service_name selects the connection to that provider. Use this page when configuring model-specific settings or an institutional deployment such as Azure. For ordinary model selection, start with Language models; Model access explains Expected Parrot access and using your own provider keys.
After choosing access, inspect the available services and models, then the configuration you will use:
Model(model_name, service_name=provider) with identifiers selected from the catalog.
Reasoning parameters
These settings matter when your task needs a different generation budget or behavior—for example, a lengthy document analysis rather than a short category label. Set supported parameters onModel(...); they apply to the model calls in the job, rather than changing your survey questions.
A provider’s supported parameters depend on the model. For OpenAI reasoning models, reasoning_effort is forwarded for provider validation. The Responses adapter also accepts a reasoning dictionary; an explicitly supplied effort overrides the effort inside that dictionary. Check the adapter and provider documentation for the selected model before choosing values. Do not assume temperature or token-limit semantics are shared across models.
Azure deployments
The current Azure adapter readsAZURE_ENDPOINT_URL_AND_KEY as comma-separated endpoint:key pairs. Configure this credential securely without printing it. For Azure OpenAI, the endpoint includes the deployment path and its api-version query parameter. Select the azure:<deployment> identifier reported for that configuration and pass service_name="azure".
Deployment names and endpoint versions come from your Azure account. See the Azure adapter source for the format supported by your installed EDSL version.
