diff --git a/dsRag/Config/Config.py b/dsRag/Config/Config.py
index 2588e16a..33184b88 100644
--- a/dsRag/Config/Config.py
+++ b/dsRag/Config/Config.py
@@ -5,6 +5,16 @@ MYSQL_USER = "root"
 MYSQL_PASSWORD = "DsideaL147258369"
 MYSQL_DB_NAME = "base_db"
 
+# Elasticsearch配置
+ES_CONFIG = {
+    "hosts": "https://10.10.14.206:9200",
+    "basic_auth": ("elastic", "jv9h8uwRrRxmDi1dq6u8"),
+    "verify_certs": False,
+    "ssl_show_warn": False,
+    "default_index": "knowledge_base"
+}
+
+
 # Milvus 服务器的主机地址
 MS_HOST = "10.10.14.207"
 # Milvus 服务器的端口号
@@ -29,10 +39,7 @@ DEEPSEEK_URL = 'https://api.deepseek.com'
 # 阿里云中用来调用 deepseek v3 的密钥【驿来特】
 MODEL_API_KEY = "sk-f6da0c787eff4b0389e4ad03a35a911f"
 MODEL_NAME = "qwen-plus"
-#MODEL_NAME = "deepseek-v3"
+# MODEL_NAME = "deepseek-v3"
 
 # Jieba分词自定义词典配置
-JIEBA_CUSTOM_WORDS = [
-    '文言虚词',
-    # 可在此添加更多自定义词语
-]
\ No newline at end of file
+JIEBA_CUSTOM_WORDS = ['文言虚词', '花呗']
diff --git a/dsRag/Config/__pycache__/Config.cpython-310.pyc b/dsRag/Config/__pycache__/Config.cpython-310.pyc
index dd5b8039..93fec986 100644
Binary files a/dsRag/Config/__pycache__/Config.cpython-310.pyc and b/dsRag/Config/__pycache__/Config.cpython-310.pyc differ
diff --git a/dsRag/Dict/custom_dict.txt b/dsRag/Dict/custom_dict.txt
deleted file mode 100644
index 0653b557..00000000
--- a/dsRag/Dict/custom_dict.txt
+++ /dev/null
@@ -1 +0,0 @@
-花呗
\ No newline at end of file
diff --git a/dsRag/ElasticSearch/T1_HelloWord.py b/dsRag/ElasticSearch/T1_HelloWord.py
new file mode 100644
index 00000000..3d12d9f5
--- /dev/null
+++ b/dsRag/ElasticSearch/T1_HelloWord.py
@@ -0,0 +1,20 @@
+# 初始化ES连接
+import urllib3
+from elasticsearch import Elasticsearch
+
+from Config import Config
+
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
+# 初始化ES连接时添加verify_certs=False
+es = Elasticsearch(
+    hosts=Config.ES_CONFIG['hosts'],
+    basic_auth=Config.ES_CONFIG['basic_auth'],
+    verify_certs=False  # 禁用证书验证
+)
+
+# 查询所有index
+indices = es.cat.indices(format='json')
+print(f"当前ES集群中共有 {len(indices)} 个index:")
+for idx in indices:
+    print(f"- {idx['index']}")
\ No newline at end of file
diff --git a/dsRag/ElasticSearch/__init__.py b/dsRag/ElasticSearch/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/dsRag/StartEs.py b/dsRag/StartEs.py
new file mode 100644
index 00000000..8731daf0
--- /dev/null
+++ b/dsRag/StartEs.py
@@ -0,0 +1,265 @@
+import os
+import subprocess
+import tempfile
+import urllib.parse
+import uuid
+from contextlib import asynccontextmanager
+from io import BytesIO
+from logging.handlers import RotatingFileHandler
+from typing import List
+
+import jieba  # 导入 jieba 分词库
+import uvicorn
+from fastapi import FastAPI, Request, HTTPException
+from fastapi.staticfiles import StaticFiles
+from gensim.models import KeyedVectors
+from pydantic import BaseModel, Field, ValidationError
+from starlette.responses import StreamingResponse
+
+from Config.Config import MS_MODEL_PATH, MS_MODEL_LIMIT, MS_HOST, MS_PORT, MS_MAX_CONNECTIONS, MS_NPROBE, \
+    MS_COLLECTION_NAME
+from Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
+from Milvus.Utils.MilvusConnectionPool import *
+from Milvus.Utils.MilvusConnectionPool import MilvusConnectionPool
+from Util.ALiYunUtil import ALiYunUtil
+
+# 初始化日志
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+handler = RotatingFileHandler('Logs/start.log', maxBytes=1024 * 1024, backupCount=5)
+handler.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(message)s'))
+logger.addHandler(handler)
+
+# 1. 加载预训练的 Word2Vec 模型
+model = KeyedVectors.load_word2vec_format(MS_MODEL_PATH, binary=False, limit=MS_MODEL_LIMIT)
+logger.info(f"模型加载成功，词向量维度: {model.vector_size}")
+
+
+# 将HTML文件转换为Word文件
+def html_to_word_pandoc(html_file, output_file):
+    subprocess.run(['pandoc', html_file, '-o', output_file])
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # 初始化Milvus连接池
+    app.state.milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)
+
+    # 初始化集合管理器
+    app.state.collection_manager = MilvusCollectionManager(MS_COLLECTION_NAME)
+    app.state.collection_manager.load_collection()
+
+    # 初始化阿里云大模型工具
+    app.state.aliyun_util = ALiYunUtil()
+
+    yield
+    # 关闭Milvus连接池
+    app.state.milvus_pool.close()
+
+
+app = FastAPI(lifespan=lifespan)
+
+# 挂载静态文件目录
+app.mount("/static", StaticFiles(directory="Static"), name="static")
+
+
+# 将文本转换为嵌入向量
+def text_to_embedding(text):
+    words = jieba.lcut(text)  # 使用 jieba 分词
+    print(f"文本: {text}, 分词结果: {words}")
+    embeddings = [model[word] for word in words if word in model]
+    logger.info(f"有效词向量数量: {len(embeddings)}")
+    if embeddings:
+        avg_embedding = sum(embeddings) / len(embeddings)
+        logger.info(f"生成的平均向量: {avg_embedding[:5]}...")  # 打印前 5 维
+        return avg_embedding
+    else:
+        logger.warning("未找到有效词，返回零向量")
+        return [0.0] * model.vector_size
+
+
+async def generate_stream(client, milvus_pool, collection_manager, query, documents):
+    # 从连接池获取连接
+    connection = milvus_pool.get_connection()
+    try:
+        # 1. 将查询文本转换为向量
+        current_embedding = text_to_embedding(query)
+
+        # 2. 搜索相关数据
+        search_params = {
+            "metric_type": "L2",  # 使用 L2 距离度量方式
+            "params": {"nprobe": MS_NPROBE}  # 设置 IVF_FLAT 的 nprobe 参数
+        }
+        # 动态生成expr表达式
+        if documents:
+            conditions = [f"array_contains(tags['tags'], '{doc}')" for doc in documents]
+            expr = " OR ".join(conditions)
+        else:
+            expr = ""  # 如果没有选择文档，返回空字符串
+        # 7. 将文本转换为嵌入向量
+        results = collection_manager.search(current_embedding,
+                                            search_params,
+                                            expr=expr,  # 使用in操作符
+                                            limit=5)  # 返回 5 条结果
+
+        # 3. 处理搜索结果
+        logger.info("最相关的知识库内容:")
+        context = ""
+        if results:
+            for hits in results:
+                for hit in hits:
+                    try:
+                        # 查询非向量字段
+                        record = collection_manager.query_by_id(hit.id)
+                        if hit.distance < 0.88:  # 设置距离阈值
+                            logger.info(f"ID: {hit.id}")
+                            logger.info(f"标签: {record['tags']}")
+                            logger.info(f"用户问题: {record['user_input']}")
+                            logger.info(f"时间: {record['timestamp']}")
+                            logger.info(f"距离: {hit.distance}")
+                            logger.info("-" * 40)  # 分隔线
+                            # 获取完整内容
+                            full_content = record['tags'].get('full_content', record['user_input'])
+                            context = context + full_content
+                        else:
+                            logger.warning(f"距离太远，忽略此结果: {hit.id}")
+                            logger.info(f"标签: {record['tags']}")
+                            logger.info(f"用户问题: {record['user_input']}")
+                            logger.info(f"时间: {record['timestamp']}")
+                            logger.info(f"距离: {hit.distance}")
+                            continue
+
+                    except Exception as e:
+                        logger.error(f"查询失败: {e}")
+        else:
+            logger.warning("未找到相关历史对话，请检查查询参数或数据。")
+
+        prompt = f"""
+信息检索与回答助手
+根据以下关于'{query}'的相关信息：
+
+基本信息
+- 语言: 中文
+- 描述: 根据提供的材料检索信息并回答问题
+- 特点: 快速准确提取关键信息，清晰简洁地回答
+
+相关信息
+{context}
+
+回答要求
+1. 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。
+2. 如果未提供相关信息，请不要回答。
+3. 如果发现相关信息与原来的问题契合度低，也不要回答
+4. 使用HTML格式返回，包含适当的段落、列表和标题标签
+5. 确保内容结构清晰，便于前端展示
+"""
+
+        # 调用阿里云大模型
+        if len(context) > 0:
+            html_content = client.chat(prompt)
+            yield {"data": html_content}
+        else:
+            yield {"data": "没有在知识库中找到相关的信息，无法回答此问题。"}
+    except Exception as e:
+        yield {"data": f"生成报告时出错: {str(e)}"}
+    finally:
+        # 释放连接
+        milvus_pool.release_connection(connection)
+
+
+"""
+http://10.10.21.22:8000/static/ai.html
+
+知识库中有的内容：
+小学数学中有哪些模型？
+帮我写一下 “如何理解点、线、面、体、角”的教学设计
+
+知识库中没有的内容：
+你知道黄海是谁吗？
+"""
+
+
+class QueryRequest(BaseModel):
+    query: str = Field(..., description="用户查询的问题")
+    documents: List[str] = Field(..., description="用户上传的文档")
+
+
+class SaveWordRequest(BaseModel):
+    html: str = Field(..., description="要保存为Word的HTML内容")
+
+
+@app.post("/api/save-word")
+async def save_to_word(request: Request):
+    temp_html = None
+    output_file = None
+    try:
+        # Parse request data
+        try:
+            data = await request.json()
+            html_content = data.get('html_content', '')
+            if not html_content:
+                raise ValueError("Empty HTML content")
+        except Exception as e:
+            logger.error(f"Request parsing failed: {str(e)}")
+            raise HTTPException(status_code=400, detail=f"Invalid request: {str(e)}")
+
+        # 创建临时HTML文件
+        temp_html = os.path.join(tempfile.gettempdir(), uuid.uuid4().hex + ".html")
+        with open(temp_html, "w", encoding="utf-8") as f:
+            f.write(html_content)
+
+        # 使用pandoc转换
+        output_file = os.path.join(tempfile.gettempdir(), "小学数学问答.docx")
+        subprocess.run(['pandoc', temp_html, '-o', output_file], check=True)
+
+        # 读取生成的Word文件
+        with open(output_file, "rb") as f:
+            stream = BytesIO(f.read())
+
+        # 返回响应
+        encoded_filename = urllib.parse.quote("小学数学问答.docx")
+        return StreamingResponse(
+            stream,
+            media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+            headers={"Content-Disposition": f"attachment; filename*=UTF-8''{encoded_filename}"})
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Unexpected error: {str(e)}")
+        raise HTTPException(status_code=500, detail="Internal server error")
+    finally:
+        # 清理临时文件
+        try:
+            if temp_html and os.path.exists(temp_html):
+                os.remove(temp_html)
+            if output_file and os.path.exists(output_file):
+                os.remove(output_file)
+        except Exception as e:
+            logger.warning(f"Failed to clean up temp files: {str(e)}")
+
+
+@app.post("/api/rag")
+async def rag_stream(request: Request):
+    try:
+        data = await request.json()
+        query_request = QueryRequest(**data)
+    except ValidationError as e:
+        logger.error(f"请求体验证失败: {e.errors()}")
+        raise HTTPException(status_code=422, detail=e.errors())
+    except Exception as e:
+        logger.error(f"请求解析失败: {str(e)}")
+        raise HTTPException(status_code=400, detail="无效的请求格式")
+    """RAG+ALiYun接口"""
+    async for chunk in generate_stream(
+            request.app.state.aliyun_util,
+            request.app.state.milvus_pool,
+            request.app.state.collection_manager,
+            query_request.query,
+            query_request.documents
+    ):
+        return chunk
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)