DRAFT: Miscellaneous updates to HTTP API. Tried to finish off Python API ref… (#2909)

…erence but failed.

### What problem does this PR solve?



### Type of change


- [x] Documentation Update

api/python_api_reference.md

 
 #### keywords: `str`
 
-The keywords to match document titles. Defaults to `None`.
+The keywords used to match document titles. Defaults to `None`.
 
 #### offset: `int`
 
 - `created_by`: `str` The creator of the document. Defaults to `""`.
 - `size`: `int` The document size in bytes. Defaults to `0`.
 - `token_count`: `int` The number of tokens in the document. Defaults to `0`.
-- `chunk_count`: `int` The number of chunks that the document is split into. Defaults to `0`.
+- `chunk_count`: `int` The number of chunks in the document. Defaults to `0`.
 - `progress`: `float` The current processing progress as a percentage. Defaults to `0.0`.
 - `progress_msg`: `str` A message indicating the current progress status. Defaults to `""`.
 - `process_begin_at`: `datetime` The start time of document processing. Defaults to `None`.
 ```python
 from ragflow import RAGFlow
 
-rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
-dataset = rag.create_dataset(name="kb_1")
+rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
+dataset = rag_object.create_dataset(name="kb_1")
 
 filename1 = "~/ragflow.txt"
 blob = open(filename1 , "rb").read()
 DataSet.async_parse_documents(document_ids:list[str]) -> None
 ```
 
+Parses documents in the current dataset.
+
 ### Parameters
 
 #### document_ids: `list[str]`, *Required*
 
 ### Returns
 
-- Success: No value is returned.
+- Success: No value is returned.????????????????????
 - Failure: `Exception`
 
 ### Examples
 DataSet.async_cancel_parse_documents(document_ids:list[str])-> None
 ```
 
+Stops parsing specified documents.
+
 ### Parameters
 
 #### document_ids: `list[str]`, *Required*
 
 ---
 
-## List chunks
+## Add chunk
 
 ```python
-Document.list_chunks(keywords: str = None, offset: int = 0, limit: int = -1, id : str = None) -> list[Chunk]
+Document.add_chunk(content:str) -> Chunk ?????????????????????
 ```
 
-Retrieves a list of document chunks.
+Adds a chunk to the current document.
 
 ### Parameters
 
-#### keywords: `str`  
-  
-List chunks whose name has the given keywords. Defaults to `None`
-
-#### offset: `int`
-
-The starting index for the chunks to retrieve. Defaults to `1`
+#### content: `str`, *Required*
 
-#### limit  
+The text content of the chunk.
 
-The maximum number of chunks to retrieve.  Default: `30`
+#### important_keywords: `list[str]`  ??????????????????????
 
-#### id
-
-The ID of the chunk to retrieve. Default: `None`
+The key terms or phrases to tag with the chunk.
 
 ### Returns
 
-- Success: A list of `Chunk` objects.
+- Success: A `Chunk` object.
 - Failure: `Exception`.
 
+A `Chunk` object contains the following attributes:
+
+- `id`: `str` 
+- `content`: `str` Content of the chunk.
+- `important_keywords`: `list[str]` A list of key terms or phrases to tag with the chunk.
+- `create_time`: `str` The time when the chunk was created (added to the document).
+- `create_timestamp`: `float` The timestamp representing the creation time of the chunk, expressed in seconds since January 1, 1970.
+- `knowledgebase_id`: `str` The ID of the associated dataset.
+- `document_name`: `str` The name of the associated document.
+- `document_id`: `str` The ID of the associated document.
+- `available`: `int`???? The chunk's availability status in the dataset. Value options:
+  - `0`: Unavailable
+  - `1`: Available
+
+
 ### Examples
 
 ```python
 from ragflow import RAGFlow
 
 rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
-dataset = rag_object.list_datasets("123")
-dataset = dataset[0]
-dataset.async_parse_documents(["wdfxb5t547d"])
-for chunk in doc.list_chunks(keywords="rag", offset=0, limit=12):
-    print(chunk)
+dataset = rag_object.list_datasets(id="123")
+dtaset = dataset[0]
+doc = dataset.list_documents(id="wdfxb5t547d")
+doc = doc[0]
+chunk = doc.add_chunk(content="xxxxxxx")
 ```
 
-## Add chunk
+---
+
+## List chunks
 
 ```python
-Document.add_chunk(content:str) -> Chunk
+Document.list_chunks(keywords: str = None, offset: int = 0, limit: int = -1, id : str = None) -> list[Chunk]
 ```
 
+Retrieves a list of chunks from the current document.
+
 ### Parameters
 
-#### content: *Required*
+#### keywords: `str`  
+  
+The keywords used to match chunk content. Defaults to `None`
 
-The text content of the chunk.
+#### offset: `int`
+
+The starting index for the chunks to retrieve. Defaults to `1`??????
+
+#### limit  
 
-#### important_keywords :`list[str]`
+The maximum number of chunks to retrieve.  Default: `30`?????????
 
-List the key terms or phrases that are significant or central to the chunk's content.
+#### id
+
+The ID of the chunk to retrieve. Default: `None`
 
 ### Returns
 
-chunk
+- Success: A list of `Chunk` objects.
+- Failure: `Exception`.
 
 ### Examples
 
 ```python
 from ragflow import RAGFlow
 
-rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
-dataset = rag.list_datasets(id="123")
-dtaset = dataset[0]
-doc = dataset.list_documents(id="wdfxb5t547d")
-doc = doc[0]
-chunk = doc.add_chunk(content="xxxxxxx")
+rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
+dataset = rag_object.list_datasets("123")
+dataset = dataset[0]
+dataset.async_parse_documents(["wdfxb5t547d"])
+for chunk in doc.list_chunks(keywords="rag", offset=0, limit=12):
+    print(chunk)
 ```
 
 ---
 
-## Delete chunk
+## Delete chunks
 
 ```python
 Document.delete_chunks(chunk_ids: list[str])
 ```python
 from ragflow import RAGFlow
 
-rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
-ds = rag.list_datasets(id="123")
-ds = ds[0]
-doc = ds.list_documents(id="wdfxb5t547d")
+rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
+dataset = rag_object.list_datasets(id="123")
+dataset = dataset[0]
+doc = dataset.list_documents(id="wdfxb5t547d")
 doc = doc[0]
 chunk = doc.add_chunk(content="xxxxxxx")
 doc.delete_chunks(["id_1","id_2"])
 A dictionary representing the attributes to update, with the following keys:
 
 - `"content"`: `str` Content of the chunk.
-- `"important_keywords"`: `list[str]` A list of key terms to attach to the chunk.
+- `"important_keywords"`: `list[str]` A list of key terms or phrases to tag with the chunk.
 - `"available"`: `int` The chunk's availability status in the dataset. Value options:
   - `0`: Unavailable
   - `1`: Available
 RAGFlow.retrieve(question:str="", datasets:list[str]=None, document=list[str]=None, offset:int=1, limit:int=30, similarity_threshold:float=0.2, vector_similarity_weight:float=0.3, top_k:int=1024,rerank_id:str=None,keyword:bool=False,higlight:bool=False) -> list[Chunk]
 ```
 
+???????
+
 ### Parameters
 
 #### question: `str` *Required*
 
 The user query or query keywords. Defaults to `""`.
 
-#### datasets: `list[str]`, *Required*
+#### datasets: `list[str]`, *Required*?????
 
 The datasets to search from.
 
 
 #### limit: `int`
 
-The maximum number of chunks to retrieve. Defaults to `6`.
+The maximum number of chunks to retrieve. Defaults to `6`.???????????????
 
 #### Similarity_threshold: `float`
 
 
 The number of chunks engaged in vector cosine computaton. Defaults to `1024`.
 
-#### rerank_id
+#### rerank_id: `str`
 
-The ID of the rerank model.  Defaults to `None`.
+The ID of the rerank model. Defaults to `None`.
 
-#### keyword
+#### keyword: `bool`
 
 Indicates whether keyword-based matching is enabled:
 
 - `True`: Enabled.
-- `False`: Disabled.
+- `False`: Disabled (default).
 
-#### highlight:`bool`
+#### highlight: `bool`
 
 Specifying whether to enable highlighting of matched terms in the results (True) or not (False).
 
 from ragflow import RAGFlow
 
 rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
-ds = rag_object.list_datasets(name="ragflow")
-ds = ds[0]
+dataset = rag_object.list_datasets(name="ragflow")
+dataset = dataset[0]
 name = 'ragflow_test.txt'
 path = './test_data/ragflow_test.txt'
-rag_object.create_document(ds, name=name, blob=open(path, "rb").read())
-doc = ds.list_documents(name=name)
+rag_object.create_document(dataset, name=name, blob=open(path, "rb").read())
+doc = dataset.list_documents(name=name)
 doc = doc[0]
-ds.async_parse_documents([doc.id])
+dataset.async_parse_documents([doc.id])
 for c in rag_object.retrieve(question="What's ragflow?", 
-             datasets=[ds.id], documents=[doc.id], 
+             datasets=[dataset.id], documents=[doc.id], 
              offset=1, limit=30, similarity_threshold=0.2, 
              vector_similarity_weight=0.3,
              top_k=1024
 
 The following shows the attributes of a `Chat` object:
 
-#### name: *Required*
+#### name: `str`, *Required*????????
 
 The name of the chat assistant. Defaults to `"assistant"`.
 
-#### avatar
+#### avatar: `str`
 
 Base64 encoding of the avatar. Defaults to `""`.
 
 
 The IDs of the associated datasets. Defaults to `[""]`.
 
-#### llm
+#### llm: `Chat.LLM`
 
 The llm of the created chat. Defaults to `None`. When the value is `None`, a dictionary with the following values will be generated as the default.
 
 - `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
+#### prompt: `Chat.Prompt`
 
 Instructions for the LLM to follow.  A `Prompt` object contains the following attributes:
 
 ```python
 from ragflow import RAGFlow
 
-rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
-kbs = rag.list_datasets(name="kb_1")
-list_kb=[]
-for kb in kbs:
-    list_kb.append(kb.id)
-assi = rag.create_chat("Miss R", knowledgebases=list_kb)
+rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
+datasets = rag_object.list_datasets(name="kb_1")
+dataset_ids = []
+for dataset in datasets:
+    dataset_ids.append(dataset.id)
+assistant = rag_object.create_chat("Miss R", knowledgebases=dataset_ids)
 ```
 
 ---
 
-## Update chat
+## Update chat assistant
 
 ```python
 Chat.update(update_message: dict)
 ```python
 from ragflow import RAGFlow
 
-rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
-knowledge_base = rag.list_datasets(name="kb_1")
-assistant = rag.create_chat("Miss R", knowledgebases=knowledge_base)
+rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
+datasets = rag_object.list_datasets(name="kb_1")
+assistant = rag_object.create_chat("Miss R", knowledgebases=datasets)
 assistant.update({"name": "Stefan", "llm": {"temperature": 0.8}, "prompt": {"top_n": 8}})
 ```
 
 ---
 
-## Delete chats
+## Delete chat assistants
 
 ```python
 RAGFlow.delete_chats(ids: list[str] = None)
 ```python
 from ragflow import RAGFlow
 
-rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
-rag.delete_chats(ids=["id_1","id_2"])
+rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
+rag_object.delete_chats(ids=["id_1","id_2"])
 ```
 
 ---
 
-## List chats
+## List chat assistants
 
 ```python
 RAGFlow.list_chats(
 
 ### Parameters
 
-#### page
+#### page: `int`
 
 Specifies the page on which the chat assistants will be displayed. Defaults to `1`.
 
-#### page_size
+#### page_size: `int`
 
 The number of chat assistants on each page. Defaults to `1024`.
 
-#### order_by
+#### orderby: `str`
 
-The attribute by which the results are sorted. Defaults to `"create_time"`.
+The attribute by which the results are sorted. Available options:
 
-#### desc
+- `"create_time"` (default)
+- `"update_time"`
+
+#### desc: `bool`
 
 Indicates whether the retrieved chat assistants should be sorted in descending order. Defaults to `True`.
 
-#### id: `string`  
+#### id: `str`  
 
-The ID of the chat to retrieve. Defaults to `None`.
+The ID of the chat assistant to retrieve. Defaults to `None`.
 
-#### name: `string`  
+#### name: `str`  
 
-The name of the chat to retrieve. Defaults to `None`.
+The name of the chat assistant to retrieve. Defaults to `None`.
 
 ### Returns
 
 ---
 
 :::tip API GROUPING
-Chat-session APIs
+Chat Session APIs
 :::
 
 ---
 
 ### Parameters
 
-#### name
+#### name: `str`
 
 The name of the chat session to create.
 
 ```python
 from ragflow import RAGFlow
 
-rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
-assistant = rag.list_chats(name="Miss R")
+rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
+assistant = rag_object.list_chats(name="Miss R")
 assistant = assistant[0]
 session = assistant.create_session()
 ```
 
+---
+
 ## Update session
 
 ```python
 ```python
 from ragflow import RAGFlow
 
-rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
-assistant = rag.list_chats(name="Miss R")
+rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
+assistant = rag_object.list_chats(name="Miss R")
 assistant = assistant[0]
 session = assistant.create_session("session_name")
 session.update({"name": "updated_name"})
 
 ---
 
-## Chat
-
-```python
-Session.ask(question: str, stream: bool = False) -> Optional[Message, iter[Message]]
-```
-
-Asks a question to start a conversation.
-
-### Parameters
-
-#### question *Required*
-
-The question to start an AI chat. Defaults to `None`.
-
-#### stream
-
-Indicates whether to output responses in a streaming way:
-
-- `True`: Enable streaming.
-- `False`: (Default) Disable streaming.
-
-### Returns
-
-- A `Message` object containing the response to the question if `stream` is set to `False`
-- An iterator containing multiple `message` objects (`iter[Message]`) if `stream` is set to `True`
-
-The following shows the attributes of a `Message` object:
-
-#### id: `str`
-
-The auto-generated message ID.
-
-#### content: `str`
-
-The content of the message. Defaults to `"Hi! I am your assistant, can I help you?"`.
-
-#### reference: `list[Chunk]`
-
-A list of `Chunk` objects representing references to the message, each containing the following attributes:
-
-- `id` `str`  
-  The chunk ID.
-- `content` `str`  
-  The content of the chunk.
-- `image_id` `str`  
-  The ID of the snapshot of the chunk.
-- `document_id` `str`  
-  The ID of the referenced document.
-- `document_name` `str`  
-  The name of the referenced document.
-- `position` `list[str]`  
-  The location information of the chunk within the referenced document.
-- `knowledgebase_id` `str`  
-  The ID of the dataset to which the referenced document belongs.
-- `similarity` `float`
-  A composite similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity.
-- `vector_similarity` `float`  
-  A vector similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity between vector embeddings.
-- `term_similarity` `float`  
-  A keyword similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity between keywords.
-
-
-### Examples
-
-```python
-from ragflow import RAGFlow
-
-rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
-assistant = rag.list_chats(name="Miss R")
-assistant = assistant[0]
-session = assistant.create_session()    
-
-print("\n==================== Miss R =====================\n")
-print(assistant.get_prologue())
-
-while True:
-    question = input("\n==================== User =====================\n> ")
-    print("\n==================== Miss R =====================\n")
-    
-    cont = ""
-    for ans in session.ask(question, stream=True):
-        print(answer.content[len(cont):], end='', flush=True)
-        cont = answer.content
-```
-
----
-
 ## List sessions
 
 ```python
 
 ### Parameters
 
-#### page
+#### page: `int`
 
 Specifies the page on which the sessions will be displayed. Defaults to `1`.
 
-#### page_size
+#### page_size: `int`
 
 The number of sessions on each page. Defaults to `1024`.
 
-#### orderby
+#### orderby: `str`
 
 The field by which sessions should be sorted. Available options:
 
 - `"create_time"` (default)
 - `"update_time"`
 
-#### desc
+#### desc: `bool`
 
 Indicates whether the retrieved sessions should be sorted in descending order. Defaults to `True`.
 
-#### id
+#### id: `str`
 
 The ID of the chat session to retrieve. Defaults to `None`.
 
-#### name
+#### name: `str`
 
-The name of the chat to retrieve. Defaults to `None`.
+The name of the chat session to retrieve. Defaults to `None`.
 
 ### Returns
 
 ```python
 from ragflow import RAGFlow
 
-rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
-assistant = rag.list_chats(name="Miss R")
+rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
+assistant = rag_object.list_chats(name="Miss R")
 assistant = assistant[0]
 assistant.delete_sessions(ids=["id_1","id_2"])
+```
+
+---
+
+## Chat
+
+```python
+Session.ask(question: str, stream: bool = False) -> Optional[Message, iter[Message]]
+```
+
+Asks a question to start a conversation.
+
+### Parameters
+
+#### question: `str` *Required*
+
+The question to start an AI chat.
+
+#### stream: `str`
+
+Indicates whether to output responses in a streaming way:
+
+- `True`: Enable streaming.
+- `False`: (Default) Disable streaming.
+
+### Returns
+
+- A `Message` object containing the response to the question if `stream` is set to `False`
+- An iterator containing multiple `message` objects (`iter[Message]`) if `stream` is set to `True`
+
+The following shows the attributes of a `Message` object:
+
+#### id: `str`
+
+The auto-generated message ID.
+
+#### content: `str`
+
+The content of the message. Defaults to `"Hi! I am your assistant, can I help you?"`.
+
+#### reference: `list[Chunk]`
+
+A list of `Chunk` objects representing references to the message, each containing the following attributes:
+
+- `id` `str`  
+  The chunk ID.
+- `content` `str`  
+  The content of the chunk.
+- `image_id` `str`  
+  The ID of the snapshot of the chunk.
+- `document_id` `str`  
+  The ID of the referenced document.
+- `document_name` `str`  
+  The name of the referenced document.
+- `position` `list[str]`  
+  The location information of the chunk within the referenced document.
+- `knowledgebase_id` `str`  
+  The ID of the dataset to which the referenced document belongs.
+- `similarity` `float`
+  A composite similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity.
+- `vector_similarity` `float`  
+  A vector similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity between vector embeddings.
+- `term_similarity` `float`  
+  A keyword similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity between keywords.
+
+
+### Examples
+
+```python
+from ragflow import RAGFlow
+
+rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
+assistant = rag_object.list_chats(name="Miss R")
+assistant = assistant[0]
+session = assistant.create_session()    
+
+print("\n==================== Miss R =====================\n")
+print(assistant.get_prologue())
+
+while True:
+    question = input("\n==================== User =====================\n> ")
+    print("\n==================== Miss R =====================\n")
+    
+    cont = ""
+    for ans in session.ask(question, stream=True):
+        print(answer.content[len(cont):], end='', flush=True)
+        cont = answer.content
 ```