tessl install tessl/pypi-azure-ai-documentintelligence@1.0.0Azure AI Document Intelligence client library for Python - a cloud service that uses machine learning to analyze text and structured data from documents
Agent Success
Agent success rate when using this tile
76%
Improvement
Agent success rate improvement when using this tile compared to baseline
1.19x
Baseline
Agent success rate without this tile
64%
{
"context": "Evaluates how well the solution uses azure-ai-documentintelligence to manage the document model lifecycle. Checks correct use of building from blob training data, composing existing models, inspecting catalogs/quotas, deleting models, and checking operation status.",
"type": "weighted_checklist",
"checklist": [
{
"name": "Admin client setup",
"description": "Initializes DocumentIntelligenceAdministrationClient with the provided endpoint and AzureKeyCredential (or compatible TokenCredential) from azure.ai.documentintelligence.",
"max_score": 10
},
{
"name": "Build request",
"description": "Creates a BuildDocumentModelRequest using AzureBlobContentSource and DocumentBuildMode, calls begin_build_document_model with the request, waits for poller.result(), and surfaces model_id/description from the returned DocumentModelDetails.",
"max_score": 20
},
{
"name": "Compose request",
"description": "Constructs a ComposeDocumentModelRequest with ordered ComponentDocumentModelDetails, invokes begin_compose_model, waits for the poller to complete, and uses the composed DocumentModelDetails to return model_id and component ordering.",
"max_score": 20
},
{
"name": "Model listing",
"description": "Uses list_models to retrieve DocumentModelDetails objects and maps them into summaries containing model_id and created_date_time for the workflow’s models.",
"max_score": 15
},
{
"name": "Resource quotas",
"description": "Calls get_resource_details and reads custom_document_models (and/or custom_neural_document_model_builds) count/limit to populate quota information in the response.",
"max_score": 10
},
{
"name": "Deletion cleanup",
"description": "Deletes models via delete_model using the supplied identifier and ensures subsequent list_models calls omit the deleted model.",
"max_score": 10
},
{
"name": "Operation lookup",
"description": "Resolves operation status by calling get_operation (or list_operations filtered by ID) to return status, last_updated_date_time, and percent_completed from DocumentIntelligenceOperationDetails.",
"max_score": 15
}
]
}