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AIModelsApi.md

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quantcdn.AIModelsApi

All URIs are relative to https://dashboard.quantcdn.io

Method HTTP request Description
get_ai_model GET /api/v3/organizations/{organisation}/ai/models/{modelId} Get AI Model Details
list_ai_models GET /api/v3/organizations/{organisation}/ai/models List available AI models for an organization

get_ai_model

GetAIModel200Response get_ai_model(organisation, model_id)

Get AI Model Details

Retrieves detailed information about a specific Bedrock model from the catalog. * * Features: * - Complete pricing breakdown (input/output per million tokens) * - Context window and output token limits * - Supported features (chat, vision, streaming, embeddings) * - Model availability and deprecation status * - Release date for version tracking * * Example Model IDs: * - amazon.nova-lite-v1:0 - Default multimodal model * - anthropic.claude-3-5-sonnet-20241022-v2:0 - Latest Claude * - amazon.titan-embed-text-v2:0 - Latest embeddings

Example

  • Bearer (JWT) Authentication (BearerAuth):
import quantcdn
from quantcdn.models.get_ai_model200_response import GetAIModel200Response
from quantcdn.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://dashboard.quantcdn.io
# See configuration.py for a list of all supported configuration parameters.
configuration = quantcdn.Configuration(
    host = "https://dashboard.quantcdn.io"
)

# 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 (JWT): BearerAuth
configuration = quantcdn.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with quantcdn.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = quantcdn.AIModelsApi(api_client)
    organisation = 'organisation_example' # str | The organisation ID
    model_id = 'amazon.nova-lite-v1:0' # str | The model identifier (e.g., amazon.nova-lite-v1:0)

    try:
        # Get AI Model Details
        api_response = api_instance.get_ai_model(organisation, model_id)
        print("The response of AIModelsApi->get_ai_model:\n")
        pprint(api_response)
    except Exception as e:
        print("Exception when calling AIModelsApi->get_ai_model: %s\n" % e)

Parameters

Name Type Description Notes
organisation str The organisation ID
model_id str The model identifier (e.g., amazon.nova-lite-v1:0)

Return type

GetAIModel200Response

Authorization

BearerAuth

HTTP request headers

  • Content-Type: Not defined
  • Accept: application/json

HTTP response details

Status code Description Response headers
200 Model details retrieved successfully -
404 Model not found in catalog -
500 Failed to fetch model details -

[Back to top] [Back to API list] [Back to Model list] [Back to README]

list_ai_models

ListAIModels200Response list_ai_models(organisation, feature=feature)

List available AI models for an organization

Example

  • Bearer (JWT) Authentication (BearerAuth):
import quantcdn
from quantcdn.models.list_ai_models200_response import ListAIModels200Response
from quantcdn.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://dashboard.quantcdn.io
# See configuration.py for a list of all supported configuration parameters.
configuration = quantcdn.Configuration(
    host = "https://dashboard.quantcdn.io"
)

# 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 (JWT): BearerAuth
configuration = quantcdn.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with quantcdn.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = quantcdn.AIModelsApi(api_client)
    organisation = 'organisation_example' # str | The organisation ID
    feature = all # str | Filter models by supported feature (optional) (default to all)

    try:
        # List available AI models for an organization
        api_response = api_instance.list_ai_models(organisation, feature=feature)
        print("The response of AIModelsApi->list_ai_models:\n")
        pprint(api_response)
    except Exception as e:
        print("Exception when calling AIModelsApi->list_ai_models: %s\n" % e)

Parameters

Name Type Description Notes
organisation str The organisation ID
feature str Filter models by supported feature [optional] [default to all]

Return type

ListAIModels200Response

Authorization

BearerAuth

HTTP request headers

  • Content-Type: Not defined
  • Accept: application/json

HTTP response details

Status code Description Response headers
200 List of available AI models -
500 Failed to fetch models -

[Back to top] [Back to API list] [Back to Model list] [Back to README]