feat: add embedder with local/API dual mode

src/core/embedder.py

@@ -0,0 +1,59 @@
+"""嵌入模型抽象层."""
+from sentence_transformers import SentenceTransformer
+
+from src.core.config import EmbedConfig
+
+
+class Embedder:
+    """文本嵌入器, 支持本地模型和 API 两种模式."""
+
+    def __init__(self, config: EmbedConfig):
+        self._config = config
+        if config.mode == "local":
+            self._model = SentenceTransformer(config.local_model)
+        elif config.mode == "api":
+            self._model = None  # 延迟初始化, 需要 openai 包
+            self._api_base = config.api_base
+            self._api_key = config.api_key
+        else:
+            raise ValueError(f"不支持的嵌入模式: {config.mode}")
+
+    @property
+    def mode(self) -> str:
+        """当前嵌入模式."""
+        return self._config.mode
+
+    @property
+    def dimension(self) -> int:
+        """嵌入向量维度."""
+        if self.mode == "local":
+            return self._model.get_embedding_dimension()
+        else:
+            # 默认 OpenAI text-embedding-ada-002 / text-embedding-3-small 维度
+            return 1536
+
+    def embed(self, texts: list[str]) -> list[list[float]]:
+        """对文本列表进行嵌入, 返回向量列表."""
+        if not texts:
+            raise ValueError("文本列表不能为空")
+        if self.mode == "local":
+            embeddings = self._model.encode(texts, normalize_embeddings=True)
+            return embeddings.tolist()
+        else:
+            return self._embed_via_api(texts)
+
+    def _embed_via_api(self, texts: list[str]) -> list[list[float]]:
+        """通过 OpenAI 兼容 API 嵌入（延迟导入 openai）."""
+        from openai import OpenAI
+
+        client = OpenAI(base_url=self._api_base, api_key=self._api_key)
+        response = client.embeddings.create(
+            model="text-embedding-3-small",
+            input=texts,
+        )
+        return [d.embedding for d in response.data]
+
+
+def create_embedder(config: EmbedConfig) -> Embedder:
+    """工厂函数: 根据配置创建嵌入器."""
+    return Embedder(config)