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+# DRAFT Python API Reference
+
+:::tip NOTE
+Knowledgebase APIs
+:::
+
+## Create knowledge base
+
+```python
+RAGFlow.create_dataset(
+    name: str,
+    avatar: str = "",
+    description: str = "",
+    language: str = "English",
+    permission: str = "me", 
+    document_count: int = 0,
+    chunk_count: int = 0,
+    parse_method: str = "naive",
+    parser_config: DataSet.ParserConfig = None
+) -> DataSet
+```
+
+Creates a knowledge base (dataset).
+
+### Parameters
+
+#### name: `str`, *Required*
+
+The unique name of the dataset to create. It must adhere to the following requirements:
+
+- Permitted characters include:
+  - English letters (a-z, A-Z)
+  - Digits (0-9)
+  - "_" (underscore)
+- Must begin with an English letter or underscore.
+- Maximum 65,535 characters.
+- Case-insensitive.
+
+#### avatar: `str`
+
+The url or ???????????????????????? path to the avatar image associated with the created dataset. Defaults to `""`
+
+#### tenant_id: `str` ?????????????????
+
+The id of the tenant associated with the created dataset is used to identify different users. Defaults to `None`.
+
+- If creating a dataset, tenant_id must not be provided.
+- If updating a dataset, tenant_id can't be changed.
+
+#### description: `str`
+
+The description of the created dataset. Defaults to `""`.
+
+#### language: `str`
+
+The language setting of the created dataset. Defaults to `"English"`. ????????????
+
+#### embedding_model: `str`       ????????????????
+
+The specific model or algorithm used by the dataset to generate vector embeddings. Defaults to `""`.
+
+- If creating a dataset, embedding_model must not be provided.
+- If updating a dataset, embedding_model can't be changed.
+
+#### permission: `str`
+
+Specify who can operate on the dataset. Defaults to `"me"`.
+
+#### document_count: `int`
+
+The number of documents associated with the dataset. Defaults to `0`.
+
+- If updating a dataset, `document_count` can't be changed.
+
+#### chunk_count: `int`
+
+The number of data chunks generated or processed by the created dataset. Defaults to `0`.
+
+- If updating a dataset, chunk_count can't be changed.
+
+#### parse_method, `str`
+
+The method used by the dataset to parse and process data.
+
+- If updating parse_method in a dataset, chunk_count must be greater than 0. Defaults to `"naive"`.
+
+#### parser_config, `Dataset.ParserConfig`
+
+The configuration settings for the parser used by the dataset.
+
+### Returns
+
+- Success: An `infinity.local_infinity.table.LocalTable` object in Python module mode or an `infinity.remote_thrift.table.RemoteTable` object in client-server mode.
+- Failure: `InfinityException`
+  - `error_code`: `int` A non-zero value indicating a specific error condition.
+  - `error_msg`: `str` A message providing additional details about the error.
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+ds = rag.create_dataset(name="kb_1")
+```
+
+---
+
+## Delete knowledge base
+
+```python
+DataSet.delete() -> bool
+```
+
+Deletes a knowledge base. 
+
+### Returns
+
+`bool`
+
+description:the case of updating an dateset, `True` or `False`.
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+ds = rag.create_dataset(name="kb_1")
+ds.delete()
+```
+
+---
+
+## List knowledge bases
+
+```python
+RAGFlow.list_datasets(
+    page: int = 1, 
+    page_size: int = 1024, 
+    orderby: str = "create_time", 
+    desc: bool = True
+) -> List[DataSet]
+```
+
+Lists all knowledge bases in the RAGFlow system. 
+
+### Parameters
+
+#### page: `int`
+
+The current page number to retrieve from the paginated data. This parameter determines which set of records will be fetched. Defaults to `1`.
+
+#### page_size: `int`
+
+The number of records to retrieve per page. This controls how many records will be included in each page. Defaults to `1024`.
+
+#### order_by: `str`
+
+The field by which the records should be sorted. This specifies the attribute or column used to order the results. Defaults to `"create_time"`.
+
+#### desc: `bool`
+
+Whether the sorting should be in descending order. Defaults to `True`.
+
+### Returns
+
+```python
+List[DataSet]
+description:the list of datasets.
+```
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+for ds in rag.list_datasets():
+    print(ds)
+```
+
+---
+
+## Retrieve knowledge base
+
+```python
+RAGFlow.get_dataset(
+    id: str = None, 
+    name: str = None
+) -> DataSet
+```
+
+Retrieves a knowledge base by name.
+
+### Parameters
+
+#### name: `str`
+
+The name of the dataset to be got. If `id` is not provided,  `name` is required.
+
+#### id: `str`
+
+The id of the dataset to be got. If `name` is not provided,  `id` is required.
+
+### Returns
+
+```python
+DataSet
+description: dataset object
+```
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+ds = rag.get_dataset(name="ragflow")
+print(ds)
+```
+
+---
+
+## Save knowledge base configurations
+
+```python
+DataSet.save() -> bool
+```
+
+### Returns
+
+```python
+bool
+description:the case of updating an dateset, True or False.
+```
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+ds = rag.get_dataset(name="kb_1")
+ds.parse_method = "manual"
+ds.save()
+```
+
+---
+
+:::tip API GROUPING
+File management inside knowledge base
+:::
+
+## Upload document
+
+```python
+RAGFLOW.upload_document(ds:DataSet, name:str, blob:bytes)-> bool
+```
+
+### Parameters
+
+#### ds
+
+#### name
+
+#### blob
+
+
+
+### Returns
+
+
+### Examples
+
+---
+
+## Retrieve document
+
+```python
+RAGFlow.get_document(id:str=None,name:str=None) -> Document
+```
+
+### Parameters
+
+#### id: `str`, *Required*
+
+ID of the document to retrieve.
+
+#### name: `str`
+
+Name or title of the document.
+
+### Returns
+
+A document object containing the following attributes:
+
+#### id: `str`
+
+Id of the retrieved document. Defaults to `""`.
+
+#### thumbnail: `str`
+
+Thumbnail image of the retrieved document. Defaults to `""`.
+
+#### knowledgebase_id: `str`
+
+Knowledge base ID related to the document. Defaults to `""`.
+
+#### parser_method: `str`
+
+Method used to parse the document. Defaults to `""`.
+
+#### parser_config: `ParserConfig`
+
+Configuration object for the parser. Defaults to `None`.
+
+#### source_type: `str`
+
+Source type of the document. Defaults to `""`.
+
+#### type: `str`
+
+Type or category of the document. Defaults to `""`.
+
+#### created_by: `str`
+
+Creator of the document. Defaults to `""`.
+
+#### name: `str`
+string
+''
+Name or title of the document. Defaults to `""`.
+
+#### size: `int`
+
+Size of the document in bytes or some other unit. Defaults to `0`.
+
+#### token_count: `int`
+
+Number of tokens in the document. Defaults to `""`.
+
+#### chunk_count: `int`
+
+Number of chunks the document is split into. Defaults to `0`.
+
+#### progress: `float`
+
+Current processing progress as a percentage. Defaults to `0.0`.
+
+#### progress_msg: `str`
+
+Message indicating current progress status. Defaults to `""`.
+
+#### process_begin_at: `datetime`
+
+Start time of the document processing. Defaults to `None`.
+
+#### process_duation: `float`
+
+Duration of the processing in seconds or minutes. Defaults to `0.0`.
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+doc = rag.get_document(id="wdfxb5t547d",name='testdocument.txt')
+print(doc)
+```
+
+---
+
+## Save document settings
+
+```python
+Document.save() -> bool
+```
+
+### Returns
+
+bool
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+doc = rag.get_document(id="wdfxb5t547d")
+doc.parser_method= "manual"
+doc.save()
+```
+
+---
+
+## Download document
+
+```python
+Document.download() -> bytes
+```
+
+### Returns
+
+bytes of the document.
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+doc = rag.get_document(id="wdfxb5t547d")
+open("~/ragflow.txt", "w+").write(doc.download())
+print(doc) 
+```
+
+---
+
+## List documents
+
+```python
+Dataset.list_docs(keywords: str=None, offset: int=0, limit:int = -1) -> List[Document]
+```
+
+### Parameters
+
+#### keywords: `str`
+
+List documents whose name has the given keywords. Defaults to `None`.
+
+#### offset: `int`
+
+The beginning number of records for paging. Defaults to `0`.
+
+#### limit: `int`
+
+Records number to return, -1 means all of them. Records number to return, -1 means all of them.
+
+### Returns
+
+List[Document]
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+ds = rag.create_dataset(name="kb_1")
+
+filename1 = "~/ragflow.txt"
+rag.create_document(ds, name=filename1 , blob=open(filename1 , "rb").read())
+
+filename2 = "~/infinity.txt"
+rag.create_document(ds, name=filename2 , blob=open(filename2 , "rb").read())
+
+for d in ds.list_docs(keywords="rag", offset=0, limit=12):
+    print(d)
+```
+
+---
+
+## Delete documents
+
+```python
+Document.delete() -> bool
+```
+### Returns
+
+bool
+description: delete success or not
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+ds = rag.create_dataset(name="kb_1")
+
+filename1 = "~/ragflow.txt"
+rag.create_document(ds, name=filename1 , blob=open(filename1 , "rb").read())
+
+filename2 = "~/infinity.txt"
+rag.create_document(ds, name=filename2 , blob=open(filename2 , "rb").read())
+for d in ds.list_docs(keywords="rag", offset=0, limit=12):
+    d.delete()
+```
+
+---
+
+## Parse document
+
+```python
+Document.async_parse() -> None
+RAGFLOW.async_parse_documents() -> None
+```
+
+### Parameters
+
+????????????????????????????????????????????????????
+
+### Returns
+
+????????????????????????????????????????????????????
+
+### Examples
+
+```python
+#document parse and cancel
+rag = RAGFlow(API_KEY, HOST_ADDRESS)
+ds = rag.create_dataset(name="dataset_name")
+name3 = 'ai.pdf'
+path = 'test_data/ai.pdf'
+rag.create_document(ds, name=name3, blob=open(path, "rb").read())
+doc = rag.get_document(name="ai.pdf")
+doc.async_parse()
+print("Async parsing initiated")
+```
+
+---
+
+## Cancel document parsing
+
+```python
+rag.async_cancel_parse_documents(ids)
+RAGFLOW.async_cancel_parse_documents()-> None
+```
+
+### Parameters
+
+#### ids, `list[]`
+
+### Returns
+
+?????????????????????????????????????????????????
+
+### Examples
+
+```python
+#documents parse and cancel
+rag = RAGFlow(API_KEY, HOST_ADDRESS)
+ds = rag.create_dataset(name="God5")
+documents = [
+    {'name': 'test1.txt', 'path': 'test_data/test1.txt'},
+    {'name': 'test2.txt', 'path': 'test_data/test2.txt'},
+    {'name': 'test3.txt', 'path': 'test_data/test3.txt'}
+]
+
+# Create documents in bulk
+for doc_info in documents:
+    with open(doc_info['path'], "rb") as file:
+        created_doc = rag.create_document(ds, name=doc_info['name'], blob=file.read())
+docs = [rag.get_document(name=doc_info['name']) for doc_info in documents]
+ids = [doc.id for doc in docs]
+
+rag.async_parse_documents(ids)
+print("Async bulk parsing initiated")
+
+for doc in docs:
+    for progress, msg in doc.join(interval=5, timeout=10):
+        print(f"{doc.name}: Progress: {progress}, Message: {msg}")
+
+cancel_result = rag.async_cancel_parse_documents(ids)
+print("Async bulk parsing cancelled")
+```
+
+---
+
+## Join document
+
+??????????????????
+
+```python
+Document.join(interval=15, timeout=3600) -> iteral[Tuple[float, str]]
+```
+
+### Parameters
+
+#### interval: `int`
+
+Time interval in seconds for progress report. Defaults to `15`.
+
+#### timeout: `int`
+
+Timeout in seconds. Defaults to `3600`.
+
+### Returns
+
+iteral[Tuple[float, str]]
+
+## Add chunk
+
+```python
+Document.add_chunk(content:str) -> Chunk
+```
+
+### Parameters
+
+#### content: `str`, *Required*
+
+### Returns
+
+chunk
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+doc = rag.get_document(id="wdfxb5t547d")
+chunk = doc.add_chunk(content="xxxxxxx")
+```
+
+---
+
+## Delete chunk
+
+```python
+Chunk.delete() -> bool
+```
+
+### Returns
+
+bool
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+doc = rag.get_document(id="wdfxb5t547d")
+chunk = doc.add_chunk(content="xxxxxxx")
+chunk.delete()
+```
+
+---
+
+## Save chunk contents
+
+```python
+Chunk.save() -> bool
+```
+
+### Returns
+
+bool
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+doc = rag.get_document(id="wdfxb5t547d")
+chunk = doc.add_chunk(content="xxxxxxx")
+chunk.content = "sdfx"
+chunk.save()
+```
+
+---
+
+## Retrieval
+
+```python
+RAGFlow.retrieval(question:str, datasets:List[Dataset], document=List[Document]=None,     offset:int=0, limit:int=6, similarity_threshold:float=0.1, vector_similarity_weight:float=0.3, top_k:int=1024) -> List[Chunk]
+```
+
+### Parameters
+
+#### question: `str`, *Required*
+
+The user query or query keywords. Defaults to `""`.
+
+#### datasets: `List[Dataset]`, *Required*
+
+The scope of datasets.
+
+#### document: `List[Document]`
+
+The scope of document. `None` means no limitation. Defaults to `None`.
+
+#### offset: `int`
+
+The beginning point of retrieved records. Defaults to `0`.
+
+#### limit: `int`
+
+The maximum number of records needed to return. Defaults to `6`.
+
+#### Similarity_threshold: `float`
+
+The minimum similarity score. Defaults to `0.2`.
+
+#### similarity_threshold_weight: `float`
+
+The weight of vector cosine similarity, 1 - x is the term similarity weight. Defaults to `0.3`.
+
+#### top_k: `int`
+
+Number of records engaged in vector cosine computaton. Defaults to `1024`.
+
+### Returns
+
+List[Chunk]
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+ds = rag.get_dataset(name="ragflow")
+name = 'ragflow_test.txt'
+path = 'test_data/ragflow_test.txt'
+rag.create_document(ds, name=name, blob=open(path, "rb").read())
+doc = rag.get_document(name=name)
+doc.async_parse()
+# Wait for parsing to complete 
+for progress, msg in doc.join(interval=5, timeout=30):
+    print(progress, msg)
+for c in rag.retrieval(question="What's ragflow?", 
+             datasets=[ds], documents=[doc], 
+             offset=0, limit=6, similarity_threshold=0.1, 
+             vector_similarity_weight=0.3,
+             top_k=1024
+             ):
+    print(c)
+```
+
+---
+
+:::tip API GROUPING
+Chat assistant APIs
+:::
+
+## Create assistant
+
+```python
+RAGFlow.create_assistant(
+    name: str = "assistant", 
+    avatar: str = "path", 
+    knowledgebases: List[DataSet] = ["kb1"], 
+    llm: Assistant.LLM = None, 
+    prompt: Assistant.Prompt = None
+) -> Assistant
+```
+
+### Returns
+
+Assistant object.
+
+#### name: `str`
+
+The name of the created assistant. Defaults to `"assistant"`.
+
+#### avatar: `str`
+
+The icon of the created assistant. Defaults to `"path"`. 
+
+#### knowledgebases: `List[DataSet]`
+
+Select knowledgebases associated. Defaults to `["kb1"]`.
+
+#### id: `str`
+
+The id of the created assistant. Defaults to `""`.
+
+#### llm: `LLM`
+
+The llm of the created assistant. Defaults to `None`. When the value is `None`, a dictionary with the following values will be generated as the default.
+
+- **model_name**, `str`  
+  Large language chat model. If it is `None`, it will return the user's default model.  
+- **temperature**, `float`  
+  This parameter controls the randomness of predictions by the model. A lower temperature makes the model more confident in its responses, while a higher temperature makes it more creative and diverse. Defaults to `0.1`.  
+- **top_p**, `float`  
+  Also known as “nucleus sampling,” this parameter sets a threshold to select a smaller set of words to sample from. It focuses on the most likely words, cutting off the less probable ones. Defaults to `0.3`  
+- **presence_penalty**, `float`  
+  This discourages the model from repeating the same information by penalizing words that have already appeared in the conversation. Defaults to `0.2`.
+- **frequency penalty**, `float`  
+  Similar to the presence penalty, this reduces the model’s tendency to repeat the same words frequently. Defaults to `0.7`.
+- **max_token**, `int`  
+  This sets the maximum length of the model’s output, measured in the number of tokens (words or pieces of words). Defaults to `512`.
+
+#### Prompt: `str`
+
+Instructions you need LLM to follow when LLM answers questions, like character design, answer length and answer language etc. 
+
+Defaults:
+```
+You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence "The answer you are looking for is not found in the knowledge base!" Answers need to consider chat history.
+      Here is the knowledge base:
+      {knowledge}
+      The above is the knowledge base.
+```
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+kb = rag.get_dataset(name="kb_1")
+assi = rag.create_assistant("Miss R", knowledgebases=[kb])
+```
+
+---
+
+## Save updates to a chat assistant
+
+```python
+Assistant.save() -> bool
+```
+
+### Returns
+
+```python
+bool
+description:the case of updating an assistant, True or False.
+```
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+kb = rag.get_knowledgebase(name="kb_1")
+assi = rag.create_assistant("Miss R"， knowledgebases=[kb])
+assi.llm.temperature = 0.8
+assi.save()
+```
+
+---
+
+## Delete assistant
+
+```python
+Assistant.delete() -> bool
+```
+
+### Returns
+
+```python
+bool
+description:the case of deleting an assistant, True or False.
+```
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+kb = rag.get_knowledgebase(name="kb_1")
+assi = rag.create_assistant("Miss R"， knowledgebases=[kb])
+assi.delete()
+```
+
+---
+
+## Retrieve assistant
+
+```python
+RAGFlow.get_assistant(id: str = None, name: str = None) -> Assistant
+```
+
+### Parameters
+
+#### id: `str`
+
+ID of the assistant to retrieve. If `name` is not provided,  `id` is required.
+
+#### name: `str`
+
+Name of the assistant to retrieve. If `id` is not provided,  `name` is required.
+
+### Returns
+
+Assistant object.
+
+#### name: `str`
+
+The name of the created assistant. Defaults to `"assistant"`.
+
+#### avatar: `str`
+
+The icon of the created assistant. Defaults to `"path"`. 
+
+#### knowledgebases: `List[DataSet]`
+
+Select knowledgebases associated. Defaults to `["kb1"]`.
+
+#### id: `str`
+
+The id of the created assistant. Defaults to `""`.
+
+#### llm: `LLM`
+
+The llm of the created assistant. Defaults to `None`. When the value is `None`, a dictionary with the following values will be generated as the default.
+
+- **model_name**, `str`  
+  Large language chat model. If it is `None`, it will return the user's default model.  
+- **temperature**, `float`  
+  This parameter controls the randomness of predictions by the model. A lower temperature makes the model more confident in its responses, while a higher temperature makes it more creative and diverse. Defaults to `0.1`.  
+- **top_p**, `float`  
+  Also known as “nucleus sampling,” this parameter sets a threshold to select a smaller set of words to sample from. It focuses on the most likely words, cutting off the less probable ones. Defaults to `0.3`  
+- **presence_penalty**, `float`  
+  This discourages the model from repeating the same information by penalizing words that have already appeared in the conversation. Defaults to `0.2`.
+- **frequency penalty**, `float`  
+  Similar to the presence penalty, this reduces the model’s tendency to repeat the same words frequently. Defaults to `0.7`.
+- **max_token**, `int`  
+  This sets the maximum length of the model’s output, measured in the number of tokens (words or pieces of words). Defaults to `512`.
+
+#### Prompt: `str`
+
+Instructions you need LLM to follow when LLM answers questions, like character design, answer length and answer language etc. 
+
+Defaults:
+```
+You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence "The answer you are looking for is not found in the knowledge base!" Answers need to consider chat history.
+      Here is the knowledge base:
+      {knowledge}
+      The above is the knowledge base.
+```
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+assi = rag.get_assistant(name="Miss R")
+```
+
+---
+
+## List assistants
+
+```python
+RAGFlow.list_assistants() -> List[Assistant]
+```
+
+### Returns
+
+A list of assistant objects.
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+for assi in rag.list_assistants():
+    print(assi)
+```
+
+---
+
+:::tip API GROUPING
+Chat-session APIs
+:::
+
+## Create session
+
+```python
+assistant_1.create_session(name: str = "New session") -> Session
+```
+
+### Returns
+
+A `session` object.
+
+#### id: `str`
+
+The id of the created session is used to identify different sessions.
+- `id` cannot be provided in creating
+- `id` is required in updating
+
+#### name: `str`
+
+The name of the created session. Defaults to `"New session"`.
+
+#### messages: `List[Message]`
+
+The messages of the created session.
+- messages cannot be provided.
+
+Defaults:
+
+??????????????????????????????????????????????????????????????????????????????????????????????
+
+```
+[{"role": "assistant", "content": "Hi! I am your assistant，can I help you?"}]
+```
+
+#### assistant_id: `str`
+
+The id of associated assistant. Defaults to `""`.
+- `assistant_id` is required in creating if you use HTTP API.
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+assi = rag.get_assistant(name="Miss R")
+sess = assi.create_session()
+```
+
+## Retrieve session
+
+```python
+Assistant.get_session(id: str) -> Session
+```
+
+### Parameters
+
+#### id: `str`, *Required*
+
+???????????????????????????????
+
+### Returns
+
+### Returns
+
+A `session` object.
+
+#### id: `str`
+
+The id of the created session is used to identify different sessions.
+- `id` cannot be provided in creating
+- `id` is required in updating
+
+#### name: `str`
+
+The name of the created session. Defaults to `"New session"`.
+
+#### messages: `List[Message]`
+
+The messages of the created session.
+- messages cannot be provided.
+
+Defaults:
+
+??????????????????????????????????????????????????????????????????????????????????????????????
+
+```
+[{"role": "assistant", "content": "Hi! I am your assistant，can I help you?"}]
+```
+
+#### assistant_id: `str`
+
+
+???????????????????????????????????????How to get
+
+The id of associated assistant. Defaults to `""`.
+- `assistant_id` is required in creating if you use HTTP API.
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+assi = rag.get_assistant(name="Miss R")
+sess = assi.get_session(id="d5c55d2270dd11ef9bd90242ac120007")
+```
+
+---
+
+## Save session settings
+
+```python
+Session.save() -> bool
+```
+
+### Returns
+
+bool
+description:the case of updating a session, True or False.
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+assi = rag.get_assistant(name="Miss R")
+sess = assi.get_session(id="d5c55d2270dd11ef9bd90242ac120007")
+sess.name = "Updated session"
+sess.save()
+```
+
+---
+
+## Chat
+
+```python
+Session.chat(question: str, stream: bool = False) -> Optional[Message, iter[Message]]
+```
+
+### Parameters
+
+#### question: `str`, *Required*
+
+The question to start an AI chat. Defaults to `None`. ???????????????????
+
+#### stream: `bool`
+
+The approach of streaming text generation. When stream is True, it outputs results in a streaming fashion; otherwise, it outputs the complete result after the model has finished generating.
+
+#### session_id: `str` ??????????????????
+
+### Returns
+
+[Message, iter[Message]]
+
+#### id: `str`
+
+The id of the message. `id` is automatically generated. Defaults to `None`. ???????????????????
+
+#### content: `str`
+
+The content of the message. Defaults to `"Hi! I am your assistant, can I help you?"`.
+
+#### reference: `List[Chunk]`
+
+The auto-generated reference of the message. Each `chunk` object includes the following attributes:
+
+- **id**: `str`  
+  The id of the chunk. ?????????????????  
+- **content**: `str`  
+  The content of the chunk. Defaults to `None`. ?????????????????????  
+- **document_id**: `str`  
+  The ID of the document being referenced. Defaults to `""`.  
+- **document_name**: `str`  
+  The name of the referenced document being referenced. Defaults to `""`.  
+- **knowledgebase_id**: `str`  
+  The id of the knowledge base to which the relevant document belongs. Defaults to `""`.  
+- **image_id**: `str`  
+  The id of the image related to the chunk. Defaults to `""`.  
+- **similarity**: `float`
+  A general similarity score, usually a composite score derived from various similarity measures . This score represents the degree of similarity between two objects. The value ranges between 0 and 1, where a value closer to 1 indicates higher similarity. Defaults to `None`. ????????????????????????????????????   
+- **vector_similarity**: `float`  
+  A similarity score based on vector representations. This score is obtained by converting texts, words, or objects into vectors and then calculating the cosine similarity or other distance measures between these vectors to determine the similarity in vector space. A higher value indicates greater similarity in the vector space. Defaults to `None`. ?????????????????????????????????
+- **term_similarity**: `float`  
+  The similarity score based on terms or keywords. This score is calculated by comparing the similarity of key terms between texts or datasets, typically measuring how similar two words or phrases are in meaning or context. A higher value indicates a stronger similarity between terms. Defaults to `None`. ???????????????????  
+- **position**: `List[string]`  
+  Indicates the position or index of keywords or specific terms within the text. An array is typically used to mark the location of keywords or specific elements, facilitating precise operations or analysis of the text. Defaults to `None`. ??????????????
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+assi = rag.get_assistant(name="Miss R")
+sess = assi.create_session()    
+
+print("\n==================== Miss R =====================\n")
+print(assi.get_prologue())
+
+while True:
+    question = input("\n==================== User =====================\n> ")
+    print("\n==================== Miss R =====================\n")
+    
+    cont = ""
+    for ans in sess.chat(question, stream=True):
+        print(ans.content[len(cont):], end='', flush=True)
+        cont = ans.content
+```
+
+---
+
+## List sessions
+
+```python
+Assistant.list_session() -> List[Session]
+```
+
+### Returns
+
+List[Session]
+description: the List contains information about multiple assistant object, with each dictionary containing information about one assistant.
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+assi = rag.get_assistant(name="Miss R")
+
+for sess in assi.list_session():
+    print(sess)
+```
+
+---
+
+## Delete session
+
+```python
+Session.delete() -> bool
+```
+
+### Returns
+
+bool
+description:the case of deleting a session, True or False.
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
+assi = rag.get_assistant(name="Miss R")
+sess = assi.create_session()
+sess.delete()
+```
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