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retriver/langgraph/es_vector_retriever.py

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+"""
+ES向量检索器
+用于直接与ES向量库进行向量匹配检索
+"""
+
+import os
+import sys
+from typing import List, Dict, Any, Optional
+from langchain_core.documents import Document
+
+# 添加路径
+project_root = os.path.join(os.path.dirname(__file__), '..', '..')
+sys.path.append(project_root)
+
+from retriver.langgraph.dashscope_embedding import DashScopeEmbeddingModel
+from elasticsearch_vectorization.es_client_wrapper import ESClientWrapper
+from elasticsearch_vectorization.config import ElasticsearchConfig
+
+
+class ESVectorRetriever:
+    """ES向量检索器，用于直接进行向量相似度匹配"""
+    
+    def __init__(
+        self,
+        keyword: str,
+        top_k: int = 3,
+        oneapi_key: Optional[str] = None,
+        oneapi_base_url: Optional[str] = None,
+        embed_model_name: Optional[str] = None
+    ):
+        """
+        初始化ES向量检索器
+        
+        Args:
+            keyword: ES索引关键词
+            top_k: 返回的文档数量
+            oneapi_key: OneAPI密钥
+            oneapi_base_url: OneAPI基础URL  
+            embed_model_name: 嵌入模型名称
+        """
+        self.keyword = keyword
+        self.top_k = top_k
+        
+        # 初始化嵌入模型
+        self.embedding_model = DashScopeEmbeddingModel(
+            api_key=oneapi_key,
+            model_name=embed_model_name
+        )
+        
+        # 初始化ES客户端
+        self.es_client = ESClientWrapper()
+        
+        # 设置索引名称
+        self.passages_index = ElasticsearchConfig.get_passages_index_name(keyword)
+        
+        print(f"ES向量检索器初始化完成，目标索引: {self.passages_index}")
+    
+    def retrieve(self, query: str) -> List[Document]:
+        """
+        检索相关文档
+        
+        Args:
+            query: 查询文本
+            
+        Returns:
+            检索到的文档列表
+        """
+        try:
+            # 生成查询向量
+            query_embedding = self.embedding_model.encode([query], normalize_embeddings=True)[0]
+            
+            # 确保向量是列表格式
+            if hasattr(query_embedding, 'tolist'):
+                query_vector = query_embedding.tolist()
+            else:
+                query_vector = list(query_embedding)
+            
+            # 执行向量搜索
+            search_result = self.es_client.vector_search(
+                index_name=self.passages_index,
+                vector=query_vector,
+                field="embedding",
+                size=self.top_k
+            )
+            
+            # 解析搜索结果
+            documents = []
+            hits = search_result.get("hits", {}).get("hits", [])
+            
+            for hit in hits:
+                source = hit["_source"]
+                score = hit["_score"]
+                
+                # 创建Document对象
+                doc = Document(
+                    page_content=source.get("content", ""),
+                    metadata={
+                        "passage_id": source.get("passage_id", ""),
+                        "file_id": source.get("file_id", ""),
+                        "evidence": source.get("evidence", ""),
+                        "score": score,
+                        "source": "es_vector_search"
+                    }
+                )
+                documents.append(doc)
+            
+            print(f"ES向量检索完成，找到 {len(documents)} 个相关文档")
+            return documents
+            
+        except Exception as e:
+            print(f"ES向量检索失败: {e}")
+            return []
+    
+    def test_connection(self) -> bool:
+        """测试ES连接"""
+        try:
+            return self.es_client.ping()
+        except:
+            return False
+    
+    def get_index_stats(self) -> Dict[str, Any]:
+        """获取索引统计信息"""
+        try:
+            query = {"match_all": {}}
+            result = self.es_client.search(self.passages_index, query, size=0)
+            total = result.get("hits", {}).get("total", 0)
+            
+            # 兼容不同ES版本的total格式
+            if isinstance(total, dict):
+                count = total.get("value", 0)
+            else:
+                count = total
+                
+            return {
+                "index_name": self.passages_index,
+                "document_count": count,
+                "top_k": self.top_k
+            }
+        except Exception as e:
+            print(f"获取索引统计信息失败: {e}")
+            return {
+                "index_name": self.passages_index,
+                "document_count": 0,
+                "top_k": self.top_k,
+                "error": str(e)
+            }
+
+
+def create_es_vector_retriever(
+    keyword: str,
+    top_k: int = 3,
+    **kwargs
+) -> ESVectorRetriever:
+    """
+    创建ES向量检索器的便捷函数
+    
+    Args:
+        keyword: ES索引关键词
+        top_k: 返回的文档数量
+        **kwargs: 其他参数
+        
+    Returns:
+        ES向量检索器实例
+    """
+    return ESVectorRetriever(
+        keyword=keyword,
+        top_k=top_k,
+        **kwargs
+    )