refactor: consolidate stream smoothing into StreamProcessor with intelligent timing

- Move StreamSmoother functionality directly into StreamProcessor for better integration
- Create ContentExtractor strategy pattern for format-agnostic content extraction
- Implement intelligent dynamic delay calculation based on text length
- Support three text length tiers: short (char-by-char), medium (chunked), long (chunked)
- Remove manual chunk_size and delay_ms configuration - now auto-calculated
- Simplify admin UI to single toggle switch with auto timing adjustment
- Extract format detection logic to reusable content_extractors module
- Improve code maintainability with cleaner architecture

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())