From 17e52bc315ed0e352cd10cf2d4725e33f83f53f8 Mon Sep 17 00:00:00 2001
From: HuangHai <10402852@qq.com>
Date: Tue, 19 Aug 2025 08:00:57 +0800
Subject: [PATCH] 'commit'

---
 dsSchoolBuddy/ElasticSearch/T2_BgeM3.py | 68 +++++++++++++++++++++----
 1 file changed, 57 insertions(+), 11 deletions(-)

diff --git a/dsSchoolBuddy/ElasticSearch/T2_BgeM3.py b/dsSchoolBuddy/ElasticSearch/T2_BgeM3.py
index 44799a92..1e9937c7 100644
--- a/dsSchoolBuddy/ElasticSearch/T2_BgeM3.py
+++ b/dsSchoolBuddy/ElasticSearch/T2_BgeM3.py
@@ -1,10 +1,15 @@
-# pip install pydantic
+# pip install pydantic requests
 from langchain_core.documents import Document
 from langchain_core.vectorstores import InMemoryVectorStore
 from langchain_openai import OpenAIEmbeddings
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 from pydantic import SecretStr  # 导入 SecretStr
-from Config.Config import EMBED_MODEL_NAME, EMBED_BASE_URL, EMBED_API_KEY
+import requests
+import json
+from Config.Config import (
+    EMBED_MODEL_NAME, EMBED_BASE_URL, EMBED_API_KEY,
+    RERANK_MODEL, RERANK_BASE_URL, RERANK_BINDING_API_KEY
+)
 
 
 # 模拟长字符串文档内容
@@ -43,15 +48,56 @@ embeddings = OpenAIEmbeddings(
 vector_store = InMemoryVectorStore(embeddings)
 ids = vector_store.add_documents(documents=all_splits)
 
-# 向量查询
-results = vector_store.similarity_search(
-    "混凝土", k=2
-)
+# 向量查询 - 获取更多结果用于重排
+query = "混凝土"
+results = vector_store.similarity_search(query, k=4)  # 获取4个结果用于重排
 
-# 打印所有查询结果
-print("查询结果数量：", len(results))
-print("查询结果：")
-for i, result in enumerate(results):
-    print(f"结果 {i+1}:")
+print("向量搜索结果数量：", len(results))
+
+# 存储重排后的文档和分数
+reranked_docs_with_scores = []
+
+# 调用重排模型
+if len(results) > 1:
+    # 准备重排请求数据
+    rerank_data = {
+        "model": RERANK_MODEL,
+        "query": query,
+        "documents": [doc.page_content for doc in results],
+        "top_n": len(results)
+    }
+
+    # 调用SiliconFlow API进行重排
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {RERANK_BINDING_API_KEY}"
+    }
+
+    try:
+        response = requests.post(RERANK_BASE_URL, headers=headers, data=json.dumps(rerank_data))
+        response.raise_for_status()  # 检查请求是否成功
+        rerank_result = response.json()
+
+        # 处理重排结果，保留分数
+        for item in rerank_result.get("results", []):
+            doc_idx = item.get("index")
+            score = item.get("score", 0.0)  # 获取可信度分数
+            if 0 <= doc_idx < len(results):
+                reranked_docs_with_scores.append((results[doc_idx], score))
+
+        print("重排后结果数量：", len(reranked_docs_with_scores))
+    except Exception as e:
+        print(f"重排模型调用失败: {e}")
+        print("将使用原始搜索结果")
+        # 使用原始结果，分数设为0.0
+        reranked_docs_with_scores = [(doc, 0.0) for doc in results]
+else:
+    # 只有一个结果，无需重排
+    reranked_docs_with_scores = [(doc, 1.0) for doc in results]  # 单个结果可信度设为1.0
+
+# 打印所有查询结果及其可信度
+print("最终查询结果：")
+for i, (result, score) in enumerate(reranked_docs_with_scores):
+    print(f"结果 {i+1} (可信度: {score:.4f}):")
     print(result.page_content)
     print("---")