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Python API

A complete reference for AIBuilder's Python APIs. Before proceeding, please ensure you have your AIBuilder API key ready for authentication.

NOTE

Run the following command to download the Python SDK:

pip install AIBuilder-sdk

OpenAI-Compatible API#

Create chat completion#

Creates a model response for the given historical chat conversation via OpenAI's API.

Parameters#

model: str, Required#

The model used to generate the response. The server will parse this automatically, so you can set it to any value for now.

messages: list[object], Required#

A list of historical chat messages used to generate the response. This must contain at least one message with the user role.

stream: boolean#

Whether to receive the response as a stream. Set this to false explicitly if you prefer to receive the entire response in one go instead of as a stream.

Returns#

  • Success: Response message like OpenAI
  • Failure: Exception

Examples#

from openai import OpenAI
model = "model"
client = OpenAI(api_key="AIBuilder-api-key", base_url=f"http://AIBuilder_address/api/v1/chats_openai/<chat_id>")
completion = client.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Who are you?"},
],
stream=True
)
stream = True
if stream:
for chunk in completion:
print(chunk)
else:
print(completion.choices[0].message.content)

DATASET MANAGEMENT#

Create dataset#

AIBuilder.create_dataset(
name: str,
avatar: str = "",
description: str = "",
embedding_model: str = "BAAI/bge-large-zh-v1.5",
permission: str = "me",
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:

  • 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 "".

permission#

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.
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
    Ensure your LLM is properly configured on the Settings page before selecting this. Please also note that Knowledge Graph consumes a large number of Tokens!
  • "email": Email
parser_config#

The parser configuration of the dataset. A ParserConfig object's attributes vary based on the selected chunk_method:

  • chunk_method="naive":
    {"chunk_token_num":128,"delimiter":"\\n!?;。；！？","html4excel":False,"layout_recognize":True,"raptor":{"user_raptor":False}}.
  • chunk_method="qa":
    {"raptor": {"user_raptor": False}}
  • chunk_method="manuel":
    {"raptor": {"user_raptor": False}}
  • chunk_method="table":
    None
  • chunk_method="paper":
    {"raptor": {"user_raptor": False}}
  • chunk_method="book":
    {"raptor": {"user_raptor": False}}
  • chunk_method="laws":
    {"raptor": {"user_raptor": False}}
  • chunk_method="picture":
    None
  • chunk_method="presentation":
    {"raptor": {"user_raptor": False}}
  • chunk_method="one":
    None
  • chunk_method="knowledge-graph":
    {"chunk_token_num":128,"delimiter":"\\n!?;。；！？","entity_types":["organization","person","location","event","time"]}
  • chunk_method="email":
    None

Returns#

  • Success: A dataset object.
  • Failure: Exception

Examples#

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

Delete datasets#

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

Deletes datasets by ID.

Parameters#

ids: list[str], Required#

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

Returns#

  • Success: No value is returned.
  • Failure: Exception

Examples#

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

List datasets#

AIBuilder.list_datasets(
page: int = 1,
page_size: int = 30,
orderby: str = "create_time",
desc: bool = True,
id: str = None,
name: str = None
) -> list[DataSet]

Lists 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 30.

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 revised name of the dataset.
  • "embedding_model": str The updated embedding model name.
    • 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
    • "email": Email

Returns#

  • Success: No value is returned.
  • Failure: Exception

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(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"})

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:

  • "display_name": str The name of the document to update.
  • "meta_fields": dict[str, Any] The meta fields of the document.
  • "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
      Ensure your LLM is properly configured on the Settings page before selecting this. Please also note that Knowledge Graph consumes a large number of Tokens!
    • "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_token_num":128,"delimiter":"\\n!?;。；！？","html4excel":False,"layout_recognize":True,"raptor":{"user_raptor":False}}.
    • chunk_method="qa":
      {"raptor": {"user_raptor": False}}
    • chunk_method="manuel":
      {"raptor": {"user_raptor": False}}
    • chunk_method="table":
      None
    • chunk_method="paper":
      {"raptor": {"user_raptor": False}}
    • chunk_method="book":
      {"raptor": {"user_raptor": False}}
    • chunk_method="laws":
      {"raptor": {"user_raptor": False}}
    • chunk_method="presentation":
      {"raptor": {"user_raptor": False}}
    • chunk_method="picture":
      None
    • chunk_method="one":
      None
    • chunk_method="knowledge-graph":
      {"chunk_token_num":128,"delimiter":"\\n!?;。；！？","entity_types":["organization","person","location","event","time"]}
    • chunk_method="email":
      None

Returns#

  • Success: No value is returned.
  • Failure: Exception

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(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 AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(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("~/AIBuilder.txt", "wb+").write(doc.download())
print(doc)

List documents#

Dataset.list_documents(id:str =None, keywords: str=None, page: int=1, page_size:int = 30, order_by:str = "create_time", desc: bool = True) -> list[Document]

Lists documents in the current dataset.

Parameters#

id: str#

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

keywords: str#

The keywords used to match document titles. Defaults to None.

page: int#

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

page_size: int#

The maximum number of documents on each page. Defaults to 30.

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.
  • dataset_id: The dataset ID associated with the document. Defaults to None.
  • chunk_method The chunk method name. Defaults to "naive".
  • source_type: The source type of the document. Defaults to "local".
  • type: Type or category of the document. Defaults to "". Reserved for future use.
  • 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 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.
  • process_duation: float Duration of the processing in seconds. Defaults to 0.0.
  • run: str The document's processing status:
    • "UNSTART" (default)
    • "RUNNING"
    • "CANCEL"
    • "DONE"
    • "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_token_num":128,"delimiter":"\\n!?;。；！？","html4excel":False,"layout_recognize":True,"raptor":{"user_raptor":False}}.
    • chunk_method="qa":
      {"raptor": {"user_raptor": False}}
    • chunk_method="manuel":
      {"raptor": {"user_raptor": False}}
    • chunk_method="table":
      None
    • chunk_method="paper":
      {"raptor": {"user_raptor": False}}
    • chunk_method="book":
      {"raptor": {"user_raptor": False}}
    • chunk_method="laws":
      {"raptor": {"user_raptor": False}}
    • chunk_method="presentation":
      {"raptor": {"user_raptor": False}}
    • chunk_method="picure":
      None
    • chunk_method="one":
      None
    • chunk_method="knowledge-graph":
      {"chunk_token_num":128,"delimiter": "\\n!?;。；！？","entity_types":["organization","person","location","event","time"]}
    • chunk_method="email":
      None

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag_object.create_dataset(name="kb_1")
filename1 = "~/AIBuilder.txt"
blob = open(filename1 , "rb").read()
dataset.upload_documents([{"name":filename1,"blob":blob}])
for doc in dataset.list_documents(keywords="rag", page=0, page_size=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 it is not specified, all documents in the dataset will be deleted.

Returns#

  • Success: No value is returned.
  • Failure: Exception

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(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

Parses documents in the current dataset.

Parameters#

document_ids: list[str], Required#

The IDs of the documents to parse.

Returns#

  • Success: No value is returned.
  • Failure: Exception

Examples#

rag_object = AIBuilder(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag_object.create_dataset(name="dataset_name")
documents = [
{'display_name': 'test1.txt', 'blob': open('./test_data/test1.txt',"rb").read()},
{'display_name': 'test2.txt', 'blob': open('./test_data/test2.txt',"rb").read()},
{'display_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

Stops parsing specified documents.

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 = AIBuilder(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag_object.create_dataset(name="dataset_name")
documents = [
{'display_name': 'test1.txt', 'blob': open('./test_data/test1.txt',"rb").read()},
{'display_name': 'test2.txt', 'blob': open('./test_data/test2.txt',"rb").read()},
{'display_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.")

CHUNK MANAGEMENT WITHIN DATASET#

Add chunk#

Document.add_chunk(content:str, important_keywords:list[str] = []) -> Chunk

Adds a chunk to the current document.

Parameters#

content: str, Required#

The text content of the chunk.

important_keywords: list[str]#

The key terms or phrases to tag with the chunk.

Returns#

  • Success: A Chunk object.
  • Failure: Exception.

A Chunk object contains the following attributes:

  • id: str: The chunk ID.
  • content: str The text content of the chunk.
  • important_keywords: list[str] A list of key terms or phrases tagged 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.
  • dataset_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: bool The chunk's availability status in the dataset. Value options:
    • False: Unavailable
    • True: Available (default)

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
datasets = rag_object.list_datasets(id="123")
dataset = datasets[0]
doc = dataset.list_documents(id="wdfxb5t547d")
doc = doc[0]
chunk = doc.add_chunk(content="xxxxxxx")

List chunks#

Document.list_chunks(keywords: str = None, page: int = 1, page_size: int = 30, id : str = None) -> list[Chunk]

Lists chunks in the current document.

Parameters#

keywords: str#

The keywords used to match chunk content. Defaults to None

page: int#

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

page_size: int#

The maximum number of chunks on each page. Defaults to 30.

id: str#

The ID of the chunk to retrieve. Default: None

Returns#

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

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag_object.list_datasets("123")
dataset = dataset[0]
docs = dataset.list_documents(keywords="test", page=1, page_size=12)
for chunk in docs[0].list_chunks(keywords="rag", page=0, page_size=12):
print(chunk)

Delete chunks#

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 it is not specified, all chunks of the current document will be deleted.

Returns#

  • Success: No value is returned.
  • Failure: Exception

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(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"])

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 The text content of the chunk.
  • "important_keywords": list[str] A list of key terms or phrases to tag with the chunk.
  • "available": bool The chunk's availability status in the dataset. Value options:
    • False: Unavailable
    • True: Available (default)

Returns#

  • Success: No value is returned.
  • Failure: Exception

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(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#

AIBuilder.retrieve(question:str="", dataset_ids:list[str]=None, document_ids=list[str]=None, page:int=1, page_size: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]

Retrieves chunks from specified datasets.

Parameters#

question: str, Required#

The user query or query keywords. Defaults to "".

dataset_ids: list[str], Required#

The IDs of the datasets to search. Defaults to None. If you do not set this argument, ensure that you set document_ids.

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. If you do not set this argument, ensure that you set dataset_ids.

page: int#

The starting index for the documents to retrieve. Defaults to 1.

page_size: int#

The maximum number of chunks to retrieve. Defaults to 30.

Similarity_threshold: float#

The minimum similarity score. Defaults to 0.2.

vector_similarity_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 computation. Defaults to 1024.

rerank_id: str#

The ID of the rerank model. Defaults to None.

keyword: bool#

Indicates whether to enable keyword-based matching:

  • True: Enable keyword-based matching.
  • False: Disable keyword-based matching (default).
highlight: bool#

Specifies whether to enable highlighting of matched terms in the results:

  • True: Enable highlighting of matched terms.
  • False: Disable highlighting of matched terms (default).

Returns#

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

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag_object.list_datasets(name="AIBuilder")
dataset = dataset[0]
name = 'AIBuilder_test.txt'
path = './test_data/AIBuilder_test.txt'
documents =[{"display_name":"test_retrieve_chunks.txt","blob":open(path, "rb").read()}]
docs = dataset.upload_documents(documents)
doc = docs[0]
doc.add_chunk(content="This is a chunk addition test")
for c in rag_object.retrieve(dataset_ids=[dataset.id],document_ids=[doc.id]):
print(c)

CHAT ASSISTANT MANAGEMENT#

Create chat assistant#

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

Creates a chat assistant.

Parameters#

name: str, Required#

The name of the chat assistant.

avatar: str#

Base64 encoding of the avatar. Defaults to "".

dataset_ids: list[str]#

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

llm: Chat.LLM#

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
    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
    The maximum length of the model's output, measured in the number of tokens (words or pieces of words). Defaults to 512. If disabled, you lift the maximum token limit, allowing the model to determine the number of tokens in its responses.
prompt: Chat.Prompt#

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

  • similarity_threshold: float AIBuilder employs either a combination of weighted keyword similarity and weighted vector cosine similarity, or a combination of weighted keyword similarity and weighted reranking score during retrieval. If a similarity score falls below this threshold, the corresponding chunk will be excluded from the results. The default value is 0.2.
  • keywords_similarity_weight: float This argument sets the weight of keyword similarity in the hybrid similarity score with vector cosine similarity or reranking model similarity. By adjusting this weight, you can control the influence of keyword similarity in relation to other similarity measures. The default value is 0.7.
  • top_n: int This argument specifies the number of top chunks with similarity scores above the similarity_threshold that are fed to the LLM. The LLM will only access these 'top N' chunks. The default value is 8.
  • variables: list[dict[]] This argument lists the variables to use in the 'System' field of Chat Configurations. Note that:
    • knowledge is a reserved variable, which represents the retrieved chunks.
    • All the variables in 'System' should be curly bracketed.
    • The default value is [{"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 "".
  • top_k: int Refers to the process of reordering or selecting the top-k items from a list or set based on a specific ranking criterion. Default to 1024.
  • 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 found, 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.

Returns#

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

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(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", dataset_ids=dataset_ids)

Update chat assistant#

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 revised name of the chat assistant.
  • "avatar": str Base64 encoding of the avatar. Defaults to ""
  • "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.
    • "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 The maximum length of the model's output, measured in the number of tokens (words or pieces of words). Defaults to 512. If disabled, you lift the maximum token limit, allowing the model to determine the number of tokens in its responses.
  • "prompt" : Instructions for the LLM to follow.
    • "similarity_threshold": float AIBuilder employs either a combination of weighted keyword similarity and weighted vector cosine similarity, or a combination of weighted keyword similarity and weighted rerank score during retrieval. This argument sets the threshold for similarities between the user query and chunks. If a similarity score falls below this threshold, the corresponding chunk will be excluded from the results. The default value is 0.2.
    • "keywords_similarity_weight": float This argument sets the weight of keyword similarity in the hybrid similarity score with vector cosine similarity or reranking model similarity. By adjusting this weight, you can control the influence of keyword similarity in relation to other similarity measures. The default value is 0.7.
    • "top_n": int This argument specifies the number of top chunks with similarity scores above the similarity_threshold that are fed to the LLM. The LLM will only access these 'top N' chunks. The default value is 8.
    • "variables": list[dict[]] This argument lists the variables to use in the 'System' field of Chat Configurations. Note that:
      • knowledge is a reserved variable, which represents the retrieved chunks.
      • All the variables in 'System' should be curly bracketed.
      • The default value is [{"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.

Returns#

  • Success: No value is returned.
  • Failure: Exception

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
datasets = rag_object.list_datasets(name="kb_1")
dataset_id = datasets[0].id
assistant = rag_object.create_chat("Miss R", dataset_ids=[dataset_id])
assistant.update({"name": "Stefan", "llm": {"temperature": 0.8}, "prompt": {"top_n": 8}})

Delete chat assistants#

AIBuilder.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 it is empty or not specified, all chat assistants in the system will be deleted.

Returns#

  • Success: No value is returned.
  • Failure: Exception

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
rag_object.delete_chats(ids=["id_1","id_2"])

List chat assistants#

AIBuilder.list_chats(
page: int = 1,
page_size: int = 30,
orderby: str = "create_time",
desc: bool = True,
id: str = None,
name: str = None
) -> list[Chat]

Lists chat assistants.

Parameters#

page: int#

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

page_size: int#

The number of chat assistants on each page. Defaults to 30.

orderby: str#

The attribute by which the results are sorted. Available options:

  • "create_time" (default)
  • "update_time"
desc: bool#

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

id: str#

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

name: str#

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

Returns#

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

Examples#

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

SESSION MANAGEMENT#

Create session with chat assistant#

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

Creates a session with the current chat assistant.

Parameters#

name: str#

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 opening message of the created session. 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 AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(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 chat assistant's session#

Session.update(update_message: dict)

Updates the current session of the current chat assistant.

Parameters#

update_message: dict[str, Any], Required#

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

  • "name": str The revised name of the session.

Returns#

  • Success: No value is returned.
  • Failure: Exception

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(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"})

List chat assistant's sessions#

Chat.list_sessions(
page: int = 1,
page_size: int = 30,
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: int#

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

page_size: int#

The number of sessions on each page. Defaults to 30.

orderby: str#

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

  • "create_time" (default)
  • "update_time"
desc: bool#

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

id: str#

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

name: str#

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

Returns#

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

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(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 chat assistant's sessions#

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

Deletes sessions of the current chat assistant by ID.

Parameters#

ids: list[str]#

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

Returns#

  • Success: No value is returned.
  • Failure: Exception

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(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"])

Converse with chat assistant#

Session.ask(question: str = "", stream: bool = False, **kwargs) -> Optional[Message, iter[Message]]

Asks a specified chat assistant a question to start an AI-powered conversation.

NOTE

In streaming mode, not all responses include a reference, as this depends on the system's judgement.

Parameters#

question: str, Required#

The question to start an AI-powered conversation. Default to ""

stream: bool#

Indicates whether to output responses in a streaming way:

  • True: Enable streaming (default).
  • False: Disable streaming.
**kwargs#

The parameters in prompt(system).

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.
  • 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
    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.
  • dataset_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. It is the weighted sum of vector_similarity and term_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 AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(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("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)
cont = ans.content

Create session with agent#

Agent.create_session(**kwargs) -> Session

Creates a session with the current agent.

Parameters#

**kwargs#

The parameters in begin component.

Returns#

  • Success: A Session object containing the following attributes:
    • id: str The auto-generated unique identifier 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?"}]
    • agent_id: str The ID of the associated agent.
  • Failure: Exception

Examples#

from AIBuilder_sdk import AIBuilder, Agent
rag_object = AIBuilder(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()

Converse with agent#

Session.ask(question: str="", stream: bool = False) -> Optional[Message, iter[Message]]

Asks a specified agent a question to start an AI-powered conversation.

NOTE

In streaming mode, not all responses include a reference, as this depends on the system's judgement.

Parameters#

question: str#

The question to start an AI-powered conversation. Ifthe Begin component takes parameters, a question is not required.

stream: bool#

Indicates whether to output responses in a streaming way:

  • True: Enable streaming (default).
  • False: 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. Applicable only when the source of the chunk is an image, PPT, PPTX, or PDF file.
  • 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.
  • dataset_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. It is the weighted sum of vector_similarity and term_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 AIBuilder_sdk import AIBuilder, Agent
rag_object = AIBuilder(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()
print("\n===== Miss R ====\n")
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)
cont = ans.content

List agent sessions#

Agent.list_sessions(
page: int = 1,
page_size: int = 30,
orderby: str = "update_time",
desc: bool = True,
id: str = None
) -> List[Session]

Lists sessions associated with the current agent.

Parameters#

page: int#

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

page_size: int#

The number of sessions on each page. Defaults to 30.

orderby: str#

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

  • "create_time"
  • "update_time"(default)
desc: bool#

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

id: str#

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

Returns#

  • Success: A list of Session objects associated with the current agent.
  • Failure: Exception.

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(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]
sessons = agent.list_sessions()
for session in sessions:
print(session)

Delete agent's sessions#

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

Deletes sessions of a agent by ID.

Parameters#

ids: list[str]#

The IDs of the sessions to delete. Defaults to None. If it is not specified, all sessions associated with the agent will be deleted.

Returns#

  • Success: No value is returned.
  • Failure: Exception

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(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]
agent.delete_sessions(ids=["id_1","id_2"])

AGENT MANAGEMENT#

List agents#

AIBuilder.list_agents(
page: int = 1,
page_size: int = 30,
orderby: str = "create_time",
desc: bool = True,
id: str = None,
title: str = None
) -> List[Agent]

Lists agents.

Parameters#

page: int#

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

page_size: int#

The number of agents on each page. Defaults to 30.

orderby: str#

The attribute by which the results are sorted. Available options:

  • "create_time" (default)
  • "update_time"
desc: bool#

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

id: str#

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

name: str#

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

Returns#

  • Success: A list of Agent objects.
  • Failure: Exception.

Examples#

from AIBuilder_sdk import AIBuilder
rag_object = AIBuilder(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
for agent in rag_object.list_agents():
print(agent)
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