添加硅流重排模型类，更新APIKeyManager导入路径，优化文档重排序逻辑，增强代码结构和可读性。

rag2_0/tool/ModelTool.py

@@ -16,7 +16,7 @@ from typing import List, Any
 import requests
 import os
 import logging
-from .APIKeyManager import APIKeyManager
+from rag2_0.tool.APIKeyManager import APIKeyManager
 
 class SiliconFlowEmbeddings(Embeddings):
     """SiliconFlow嵌入模型封装"""
@@ -45,7 +45,45 @@ class SiliconFlowEmbeddings(Embeddings):
 
     def embed_query(self, text: str) -> List[float]:
         return self._embed([text])[0]
-    
+
+class SiliconFlowReRankerModel:    
+    @staticmethod
+    def rerank(query: str, documents: List[str], top_k: int = 10) -> List[str]:
+        """
+        使用硅流重排模型对文档进行重新排序
+        
+        Args:
+            query: 用户查询文本
+            documents: 需要重新排序的文档列表
+            top_k: 返回排序后的前k个文档
+            
+        Returns:
+            List[dict]: 重排序后的文档列表，每个元素包含document内容、相关性分数和原始索引
+        """
+        url = "https://api.siliconflow.cn/v1/rerank"
+        payload = {
+            "model": "BAAI/bge-reranker-v2-m3",
+            "query": query,
+            "documents": documents,
+            "top_n": top_k,
+            "max_chunks_per_doc": 1024,
+            "overlap_tokens": 80,
+            "return_documents": True
+        }
+        api_key = APIKeyManager.get_api_key()
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json"
+        }
+        try:
+            response = requests.post(url, json=payload, headers=headers)
+            response.raise_for_status()
+            results = response.json()
+            return [{"document": item["document"]["text"], "score": item["relevance_score"], "index": item["index"]} for item in results["results"]]
+        except requests.exceptions.RequestException as e:
+            logging.error(f"重排序请求失败: {str(e)}")
+            return []
+        
 class XinferenceReRankerModel:
     """重排模型封装"""
 
@@ -83,6 +121,8 @@ class XinferenceReRankerModel:
             logging.error(f"重排序请求失败: {str(e)}")
             return []
 
+
+
 class OpenAiLLM:
 
     def __init__(self, **kwargs):
@@ -136,7 +176,7 @@ class OpenAiLLM:
             return completion.choices[0].message
 
 if __name__ == "__main__":
-    reranker = XinferenceReRankerModel()
+    reranker = SiliconFlowReRankerModel()
     query = "什么是AI"
     documents = ["AI是人工智能", "AI是机器学习", "AI是深度学习"]
     results = reranker.rerank(query, documents)