重构代码，统一config配置

backend/rag_core/embedders.py

@@ -0,0 +1,83 @@
+"""
+嵌入模型包装器，用于 llama.cpp 服务。
+"""
+
+import os
+from .config import LLAMACPP_EMBEDDING_URL, LLAMACPP_API_KEY
+import httpx
+from typing import List, Optional
+
+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
+        self.model = model
+
+    def as_langchain_embeddings(self) -> Embeddings:
+        """创建 LangChain 兼容的嵌入实例。"""
+        return _LlamaCppLangchainAdapter(self)
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """嵌入一批文档。"""
+        return self._call_embedding_api(texts)
+
+    def embed_query(self, text: str) -> List[float]:
+        """嵌入单个查询。"""
+        return self._call_embedding_api([text])[0]
+
+    def get_embedding_dimension(self) -> int:
+        """通过嵌入测试字符串获取嵌入维度。"""
+        test_embedding = self.embed_query("test")
+        return len(test_embedding)
+
+    def _call_embedding_api(self, texts: List[str]) -> List[List[float]]:
+        """直接调用 llama.cpp 嵌入 API。"""
+        base = self.base_url.rstrip("/")
+        if not base.endswith("/v1"):
+            base = base + "/v1"
+
+        headers = {"Content-Type": "application/json"}
+        if self.api_key:
+            headers["Authorization"] = f"Bearer {self.api_key}"
+
+        payload = {
+            "input": texts,
+            "model": self.model,
+        }
+
+        with httpx.Client(timeout=120) as client:
+            response = client.post(
+                f"{base}/embeddings",
+                headers=headers,
+                json=payload,
+            )
+            response.raise_for_status()
+            data = response.json()
+
+            if isinstance(data, list):
+                return [item["embedding"] for item in data]
+            elif isinstance(data, dict) and "data" in data:
+                return [item["embedding"] for item in sorted(data["data"], key=lambda x: x["index"])]
+            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。"""
+
+    def __init__(self, embedder: LlamaCppEmbedder):
+        self._embedder = embedder
+
+    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)