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revert white-space changes in docs (#12557)

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### What problem does this PR solve?

Trailing white-spaces in commit 6814ace1aa
got automatically trimmed by code editor may causes documentation
typesetting broken.

Mostly for double spaces for soft line breaks.  

### Type of change

- [x] Documentation Update
This commit is contained in:
Jimmy Ben Klieve
2026-01-13 09:41:02 +08:00
committed by GitHub
parent fd0a1fde6b
commit 867ec94258
71 changed files with 660 additions and 731 deletions
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docs/references/python_api_reference.md
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@ -5,7 +5,6 @@ sidebar_custom_props: {
categoryIcon: SiPython
}
---
# Python API
A complete reference for RAGFlow's Python APIs. Before proceeding, please ensure you [have your RAGFlow API key ready for authentication](https://ragflow.io/docs/dev/acquire_ragflow_api_key).
@ -111,7 +110,7 @@ RAGFlow.create_dataset(
avatar: Optional[str] = None,
description: Optional[str] = None,
embedding_model: Optional[str] = "BAAI/bge-large-zh-v1.5@BAAI",
permission: str = "me",
permission: str = "me",
chunk_method: str = "naive",
parser_config: DataSet.ParserConfig = None
) -> DataSet
@ -139,7 +138,7 @@ A brief description of the dataset to create. Defaults to `None`.
##### permission
Specifies who can access the dataset to create. Available options:
Specifies who can access the dataset to create. Available options:
- `"me"`: (Default) Only you can manage the dataset.
- `"team"`: All team members can manage the dataset.
@ -164,29 +163,29 @@ The chunking method of the dataset to create. Available options:
The parser configuration of the dataset. A `ParserConfig` object's attributes vary based on the selected `chunk_method`:
- `chunk_method`=`"naive"`:
- `chunk_method`=`"naive"`:
`{"chunk_token_num":512,"delimiter":"\\n","html4excel":False,"layout_recognize":True,"raptor":{"use_raptor":False}}`.
- `chunk_method`=`"qa"`:
- `chunk_method`=`"qa"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"manuel"`:
- `chunk_method`=`"manuel"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"table"`:
- `chunk_method`=`"table"`:
`None`
- `chunk_method`=`"paper"`:
- `chunk_method`=`"paper"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"book"`:
- `chunk_method`=`"book"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"laws"`:
- `chunk_method`=`"laws"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"picture"`:
- `chunk_method`=`"picture"`:
`None`
- `chunk_method`=`"presentation"`:
- `chunk_method`=`"presentation"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"one"`:
- `chunk_method`=`"one"`:
`None`
- `chunk_method`=`"knowledge-graph"`:
- `chunk_method`=`"knowledge-graph"`:
`{"chunk_token_num":128,"delimiter":"\\n","entity_types":["organization","person","location","event","time"]}`
- `chunk_method`=`"email"`:
- `chunk_method`=`"email"`:
`None`
#### Returns
@ -239,9 +238,9 @@ rag_object.delete_datasets(ids=["d94a8dc02c9711f0930f7fbc369eab6d","e94a8dc02c97
```python
RAGFlow.list_datasets(
page: int = 1,
page_size: int = 30,
orderby: str = "create_time",
page: int = 1,
page_size: int = 30,
orderby: str = "create_time",
desc: bool = True,
id: str = None,
name: str = None
@ -320,25 +319,25 @@ A dictionary representing the attributes to update, with the following keys:
- Basic Multilingual Plane (BMP) only
- Maximum 128 characters
- Case-insensitive
- `"avatar"`: (*Body parameter*), `string`
- `"avatar"`: (*Body parameter*), `string`
The updated base64 encoding of the avatar.
- Maximum 65535 characters
- `"embedding_model"`: (*Body parameter*), `string`
The updated embedding model name.
- `"embedding_model"`: (*Body parameter*), `string`
The updated embedding model name.
- Ensure that `"chunk_count"` is `0` before updating `"embedding_model"`.
- Maximum 255 characters
- Must follow `model_name@model_factory` format
- `"permission"`: (*Body parameter*), `string`
The updated dataset permission. Available options:
- `"permission"`: (*Body parameter*), `string`
The updated dataset permission. Available options:
- `"me"`: (Default) Only you can manage the dataset.
- `"team"`: All team members can manage the dataset.
- `"pagerank"`: (*Body parameter*), `int`
- `"pagerank"`: (*Body parameter*), `int`
refer to [Set page rank](https://ragflow.io/docs/dev/set_page_rank)
- Default: `0`
- Minimum: `0`
- Maximum: `100`
- `"chunk_method"`: (*Body parameter*), `enum<string>`
The chunking method for the dataset. Available options:
- `"chunk_method"`: (*Body parameter*), `enum<string>`
The chunking method for the dataset. Available options:
- `"naive"`: General (default)
- `"book"`: Book
- `"email"`: Email
@ -388,7 +387,7 @@ Uploads documents to the current dataset.
A list of dictionaries representing the documents to upload, each containing the following keys:
- `"display_name"`: (Optional) The file name to display in the dataset.
- `"display_name"`: (Optional) The file name to display in the dataset.
- `"blob"`: (Optional) The binary content of the file to upload.
#### Returns
@ -434,29 +433,29 @@ A dictionary representing the attributes to update, with the following keys:
- `"one"`: One
- `"email"`: Email
- `"parser_config"`: `dict[str, Any]` The parsing configuration for the document. Its attributes vary based on the selected `"chunk_method"`:
- `"chunk_method"`=`"naive"`:
- `"chunk_method"`=`"naive"`:
`{"chunk_token_num":128,"delimiter":"\\n","html4excel":False,"layout_recognize":True,"raptor":{"use_raptor":False}}`.
- `chunk_method`=`"qa"`:
- `chunk_method`=`"qa"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"manuel"`:
- `chunk_method`=`"manuel"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"table"`:
- `chunk_method`=`"table"`:
`None`
- `chunk_method`=`"paper"`:
- `chunk_method`=`"paper"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"book"`:
- `chunk_method`=`"book"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"laws"`:
- `chunk_method`=`"laws"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"presentation"`:
- `chunk_method`=`"presentation"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"picture"`:
- `chunk_method`=`"picture"`:
`None`
- `chunk_method`=`"one"`:
- `chunk_method`=`"one"`:
`None`
- `chunk_method`=`"knowledge-graph"`:
- `chunk_method`=`"knowledge-graph"`:
`{"chunk_token_num":128,"delimiter":"\\n","entity_types":["organization","person","location","event","time"]}`
- `chunk_method`=`"email"`:
- `chunk_method`=`"email"`:
`None`
#### Returns
@ -589,27 +588,27 @@ A `Document` object contains the following attributes:
- `"FAIL"`
- `status`: `str` Reserved for future use.
- `parser_config`: `ParserConfig` Configuration object for the parser. Its attributes vary based on the selected `chunk_method`:
- `chunk_method`=`"naive"`:
- `chunk_method`=`"naive"`:
`{"chunk_token_num":128,"delimiter":"\\n","html4excel":False,"layout_recognize":True,"raptor":{"use_raptor":False}}`.
- `chunk_method`=`"qa"`:
- `chunk_method`=`"qa"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"manuel"`:
- `chunk_method`=`"manuel"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"table"`:
- `chunk_method`=`"table"`:
`None`
- `chunk_method`=`"paper"`:
- `chunk_method`=`"paper"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"book"`:
- `chunk_method`=`"book"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"laws"`:
- `chunk_method`=`"laws"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"presentation"`:
- `chunk_method`=`"presentation"`:
`{"raptor": {"use_raptor": False}}`
- `chunk_method`=`"picure"`:
- `chunk_method`=`"picure"`:
`None`
- `chunk_method`=`"one"`:
- `chunk_method`=`"one"`:
`None`
- `chunk_method`=`"email"`:
- `chunk_method`=`"email"`:
`None`
#### Examples
@ -727,9 +726,9 @@ A list of tuples with detailed parsing results:
...
]
```
- `status`: The final parsing state (e.g., `success`, `failed`, `cancelled`).
- `chunk_count`: The number of content chunks created from the document.
- `token_count`: The total number of tokens processed.
- `status`: The final parsing state (e.g., `success`, `failed`, `cancelled`).
- `chunk_count`: The number of content chunks created from the document.
- `token_count`: The total number of tokens processed.
---
@ -989,11 +988,11 @@ The user query or query keywords. Defaults to `""`.
##### dataset_ids: `list[str]`, *Required*
The IDs of the datasets to search. Defaults to `None`.
The IDs of the datasets to search. Defaults to `None`.
##### document_ids: `list[str]`
The IDs of the documents to search. Defaults to `None`. You must ensure all selected documents use the same embedding model. Otherwise, an error will occur.
The IDs of the documents to search. Defaults to `None`. You must ensure all selected documents use the same embedding model. Otherwise, an error will occur.
##### page: `int`
@ -1026,7 +1025,7 @@ Indicates whether to enable keyword-based matching:
- `True`: Enable keyword-based matching.
- `False`: Disable keyword-based matching (default).
##### cross_languages: `list[string]`
##### cross_languages: `list[string]`
The languages that should be translated into, in order to achieve keywords retrievals in different languages.
@ -1067,10 +1066,10 @@ for c in rag_object.retrieve(dataset_ids=[dataset.id],document_ids=[doc.id]):
```python
RAGFlow.create_chat(
name: str,
avatar: str = "",
dataset_ids: list[str] = [],
llm: Chat.LLM = None,
name: str,
avatar: str = "",
dataset_ids: list[str] = [],
llm: Chat.LLM = None,
prompt: Chat.Prompt = None
) -> Chat
```
@ -1095,15 +1094,15 @@ The IDs of the associated datasets. Defaults to `[""]`.
The LLM settings for the chat assistant to create. Defaults to `None`. When the value is `None`, a dictionary with the following values will be generated as the default. An `LLM` object contains the following attributes:
- `model_name`: `str`
The chat model name. If it is `None`, the user's default chat model will be used.
- `temperature`: `float`
Controls the randomness of the model's predictions. A lower temperature results in more conservative responses, while a higher temperature yields more creative and diverse responses. 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`
- `model_name`: `str`
The chat model name. If it is `None`, the user's default chat model will be used.
- `temperature`: `float`
Controls the randomness of the model's predictions. A lower temperature results in more conservative responses, while a higher temperature yields more creative and diverse responses. 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`
- `frequency penalty`: `float`
Similar to the presence penalty, this reduces the model’s tendency to repeat the same words frequently. Defaults to `0.7`.
##### prompt: `Chat.Prompt`
@ -1163,8 +1162,8 @@ A dictionary representing the attributes to update, with the following keys:
- `"dataset_ids"`: `list[str]` The datasets to update.
- `"llm"`: `dict` The LLM settings:
- `"model_name"`, `str` The chat model name.
- `"temperature"`, `float` Controls the randomness of the model's predictions. A lower temperature results in more conservative responses, while a higher temperature yields more creative and diverse responses.
- `"top_p"`, `float` Also known as “nucleus sampling”, this parameter sets a threshold to select a smaller set of words to sample from.
- `"temperature"`, `float` Controls the randomness of the model's predictions. A lower temperature results in more conservative responses, while a higher temperature yields more creative and diverse responses.
- `"top_p"`, `float` Also known as “nucleus sampling”, this parameter sets a threshold to select a smaller set of words to sample from.
- `"presence_penalty"`, `float` This discourages the model from repeating the same information by penalizing words that have appeared in the conversation.
- `"frequency penalty"`, `float` Similar to presence penalty, this reduces the model’s tendency to repeat the same words.
- `"prompt"` : Instructions for the LLM to follow.
@ -1234,9 +1233,9 @@ rag_object.delete_chats(ids=["id_1","id_2"])
```python
RAGFlow.list_chats(
page: int = 1,
page_size: int = 30,
orderby: str = "create_time",
page: int = 1,
page_size: int = 30,
orderby: str = "create_time",
desc: bool = True,
id: str = None,
name: str = None
@ -1266,11 +1265,11 @@ The attribute by which the results are sorted. Available options:
Indicates whether the retrieved chat assistants should be sorted in descending order. Defaults to `True`.
##### id: `str`
##### id: `str`
The ID of the chat assistant to retrieve. Defaults to `None`.
##### name: `str`
##### name: `str`
The name of the chat assistant to retrieve. Defaults to `None`.
@ -1370,9 +1369,9 @@ session.update({"name": "updated_name"})
```python
Chat.list_sessions(
page: int = 1,
page_size: int = 30,
orderby: str = "create_time",
page: int = 1,
page_size: int = 30,
orderby: str = "create_time",
desc: bool = True,
id: str = None,
name: str = None
@ -1509,25 +1508,25 @@ The content of the message. Defaults to `"Hi! I am your assistant, can I help yo
A list of `Chunk` objects representing references to the message, each containing the following attributes:
- `id` `str`
- `id` `str`
The chunk ID.
- `content` `str`
- `content` `str`
The content of the chunk.
- `img_id` `str`
- `img_id` `str`
The ID of the snapshot of the chunk. Applicable only when the source of the chunk is an image, PPT, PPTX, or PDF file.
- `document_id` `str`
- `document_id` `str`
The ID of the referenced document.
- `document_name` `str`
- `document_name` `str`
The name of the referenced document.
- `position` `list[str]`
- `position` `list[str]`
The location information of the chunk within the referenced document.
- `dataset_id` `str`
- `dataset_id` `str`
The ID of the dataset to which the referenced document belongs.
- `similarity` `float`
- `similarity` `float`
A composite similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity. It is the weighted sum of `vector_similarity` and `term_similarity`.
- `vector_similarity` `float`
- `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`
- `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
@ -1538,7 +1537,7 @@ from ragflow_sdk 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()
session = assistant.create_session()
print("\n==================== Miss R =====================\n")
print("Hello. What can I do for you?")
@ -1546,7 +1545,7 @@ print("Hello. What can I do for you?")
while True:
question = input("\n==================== User =====================\n> ")
print("\n==================== Miss R =====================\n")
cont = ""
for ans in session.ask(question, stream=True):
print(ans.content[len(cont):], end='', flush=True)
@ -1634,25 +1633,25 @@ The content of the message. Defaults to `"Hi! I am your assistant, can I help yo
A list of `Chunk` objects representing references to the message, each containing the following attributes:
- `id` `str`
- `id` `str`
The chunk ID.
- `content` `str`
- `content` `str`
The content of the chunk.
- `image_id` `str`
- `image_id` `str`
The ID of the snapshot of the chunk. Applicable only when the source of the chunk is an image, PPT, PPTX, or PDF file.
- `document_id` `str`
- `document_id` `str`
The ID of the referenced document.
- `document_name` `str`
- `document_name` `str`
The name of the referenced document.
- `position` `list[str]`
- `position` `list[str]`
The location information of the chunk within the referenced document.
- `dataset_id` `str`
- `dataset_id` `str`
The ID of the dataset to which the referenced document belongs.
- `similarity` `float`
- `similarity` `float`
A composite similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity. It is the weighted sum of `vector_similarity` and `term_similarity`.
- `vector_similarity` `float`
- `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`
- `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
@ -1663,7 +1662,7 @@ from ragflow_sdk import RAGFlow, Agent
rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
AGENT_id = "AGENT_ID"
agent = rag_object.list_agents(id = AGENT_id)[0]
session = agent.create_session()
session = agent.create_session()
print("\n===== Miss R ====\n")
print("Hello. What can I do for you?")
@ -1671,7 +1670,7 @@ print("Hello. What can I do for you?")
while True:
question = input("\n===== User ====\n> ")
print("\n==== Miss R ====\n")
cont = ""
for ans in session.ask(question, stream=True):
print(ans.content[len(cont):], end='', flush=True)
@ -1684,9 +1683,9 @@ while True:
```python
Agent.list_sessions(
page: int = 1,
page_size: int = 30,
orderby: str = "update_time",
page: int = 1,
page_size: int = 30,
orderby: str = "update_time",
desc: bool = True,
id: str = None
) -> List[Session]
@ -1777,9 +1776,9 @@ agent.delete_sessions(ids=["id_1","id_2"])
```python
RAGFlow.list_agents(
page: int = 1,
page_size: int = 30,
orderby: str = "create_time",
page: int = 1,
page_size: int = 30,
orderby: str = "create_time",
desc: bool = True,
id: str = None,
title: str = None
@ -1809,11 +1808,11 @@ The attribute by which the results are sorted. Available options:
Indicates whether the retrieved agents should be sorted in descending order. Defaults to `True`.
##### id: `str`
##### id: `str`
The ID of the agent to retrieve. Defaults to `None`.
##### name: `str`
##### name: `str`
The name of the agent to retrieve. Defaults to `None`.