feat: multi-provider embedder — support OpenAI/DashScope with provider-specific defaults

src/core/embedder.py

@@ -1,12 +1,20 @@
 """嵌入模型抽象层 — 策略模式.
 
+支持的 Provider:
+    openai     — OpenAI / 硅基流动 / 智谱 / DeepSeek / 月之暗面 等 OpenAI 兼容服务
+    dashscope  — 阿里云 DashScope (通义千问)
+
 使用方式:
     from src.core.config import EmbedConfig
     from src.core.embedder import create_embedder, batch_embed
 
+    # 本地模型
     embedder = create_embedder(EmbedConfig(mode="local"))
-    vectors = embedder.embed(["文本1", "文本2"])
-    # 或分批嵌入避免 OOM:
+    # OpenAI 兼容 API
+    embedder = create_embedder(EmbedConfig(mode="api", provider="openai"))
+    # 阿里云 DashScope
+    embedder = create_embedder(EmbedConfig(mode="api", provider="dashscope"))
+
     vectors = batch_embed(embedder, long_text_list)
 """
 import os
@@ -17,10 +25,24 @@ from src.core.config import EmbedConfig
 
 logger = logging.getLogger(__name__)
 
-# 模型下载镜像 (仅首次下载时使用)
 _HF_MIRROR = "https://hf-mirror.com"
 
+# -- Provider 默认配置 --
+_PROVIDER_DEFAULTS: dict[str, dict[str, str | int]] = {
+    "openai": {
+        "api_base": "https://api.openai.com/v1",
+        "model": "text-embedding-3-small",
+        "dimension": 1536,
+    },
+    "dashscope": {
+        "api_base": "https://dashscope.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding",
+        "model": "text-embedding-v4",
+        "dimension": 1536,
+    },
+}
 
+
+# -- 接口 --
 class Embedder(Protocol):
     """嵌入器接口."""
     @property
@@ -28,8 +50,9 @@ class Embedder(Protocol):
     def embed(self, texts: list[str]) -> list[list[float]]: ...
 
 
+# -- 本地模型 --
 class LocalEmbedder:
-    """本地 sentence-transformers 模型嵌入器."""
+    """sentence-transformers 本地模型嵌入器."""
 
     def __init__(self, config: EmbedConfig):
         from sentence_transformers import SentenceTransformer
@@ -61,36 +84,97 @@ class LocalEmbedder:
         return embeddings.tolist()
 
 
-class APIEmbedder:
-    """OpenAI 兼容 API 嵌入器."""
+# -- API Provider 基类 --
+class _BaseAPIEmbedder:
+    """API 嵌入器基类: 统一 api_base/model 解析逻辑."""
 
-    def __init__(self, config: EmbedConfig):
-        self._api_base = config.api_base
+    def __init__(self, config: EmbedConfig, provider: str):
+        defaults = _PROVIDER_DEFAULTS.get(provider, {})
+        self._api_base = config.api_base or str(defaults.get("api_base", ""))
+        self._model = config.model or str(defaults.get("model", ""))
         self._api_key = config.api_key
+        self._dimension = int(defaults.get("dimension", 1536))
 
     @property
     def dimension(self) -> int:
-        return 1536
+        return self._dimension
+
+    def embed(self, texts: list[str]) -> list[list[float]]:
+        raise NotImplementedError
+
+
+# -- OpenAI 兼容 API --
+class OpenAIEmbedder(_BaseAPIEmbedder):
+    """OpenAI / 硅基流动 / 智谱 / DeepSeek / 月之暗面 等."""
+
+    def __init__(self, config: EmbedConfig):
+        super().__init__(config, "openai")
 
     def embed(self, texts: list[str]) -> list[list[float]]:
         if not texts:
             raise ValueError("文本列表不能为空")
         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,
-        )
+        response = client.embeddings.create(model=self._model, input=texts)
         return [d.embedding for d in response.data]
 
 
+# -- 阿里云 DashScope --
+class DashscopeEmbedder(_BaseAPIEmbedder):
+    """阿里云 DashScope 嵌入 (自定义 HTTP API, 非 OpenAI 兼容)."""
+
+    def __init__(self, config: EmbedConfig):
+        super().__init__(config, "dashscope")
+
+    def embed(self, texts: list[str]) -> list[list[float]]:
+        if not texts:
+            raise ValueError("文本列表不能为空")
+        import requests
+        resp = requests.post(
+            self._api_base,
+            headers={
+                "Authorization": f"Bearer {self._api_key}",
+                "Content-Type": "application/json",
+            },
+            json={
+                "model": self._model,
+                "input": {"texts": texts},
+            },
+            timeout=60,
+        )
+        resp.raise_for_status()
+        data = resp.json()
+        # DashScope 返回: {"output": {"embeddings": [{"text_index": 0, "embedding": [...]}, ...]}}
+        embeddings_raw = data.get("output", {}).get("embeddings", [])
+        # 按 text_index 排序确保顺序
+        embeddings_raw.sort(key=lambda x: x.get("text_index", 0))
+        return [e["embedding"] for e in embeddings_raw]
+
+
+# -- 工厂函数 --
+_PROVIDER_CLASSES: dict[str, type[_BaseAPIEmbedder]] = {
+    "openai": OpenAIEmbedder,
+    "dashscope": DashscopeEmbedder,
+}
+
+SUPPORTED_PROVIDERS = list(_PROVIDER_CLASSES.keys())
+
+
 def create_embedder(config: EmbedConfig) -> Embedder:
     """工厂函数: 根据配置创建嵌入器."""
     if config.mode == "local":
         return LocalEmbedder(config)
-    elif config.mode == "api":
-        return APIEmbedder(config)
-    else:
-        raise ValueError(f"不支持的嵌入模式: {config.mode}")
+
+    if config.mode == "api":
+        provider = config.provider or "openai"
+        cls = _PROVIDER_CLASSES.get(provider)
+        if cls is None:
+            raise ValueError(
+                f"不支持的 provider: {provider}, 可选: {SUPPORTED_PROVIDERS}"
+            )
+        return cls(config)
+
+    raise ValueError(f"不支持的嵌入模式: {config.mode}")
 
 
 def batch_embed(