diff --git a/dsRag/ElasticSearch/T2_SplitTxt.py b/dsRag/ElasticSearch/T2_SplitTxt.py
index 2d1c224f..2ecc8660 100644
--- a/dsRag/ElasticSearch/T2_SplitTxt.py
+++ b/dsRag/ElasticSearch/T2_SplitTxt.py
@@ -1,13 +1,10 @@
-import re
-import warnings
-
-import docx
-
 import os
+import re
 import shutil
-import uuid
+import warnings
 import zipfile
 
+import docx
 from docx import Document
 from docx.oxml.ns import nsmap
 
diff --git a/dsRag/ElasticSearch/T4_SelectAllData.py b/dsRag/ElasticSearch/T4_SelectAllData.py
index b389c5de..99418857 100644
--- a/dsRag/ElasticSearch/T4_SelectAllData.py
+++ b/dsRag/ElasticSearch/T4_SelectAllData.py
@@ -1,8 +1,8 @@
 import warnings
 
 from elasticsearch import Elasticsearch
+
 from Config import Config
-import urllib3
 
 # 抑制HTTPS相关警告
 warnings.filterwarnings('ignore', message='Connecting to .* using TLS with verify_certs=False is insecure')
diff --git a/dsRag/ElasticSearch/T6_XiangLiangQuery.py b/dsRag/ElasticSearch/T6_XiangLiangQuery.py
index 2c0a9a87..91a9b3d0 100644
--- a/dsRag/ElasticSearch/T6_XiangLiangQuery.py
+++ b/dsRag/ElasticSearch/T6_XiangLiangQuery.py
@@ -14,9 +14,7 @@ esClient = EsSearchUtil(ES_CONFIG)
 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')
 
-
-
-def main():
+if __name__ == "__main__":
     # 测试查询
     query = "小学数学中有哪些模型"
     query_tags = ["MATH_1"]  # 默认搜索标签，可修改
@@ -32,7 +30,7 @@ def main():
         query_embedding = esClient.text_to_embedding(query)
         print(f"2. 生成的查询向量维度: {len(query_embedding)}")
         print(f"3. 前3维向量值: {query_embedding[:3]}")
-        
+
         print("4. 正在执行Elasticsearch向量搜索...")
         vector_results = es_conn.search(
             index=ES_CONFIG['index_name'],
@@ -61,7 +59,7 @@ def main():
             }
         )
         print(f"5. 向量搜索结果数量: {len(vector_results['hits']['hits'])}")
-        
+
         # 文本精确搜索
         print("\n=== 文本精确搜索阶段 ===")
         print("1. 正在执行Elasticsearch文本精确搜索...")
@@ -88,7 +86,7 @@ def main():
             }
         )
         print(f"2. 文本搜索结果数量: {len(text_results['hits']['hits'])}")
-        
+
         # 打印详细结果
         print("\n=== 最终搜索结果 ===")
         print(f" 向量搜索结果: {len(vector_results['hits']['hits'])}条")
@@ -96,15 +94,12 @@ def main():
             print(f"   {i}. 文档ID: {hit['_id']}, 相似度分数: {hit['_score']:.2f}")
             print(f"      内容: {hit['_source']['user_input']}")
             # print(f"      详细: {hit['_source']['tags']['full_content']}")
-            
+
         print("\n文本精确搜索结果:")
         for i, hit in enumerate(text_results['hits']['hits']):
-            print(f"  {i+1}. 文档ID: {hit['_id']}, 匹配分数: {hit['_score']:.2f}")
+            print(f"  {i + 1}. 文档ID: {hit['_id']}, 匹配分数: {hit['_score']:.2f}")
             print(f"      内容: {hit['_source']['user_input']}")
             # print(f"      详细: {hit['_source']['tags']['full_content']}")
-        
+
     finally:
         esClient.es_pool.release_connection(es_conn)
-
-if __name__ == "__main__":
-    main()
diff --git a/dsRag/Start.py b/dsRag/Start.py
index 478b45dc..ca0b5094 100644
--- a/dsRag/Start.py
+++ b/dsRag/Start.py
@@ -1,13 +1,10 @@
 import json
-import logging
-import os
 import subprocess
 import tempfile
 import urllib.parse
 import uuid
 import warnings
 from io import BytesIO
-from logging.handlers import RotatingFileHandler
 
 import fastapi
 import uvicorn
@@ -18,7 +15,7 @@ from starlette.responses import StreamingResponse
 from starlette.staticfiles import StaticFiles
 
 from Config import Config
-from Util.SearchUtil import *
+from Util.EsSearchUtil import *
 
 # 初始化日志
 logger = logging.getLogger(__name__)
@@ -42,6 +39,11 @@ console_handler.setFormatter(logging.Formatter(
 logger.addHandler(file_handler)
 logger.addHandler(console_handler)
 
+# 初始化异步 OpenAI 客户端
+client = AsyncOpenAI(
+    api_key=Config.MODEL_API_KEY,
+    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+)
 
 async def lifespan(app: FastAPI):
     # 抑制HTTPS相关警告
@@ -112,11 +114,11 @@ async def rag(request: fastapi.Request):
     query = data.get('query', '')
     query_tags = data.get('tags', [])
     # 调用es进行混合搜索
-    search_results = queryByEs(query, query_tags, logger)
+    search_results = EsSearchUtil.queryByEs(query, query_tags, logger)
     # 构建提示词
     context = "\n".join([
         f"结果{i + 1}: {res['tags']['full_content']}"
-        for i, res in enumerate(search_results['vector_results'] + search_results['text_results'])
+        for i, res in enumerate(search_results['text_results'])
     ])
     # 添加图片识别提示
     prompt = f"""
@@ -135,18 +137,13 @@ async def rag(request: fastapi.Request):
             1. 严格保持原文中图片与上下文的顺序关系，确保语义相关性
             2. 图片引用使用Markdown格式: ![图片描述](图片路径)
             3. 使用Markdown格式返回，包含适当的标题、列表和代码块
-            4. 对于提供Latex公式的内容，尽量保留Latex公式
-            5. 直接返回Markdown内容，不要包含额外解释或说明
-            6. 依托给定的资料，快速准确地回答问题，可以添加一些额外的信息，但请勿重复内容
-            7. 如果未提供相关信息，请不要回答
-            8. 如果发现相关信息与原来的问题契合度低，也不要回答
-            9. 确保内容结构清晰，便于前端展示
+            4. 直接返回Markdown内容，不要包含额外解释或说明
+            5. 依托给定的资料，快速准确地回答问题，可以添加一些额外的信息，但请勿重复内容
+            6. 如果未提供相关信息，请不要回答
+            7. 如果发现相关信息与原来的问题契合度低，也不要回答
+            8. 确保内容结构清晰，便于前端展示
             """
-    # 初始化异步 OpenAI 客户端
-    client = AsyncOpenAI(
-        api_key=Config.MODEL_API_KEY,
-        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
-    )
+
     async def generate_response_stream():
         try:
             # 流式调用大模型
diff --git a/dsRag/Util/EsSearchUtil.py b/dsRag/Util/EsSearchUtil.py
index 8968bfb4..09d1c569 100644
--- a/dsRag/Util/EsSearchUtil.py
+++ b/dsRag/Util/EsSearchUtil.py
@@ -4,7 +4,7 @@ from logging.handlers import RotatingFileHandler
 import jieba
 from gensim.models import KeyedVectors
 
-from Config.Config import MODEL_LIMIT, MODEL_PATH
+from Config.Config import MODEL_LIMIT, MODEL_PATH, ES_CONFIG
 from ElasticSearch.Utils.ElasticsearchConnectionPool import ElasticsearchConnectionPool
 
 # 初始化日志
@@ -125,4 +125,100 @@ class EsSearchUtil:
         elif search_type == 'text':
             return self.text_search(query, size)
         else:
-            return self.hybrid_search(query, size)
\ No newline at end of file
+            return self.hybrid_search(query, size)
+
+    def queryByEs(query, query_tags, logger):
+        # 获取EsSearchUtil实例
+        es_search_util = EsSearchUtil(ES_CONFIG)
+
+        # 执行混合搜索
+        es_conn = es_search_util.es_pool.get_connection()
+        try:
+            # 向量搜索
+            logger.info(f"\n=== 开始执行查询 ===")
+            logger.info(f"原始查询文本: {query}")
+            logger.info(f"查询标签: {query_tags}")
+
+            logger.info("\n=== 向量搜索阶段 ===")
+            logger.info("1. 文本分词和向量化处理中...")
+            query_embedding = es_search_util.text_to_embedding(query)
+            logger.info(f"2. 生成的查询向量维度: {len(query_embedding)}")
+            logger.info(f"3. 前3维向量值: {query_embedding[:3]}")
+
+            logger.info("4. 正在执行Elasticsearch向量搜索...")
+            vector_results = es_conn.search(
+                index=ES_CONFIG['index_name'],
+                body={
+                    "query": {
+                        "script_score": {
+                            "query": {
+                                "bool": {
+                                    "should": [
+                                        {
+                                            "terms": {
+                                                "tags.tags": query_tags
+                                            }
+                                        }
+                                    ],
+                                    "minimum_should_match": 1
+                                }
+                            },
+                            "script": {
+                                "source": "double score = cosineSimilarity(params.query_vector, 'embedding'); return score >= 0 ? score : 0",
+                                "params": {"query_vector": query_embedding}
+                            }
+                        }
+                    },
+                    "size": 3
+                }
+            )
+            logger.info(f"5. 向量搜索结果数量: {len(vector_results['hits']['hits'])}")
+
+            # 文本精确搜索
+            logger.info("\n=== 文本精确搜索阶段 ===")
+            logger.info("1. 正在执行Elasticsearch文本精确搜索...")
+            text_results = es_conn.search(
+                index=ES_CONFIG['index_name'],
+                body={
+                    "query": {
+                        "bool": {
+                            "must": [
+                                {
+                                    "match": {
+                                        "user_input": query
+                                    }
+                                },
+                                {
+                                    "terms": {
+                                        "tags.tags": query_tags
+                                    }
+                                }
+                            ]
+                        }
+                    },
+                    "size": 3
+                }
+            )
+            logger.info(f"2. 文本搜索结果数量: {len(text_results['hits']['hits'])}")
+
+            # 合并vector和text结果
+            all_sources = [hit['_source'] for hit in vector_results['hits']['hits']] + \
+                          [hit['_source'] for hit in text_results['hits']['hits']]
+            
+            # 去重处理
+            unique_sources = []
+            seen_user_inputs = set()
+            
+            for source in all_sources:
+                if source['user_input'] not in seen_user_inputs:
+                    seen_user_inputs.add(source['user_input'])
+                    unique_sources.append(source)
+            
+            logger.info(f"合并后去重结果数量: {len(unique_sources)}条")
+
+            search_results = {
+                "text_results": unique_sources
+            }
+            return search_results
+        finally:
+            es_search_util.es_pool.release_connection(es_conn)
\ No newline at end of file
diff --git a/dsRag/Util/ModelUtil.py b/dsRag/Util/ModelUtil.py
deleted file mode 100644
index 2d7907ef..00000000
--- a/dsRag/Util/ModelUtil.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from typing import List
-
-from pydantic import BaseModel, Field
-
diff --git a/dsRag/Util/PdfUtil.py b/dsRag/Util/PdfUtil.py
deleted file mode 100644
index bdf85000..00000000
--- a/dsRag/Util/PdfUtil.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import PyPDF2
-import os
-
-
-def read_pdf_file(file_path):
-    """
-    读取PDF文件内容
-    :param file_path: PDF文件路径
-    :return: 文档文本内容
-    """
-    try:
-        # 检查文件是否存在
-        if not os.path.exists(file_path):
-            raise FileNotFoundError(f"文件 {file_path} 不存在")
-
-        # 检查文件是否为PDF
-        if not file_path.lower().endswith('.pdf'):
-            raise ValueError("仅支持.pdf格式的文件")
-
-        text = ""
-
-        # 以二进制模式打开PDF文件
-        with open(file_path, 'rb') as file:
-            reader = PyPDF2.PdfReader(file)
-
-            # 逐页读取内容
-            for page in reader.pages:
-                text += page.extract_text() + "\n"
-
-        return text.strip()
-
-    except Exception as e:
-        print(f"读取PDF文件时出错: {str(e)}")
-        return None
\ No newline at end of file
diff --git a/dsRag/Util/SearchUtil.py b/dsRag/Util/SearchUtil.py
deleted file mode 100644
index 1c47d2b2..00000000
--- a/dsRag/Util/SearchUtil.py
+++ /dev/null
@@ -1,124 +0,0 @@
-from Config.Config import ES_CONFIG
-from Util.EsSearchUtil import EsSearchUtil
-
-
-def queryByEs(query, query_tags,logger):
-    # 获取EsSearchUtil实例
-    es_search_util = EsSearchUtil(ES_CONFIG)
-
-    # 执行混合搜索
-    es_conn = es_search_util.es_pool.get_connection()
-    try:
-        # 向量搜索
-        logger.info(f"\n=== 开始执行查询 ===")
-        logger.info(f"原始查询文本: {query}")
-        logger.info(f"查询标签: {query_tags}")
-
-        logger.info("\n=== 向量搜索阶段 ===")
-        logger.info("1. 文本分词和向量化处理中...")
-        query_embedding = es_search_util.text_to_embedding(query)
-        logger.info(f"2. 生成的查询向量维度: {len(query_embedding)}")
-        logger.info(f"3. 前3维向量值: {query_embedding[:3]}")
-
-        logger.info("4. 正在执行Elasticsearch向量搜索...")
-        vector_results = es_conn.search(
-            index=ES_CONFIG['index_name'],
-            body={
-                "query": {
-                    "script_score": {
-                        "query": {
-                            "bool": {
-                                "should": [
-                                    {
-                                        "terms": {
-                                            "tags.tags": query_tags
-                                        }
-                                    }
-                                ],
-                                "minimum_should_match": 1
-                            }
-                        },
-                        "script": {
-                            "source": "double score = cosineSimilarity(params.query_vector, 'embedding'); return score >= 0 ? score : 0",
-                            "params": {"query_vector": query_embedding}
-                        }
-                    }
-                },
-                "size": 3
-            }
-        )
-        logger.info(f"5. 向量搜索结果数量: {len(vector_results['hits']['hits'])}")
-
-        # 文本精确搜索
-        logger.info("\n=== 文本精确搜索阶段 ===")
-        logger.info("1. 正在执行Elasticsearch文本精确搜索...")
-        text_results = es_conn.search(
-            index=ES_CONFIG['index_name'],
-            body={
-                "query": {
-                    "bool": {
-                        "must": [
-                            {
-                                "match": {
-                                    "user_input": query
-                                }
-                            },
-                            {
-                                "terms": {
-                                    "tags.tags": query_tags
-                                }
-                            }
-                        ]
-                    }
-                },
-                "size": 3
-            }
-        )
-        logger.info(f"2. 文本搜索结果数量: {len(text_results['hits']['hits'])}")
-
-        # 合并结果
-        logger.info("\n=== 最终搜索结果 ===")
-        logger.info(f"向量搜索结果: {len(vector_results['hits']['hits'])}条")
-        for i, hit in enumerate(vector_results['hits']['hits'], 1):
-            logger.info(f"   {i}. 文档ID: {hit['_id']}, 相似度分数: {hit['_score']:.2f}")
-            logger.info(f"      内容: {hit['_source']['user_input']}")
-
-        logger.info("文本精确搜索结果:")
-        for i, hit in enumerate(text_results['hits']['hits']):
-            logger.info(f"  {i + 1}. 文档ID: {hit['_id']}, 匹配分数: {hit['_score']:.2f}")
-            logger.info(f"      内容: {hit['_source']['user_input']}")
-
-        # 去重处理：去除vector_results和text_results中重复的user_input
-        vector_sources = [hit['_source'] for hit in vector_results['hits']['hits']]
-        text_sources = [hit['_source'] for hit in text_results['hits']['hits']]
-
-        # 构建去重后的结果
-        unique_text_sources = []
-        text_user_inputs = set()
-
-        # 先处理text_results，保留所有
-        for source in text_sources:
-            text_user_inputs.add(source['user_input'])
-            unique_text_sources.append(source)
-
-        # 处理vector_results，只保留不在text_results中的
-        unique_vector_sources = []
-        for source in vector_sources:
-            if source['user_input'] not in text_user_inputs:
-                unique_vector_sources.append(source)
-
-        # 计算优化掉的记录数量和节约的tokens
-        removed_count = len(vector_sources) - len(unique_vector_sources)
-        saved_tokens = sum(len(source['user_input']) for source in vector_sources
-                           if source['user_input'] in text_user_inputs)
-
-        logger.info(f"优化掉 {removed_count} 条重复记录，节约约 {saved_tokens} tokens")
-
-        search_results = {
-            "vector_results": unique_vector_sources,
-            "text_results": unique_text_sources
-        }
-        return search_results
-    finally:
-        es_search_util.es_pool.release_connection(es_conn)
-
diff --git a/dsRag/Util/SplitDocxUtil.py b/dsRag/Util/SplitDocxUtil.py
deleted file mode 100644
index 5774b3fc..00000000
--- a/dsRag/Util/SplitDocxUtil.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import docx
-
-
-class SplitDocxUtil:
-    @staticmethod
-    def read_docx(file_path):
-        """读取docx文件内容"""
-        try:
-            doc = docx.Document(file_path)
-            return "\n".join([para.text for para in doc.paragraphs if para.text])
-        except Exception as e:
-            print(f"读取docx文件出错: {str(e)}")
-            return ""
\ No newline at end of file
diff --git a/dsRag/Util/WordImageUtil.py b/dsRag/Util/WordImageUtil.py
deleted file mode 100644
index a887f482..00000000
--- a/dsRag/Util/WordImageUtil.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import os
-import shutil
-import uuid
-import zipfile
-
-from docx import Document
-from docx.oxml.ns import nsmap
-
-
-def extract_images_from_docx(docx_path, output_folder):
-    """
-    从docx提取图片并记录位置
-    :param docx_path: Word文档路径
-    :param output_folder: 图片输出文件夹
-    :return: 包含图片路径和位置的列表
-    """
-    # 创建一个List<String> 记录每个图片的名称和序号
-    image_data = []
-    # 创建临时解压目录
-    temp_dir = os.path.join(output_folder, "temp_docx")
-    os.makedirs(temp_dir, exist_ok=True)
-
-    # 解压docx文件
-    with zipfile.ZipFile(docx_path, 'r') as zip_ref:
-        zip_ref.extractall(temp_dir)
-
-    # 读取主文档关系
-    with open(os.path.join(temp_dir, 'word', '_rels', 'document.xml.rels'), 'r') as rels_file:
-        rels_content = rels_file.read()
-
-    # 加载主文档
-    doc = Document(docx_path)
-    img_counter = 1
-
-    # 遍历所有段落
-    for para_idx, paragraph in enumerate(doc.paragraphs):
-        for run_idx, run in enumerate(paragraph.runs):
-            # 检查运行中的图形
-            for element in run._element:
-                if element.tag.endswith('drawing'):
-                    # 提取图片关系ID
-                    blip = element.find('.//a:blip', namespaces=nsmap)
-                    if blip is not None:
-                        embed_id = blip.get('{%s}embed' % nsmap['r'])
-
-                        # 从关系文件中获取图片文件名
-                        rel_entry = f'<Relationship Id="{embed_id}"'
-                        if rel_entry in rels_content:
-                            start = rels_content.find(rel_entry)
-                            target_start = rels_content.find('Target="', start) + 8
-                            target_end = rels_content.find('"', target_start)
-                            image_path = rels_content[target_start:target_end]
-
-                            # 构建图片源路径
-                            src_path = os.path.join(temp_dir, 'word', image_path.replace('..', '').lstrip('/'))
-
-                            if os.path.exists(src_path):
-                                # 创建输出文件名
-                                ext = os.path.splitext(src_path)[1]
-                                # 名称为uuid
-                                fileName=uuid.uuid4().hex
-                                img_name = f"{fileName}{ext}"
-                                image_data.append(img_name)
-                                dest_path = os.path.join(output_folder, img_name)
-                                # 复制图片
-                                shutil.copy(src_path, dest_path)
-
-                                img_counter += 1
-
-    # 清理临时目录
-    shutil.rmtree(temp_dir)
-    return image_data
diff --git a/dsRag/Util/__pycache__/EsSearchUtil.cpython-310.pyc b/dsRag/Util/__pycache__/EsSearchUtil.cpython-310.pyc
index 75397822..51fbd0ac 100644
Binary files a/dsRag/Util/__pycache__/EsSearchUtil.cpython-310.pyc and b/dsRag/Util/__pycache__/EsSearchUtil.cpython-310.pyc differ
diff --git a/dsRag/Util/__pycache__/PdfUtil.cpython-310.pyc b/dsRag/Util/__pycache__/PdfUtil.cpython-310.pyc
deleted file mode 100644
index d539d234..00000000
Binary files a/dsRag/Util/__pycache__/PdfUtil.cpython-310.pyc and /dev/null differ
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