diff --git a/dsSchoolBuddy/ElasticSearch/T6_SelectByVector.py b/dsSchoolBuddy/ElasticSearch/T6_SelectByVector.py
new file mode 100644
index 00000000..29f08e24
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+++ b/dsSchoolBuddy/ElasticSearch/T6_SelectByVector.py
@@ -0,0 +1,198 @@
+import json
+import warnings
+
+import requests
+from elasticsearch import Elasticsearch
+from langchain_openai import OpenAIEmbeddings
+from pydantic import SecretStr
+
+from Config import Config
+from Config.Config import ES_CONFIG
+
+# 抑制HTTPS相关警告
+warnings.filterwarnings('ignore', message='Connecting to .* using TLS with verify_certs=False is insecure')
+warnings.filterwarnings('ignore', message='Unverified HTTPS request is being made to host')
+
+# 从配置中获取重排模型参数
+RERANK_MODEL = Config.RERANK_MODEL
+RERANK_BASE_URL = Config.RERANK_BASE_URL
+RERANK_BINDING_API_KEY = Config.RERANK_BINDING_API_KEY
+
+
+def init_es_connection() -> Elasticsearch:
+    """
+    初始化Elasticsearch连接
+
+    返回:
+        Elasticsearch: ES连接对象
+    """
+    return Elasticsearch(
+        hosts=Config.ES_CONFIG['hosts'],
+        basic_auth=Config.ES_CONFIG['basic_auth'],
+        verify_certs=False
+    )
+
+
+def get_query_embedding(query: str) -> list:
+    """
+    将查询文本转换为向量
+
+    参数:
+        query: 查询文本
+
+    返回:
+        list: 向量表示
+    """
+    # 创建嵌入模型
+    embeddings = OpenAIEmbeddings(
+        model=Config.EMBED_MODEL_NAME,
+        base_url=Config.EMBED_BASE_URL,
+        api_key=SecretStr(Config.EMBED_API_KEY)
+    )
+
+    # 生成查询向量
+    query_embedding = embeddings.embed_query(query)
+    return query_embedding
+
+
+def search_by_vector(es: Elasticsearch, index_name: str, query_embedding: list, k: int = 10) -> list:
+    """
+    在Elasticsearch中按向量搜索
+
+    参数:
+        es: ES连接对象
+        index_name: 索引名称
+        query_embedding: 查询向量
+        k: 返回结果数量
+
+    返回:
+        list: 搜索结果
+    """
+    # 构建向量查询DSL
+    query = {
+        "query": {
+            "script_score": {
+                "query": {"match_all": {}},
+                "script": {
+                    "source": "cosineSimilarity(params.query_vector, 'embedding') + 1.0",
+                    "params": {
+                        "query_vector": query_embedding
+                    }
+                }
+            }
+        },
+        "size": k
+    }
+
+    # 执行查询
+    try:
+        response = es.search(index=index_name, body=query)
+        return response['hits']['hits']
+    except Exception as e:
+        print(f"向量查询失败: {e}")
+        return []
+
+
+def rerank_results(query: str, results: list) -> list:
+    """
+    使用重排模型对结果进行排序
+
+    参数:
+        query: 查询文本
+        results: 初始搜索结果
+
+    返回:
+        list: 重排后的结果
+    """
+    if len(results) <= 1:
+        # 结果太少，无需重排
+        return [(result, 1.0) for result in results]
+
+    # 准备重排请求数据
+    rerank_data = {
+        "model": RERANK_MODEL,
+        "query": query,
+        "documents": [result['_source']['user_input'] for result in results],
+        "top_n": len(results)
+    }
+
+    # 调用重排API
+    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()
+        rerank_result = response.json()
+
+        # 处理重排结果
+        reranked_results = []
+        if "results" in rerank_result:
+            for item in rerank_result["results"]:
+                doc_idx = item.get("index")
+                score = item.get("relevance_score", 0.0)
+                if 0 <= doc_idx < len(results):
+                    reranked_results.append((results[doc_idx], score))
+        else:
+            print("警告: 无法识别重排API响应格式")
+            reranked_results = [(result, 0.0) for result in results]
+
+        return reranked_results
+    except Exception as e:
+        print(f"重排模型调用失败: {e}")
+        return [(result, 0.0) for result in results]
+
+
+def display_results(results: list) -> None:
+    """
+    展示查询结果
+
+    参数:
+        results: 查询结果列表，每个元素是(结果对象, 分数)的元组
+    """
+    if not results:
+        print("未找到相关数据。")
+        return
+
+    print(f"找到 {len(results)} 条相关数据：")
+    for i, (result, score) in enumerate(results, 1):
+        source = result['_source']
+        print(f"{i}. ID: {result['_id']}")
+        print(f"   相似度分数: {score:.4f}")
+        print(f"   内容: {source.get('user_input', '')}")
+        print(f"   标签: {source['tags']['tags']}")
+        print(f"   时间: {source['timestamp']}")
+        print("-" * 50)
+
+
+def main():
+    # 初始化ES连接
+    es = init_es_connection()
+
+    # 获取用户输入
+    query_text = input("请输入查询关键词(例如: 高性能的混凝土): ")
+    if not query_text:
+        query_text = "高性能的混凝土"
+        print(f"未输入查询关键词，使用默认值: {query_text}")
+
+    # 生成查询向量
+    print("正在生成查询向量...")
+    query_embedding = get_query_embedding(query_text)
+
+    # 执行向量搜索
+    print("正在执行向量搜索...")
+    search_results = search_by_vector(es, ES_CONFIG['index_name'], query_embedding, k=10)
+    print(f"向量搜索结果数量: {len(search_results)}")
+
+    # 重排结果
+    print("正在重排结果...")
+    reranked_results = rerank_results(query_text, search_results)
+
+    # 展示结果
+    display_results(reranked_results)
+
+
+if __name__ == "__main__":
+    main()
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