Initial commit: QMDSearch 分层信息检索服务

- FastAPI + Qdrant + Redis + Ollama 技术栈
- L1→L2→L3→chunk 四层分层检索（dense + sparse RRF 融合）
- 文档三级总结与 2.5 级回退
- query 解析路由与分类
- /admin 管理页面

app/core/embeddings.py

@@ -0,0 +1,99 @@
+"""Dense 向量嵌入服务
+
+提供统一的 EmbeddingService 接口，支持两种 provider：
+- openai：OpenAI 兼容 API（AsyncOpenAI）
+- local：本地 Ollama /api/embed 接口
+"""
+
+from typing import Protocol, runtime_checkable
+
+import httpx
+import structlog
+from openai import AsyncOpenAI
+
+from app.config import settings
+
+logger = structlog.get_logger()
+
+
+@runtime_checkable
+class EmbeddingService(Protocol):
+    """统一嵌入服务接口"""
+
+    async def embed(self, texts: list[str]) -> list[list[float]]:
+        """批量生成文本向量
+
+        Args:
+            texts: 待嵌入文本列表，空列表时直接返回空列表
+
+        Returns:
+            与输入等长的向量列表
+        """
+        ...
+
+
+def _check_dimension(vectors: list[list[float]], provider: str) -> None:
+    """返回维度与配置不一致时告警（不抛错），每次调用最多提示一次"""
+    for vec in vectors:
+        if len(vec) != settings.embedding_dimension:
+            logger.warning(
+                "嵌入向量维度与配置不一致",
+                provider=provider,
+                actual=len(vec),
+                expected=settings.embedding_dimension,
+            )
+            return
+
+
+class OpenAIEmbeddingService:
+    """OpenAI 兼容 API 嵌入服务"""
+
+    def __init__(self, api_key: str, base_url: str, model: str) -> None:
+        self._client = AsyncOpenAI(api_key=api_key, base_url=base_url)
+        self._model = model
+
+    async def embed(self, texts: list[str]) -> list[list[float]]:
+        if not texts:
+            return []
+        resp = await self._client.embeddings.create(model=self._model, input=texts)
+        vectors = [item.embedding for item in resp.data]
+        _check_dimension(vectors, provider="openai")
+        logger.debug("OpenAI 嵌入完成", model=self._model, count=len(vectors))
+        return vectors
+
+
+class LocalEmbeddingService:
+    """本地 Ollama 嵌入服务（/api/embed）"""
+
+    def __init__(self, base_url: str, model: str, timeout: float = 60.0) -> None:
+        self.base_url = base_url.rstrip("/")
+        self.model = model
+        self.timeout = timeout
+
+    async def embed(self, texts: list[str]) -> list[list[float]]:
+        if not texts:
+            return []
+        url = f"{self.base_url}/api/embed"
+        payload = {"model": self.model, "input": texts}
+        async with httpx.AsyncClient(timeout=self.timeout) as client:
+            resp = await client.post(url, json=payload)
+            resp.raise_for_status()
+            data = resp.json()
+        vectors: list[list[float]] = data.get("embeddings", [])
+        _check_dimension(vectors, provider="local")
+        logger.debug("Ollama 嵌入完成", model=self.model, count=len(vectors))
+        return vectors
+
+
+def create_embedding_service() -> EmbeddingService:
+    """按 settings.embedding_provider 创建嵌入服务实例"""
+    if settings.embedding_provider == "local":
+        return LocalEmbeddingService(
+            base_url=settings.ollama_base_url,
+            model=settings.ollama_embedding_model,
+        )
+    return OpenAIEmbeddingService(
+        api_key=settings.openai_api_key,
+        base_url=settings.openai_base_url,
+        model=settings.embedding_model,
+    )