feat: 完成全量功能开发，包括前端管理后台与后端服务优化

此提交实现了完整的知识库管理系统：
1. 新增Vue3 + Antd Vue前端管理后台，包含登录、文档管理、检索、类目设置等完整页面
2. 重构后端LLM调用抽象层，支持Ollama与OpenAI兼容服务动态切换
3. 调整默认嵌入模型配置为本地bge-m3模式
4. 优化入库任务去重逻辑与缓存清理机制
5. 完善Docker镜像构建与docker-compose部署配置
6. 修复多项测试用例与兼容性问题
7. 新增运行时配置API，支持动态调整系统参数

app/services/llm.py

@@ -0,0 +1,179 @@
+"""LLM 客户端抽象层
+
+提供统一的 `generate(prompt, json_mode)` 接口，底层实现可切换：
+- OllamaLLMClient：走 Ollama HTTP /api/generate（本地或远程 Ollama 服务）
+- OpenAICompatibleLLMClient：走 OpenAI 兼容 /v1/chat/completions（OpenAI / DeepSeek / 智谱 / Qwen API 等）
+
+工厂 `create_llm_client(purpose)` 根据 runtime_settings 选择实现与参数。
+所有客户端共享同一接口，业务侧无需感知底层协议差异。
+"""
+
+from __future__ import annotations
+
+from typing import Literal, Protocol, runtime_checkable
+
+import httpx
+import structlog
+
+from app.config import settings
+from app.core.runtime_settings import LlmProviderConfig, get_runtime_settings
+from app.services.ollama import OllamaClient
+
+logger = structlog.get_logger()
+
+LlmPurpose = Literal["summarize", "query", "classify"]
+
+
+@runtime_checkable
+class LLMClient(Protocol):
+    """LLM 客户端统一接口"""
+
+    async def generate(self, prompt: str, json_mode: bool = False) -> str:
+        """生成文本
+
+        Args:
+            prompt: 输入提示词
+            json_mode: True 时约束输出为 JSON（不支持时降级为普通生成）
+
+        Returns:
+            生成的文本内容
+        """
+        ...
+
+    async def is_available(self) -> bool:
+        """检查服务是否可用"""
+        ...
+
+
+class OllamaLLMClient:
+    """Ollama HTTP API 客户端（包装现有 OllamaClient，便于统一接口）"""
+
+    def __init__(self, base_url: str, model: str, timeout: float = 120.0) -> None:
+        self._inner = OllamaClient(base_url=base_url, model=model, timeout=timeout)
+
+    async def generate(self, prompt: str, json_mode: bool = False) -> str:
+        return await self._inner.generate(prompt, json_mode=json_mode)
+
+    async def is_available(self) -> bool:
+        return await self._inner.is_available()
+
+
+class OpenAICompatibleLLMClient:
+    """OpenAI 兼容 chat/completions 客户端
+
+    适用于 OpenAI 官方 API、DeepSeek、智谱 ChatGLM、Qwen DashScope、Moonshot 等
+    所有兼容 OpenAI /v1/chat/completions 协议的服务。
+    """
+
+    def __init__(self, base_url: str, api_key: str, model: str, timeout: float = 120.0, temperature: float = 0.3) -> None:
+        self.base_url = base_url.rstrip("/")
+        self.api_key = api_key
+        self.model = model
+        self.timeout = timeout
+        self.temperature = temperature
+
+    async def generate(self, prompt: str, json_mode: bool = False) -> str:
+        url = f"{self.base_url}/chat/completions"
+        payload: dict = {
+            "model": self.model,
+            "messages": [{"role": "user", "content": prompt}],
+            "temperature": self.temperature,
+            "stream": False,
+        }
+        if json_mode:
+            payload["response_format"] = {"type": "json_object"}
+
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+        async with httpx.AsyncClient(timeout=self.timeout) as client:
+            resp = await client.post(url, json=payload, headers=headers)
+            resp.raise_for_status()
+            data = resp.json()
+
+        # OpenAI 标准响应结构：choices[0].message.content
+        choices = data.get("choices") or []
+        if not choices:
+            logger.warning("OpenAI 兼容响应无 choices", model=self.model, raw_keys=list(data.keys()))
+            return ""
+        message = choices[0].get("message") or {}
+        content = message.get("content") or ""
+        logger.debug("OpenAI 兼容生成完成", model=self.model, output_length=len(content))
+        return content
+
+    async def is_available(self) -> bool:
+        """简单探测：调 /models 列表接口（多数 OpenAI 兼容服务支持）"""
+        try:
+            async with httpx.AsyncClient(timeout=5.0) as client:
+                resp = await client.get(
+                    f"{self.base_url}/models",
+                    headers={"Authorization": f"Bearer {self.api_key}"},
+                )
+                return resp.status_code == 200
+        except httpx.HTTPError:
+            return False
+
+
+# ---------------------------------------------------------------------------- #
+# 工厂
+# ---------------------------------------------------------------------------- #
+
+# 进程级客户端缓存（避免每请求新建 httpx client）
+_client_cache: dict[str, LLMClient] = {}
+
+
+def _resolve_provider_config(purpose: LlmPurpose) -> LlmProviderConfig:
+    """从 runtime_settings 取指定用途的 LLM 配置"""
+    rt = get_runtime_settings()
+    return getattr(rt.models, purpose)
+
+
+def _build_client(purpose: LlmPurpose) -> LLMClient:
+    """按 runtime_settings 构造 LLM 客户端"""
+    cfg = _resolve_provider_config(purpose)
+
+    if cfg.provider == "ollama":
+        base_url = cfg.base_url or settings.ollama_base_url
+        model = cfg.model or settings.ollama_model
+        return OllamaLLMClient(base_url=base_url, model=model, timeout=cfg.timeout)
+
+    if cfg.provider == "openai_compatible":
+        base_url = cfg.base_url or settings.openai_base_url
+        api_key = cfg.api_key or settings.openai_api_key
+        # openai_compatible 默认模型：若 cfg.model 为空，退化到 OpenAI 通用 chat 模型
+        model = cfg.model or "gpt-4o-mini"
+        if not api_key:
+            logger.warning("OpenAI 兼容 provider 缺少 api_key，调用大概率会失败", purpose=purpose)
+        return OpenAICompatibleLLMClient(
+            base_url=base_url, api_key=api_key, model=model, timeout=cfg.timeout, temperature=cfg.temperature
+        )
+
+    raise ValueError(f"未知 LLM provider: {cfg.provider}")
+
+
+def create_llm_client(purpose: LlmPurpose, *, use_cache: bool = True) -> LLMClient:
+    """创建指定用途的 LLM 客户端
+
+    Args:
+        purpose: 用途（summarize / query / classify）
+        use_cache: True 时复用进程级客户端单例（默认）；False 每次新建（测试用）
+
+    Returns:
+        LLMClient 实例
+    """
+    if not use_cache:
+        return _build_client(purpose)
+
+    cache_key = f"{purpose}"
+    if cache_key not in _client_cache:
+        _client_cache[cache_key] = _build_client(purpose)
+    return _client_cache[cache_key]
+
+
+def invalidate_llm_client_cache(purpose: LlmPurpose | None = None) -> None:
+    """清除客户端缓存（runtime_settings 更新后调用，确保后续读取新配置）"""
+    if purpose is None:
+        _client_cache.clear()
+    else:
+        _client_cache.pop(purpose, None)