DRAFT Python API Reference

THE API REFERENCES BELOW ARE STILL UNDER DEVELOPMENT.

:::tip NOTE Dataset Management :::

Create dataset

RAGFlow.create_dataset(
    name: str,
    avatar: str = "",
    description: str = "",
    language: str = "English",
    permission: str = "me", 
    document_count: int = 0,
    chunk_count: int = 0,
    chunk_method: str = "naive",
    parser_config: DataSet.ParserConfig = None
) -> DataSet

Creates a 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`

Base64 encoding of the avatar. Defaults to ""

description: `str`

A brief description of the dataset to create. Defaults to "".

language: `str`

The language setting of the dataset to create. Available options:

"English" (Default)
"Chinese"

permission

Specifies who can access the dataset to create. You can set it only to "me" for now.

chunk_method, `str`

The chunking method of the dataset to create. Available options:

"naive": General (default)
"manual: Manual
"qa": Q&A
"table": Table
"paper": Paper
"book": Book
"laws": Laws
"presentation": Presentation
"picture": Picture
"one":One
"knowledge_graph": Knowledge Graph
"email": Email

parser_config

The parser configuration of the dataset. A ParserConfig object contains the following attributes:

chunk_token_count: Defaults to 128.
layout_recognize: Defaults to True.
delimiter: Defaults to "\n!?。；！？".
task_page_size: Defaults to 12.

Returns

Success: A dataset object.
Failure: Exception

Examples

from ragflow import RAGFlow

rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag_object.create_dataset(name="kb_1")

Delete datasets

RAGFlow.delete_datasets(ids: list[str] = None)

Deletes specified datasets or all datasets in the system.

Parameters

ids: `list[str]`

The IDs of the datasets to delete. Defaults to None. If not specified, all datasets in the system will be deleted.

Returns

Success: No value is returned.
Failure: Exception

Examples

rag_object.delete_datasets(ids=["id_1","id_2"])

List datasets

RAGFlow.list_datasets(
    page: int = 1, 
    page_size: int = 1024, 
    orderby: str = "create_time", 
    desc: bool = True,
    id: str = None,
    name: str = None
) -> list[DataSet]

Retrieves a list of datasets.

Parameters

page: `int`

Specifies the page on which the datasets will be displayed. Defaults to 1.

page_size: `int`

The number of datasets on each page. Defaults to 1024.

orderby: `str`

The field by which datasets should be sorted. Available options:

"create_time" (default)
"update_time"

desc: `bool`

Indicates whether the retrieved datasets should be sorted in descending order. Defaults to True.

id: `str`

The ID of the dataset to retrieve. Defaults to None.

name: `str`

The name of the dataset to retrieve. Defaults to None.

Returns

Success: A list of DataSet objects.
Failure: Exception.

Examples

List all datasets

for dataset in rag_object.list_datasets():
    print(dataset)

Retrieve a dataset by ID

dataset = rag_object.list_datasets(id = "id_1")
print(dataset[0])

Update dataset

DataSet.update(update_message: dict)

Updates configurations for the current dataset.

Parameters

update_message: `dict[str, str|int]`, Required

A dictionary representing the attributes to update, with the following keys:

"name": str The name of the dataset to update.
"embedding_model": str The embedding model name to update.
- Ensure that "chunk_count" is 0 before updating "embedding_model".
"chunk_method": str The chunking method for the dataset. Available options:
- "naive": General
- "manual: Manual
- "qa": Q&A
- "table": Table
- "paper": Paper
- "book": Book
- "laws": Laws
- "presentation": Presentation
- "picture": Picture
- "one":One
- "knowledge_graph": Knowledge Graph
- "email": Email

Returns

Success: No value is returned.
Failure: Exception

Examples

from ragflow import RAGFlow

rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag_object.list_datasets(name="kb_name")
dataset.update({"embedding_model":"BAAI/bge-zh-v1.5", "chunk_method":"manual"})

:::tip API GROUPING File Management within Dataset :::

Upload documents

DataSet.upload_documents(document_list: list[dict])

Uploads documents to the current dataset.

Parameters

document_list: `list[dict]`, Required

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.
"blob": (Optional) The binary content of the file to upload.

Returns

Success: No value is returned.
Failure: Exception

Examples

dataset = rag_object.create_dataset(name="kb_name")
dataset.upload_documents([{"display_name": "1.txt", "blob": "<BINARY_CONTENT_OF_THE_DOC>"}, {"display_name": "2.pdf", "blob": "<BINARY_CONTENT_OF_THE_DOC>"}])

Update document

Document.update(update_message:dict)

Updates configurations for the current document.

Parameters

update_message: `dict[str, str|dict[]]`, Required

A dictionary representing the attributes to update, with the following keys:

"name": str The name of the document to update.
"parser_config": dict[str, Any] The parsing configuration for the document:
- "chunk_token_count": Defaults to 128.
- "layout_recognize": Defaults to True.
- "delimiter": Defaults to '\n!?。；！？'.
- "task_page_size": Defaults to 12.
"chunk_method": str The parsing method to apply to the document.
- "naive": General
- "manual: Manual
- "qa": Q&A
- "table": Table
- "paper": Paper
- "book": Book
- "laws": Laws
- "presentation": Presentation
- "picture": Picture
- "one": One
- "knowledge_graph": Knowledge Graph
- "email": Email

Returns

Success: No value is returned.
Failure: Exception

Examples

from ragflow import RAGFlow

rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag_object.list_datasets(id='id')
dataset = dataset[0]
doc = dataset.list_documents(id="wdfxb5t547d")
doc = doc[0]
doc.update([{"parser_config": {"chunk_token_count": 256}}, {"chunk_method": "manual"}])

Download document

Document.download() -> bytes

Downloads the current document.

Returns

The downloaded document in bytes.

Examples

from ragflow import RAGFlow

rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag_object.list_datasets(id="id")
dataset = dataset[0]
doc = dataset.list_documents(id="wdfxb5t547d")
doc = doc[0]
open("~/ragflow.txt", "wb+").write(doc.download())
print(doc)

List documents

Dataset.list_documents(id:str =None, keywords: str=None, offset: int=0, limit:int = 1024,order_by:str = "create_time", desc: bool = True) -> list[Document]

Retrieves a list of documents from the current dataset.

Parameters

id: `str`

The ID of the document to retrieve. Defaults to None.

keywords: `str`

The keywords to match document titles. Defaults to None.

offset: `int`

The starting index for the documents to retrieve. Typically used in confunction with limit. Defaults to 0.

limit: `int`

The maximum number of documents to retrieve. Defaults to 1024. A value of -1 indicates that all documents should be returned.

orderby: `str`

The field by which documents should be sorted. Available options:

"create_time" (default)
"update_time"

desc: `bool`

Indicates whether the retrieved documents should be sorted in descending order. Defaults to True.

Returns

Success: A list of Document objects.
Failure: Exception.

A Document object contains the following attributes:

id: The document ID. Defaults to "".
name: The document name. Defaults to "".
thumbnail: The thumbnail image of the document. Defaults to None.
knowledgebase_id: The dataset ID associated with the document. Defaults to None.
chunk_method The chunk method name. Defaults to "". ?????naive??????
parser_config: ParserConfig Configuration object for the parser. Defaults to {"pages": [[1, 1000000]]}.
source_type: The source type of the document. Defaults to "local".
type: Type or category of the document???????????. Defaults to "".
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.
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.
process_duation: float Duration of the processing in seconds or minutes.??????? Defaults to 0.0.
run: str ?????????????????? Defaults to "0".
status: str ??????????????????? Defaults to "1".

Examples

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")

filename1 = "~/ragflow.txt"
blob = open(filename1 , "rb").read()
dataset.upload_documents([{"name":filename1,"blob":blob}])
for doc in dataset.list_documents(keywords="rag", offset=0, limit=12):
    print(doc)

Delete documents

DataSet.delete_documents(ids: list[str] = None)

Deletes documents by ID.

Parameters

ids: `list[list]`

The IDs of the documents to delete. Defaults to None. If not specified, all documents in the dataset will be deleted.

Returns

Success: No value is returned.
Failure: Exception

Examples

from ragflow import RAGFlow

rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag_object.list_datasets(name="kb_1")
dataset = dataset[0]
dataset.delete_documents(ids=["id_1","id_2"])

Parse documents

DataSet.async_parse_documents(document_ids:list[str]) -> None

Parameters

document_ids: `list[str]`, Required

The IDs of the documents to parse.

Returns

Success: No value is returned.
Failure: Exception

Examples

rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag_object.create_dataset(name="dataset_name")
documents = [
    {'name': 'test1.txt', 'blob': open('./test_data/test1.txt',"rb").read()},
    {'name': 'test2.txt', 'blob': open('./test_data/test2.txt',"rb").read()},
    {'name': 'test3.txt', 'blob': open('./test_data/test3.txt',"rb").read()}
]
dataset.upload_documents(documents)
documents = dataset.list_documents(keywords="test")
ids = []
for document in documents:
    ids.append(document.id)
dataset.async_parse_documents(ids)
print("Async bulk parsing initiated.")

Stop parsing documents

DataSet.async_cancel_parse_documents(document_ids:list[str])-> None

Parameters

document_ids: `list[str]`, Required

The IDs of the documents for which parsing should be stopped.

Returns

Success: No value is returned.
Failure: Exception

Examples

rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag_object.create_dataset(name="dataset_name")
documents = [
    {'name': 'test1.txt', 'blob': open('./test_data/test1.txt',"rb").read()},
    {'name': 'test2.txt', 'blob': open('./test_data/test2.txt',"rb").read()},
    {'name': 'test3.txt', 'blob': open('./test_data/test3.txt',"rb").read()}
]
dataset.upload_documents(documents)
documents = dataset.list_documents(keywords="test")
ids = []
for document in documents:
    ids.append(document.id)
dataset.async_parse_documents(ids)
print("Async bulk parsing initiated.")
dataset.async_cancel_parse_documents(ids)
print("Async bulk parsing cancelled.")

List chunks

Document.list_chunks(keywords: str = None, offset: int = 0, limit: int = -1, id : str = None) -> list[Chunk]

Retrieves a list of document chunks.

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

limit

The maximum number of chunks to retrieve. Default: 30

id

The ID of the chunk to retrieve. Default: None

Returns

Success: A list of Chunk objects.
Failure: Exception.

Examples

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)

Add chunk

Document.add_chunk(content:str) -> Chunk

Parameters

content: Required

The text content of the chunk.

important_keywords :`list[str]`

List the key terms or phrases that are significant or central to the chunk’s content.

Returns

chunk

Examples

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")

Delete chunk

Document.delete_chunks(chunk_ids: list[str])

Deletes chunks by ID.

Parameters

chunk_ids: `list[str]`

The IDs of the chunks to delete. Defaults to None. If not specified, all chunks of the current document will be deleted.

Returns

Success: No value is returned.
Failure: Exception

Examples

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")
doc = doc[0]
chunk = doc.add_chunk(content="xxxxxxx")
doc.delete_chunks(["id_1","id_2"])

Update chunk

Chunk.update(update_message: dict)

Updates content or configurations for the current chunk.

Parameters

update_message: `dict[str, str|list[str]|int]` Required

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.
"available": int The chunk’s availability status in the dataset. Value options:
- 0: Unavailable
- 1: Available

Returns

Success: No value is returned.
Failure: Exception

Examples

from ragflow import RAGFlow

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")
chunk.update({"content":"sdfx..."})

Retrieve chunks

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

The datasets to search from.

document: `list[str]`

The documents to search from. None means no limitation. Defaults to None.

offset: `int`

The starting index for the documents to retrieve. Defaults to 0??????.

limit: `int`

The maximum number of chunks to retrieve. Defaults to 6.

Similarity_threshold: `float`

The minimum similarity score. Defaults to 0.2.

similarity_threshold_weight: `float`

The weight of vector cosine similarity. Defaults to 0.3. If x represents the vector cosine similarity, then (1 - x) is the term similarity weight.

top_k: `int`

The number of chunks engaged in vector cosine computaton. Defaults to 1024.

rerank_id

The ID of the rerank model. Defaults to None.

keyword

Indicates whether keyword-based matching is enabled:

True: Enabled.
False: Disabled.

highlight:`bool`

Specifying whether to enable highlighting of matched terms in the results (True) or not (False).

Returns

Success: A list of Chunk objects representing the document chunks.
Failure: Exception

Examples

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]
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)
doc = doc[0]
ds.async_parse_documents([doc.id])
for c in rag_object.retrieve(question="What's ragflow?", 
             datasets=[ds.id], documents=[doc.id], 
             offset=1, limit=30, similarity_threshold=0.2, 
             vector_similarity_weight=0.3,
             top_k=1024
             ):
    print(c)

:::tip API GROUPING Chat Assistant Management :::

Create chat assistant

RAGFlow.create_chat(
    name: str, 
    avatar: str = "", 
    knowledgebases: list[str] = [], 
    llm: Chat.LLM = None, 
    prompt: Chat.Prompt = None
) -> Chat

Creates a chat assistant.

Parameters

The following shows the attributes of a Chat object:

name: Required

The name of the chat assistant. Defaults to "assistant".

avatar

Base64 encoding of the avatar. Defaults to "".

knowledgebases: `list[str]`

The IDs of the associated datasets. Defaults to [""].

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.

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 returned.
temperature, float
Controls the randomness of the model’s predictions. A lower temperature increases the model’s conficence in its responses; a higher temperature increases creativity and diversity. 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

Instructions for the LLM to follow. A Prompt object contains the following attributes:

"similarity_threshold": float A similarity score to evaluate distance between two lines of text. It’s weighted keywords similarity and vector cosine similarity. If the similarity between query and chunk is less than this threshold, the chunk will be filtered out. Defaults to 0.2.
"keywords_similarity_weight": float It’s weighted keywords similarity and vector cosine similarity or rerank score (0~1). Defaults to 0.7.
"top_n": int Not all the chunks whose similarity score is above the ‘similarity threshold’ will be feed to LLMs. LLM can only see these ‘Top N’ chunks. Defaults to 8.
"variables": list[dict[]] If you use dialog APIs, the variables might help you chat with your clients with different strategies. The variables are used to fill in the ‘System’ part in prompt in order to give LLM a hint. The ‘knowledge’ is a very special variable which will be filled-in with the retrieved chunks. All the variables in ‘System’ should be curly bracketed. Defaults to [{"key": "knowledge", "optional": True}]
"rerank_model": str If it is not specified, vector cosine similarity will be used; otherwise, reranking score will be used. Defaults to "".
"empty_response": str If nothing is retrieved in the dataset for the user’s question, this will be used as the response. To allow the LLM to improvise when nothing is retrieved, leave this blank. Defaults to None.
"opener": str The opening greeting for the user. Defaults to "Hi! I am your assistant, can I help you?".
"show_quote: bool Indicates whether the source of text should be displayed Defaults to True.
"prompt": str The prompt content. Defaults to You are an intelligent assistant. Please summarize the content of the dataset 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..

Returns

Success: A Chat object representing the chat assistant.
Failure: Exception

Examples

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)

Update chat

Chat.update(update_message: dict)

Updates configurations for the current chat assistant.

Parameters

update_message: `dict[str, str|list[str]|dict[]]`, Required

A dictionary representing the attributes to update, with the following keys:

"name": str The name of the chat assistant to update.
"avatar": str Base64 encoding of the avatar. Defaults to ""
"knowledgebases": 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.
- "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.
- "max_token", int This sets the maximum length of the model’s output, measured in the number of tokens (words or pieces of words).
"prompt" : Instructions for the LLM to follow.
- "similarity_threshold": float A score to evaluate distance between two lines of text. It’s weighted keywords similarity and vector cosine similarity. If the similarity between query and chunk is less than this threshold, the chunk will be filtered out. Defaults to 0.2.
- "keywords_similarity_weight": float It’s weighted keywords similarity and vector cosine similarity or rerank score (0~1). Defaults to 0.7.
- "top_n": int Not all the chunks whose similarity score is above the ‘similarity threshold’ will be feed to LLMs. LLM can only see these ‘Top N’ chunks. Defaults to 8.
- "variables": list[dict[]] If you use dialog APIs, the variables might help you chat with your clients with different strategies. The variables are used to fill in the ‘System’ part in prompt in order to give LLM a hint. The ‘knowledge’ is a very special variable which will be filled-in with the retrieved chunks. All the variables in ‘System’ should be curly bracketed. Defaults to [{"key": "knowledge", "optional": True}]
- "rerank_model": str If it is not specified, vector cosine similarity will be used; otherwise, reranking score will be used. Defaults to "".
- "empty_response": str If nothing is retrieved in the dataset for the user’s question, this will be used as the response. To allow the LLM to improvise when nothing is retrieved, leave this blank. Defaults to None.
- "opener": str The opening greeting for the user. Defaults to "Hi! I am your assistant, can I help you?".
- "show_quote: bool Indicates whether the source of text should be displayed Defaults to True.
- "prompt": str The prompt content. Defaults to 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..

Returns

Success: No value is returned.
Failure: Exception

Examples

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)
assistant.update({"name": "Stefan", "llm": {"temperature": 0.8}, "prompt": {"top_n": 8}})

Delete chats

RAGFlow.delete_chats(ids: list[str] = None)

Deletes chat assistants by ID.

Parameters

ids: `list[str]`

The IDs of the chat assistants to delete. Defaults to None. If not specified, all chat assistants in the system will be deleted.

Returns

Success: No value is returned.
Failure: Exception

Examples

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"])

List chats

RAGFlow.list_chats(
    page: int = 1, 
    page_size: int = 1024, 
    orderby: str = "create_time", 
    desc: bool = True,
    id: str = None,
    name: str = None
) -> list[Chat]

Retrieves a list of chat assistants.

Parameters

page

Specifies the page on which the chat assistants will be displayed. Defaults to 1.

page_size

The number of chat assistants on each page. Defaults to 1024.

order_by

The attribute by which the results are sorted. Defaults to "create_time".

desc

Indicates whether the retrieved chat assistants should be sorted in descending order. Defaults to True.

id: `string`

The ID of the chat to retrieve. Defaults to None.

name: `string`

The name of the chat to retrieve. Defaults to None.

Returns

Success: A list of Chat objects.
Failure: Exception.

Examples

from ragflow import RAGFlow

rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
for assistant in rag_object.list_chats():
    print(assistant)

:::tip API GROUPING Chat-session APIs :::

Create session

Chat.create_session(name: str = "New session") -> Session

Creates a chat session.

Parameters

name

The name of the chat session to create.

Returns

Success: A Session object containing the following attributes:
- id: str The auto-generated unique identifier of the created session.
- name: str The name of the created session.
- message: list[Message] The messages of the created session assistant. Default: [{"role": "assistant", "content": "Hi! I am your assistant，can I help you?"}]
- chat_id: str The ID of the associated chat assistant.
Failure: Exception

Examples

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()

Update session

Session.update(update_message: dict)

Updates the current session name.

Parameters

update_message: `dict[str, Any]`, Required

A dictionary representing the attributes to update, with only one key:

"name": str The name of the session to update.

Returns

Success: No value is returned.
Failure: Exception

Examples

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("session_name")
session.update({"name": "updated_name"})

Chat

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

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

Chat.list_sessions(
    page: int = 1, 
    page_size: int = 1024, 
    orderby: str = "create_time", 
    desc: bool = True,
    id: str = None,
    name: str = None
) -> list[Session]

Lists sessions associated with the current chat assistant.

Parameters

page

Specifies the page on which the sessions will be displayed. Defaults to 1.

page_size

The number of sessions on each page. Defaults to 1024.

orderby

The field by which sessions should be sorted. Available options:

"create_time" (default)
"update_time"

desc

Indicates whether the retrieved sessions should be sorted in descending order. Defaults to True.

id

The ID of the chat session to retrieve. Defaults to None.

name

The name of the chat to retrieve. Defaults to None.

Returns

Success: A list of Session objects associated with the current chat assistant.
Failure: Exception.

Examples

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]
for session in assistant.list_sessions():
    print(session)

Delete sessions

Chat.delete_sessions(ids:list[str] = None)

Deletes sessions by ID.

Parameters

ids: `list[str]`

The IDs of the sessions to delete. Defaults to None. If not specified, all sessions associated with the current chat assistant will be deleted.

Returns

Success: No value is returned.
Failure: Exception

Examples

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]
assistant.delete_sessions(ids=["id_1","id_2"])

python_api_reference.md 34KB Raw Blame History

DRAFT Python API Reference

Create dataset

Parameters

name: str, Required

avatar: str

description: str

language: str

permission

chunk_method, str

parser_config

Returns

Examples

Delete datasets

Parameters

ids: list[str]

Returns

Examples

List datasets

Parameters

page: int

page_size: int

orderby: str

desc: bool

id: str

name: str

Returns

Examples

List all datasets

Retrieve a dataset by ID

Update dataset

Parameters

update_message: dict[str, str|int], Required

Returns

Examples

Upload documents

Parameters

document_list: list[dict], Required

Returns

Examples

Update document

Parameters

update_message: dict[str, str|dict[]], Required

Returns

Examples

Download document

Returns

Examples

List documents

Parameters

id: str

keywords: str

offset: int

limit: int

orderby: str

desc: bool

Returns

Examples

Delete documents

Parameters

ids: list[list]

Returns

Examples

Parse documents

Parameters

document_ids: list[str], Required

Returns

Examples

Stop parsing documents

Parameters

document_ids: list[str], Required

Returns

Examples

List chunks

Parameters

keywords: str

offset: int

limit

id

Returns

python_api_reference.md 34KB

Raw Blame History

name: `str`, Required

avatar: `str`

description: `str`

language: `str`

chunk_method, `str`

ids: `list[str]`

page: `int`

page_size: `int`

orderby: `str`

desc: `bool`

id: `str`

name: `str`

update_message: `dict[str, str|int]`, Required

document_list: `list[dict]`, Required

update_message: `dict[str, str|dict[]]`, Required

id: `str`

keywords: `str`

offset: `int`

limit: `int`

orderby: `str`

desc: `bool`

ids: `list[list]`

document_ids: `list[str]`, Required

document_ids: `list[str]`, Required

keywords: `str`

offset: `int`

important_keywords :`list[str]`

chunk_ids: `list[str]`

update_message: `dict[str, str|list[str]|int]` Required

question: `str` Required

datasets: `list[str]`, Required

document: `list[str]`

offset: `int`

limit: `int`

Similarity_threshold: `float`

similarity_threshold_weight: `float`

top_k: `int`

highlight:`bool`

knowledgebases: `list[str]`

update_message: `dict[str, str|list[str]|dict[]]`, Required

ids: `list[str]`

id: `string`

name: `string`

update_message: `dict[str, Any]`, Required