warning(f"ID:{self.request_id} | Client disconnected")
+                            ctx.status_code = 499
+                            ctx.error_message = "client_disconnected"
+                        break
+                    yield chunk
+            finally:
+                check_task.cancel()
+                try:
+                    await check_task
+                except asyncio.CancelledError:
+                    pass
+        except asyncio.CancelledError:
+            # 如果响应已完成，不标记为失败
+            if not ctx.has_completion:
+                ctx.status_code = 499
+                ctx.error_message = "client_disconnected"
             raise
         except Exception as e:
             ctx.status_code = 500
             ctx.error_message = str(e)
             raise
 
+    async def create_smoothed_stream(
+        self,
+        stream_generator: AsyncGenerator[bytes, None],
+    ) -> AsyncGenerator[bytes, None]:
+        """
+        创建平滑输出的流生成器
+
+        如果启用了平滑输出，将大 chunk 拆分成小块并添加微小延迟。
+        否则直接透传原始流。
+
+        Args:
+            stream_generator: 原始流生成器
+
+        Yields:
+            平滑处理后的响应数据块
+        """
+        if not self.smoothing_config.enabled:
+            # 未启用平滑输出，直接透传
+            async for chunk in stream_generator:
+                yield chunk
+            return
+
+        # 启用平滑输出
+        buffer = b""
+        is_first_content = True
+
+        async for chunk in stream_generator:
+            buffer += chunk
+
+            # 按双换行分割 SSE 事件（标准 SSE 格式）
+            while b"\n\n" in buffer:
+                event_block, buffer = buffer.split(b"\n\n", 1)
+                event_str = event_block.decode("utf-8", errors="replace")
+
+                # 解析事件块
+                lines = event_str.strip().split("\n")
+                data_str = None
+                event_type = ""
+
+                for line in lines:
+                    line = line.rstrip("\r")
+                    if line.startswith("event: "):
+                        event_type = line[7:].strip()
+                    elif line.startswith("data: "):
+                        data_str = line[6:]
+
+                # 没有 data 行，直接透传
+                if data_str is None:
+                    yield event_block + b"\n\n"
+                    continue
+
+                # [DONE] 直接透传
+                if data_str.strip() == "[DONE]":
+                    yield event_block + b"\n\n"
+                    continue
+
+                # 尝试解析 JSON
+                try:
+                    data = json.loads(data_str)
+                except json.JSONDecodeError:
+                    yield event_block + b"\n\n"
+                    continue
+
+                # 检测格式并提取内容
+                content, extractor = self._detect_format_and_extract(data)
+
+                # 只有内容长度大于 1 才需要平滑处理
+                if content and len(content) > 1 and extractor:
+                    # 获取配置的延迟
+                    delay_seconds = self._calculate_delay()
+
+                    # 拆分内容
+                    content_chunks = self._split_content(content)
+
+                    for i, sub_content in enumerate(content_chunks):
+                        is_first = is_first_content and i == 0
+
+                        # 使用提取器创建新 chunk
+                        sse_chunk = extractor.create_chunk(
+                            data,
+                            sub_content,
+                            event_type=event_type,
+                            is_first=is_first,
+                        )
+
+                        yield sse_chunk
+
+                        # 除了最后一个块，其他块之间加延迟
+                        if i < len(content_chunks) - 1:
+                            await asyncio.sleep(delay_seconds)
+
+                    is_first_content = False
+                else:
+                    # 不需要拆分，直接透传
+                    yield event_block + b"\n\n"
+                    if content:
+                        is_first_content = False
+
+        # 处理剩余数据
+        if buffer:
+            yield buffer
+
+    def _get_extractor(self, format_name: str) -> Optional[ContentExtractor]:
+        """获取或创建格式对应的提取器（带缓存）"""
+        if format_name not in self._extractors:
+            extractor = get_extractor(format_name)
+            if extractor:
+                self._extractors[format_name] = extractor
+        return self._extractors.get(format_name)
+
+    def _detect_format_and_extract(
+        self, data: dict
+    ) -> tuple[Optional[str], Optional[ContentExtractor]]:
+        """
+        检测数据格式并提取内容
+
+        依次尝试各格式的提取器，返回第一个成功提取内容的结果。
+
+        Returns:
+            (content, extractor): 提取的内容和对应的提取器
+        """
+        for format_name in get_extractor_formats():
+            extractor = self._get_extractor(format_name)
+            if extractor:
+                content = extractor.extract_content(data)
+                if content is not None:
+                    return content, extractor
+
+        return None, None
+
+    def _calculate_delay(self) -> float:
+        """获取配置的延迟（秒）"""
+        return self.smoothing_config.delay_ms / 1000.0
+
+    def _split_content(self, content: str) -> list[str]:
+        """
+        按块拆分文本
+        """
+        chunk_size = self.smoothing_config.chunk_size
+        text_length = len(content)
+
+        if text_length <= chunk_size:
+            return [content]
+
+        # 按块拆分
+        chunks = []
+        for i in range(0, text_length, chunk_size):
+            chunks.append(content[i : i + chunk_size])
+        return chunks
+
     async def _cleanup(
         self,
         response_ctx: Any,
@@ -440,3 +638,128 @@ class StreamProcessor:
             await http_client.aclose()
         except Exception:
             pass
+
+
+async def create_smoothed_stream(
+    stream_generator: AsyncGenerator[bytes, None],
+    chunk_size: int = 20,
+    delay_ms: int = 8,
+) -> AsyncGenerator[bytes, None]:
+    """
+    独立的平滑流生成函数
+
+    供 CLI handler 等场景使用，无需创建完整的 StreamProcessor 实例。
+
+    Args:
+        stream_generator: 原始流生成器
+        chunk_size: 每块字符数
+        delay_ms: 每块之间的延迟毫秒数
+
+    Yields:
+        平滑处理后的响应数据块
+    """
+    processor = _LightweightSmoother(chunk_size=chunk_size, delay_ms=delay_ms)
+    async for chunk in processor.smooth(stream_generator):
+        yield chunk
+
+
+class _LightweightSmoother:
+    """
+    轻量级平滑处理器
+
+    只包含平滑输出所需的最小逻辑，不依赖 StreamProcessor 的其他功能。
+    """
+
+    def __init__(self, chunk_size: int = 20, delay_ms: int = 8) -> None:
+        self.chunk_size = chunk_size
+        self.delay_ms = delay_ms
+        self._extractors: dict[str, ContentExtractor] = {}
+
+    def _get_extractor(self, format_name: str) -> Optional[ContentExtractor]:
+        if format_name not in self._extractors:
+            extractor = get_extractor(format_name)
+            if extractor:
+                self._extractors[format_name] = extractor
+        return self._extractors.get(format_name)
+
+    def _detect_format_and_extract(
+        self, data: dict
+    ) -> tuple[Optional[str], Optional[ContentExtractor]]:
+        for format_name in get_extractor_formats():
+            extractor = self._get_extractor(format_name)
+            if extractor:
+                content = extractor.extract_content(data)
+                if content is not None:
+                    return content, extractor
+        return None, None
+
+    def _calculate_delay(self) -> float:
+        return self.delay_ms / 1000.0
+
+    def _split_content(self, content: str) -> list[str]:
+        text_length = len(content)
+        if text_length <= self.chunk_size:
+            return [content]
+        return [content[i : i + self.chunk_size] for i in range(0, text_length, self.chunk_size)]
+
+    async def smooth(
+        self, stream_generator: AsyncGenerator[bytes, None]
+    ) -> AsyncGenerator[bytes, None]:
+        buffer = b""
+        is_first_content = True
+
+        async for chunk in stream_generator:
+            buffer += chunk
+
+            while b"\n\n" in buffer:
+                event_block, buffer = buffer.split(b"\n\n", 1)
+                event_str = event_block.decode("utf-8", errors="replace")
+
+                lines = event_str.strip().split("\n")
+                data_str = None
+                event_type = ""
+
+                for line in lines:
+                    line = line.rstrip("\r")
+                    if line.startswith("event: "):
+                        event_type = line[7:].strip()
+                    elif line.startswith("data: "):
+                        data_str = line[6:]
+
+                if data_str is None:
+                    yield event_block + b"\n\n"
+                    continue
+
+                if data_str.strip() == "[DONE]":
+                    yield event_block + b"\n\n"
+                    continue
+
+                try:
+                    data = json.loads(data_str)
+                except json.JSONDecodeError:
+                    yield event_block + b"\n\n"
+                    continue
+
+                content, extractor = self._detect_format_and_extract(data)
+
+                if content and len(content) > 1 and extractor:
+                    delay_seconds = self._calculate_delay()
+                    content_chunks = self._split_content(content)
+
+                    for i, sub_content in enumerate(content_chunks):
+                        is_first = is_first_content and i == 0
+                        sse_chunk = extractor.create_chunk(
+                            data, sub_content, event_type=event_type, is_first=is_first
+                        )
+                        yield sse_chunk
+                        if i < len(content_chunks) - 1:
+                            await asyncio.sleep(delay_seconds)
+
+                    is_first_content = False
+                else:
+                    yield event_block + b"\n\n"
+                    if content:
+                        is_first_content = False
+
+        if buffer:
+            yield buffer

src/config/settings.py

@@ -143,6 +143,7 @@ class Config:
         # STREAM_STATS_DELAY: 统计记录延迟（秒），等待流完全关闭
         self.stream_prefetch_lines = int(os.getenv("STREAM_PREFETCH_LINES", "5"))
         self.stream_stats_delay = float(os.getenv("STREAM_STATS_DELAY", "0.1"))
+        # 注：流式平滑输出配置已移至数据库系统设置（stream_smoothing_*）
 
         # 验证连接池配置
         self._validate_pool_config()

src/services/model/service.py

@@ -13,6 +13,7 @@ from src.core.exceptions import InvalidRequestException, NotFoundException
 from src.core.logger import logger
 from src.models.api import ModelCreate, ModelResponse, ModelUpdate
 from src.models.database import Model, Provider
+from src.api.base.models_service import invalidate_models_list_cache
 from src.services.cache.invalidation import get_cache_invalidation_service
 from src.services.cache.model_cache import ModelCacheService
 
@@ -75,6 +76,10 @@ class ModelService:
                 )
 
             logger.info(f"创建模型成功: provider={provider.name}, model={model.provider_model_name}, global_model_id={model.global_model_id}")
+
+            # 清除 /v1/models 列表缓存
+            asyncio.create_task(invalidate_models_list_cache())
+
             return model
 
         except IntegrityError as e:
@@ -197,6 +202,9 @@ class ModelService:
                 cache_service = get_cache_invalidation_service()
                 cache_service.on_model_changed(model.provider_id, model.global_model_id)
 
+            # 清除 /v1/models 列表缓存
+            asyncio.create_task(invalidate_models_list_cache())
+
             logger.info(f"更新模型成功: id={model_id}, 最终 supports_vision: {model.supports_vision}, supports_function_calling: {model.supports_function_calling}, supports_extended_thinking: {model.supports_extended_thinking}")
             return model
         except IntegrityError as e:
@@ -261,6 +269,9 @@ class ModelService:
                 cache_service = get_cache_invalidation_service()
                 cache_service.on_model_changed(cache_info["provider_id"], cache_info["global_model_id"])
 
+            # 清除 /v1/models 列表缓存
+            asyncio.create_task(invalidate_models_list_cache())
+
             logger.info(f"删除模型成功: id={model_id}, provider_model_name={cache_info['provider_model_name']}, "
                 f"global_model_id={cache_info['global_model_id'][:8] if cache_info['global_model_id'] else 'None'}...")
         except Exception as e:
@@ -295,6 +306,9 @@ class ModelService:
             cache_service = get_cache_invalidation_service()
             cache_service.on_model_changed(model.provider_id, model.global_model_id)
 
+        # 清除 /v1/models 列表缓存
+        asyncio.create_task(invalidate_models_list_cache())
+
         status = "可用" if is_available else "不可用"
         logger.info(f"更新模型可用状态: id={model_id}, status={status}")
         return model
@@ -358,6 +372,9 @@ class ModelService:
                 for model in created_models:
                     db.refresh(model)
                 logger.info(f"批量创建 {len(created_models)} 个模型成功")
+
+                # 清除 /v1/models 列表缓存
+                asyncio.create_task(invalidate_models_list_cache())
             except IntegrityError as e:
                 db.rollback()
                 logger.error(f"批量创建模型失败: {str(e)}")

src/services/system/config.py

@@ -12,7 +12,6 @@ from src.core.logger import logger
 from src.models.database import Provider, SystemConfig
 
 
-
 class LogLevel(str, Enum):
     """日志记录级别"""
 
@@ -79,6 +78,19 @@ class SystemConfigService:
             "value": False,
             "description": "是否自动删除过期的API Key（True=物理删除，False=仅禁用），仅管理员可配置",
         },
+        # 流式平滑输出配置
+        "stream_smoothing_enabled": {
+            "value": False,
+            "description": "是否启用流式平滑输出，自动根据文本长度调整输出速度",
+        },
+        "stream_smoothing_chunk_size": {
+            "value": 20,
+            "description": "流式平滑输出每个小块的字符数",
+        },
+        "stream_smoothing_delay_ms": {
+            "value": 8,
+            "description": "流式平滑输出每个小块之间的延迟毫秒数",
+        },
     }
 
     @classmethod
@@ -94,6 +106,35 @@ class SystemConfigService:
 
         return default
 
+    @classmethod
+    def get_configs(cls, db: Session, keys: List[str]) -> Dict[str, Any]:
+        """
+        批量获取系统配置值
+
+        Args:
+            db: 数据库会话
+            keys: 配置键列表
+
+        Returns:
+            配置键值字典
+        """
+        result = {}
+
+        # 一次查询获取所有配置
+        configs = db.query(SystemConfig).filter(SystemConfig.key.in_(keys)).all()
+        config_map = {c.key: c.value for c in configs}
+
+        # 填充结果，不存在的使用默认值
+        for key in keys:
+            if key in config_map:
+                result[key] = config_map[key]
+            elif key in cls.DEFAULT_CONFIGS:
+                result[key] = cls.DEFAULT_CONFIGS[key]["value"]
+            else:
+                result[key] = None
+
+        return result
+
     @staticmethod
     def set_config(db: Session, key: str, value: Any, description: str = None) -> SystemConfig:
         """设置系统配置值"""
@@ -111,6 +152,7 @@ class SystemConfigService:
 
         db.commit()
         db.refresh(config)
+
         return config
 
     @staticmethod
@@ -153,8 +195,8 @@ class SystemConfigService:
             for config in configs
         ]
 
-    @staticmethod
+    @classmethod
-    def delete_config(db: Session, key: str) -> bool:
+    def delete_config(cls, db: Session, key: str) -> bool:
         """删除系统配置"""
         config = db.query(SystemConfig).filter(SystemConfig.key == key).first()
         if config: