diff --git a/dsSchoolBuddy/ElasticSearch/T3_InsertData.py b/dsSchoolBuddy/ElasticSearch/T3_InsertData.py
index a0c23e81..c98291ed 100644
--- a/dsSchoolBuddy/ElasticSearch/T3_InsertData.py
+++ b/dsSchoolBuddy/ElasticSearch/T3_InsertData.py
@@ -3,8 +3,10 @@ import time
 import warnings
 
 from elasticsearch import Elasticsearch
-from langchain_openai import OpenAIEmbeddings  # 直接导入嵌入模型
-from pydantic import SecretStr  # 用于包装API密钥
+from langchain_core.documents import Document
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_openai import OpenAIEmbeddings
+from pydantic import SecretStr
 
 from Config import Config
 
@@ -13,14 +15,39 @@ warnings.filterwarnings('ignore', message='Connecting to .* using TLS with verif
 warnings.filterwarnings('ignore', message='Unverified HTTPS request is being made to host')
 
 
+def split_text_into_chunks(text: str, chunk_size: int = 200, chunk_overlap: int = 100) -> list:
+    """
+    将文本切割成块
+
+    参数:
+        text: 要切割的文本
+        chunk_size: 每个块的大小
+        chunk_overlap: 块之间的重叠大小
+
+    返回:
+        list: 文本块列表
+    """
+    # 创建文档对象
+    docs = [Document(page_content=text, metadata={"source": "simulated_document"})]
+
+    # 切割文档
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size, chunk_overlap=chunk_overlap, add_start_index=True
+    )
+    all_splits = text_splitter.split_documents(docs)
+    print(f"切割后的文档块数量：{len(all_splits)}")
+
+    return [split.page_content for split in all_splits]
+
+
 def insert_long_text_to_es(long_text: str, tags: list = None) -> bool:
     """
-    将长文本向量化并插入到Elasticsearch，基于文本内容哈希实现去重
-    
+    将长文本切割后向量化并插入到Elasticsearch，基于文本内容哈希实现去重
+
     参数:
         long_text: 要插入的长文本
         tags: 可选的标签列表
-    
+
     返回:
         bool: 插入是否成功
     """
@@ -60,44 +87,48 @@ def insert_long_text_to_es(long_text: str, tags: list = None) -> bool:
             es.indices.create(index=index_name, body=mapping)
             print(f"索引 '{index_name}' 创建成功")
 
-        # 3. 生成文本内容的哈希值作为文档ID（实现去重）
-        doc_id = hashlib.md5(long_text.encode('utf-8')).hexdigest()
-        print(f"文本哈希值: {doc_id}")
+        # 3. 切割文本
+        text_chunks = split_text_into_chunks(long_text)
 
-        # 4. 检查文档是否已存在
-        if es.exists(index=index_name, id=doc_id):
-            print(f"文档已存在，跳过插入: {doc_id}")
-            return True
-
-        # 5. 准备标签
+        # 4. 准备标签
         if tags is None:
             tags = ["general_text"]
-        tags_dict = {"tags": tags, "full_content": long_text}
 
-        # 6. 获取当前时间
+        # 5. 获取当前时间
         timestamp = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
 
-        # 7. 直接创建嵌入模型并生成向量
+        # 6. 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)
         )
 
-        # 8. 生成文本嵌入向量
-        embedding = embeddings.embed_documents([long_text])[0]
+        # 7. 为每个文本块生成向量并插入
+        for i, chunk in enumerate(text_chunks):
+            # 生成文本块的哈希值作为文档ID
+            doc_id = hashlib.md5(chunk.encode('utf-8')).hexdigest()
 
-        # 9. 准备文档数据
-        doc = {
-            'tags': tags_dict,
-            'user_input': long_text[:500],  # 取前500个字符作为摘要
-            'timestamp': timestamp,
-            'embedding': embedding
-        }
+            # 检查文档是否已存在
+            if es.exists(index=index_name, id=doc_id):
+                print(f"文档块 {i+1} 已存在，跳过插入: {doc_id}")
+                continue
+
+            # 生成文本块的嵌入向量
+            embedding = embeddings.embed_documents([chunk])[0]
+
+            # 准备文档数据
+            doc = {
+                'tags': {"tags": tags, "full_content": long_text},
+                'user_input': chunk,
+                'timestamp': timestamp,
+                'embedding': embedding
+            }
+
+            # 插入数据到Elasticsearch
+            es.index(index=index_name, id=doc_id, document=doc)
+            print(f"文档块 {i+1} 插入成功: {doc_id}")
 
-        # 10. 插入数据到Elasticsearch（使用哈希值作为ID）
-        es.index(index=index_name, id=doc_id, document=doc)
-        print(f"长文本数据插入成功: {doc_id}")
         return True
     except Exception as e:
         print(f"插入数据失败: {e}")
@@ -121,6 +152,5 @@ def main():
     insert_long_text_to_es(long_text, tags)
 
 
-
 if __name__ == "__main__":
     main()