参数配置统一

backend/rag_core/embedders.py

@@ -5,21 +5,21 @@
 import os
 from .config import LLAMACPP_EMBEDDING_URL, LLAMACPP_API_KEY
 import httpx
-from typing import List, Optional
+from typing import List
 
 from langchain_core.embeddings import Embeddings
 
+
 class LlamaCppEmbedder:
     """通过 OpenAI 兼容 API 封装 llama.cpp 嵌入服务。"""
 
-    def __init__(
-        self,
-        base_url: Optional[str] = None,
-        api_key: Optional[str] = None,
-        model: str = "Qwen3-Embedding-0.6B-Q8_0",
-    ):
-        self.base_url = base_url or LLAMACPP_EMBEDDING_URL
-        self.api_key = api_key or LLAMACPP_API_KEY
+    def __init__(self, model: str = "Qwen3-Embedding-0.6B-Q8_0"):
+        """
+        Args:
+            model: 嵌入模型名称，默认 "Qwen3-Embedding-0.6B-Q8_0"。
+        """
+        self.base_url = LLAMACPP_EMBEDDING_URL
+        self.api_key = LLAMACPP_API_KEY
         self.model = model
 
     def as_langchain_embeddings(self) -> Embeddings:
@@ -30,7 +30,7 @@ class LlamaCppEmbedder:
         """嵌入一批文档。"""
         return self._call_embedding_api(texts)
 
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> List[List[float]]:
         """嵌入单个查询。"""
         return self._call_embedding_api([text])[0]
 
@@ -70,6 +70,7 @@ class LlamaCppEmbedder:
             else:
                 raise ValueError(f"未知的嵌入 API 响应格式: {data}")
 
+
 class _LlamaCppLangchainAdapter(Embeddings):
     """将 LlamaCppEmbedder 适配为 LangChain Embeddings 接口。"""
 
@@ -79,5 +80,5 @@ class _LlamaCppLangchainAdapter(Embeddings):
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
         return self._embedder.embed_documents(texts)
 
-    def embed_query(self, text: str) -> List[float]:
-        return self._embedder.embed_query(text)
+    def embed_query(self, text: str) -> List[List[float]]:
+        return self._embedder.embed_query(text)