DRAFT Python API Reference

THE API REFERENCES BELOW ARE STILL UNDER DEVELOPMENT.

:::tip NOTE Knowledge Base Management :::

Create knowledge base

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`

Base64 encoding of the avatar. Defaults to ""

description

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". ????????????

permission

Specify who can operate on the dataset. Defaults to "me".

document_count: `int`

The number of documents associated with the dataset. Defaults to 0.

chunk_count: `int`

The number of data chunks generated or processed by the created dataset. Defaults to 0.

parse_method, `str`

The method used by the dataset to parse and process data. Defaults to "naive".

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")
ds = rag_object.create_dataset(name="kb_1")

Delete knowledge bases

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

Deletes knowledge bases by name or ID.

Parameters

ids

The IDs of the knowledge bases to delete.

Returns

Success: No value is returned.
Failure: Exception

Examples

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

List knowledge bases

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 knowledge bases.

Parameters

page: `int`

The current page number to retrieve from the paginated results. Defaults to 1.

page_size: `int`

The number of records on 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.

id: `str`

The id of the dataset to be got. Defaults to None.

name: `str`

The name of the dataset to be got. Defaults to None.

Returns

Success: A list of DataSet objects representing the retrieved knowledge bases.
Failure: Exception.

Examples

List all knowledge bases

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

Retrieve a knowledge base by ID

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

Update knowledge base

DataSet.update(update_message: dict)

Updates the current knowledge base.

Parameters

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

"name": str The name of the knowledge base to update.
"tenant_id": str The "tenant_id you get after calling create_dataset(). ?????????????????????
"embedding_model": str The embedding model for generating vector embeddings.
- Ensure that "chunk_count" is 0 before updating "embedding_model".
"parser_method": str The default parsing method for the knowledge base.
- "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 = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag.list_datasets(name="kb_name")
dataset.update({"embedding_model":"BAAI/bge-zh-v1.5", "parse_method":"manual"})

:::tip API GROUPING File Management within Knowledge Base :::

Upload documents

DataSet.upload_documents(document_list: list[dict])

Updloads documents to the current knowledge base.

Parameters

document_list

A list of dictionaries representing the documents to upload, each containing the following keys:

"name": (Optional) File path to the document to upload.
Ensure that each file path has a suffix.
"blob": (Optional) The document to upload in binary format.

Returns

Success: No value is returned.
Failure: Exception

Examples

dataset = rag.create_dataset(name="kb_name")
dataset.upload_documents([{"name": "1.txt", "blob": "123"}])

Update document

Document.update(update_message:dict)

Updates configurations for the current document.

Parameters

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

only name, parser_config, and parser_method can be changed

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")
dataset=rag.list_datasets(id='id')
dataset=dataset[0]
doc = dataset.list_documents(id="wdfxb5t547d")
doc = doc[0]
doc.update([{"parser_method": "manual"}])

Download document

Document.download() -> bytes

Returns

Bytes of the document.

Examples

from ragflow import RAGFlow

rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
ds=rag.list_datasets(id="id")
ds=ds[0]
doc = ds.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]

Parameters

id

The id of the document to retrieve.

keywords

List documents whose name has the given keywords. Defaults to None.

offset

The beginning number of records for paging. Defaults to 0.

limit

Records number to return, -1 means all of them. Records number to return, -1 means all of them.

orderby

The field by which the records should be sorted. This specifies the attribute or column used to order the results.

desc

A boolean flag indicating whether the sorting should be in descending order.

Returns

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

A Document object contains the following attributes:

id Id of the retrieved document. Defaults to "".
thumbnail Thumbnail image of the retrieved document. Defaults to "".
knowledgebase_id Knowledge base ID related to the document. Defaults to "".
parser_method Method used to parse the document. Defaults to "".
parser_config: ParserConfig Configuration object for the parser. Defaults to None.
source_type: Source type of the document. Defaults to "".
type: Type or category of the document. Defaults to "".
created_by: str Creator of the document. Defaults to "".
name 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

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()
list_files=[{"name":filename1,"blob":blob}]
dataset.upload_documents(list_files)
for d in dataset.list_documents(keywords="rag", offset=0, limit=12):
    print(d)

Delete documents

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

Deletes specified documents or all documents from the current 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")
ds = rag.list_datasets(name="kb_1")
ds = ds[0]
ds.delete_documents(ids=["id_1","id_2"])

Parse and stop parsing document

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

Parameters

document_ids: `list[str]`

The IDs of the documents to parse.

Returns

Success: No value is returned.
Failure: Exception

Examples

#documents parse and cancel
rag = RAGFlow(API_KEY, HOST_ADDRESS)
ds = rag.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()}
]
ds.upload_documents(documents)
documents=ds.list_documents(keywords="test")
ids=[]
for document in documents:
    ids.append(document.id)
ds.async_parse_documents(ids)
print("Async bulk parsing initiated")
ds.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]

Parameters

keywords

List chunks whose name has the given keywords. Defaults to None

offset

The beginning number of records for paging. Defaults to 1

limit

Records number to return. Default: 30

id

The ID of the chunk to retrieve. Default: None

Returns

list[chunk]

Examples

from ragflow import RAGFlow

rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
ds = rag.list_datasets("123")
ds = ds[0]
ds.async_parse_documents(["wdfxb5t547d"])
for c in doc.list_chunks(keywords="rag", offset=0, limit=12):
    print(c)

Add chunk

Document.add_chunk(content:str) -> Chunk

Parameters

content: Required

The main text or information 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])

Parameters

chunk_ids:`list[str]`

A list of chunk_id.

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)

Parameters

update_message: Required

content: str Contains the main text or information of the chunk
important_keywords: list[str] List the key terms or phrases that are significant or central to the chunk's content
available: int Indicating the availability status, 0 means unavailable and 1 means available

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")
dataset = rag.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..."})

Retrieval

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[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.

rerank_id:`str`

ID of the rerank model. Defaults to None.

keyword:`bool`

Indicating whether keyword-based matching is enabled (True) or disabled (False).

highlight:`bool`

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

Returns

list[Chunk]

Examples

from ragflow import RAGFlow

rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
ds = rag.list_datasets(name="ragflow")
ds = ds[0]
name = 'ragflow_test.txt'
path = './test_data/ragflow_test.txt'
rag.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.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 = "assistant", 
    avatar: str = "path", 
    knowledgebases: list[DataSet] = [], 
    llm: Chat.LLM = None, 
    prompt: Chat.Prompt = None
) -> Chat

Creates a chat assistant.

Returns

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

The following shows the attributes of a Chat object:

name: str The name of the chat assistant. Defaults to "assistant".
avatar: str Base64 encoding of the avatar. Defaults to "".
knowledgebases: list[str] The associated knowledge bases. Defaults to ["kb1"].
llm: 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.
- 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: Prompt Instructions for the LLM to follow.
- "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 knowledge base 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..

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)

Update chat

Chat.update(update_message: dict)

Updates the current chat assistant.

Parameters

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

"name": str The name of the chat assistant to update.
"avatar": str Base64 encoding of the avatar. Defaults to ""
"knowledgebases": list[str] Knowledge bases 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 knowledge base 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

Deletes specified chat assistants.

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

Parameters

ids

IDs of the chat assistants to delete. If not specified, all chat assistants 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 records will be displayed. Defaults to 1.

page_size

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

order_by

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

desc

Indicates whether to sort the results 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 = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
for assistant in rag.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.

Parameters

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

"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 knowledge base 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 records will be displayed. Defaults to 1.

page_size

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

orderby

The field by which the records should be sorted. This specifies the attribute or column used to sort the results. Defaults to "create_time".

desc

Whether the sorting should be 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 = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
assistant = rag.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 specified sessions or all sessions associated with the current chat assistant.

Parameters

ids

IDs of the sessions to delete. 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"])

DRAFT Python API Reference

Create knowledge base

Parameters

name: str, Required

avatar: str

description

tenant_id: str

description: str

language: str

permission

document_count: int

chunk_count: int

parse_method, str

parser_config

Returns

Examples

Delete knowledge bases

Parameters

ids

Returns

Examples

List knowledge bases

Parameters

page: int

page_size: int

order_by: str

desc: bool

id: str

name: str

Returns

Examples

List all knowledge bases

Retrieve a knowledge base by ID

Update knowledge base

Parameters

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

Returns

Examples

Upload documents

Parameters

document_list

Returns

Examples

Update document

Parameters

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

Returns

Examples

Download document

Returns

Examples

List documents

Parameters

id

keywords

offset

limit

orderby

desc

Returns

Examples

Delete documents

Returns

Examples

Parse and stop parsing document

Parameters

document_ids: list[str]

Returns

Examples

List chunks

Parameters

keywords

offset

limit

id

Returns

Examples

Add chunk

Parameters

content: Required

name: `str`, Required

avatar: `str`

tenant_id: `str`

description: `str`

language: `str`

document_count: `int`

chunk_count: `int`

parse_method, `str`

page: `int`

page_size: `int`

order_by: `str`

desc: `bool`

id: `str`

name: `str`

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

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

document_ids: `list[str]`

important_keywords :`list[str]`

chunk_ids:`list[str]`

question: `str`, Required

datasets: `list[Dataset]`, Required

document: `list[Document]`

offset: `int`

limit: `int`

Similarity_threshold: `float`

similarity_threshold_weight: `float`

top_k: `int`

rerank_id:`str`

keyword:`bool`

highlight:`bool`

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

id: `string`

name: `string`

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

id: `str`

content: `str`

reference: `list[Chunk]`