diff --git a/README.md b/README.md
index 5ee4f27..dd31dab 100644
--- a/README.md
+++ b/README.md
@@ -69,40 +69,44 @@ Then you can run `collection.flush() ` and `collection.num_entities` to check th
 
 ### **EnhancedQAInsertConfig**
 
-#### **Configuration for [Text Spliter](https://towhee.io/towhee/text-spliter):**
+#### **Configuration for [Text Splitter](https://towhee.io/towhee/text-splitter):**
 
-***chunk_size: int***
+***type***: str
+
+The type of splitter, defaults to 'RecursiveCharacter'. You can set this parameter in ['[RecursiveCharacter](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/recursive_text_splitter.html)', '[Markdown](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/markdown.html)', '[PythonCode](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/python.html)', '[Character](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/character_text_splitter.html#)', '[NLTK](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/nltk.html)', '[Spacy](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/spacy.html)', '[Tiktoken](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/tiktoken_splitter.html)', '[HuggingFace](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/huggingface_length_function.html)'].
+
+***chunk_size***: int
 The size of each chunk, defaults to 300.
 
 #### **Configuration for Sentence Embedding:**
 
-***model: str***
+***model***: str
 The model name in the sentence embedding pipeline, defaults to `'all-MiniLM-L6-v2'`.
 You can refer to the above [Model(s) list ](https://towhee.io/tasks/detail/operator?field_name=Natural-Language-Processing&task_name=Sentence-Embedding)to set the model, some of these models are from [HuggingFace](https://huggingface.co/) (open source), and some are from [OpenAI](https://openai.com/) (not open, required API key).
 
-***openai_api_key: str***
+***openai_api_key***: str
 The api key of openai, default to `None`. 
 This key is required if  the model is from OpenAI, you can check the model provider in the above [Model(s) list](https://towhee.io/sentence-embedding/openai).
 
-***device:*** ***int***
+***device:*** int
 The number of devices, defaults to `-1`, which means using the CPU. 
 If the setting is not `-1`, the specified GPU device will be used.
 
 #### **Configuration for [Milvus](https://towhee.io/ann-insert/milvus-client):**
 
-***host: str***
+***host***: str
 Host of Milvus vector database, default is `'127.0.0.1'`.
 
-***port: str***
+***port***: str
 Port of Milvus vector database, default is `'19530'`. 
 
-***collection_name: str***
+***collection_name***: str
 The collection name for Milvus vector database, is required when inserting data into Milvus.
 
-***user: str***
+***user***: str
 The user name for [Cloud user](https://zilliz.com/cloud), defaults to `None`.
 
-***password: str***
+***password***: str
 The user password for [Cloud user](https://zilliz.com/cloud), defaults to `None`.
 
 <br />
diff --git a/eqa_insert.py b/eqa_insert.py
index ca26215..e009b8c 100644
--- a/eqa_insert.py
+++ b/eqa_insert.py
@@ -21,7 +21,8 @@ class EnhancedQAInsertConfig:
     Config of pipeline
     """
     def __init__(self):
-        # config for text_spliter
+        # config for text_splitter
+        self.type = 'RecursiveCharacter'
         self.chunk_size = 300
         # config for sentence_embedding
         self.model = 'all-MiniLM-L6-v2'
@@ -78,7 +79,7 @@ def enhanced_qa_insert_pipe(config):
     return (
         pipe.input('doc')
             .map('doc', 'text', ops.text_loader())
-            .flat_map('text', 'sentence', ops.text_spliter(chunk_size=config.chunk_size))
+            .flat_map('text', 'sentence', ops.text_splitter(type=config.type, chunk_size=config.chunk_size))
             .map('sentence', 'embedding', sentence_embedding_op, config=sentence_embedding_config)
             .map('embedding', 'embedding', ops.towhee.np_normalize())
             .map(('doc', 'sentence', 'embedding'), 'mr', insert_milvus_op)