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AiManager

Ask question

Sends an AI request to supported LLMs and returns an answer specifically focused on the user's question given the provided context.

This operation is performed by calling function create_ai_ask.

See the endpoint docs at API Reference.

client.ai.create_ai_ask(
    CreateAiAskMode.SINGLE_ITEM_QA,
    "which direction sun rises",
    [
        AiItemAsk(
            id=file_to_ask.id,
            type=AiItemAskTypeField.FILE,
            content="Sun rises in the East",
        )
    ],
    ai_agent=ai_ask_agent_config,
)

Arguments

  • mode CreateAiAskMode
    • Box AI handles text documents with text representations up to 1MB in size, or a maximum of 25 files, whichever comes first. If the text file size exceeds 1MB, the first 1MB of text representation will be processed. Box AI handles image documents with a resolution of 1024 x 1024 pixels, with a maximum of 5 images or 5 pages for multi-page images. If the number of image or image pages exceeds 5, the first 5 images or pages will be processed. If you set mode parameter to single_item_qa, the items array can have one element only. Currently Box AI does not support multi-modal requests. If both images and text are sent Box AI will only process the text.
  • prompt str
    • The prompt provided by the client to be answered by the LLM. The prompt's length is limited to 10000 characters.
  • items List[AiItemAsk]
    • The items to be processed by the LLM, often files.
  • dialogue_history Optional[List[AiDialogueHistory]]
    • The history of prompts and answers previously passed to the LLM. This provides additional context to the LLM in generating the response.
  • include_citations Optional[bool]
    • A flag to indicate whether citations should be returned.

  • ai_agent Optional[Union[AiAgentAsk, AiAgentReference]]

  • extra_headers Optional[Dict[str, Optional[str]]]
    • Extra headers that will be included in the HTTP request.

Returns

This function returns a value of type Optional[AiResponseFull].

A successful response including the answer from the LLM.No content is available to answer the question. This is returned when the request item is a hub, but content in the hubs is not indexed. To ensure content in the hub is indexed, make sure Box AI for Hubs in the Admin Console was enabled before hub creation.

Generate text

Sends an AI request to supported Large Language Models (LLMs) and returns generated text based on the provided prompt.

This operation is performed by calling function create_ai_text_gen.

See the endpoint docs at API Reference.

client.ai.create_ai_text_gen(
    "Parapharse the document.s",
    [
        CreateAiTextGenItems(
            id=file_to_ask.id,
            type=CreateAiTextGenItemsTypeField.FILE,
            content="The Earth goes around the sun. Sun rises in the East in the morning.",
        )
    ],
    dialogue_history=[
        AiDialogueHistory(
            prompt="What does the earth go around?",
            answer="The sun",
            created_at=date_time_from_string("2021-01-01T00:00:00Z"),
        ),
        AiDialogueHistory(
            prompt="On Earth, where does the sun rise?",
            answer="East",
            created_at=date_time_from_string("2021-01-01T00:00:00Z"),
        ),
    ],
    ai_agent=ai_text_gen_agent_config,
)

Arguments

  • prompt str
    • The prompt provided by the client to be answered by the LLM. The prompt's length is limited to 10000 characters.
  • items List[CreateAiTextGenItems]
    • The items to be processed by the LLM, often files. The array can include exactly one element. Note: Box AI handles documents with text representations up to 1MB in size. If the file size exceeds 1MB, the first 1MB of text representation will be processed.
  • dialogue_history Optional[List[AiDialogueHistory]]
    • The history of prompts and answers previously passed to the LLM. This parameter provides the additional context to the LLM when generating the response.

  • ai_agent Optional[Union[AiAgentReference, AiAgentTextGen]]

  • extra_headers Optional[Dict[str, Optional[str]]]
    • Extra headers that will be included in the HTTP request.

Returns

This function returns a value of type AiResponse.

A successful response including the answer from the LLM.

Get AI agent default configuration

Get the AI agent default config

This operation is performed by calling function get_ai_agent_default_config.

See the endpoint docs at API Reference.

client.ai.get_ai_agent_default_config(GetAiAgentDefaultConfigMode.ASK, language="en-US")

Arguments

  • mode GetAiAgentDefaultConfigMode
    • The mode to filter the agent config to return.
  • language Optional[str]
    • The ISO language code to return the agent config for. If the language is not supported the default agent config is returned.
  • model Optional[str]
    • The model to return the default agent config for.
  • extra_headers Optional[Dict[str, Optional[str]]]
    • Extra headers that will be included in the HTTP request.

Returns

This function returns a value of type Union[AiAgentAsk, AiAgentTextGen, AiAgentExtract, AiAgentExtractStructured].

A successful response including the default agent configuration. This response can be one of the following four objects:

  • AI agent for questions
  • AI agent for text generation
  • AI agent for freeform metadata extraction
  • AI agent for structured metadata extraction. The response depends on the agent configuration requested in this endpoint.

Extract metadata (freeform)

Sends an AI request to supported Large Language Models (LLMs) and extracts metadata in form of key-value pairs. In this request, both the prompt and the output can be freeform. Metadata template setup before sending the request is not required.

This operation is performed by calling function create_ai_extract.

See the endpoint docs at API Reference.

client.ai.create_ai_extract(
    "firstName, lastName, location, yearOfBirth, company",
    [AiItemBase(id=file.id)],
    ai_agent=agent_ignoring_overriding_embeddings_model,
)

Arguments

  • prompt str
    • The prompt provided to a Large Language Model (LLM) in the request. The prompt can be up to 10000 characters long and it can be an XML or a JSON schema.
  • items List[AiItemBase]
    • The items that LLM will process. Currently, you can use files only.

  • ai_agent Optional[Union[AiAgentReference, AiAgentExtract]]

  • extra_headers Optional[Dict[str, Optional[str]]]
    • Extra headers that will be included in the HTTP request.

Returns

This function returns a value of type AiResponse.

A response including the answer from the LLM.

Extract metadata (structured)

Sends an AI request to supported Large Language Models (LLMs) and returns extracted metadata as a set of key-value pairs. For this request, you either need a metadata template or a list of fields you want to extract. Input is either a metadata template or a list of fields to ensure the structure. To learn more about creating templates, see Creating metadata templates in the Admin Console or use the metadata template API.

This operation is performed by calling function create_ai_extract_structured.

See the endpoint docs at API Reference.

client.ai.create_ai_extract_structured(
    [AiItemBase(id=file.id)],
    fields=[
        CreateAiExtractStructuredFields(
            key="firstName",
            display_name="First name",
            description="Person first name",
            prompt="What is the your first name?",
            type="string",
        ),
        CreateAiExtractStructuredFields(
            key="lastName",
            display_name="Last name",
            description="Person last name",
            prompt="What is the your last name?",
            type="string",
        ),
        CreateAiExtractStructuredFields(
            key="dateOfBirth",
            display_name="Birth date",
            description="Person date of birth",
            prompt="What is the date of your birth?",
            type="date",
        ),
        CreateAiExtractStructuredFields(
            key="age",
            display_name="Age",
            description="Person age",
            prompt="How old are you?",
            type="float",
        ),
        CreateAiExtractStructuredFields(
            key="hobby",
            display_name="Hobby",
            description="Person hobby",
            prompt="What is your hobby?",
            type="multiSelect",
            options=[
                CreateAiExtractStructuredFieldsOptionsField(key="guitar"),
                CreateAiExtractStructuredFieldsOptionsField(key="books"),
            ],
        ),
    ],
    ai_agent=agent_ignoring_overriding_embeddings_model,
)

Arguments

  • items List[AiItemBase]
    • The items to be processed by the LLM. Currently you can use files only.
  • metadata_template Optional[CreateAiExtractStructuredMetadataTemplate]
    • The metadata template containing the fields to extract. For your request to work, you must provide either metadata_template or fields, but not both.
  • fields Optional[List[CreateAiExtractStructuredFields]]
    • The fields to be extracted from the provided items. For your request to work, you must provide either metadata_template or fields, but not both.

  • ai_agent Optional[Union[AiAgentReference, AiAgentExtractStructured]]

  • extra_headers Optional[Dict[str, Optional[str]]]
    • Extra headers that will be included in the HTTP request.

Returns

This function returns a value of type AiExtractStructuredResponse.

A successful response including the answer from the LLM.