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docspace_api_sdk.VectorizationApi

All URIs are relative to https://your-docspace.onlyoffice.com

Method HTTP request Description
ai_vectorization_start_task POST /api/2.0/ai/vectorization/tasks Start a vectorization task

ai_vectorization_start_task

AiVectorizationStartTask200Response ai_vectorization_start_task(ai_vectorization_start_task_request)

Queues the indexing of the portal files named in the body so their contents can be retrieved during a chat round. The body is proxied unchanged to the DocSpace AI service, which validates it and owns the job. Indexing is asynchronous and fire-and-forget: the answer acknowledges the request without carrying a job handle, so there is nothing to poll and progress is not reported here. The embedding provider used is the one in GET api/2.0/ai/config/vectorization, and changing that setting does not re-index anything already indexed - queue it again for that.

For more information, see api.onlyoffice.com.

Parameters

Name Type Description Notes
ai_vectorization_start_task_request AiVectorizationStartTaskRequest The files to index, proxied unchanged to the DocSpace AI service, which owns and validates the shape.

Return type

AiVectorizationStartTask200Response

Authorization

cookieAuth, bearerAuth

Example

import docspace_api_sdk
from docspace_api_sdk.models.ai_vectorization_start_task200_response import AiVectorizationStartTask200Response
from docspace_api_sdk.models.ai_vectorization_start_task_request import AiVectorizationStartTaskRequest
from docspace_api_sdk.rest import ApiException
from pprint import pprint

configuration = docspace_api_sdk.Configuration(
    host = "https://your-docspace.onlyoffice.com"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = docspace_api_sdk.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)
# Enter a context with an instance of the API client
with docspace_api_sdk.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = docspace_api_sdk.VectorizationApi(api_client)
    ai_vectorization_start_task_request = docspace_api_sdk.AiVectorizationStartTaskRequest() # AiVectorizationStartTaskRequest | The files to index, proxied unchanged to the DocSpace AI service, which owns and validates the shape.

    try:
        # Start a vectorization task
        api_response = api_instance.ai_vectorization_start_task(ai_vectorization_start_task_request)
        print("The response of VectorizationApi->ai_vectorization_start_task:\n")
        pprint(api_response)
    except Exception as e:
        print("Exception when calling VectorizationApi->ai_vectorization_start_task: %s\n" % e)

HTTP request headers

  • Content-Type: application/json
  • Accept: application/json

HTTP response details

Status code Description Response headers
200 Confirms the indexing was queued. It carries no job handle, so there is nothing to poll. -
401 Missing asc_auth_key cookie or Authorization header. -
403 AI is disabled for this portal, the caller is a guest, or the room named by entityId is one the caller cannot open. Relayed from the DocSpace AI service or the Files API. -
413 The request body is larger than 100 KB, the JSON parser's limit on this route. -
500 Unhandled failure. The reason is logged server-side and never echoed back. -

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