[TypeSpec-Clean] Update OpenAI.Authoring (#23969)
* Changes for OpenAI.Authoring * Removing extraneous file * Removing TypeSpec files for OpenAI.Authoring
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Mark Cowlishaw
GitHub
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import "@typespec/rest";
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import "@typespec/http";
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using TypeSpec.Rest;
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using TypeSpec.Http;
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namespace Azure.OpenAI;
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// TODO: enum value types not supported in csharp emitter
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enum TypeDiscriminatorKnownValues {
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List: "list",
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FineTune: "fine-tune",
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File: "file",
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FineTuneEvent: "fine-tune-event",
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Model: "model",
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Deployment: "deployment",
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}
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@knownValues(TypeDiscriminatorKnownValues)
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scalar TypeDiscriminator extends string;
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enum ScaleTypeKnownValues {
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Manual: "manual",
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Standard: "standard",
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}
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@knownValues(ScaleTypeKnownValues)
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scalar ScaleType extends string;
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enum ErrorCodeKnownValues {
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Conflict: "conflict",
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InvalidPayload: "invalidPayload",
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Forbidden: "forbidden",
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NotFound: "notFound",
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UnexpectedEntityState: "unexpectedEntityState",
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ItemDoesAlreadyExist: "itemDoesAlreadyExist",
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ServiceUnavailable: "serviceUnavailable",
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InternalFailure: "internalFailure",
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QuotaExceeded: "quotaExceeded",
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}
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@knownValues(ErrorCodeKnownValues)
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scalar ErrorCode extends string;
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enum InnerErrorCodeKnownValues {
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InvalidPayload: "invalidPayload",
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}
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@knownValues(InnerErrorCodeKnownValues)
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scalar InnerErrorCode extends string;
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enum PurposeKnownValues {
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FineTune: "fine-tune",
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FineTuneResults: "fine-tune-results",
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}
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@knownValues(PurposeKnownValues)
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scalar Purpose extends string;
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enum State {
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NotRunning: "notRunning",
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Running: "running",
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Succeeded: "succeeded",
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Canceled: "canceled",
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Failed: "failed",
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Deleted: "deleted",
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}
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enum LogLevel {
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Info: "info",
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Warning: "warning",
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Error: "error",
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}
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@doc("Created Response")
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model CreatedResponse {
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@doc("The status code")
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@statusCode
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statusCode: 201;
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@doc("Location of the newly created item")
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@header
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@format("uri")
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location: string;
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}
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import "@azure-tools/typespec-azure-core";
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import "./models/deployment.models.tsp";
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import "./serviceCustomizations.tsp";
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namespace Azure.OpenAI;
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using TypeSpec.Http;
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using Azure.Core;
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interface Deployments {
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#suppress "@azure-tools/cadl-azure-core/use-standard-operations" "This is an existing service, we have a non-conforming operation."
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@summary("Gets the list of deployments owned by the Azure OpenAI resource.")
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@doc("Gets the list of deployments owned by the Azure OpenAI resource.")
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@route("/deployments")
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@get
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// TODO: enum value types not supported in csharp emitter, change string literal to TypeDiscriminatorKnownValues enum when fixed
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ListDeployments is ServiceCustomizations.OpenAIListOperation<Deployment, "deployment">;
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@summary("""
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Creates a new deployment for the Azure OpenAI resource according to the given
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specification.
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""")
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@doc("""
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Creates a new deployment for the Azure OpenAI resource according to the given
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specification.
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""")
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CreateDeployment is ResourceCreateWithServiceProvidedName<
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Deployment,
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{
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response: Deployment
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}
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>;
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@summary("Gets details for a single deployment specified by the given deployment_id.")
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@doc("Gets details for a single deployment specified by the given deployment_id.")
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GetDeployment is ResourceRead<Deployment>;
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@summary("Updates the mutable details of the deployment with the given deployment_id.")
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@doc("Updates the mutable details of the deployment with the given deployment_id.")
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UpdateDeployment is ResourceCreateOrUpdate<Deployment>;
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@summary("Deletes the deployment specified by the given deployment_id.")
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@doc("Deletes the deployment specified by the given deployment_id.")
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DeleteDeployment is ResourceDelete<Deployment>;
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}
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import "@azure-tools/typespec-azure-core";
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import "@typespec/rest";
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import "@typespec/http";
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import "./models/file.models.tsp";
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import "./serviceCustomizations.tsp";
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namespace Azure.OpenAI;
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using TypeSpec.Rest;
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using TypeSpec.Http;
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using Azure.Core;
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interface Files
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{
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#suppress "@azure-tools/cadl-azure-core/use-standard-operations" "This is an existing service, we have a non-conforming operation."
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@summary("""
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Gets a list of all files owned by the Azure OpenAI resource.
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These include user uploaded content like files with purpose "fine-tune" for training or
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validation of fine-tunes models as well as files that are generated by the
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service such as "fine-tune-results" which contains various metrics for the
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corresponding fine-tune job.
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""")
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@doc("""
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Gets a list of all files owned by the Azure OpenAI resource.
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These include user uploaded content like files with purpose \"fine-tune\" for training or
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validation of fine-tunes models
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as well as files that are generated by the
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service such as \"fine-tune-results\" which contains various metrics for the
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corresponding fine-tune job.
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""")
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@route("/files")
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@get
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// TODO: enum value types not supported in csharp emitter, change string literal to TypeDiscriminatorKnownValues enum when fixed
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ListFiles is ServiceCustomizations.OpenAIListOperation<File, "file">;
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@summary("""
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Creates a new file entity by uploading data from a local machine. Uploaded
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files can, for example, be used for training or evaluating fine-tuned models.
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""")
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@doc("""
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Creates a new file entity by uploading data from a local machine. Uploaded
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files can, for example, be used for training or evaluating fine-tuned models.
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""")
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UploadFile is ResourceCreateWithServiceProvidedName<
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File,
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{
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response: File
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}
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>;
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@summary("""
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Gets details for a single file specified by the given file_id including status,
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size, purpose, etc.
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""")
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@doc("""
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Gets details for a single file specified by the given file_id including status,
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size, purpose, etc.
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""")
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GetFile is ResourceRead<File>;
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@summary("""
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Deletes the file with the given file_id.
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Deletion is also allowed if a file
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was used, e.g., as training file in a fine-tune job.
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""")
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@doc("""
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Deletes the file with the given file_id.
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Deletion is also allowed if a file
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was used, e.g., as training file in a fine-tune job.
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""")
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DeleteFile is ResourceDelete<File>;
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@summary("""
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Gets the content of the file specified by the given file_id.
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Files can be user
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uploaded content or generated by the service like result metrics of a fine-tune
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job.
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""")
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@doc("""
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Gets the content of the file specified by the given file_id.
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Files can be user
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uploaded content or generated by the service like result metrics of a fine-tune
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job.
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""")
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@get
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@action("content")
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@actionSeparator("/")
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GetFileContent is ResourceAction<File, {}, FileContent>;
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#suppress "@azure-tools/cadl-azure-core/use-standard-operations" "This is an existing service, we have a non-conforming operation."
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@summary("""
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Creates a new file entity by importing data from a provided url. Uploaded files
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can, for example, be used for training or evaluating fine-tuned models.
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""")
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@doc("""
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Creates a new file entity by importing data from a provided url. Uploaded files
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can, for example, be used for training or evaluating fine-tuned models.
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""")
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@route("/files/import")
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@post
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ImportFile is Foundations.Operation<
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{
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@doc("expected schema for the body of the completion post request")
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@body
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body: FileImport;
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},
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File & CreatedResponse
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>;
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}
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@ -1,108 +0,0 @@
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import "@azure-tools/typespec-azure-core";
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import "@typespec/rest";
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import "@typespec/http";
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import "./models/finetune.models.tsp";
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import "./serviceCustomizations.tsp";
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namespace Azure.OpenAI;
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using TypeSpec.Rest;
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using TypeSpec.Http;
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using Azure.Core;
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interface FineTunes {
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#suppress "@azure-tools/cadl-azure-core/use-standard-operations" "This is an existing service, we have a non-conforming operation."
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@summary("""
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Gets a list of all fine-tune jobs owned by the Azure OpenAI resource.
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The details that are returned for each fine-tune job contain besides its
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identifier the base model, training and validation files, hyper parameters,
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time stamps, status and events. Events are created when the job status
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changes, e.g. running or complete, and when results are uploaded.
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""")
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@doc("""
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Gets a list of all fine-tune jobs owned by the Azure OpenAI resource.
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The details that are returned for each fine-tune job contain besides its
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identifier the base model, training and validation files, hyper parameters,
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time stamps, status and events. Events are created when the job status
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changes, e.g. running or complete, and when results are uploaded.
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""")
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@route("/fine-tunes")
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@get
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// TODO: enum value types not supported in csharp emitter, change string literal to TypeDiscriminatorKnownValues enum when fixed
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ListFineTunes is ServiceCustomizations.OpenAIListOperation<FineTune, "fine-tune">;
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#suppress "@azure-tools/cadl-azure-core/use-standard-operations" "This is an existing service, we have a non-conforming operation."
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@summary("""
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Creates a job that fine-tunes a specified model from a given training
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file.
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Response includes details of the enqueued job including job status and
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hyper parameters.
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The name of the fine-tuned model is added to the response
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once complete.
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""")
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@doc("""
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Creates a job that fine-tunes a specified model from a given training
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file.
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Response includes details of the enqueued job including job status and
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hyper parameters.
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The name of the fine-tuned model is added to the response
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once complete.
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""")
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@route("/fine-tunes")
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@post
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CreateFineTune is Foundations.Operation<
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FineTuneCreation,
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FineTune & CreatedResponse
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>;
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@summary("""
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Gets details for a single fine-tune job specified by the given
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fine_tune_id.
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The details contain the base model, training and validation
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files, hyper parameters, time stamps, status and events.
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Events are created
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when the job status changes, e.g. running or complete, and when results are
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uploaded.
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""")
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@doc("""
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Gets details for a single fine-tune job specified by the given
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fine_tune_id.
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The details contain the base model, training and validation
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files, hyper parameters, time stamps, status and events.
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Events are created
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when the job status changes, e.g. running or complete, and when results are
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uploaded.
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""")
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GetFineTune is ResourceRead<FineTune>;
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@summary("Deletes the fine-tune job specified by the given fine_tune_id.")
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@doc("Deletes the fine-tune job specified by the given fine_tune_id.")
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DeleteFineTune is ResourceDelete<FineTune>;
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#suppress "@azure-tools/cadl-azure-core/use-standard-operations" "This is an existing service, we have a non-conforming operation."
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@summary("""
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List events for the fine-tune job specified by the given fine_tune_id.
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Events are created when the job status changes, e.g. running or
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complete, and when results are uploaded.
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""")
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@doc("""
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List events for the fine-tune job specified by the given fine_tune_id.
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Events are created when the job status changes, e.g. running or
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complete, and when results are uploaded.
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""")
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@Cadl.Rest.actionSeparator("/")
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@get
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ListFineTuneEvents is Azure.Core.ResourceAction<FineTune,
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{
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@doc("A flag indicating whether to stream events for the fine-tune job. If set to true,\nevents will be sent as data-only server-sent events as they become available. The stream will terminate with\na data: [DONE] message when the job is finished (succeeded, cancelled, or failed).\nIf set to false, only events generated so far will be returned..")
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@query stream: boolean
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}, ServiceCustomizations.OpenAiList<Event, TypeDiscriminator>>;
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// #suppress "@azure-tools/cadl-azure-core/use-standard-operations" "This is an existing service, we have a non-conforming operation."
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@summary("Cancels the processing of the fine-tune job specified by the given fine_tune_id.")
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@doc("Cancels the processing of the fine-tune job specified by the given fine_tune_id.")
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@post
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@action("cancel")
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@actionSeparator("/")
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CancelFineTune is ResourceAction<FineTune, {}, FineTune>;
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}
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@ -1,40 +0,0 @@
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import "@typespec/rest";
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import "@typespec/http";
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import "@typespec/versioning";
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import "./common.tsp";
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import "./deployment.tsp";
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import "./file.tsp";
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import "./finetune.tsp";
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import "./model.routes.tsp";
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using TypeSpec.Rest;
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using TypeSpec.Http;
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using TypeSpec.Versioning;
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@useAuth(
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ApiKeyAuth<ApiKeyLocation.header, "api-key"> |
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OAuth2Auth<[{
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type: OAuth2FlowType.implicit,
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authorizationUrl: "https://login.microsoftonline.com/common/oauth2/v2.0/authorize",
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scopes: ["https://cognitiveservices.azure.com/.default"]
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}]>)
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@service({title: "Azure OpenAI API"})
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@versioned(Azure.OpenAI.Versions)
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@server(
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"{endpoint}/openai",
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"API for managing and utilizing Azure Open AI endpoints",
|
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{
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@doc("""
|
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Supported Cognitive Services endpoints (protocol and hostname, for example:
|
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https://westus.api.cognitive.microsoft.com).
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""")
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endpoint: string,
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||||
}
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||||
)
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@doc("API for managing and utilizing Azure Open AI endpoints")
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namespace Azure.OpenAI;
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enum Versions {
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@useDependency(Azure.Core.Versions.v1_0_Preview_1)
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v2022_06_01_preview: "2022-06-01-preview",
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}
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|
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@ -1,35 +0,0 @@
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import "@azure-tools/typespec-azure-core";
|
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import "@typespec/rest";
|
||||
import "@typespec/http";
|
||||
import "./models/model.models.tsp";
|
||||
import "./serviceCustomizations.tsp";
|
||||
|
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namespace Azure.OpenAI;
|
||||
|
||||
using Cadl.Rest;
|
||||
using Cadl.Http;
|
||||
using Azure.Core;
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||||
|
||||
interface Models {
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#suppress "@azure-tools/typespec-azure-core/use-standard-operations" "This is an existing service, we have a non-conforming operation."
|
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@summary("""
|
||||
Gets a list of all models that are accessible by the Azure OpenAI
|
||||
resource.
|
||||
These include base models as well as all successfully completed
|
||||
fine-tuned models owned by the Azure OpenAI resource.
|
||||
""")
|
||||
@doc("""
|
||||
Gets a list of all models that are accessible by the Azure OpenAI
|
||||
resource.
|
||||
These include base models as well as all successfully completed
|
||||
fine-tuned models owned by the Azure OpenAI resource.
|
||||
""")
|
||||
@route("/models")
|
||||
@get
|
||||
// TODO: enum value types not supported in csharp emitter, change string literal to TypeDiscriminatorKnownValues enum when fixed
|
||||
ListModels is ServiceCustomizations.OpenAIListOperation<Model, "model">;
|
||||
|
||||
@summary("Gets details for the model specified by the given model_id.")
|
||||
@doc("Gets details for the model specified by the given model_id.")
|
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GetModel is ResourceRead<Model>;
|
||||
}
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|
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@ -1,58 +0,0 @@
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import "@typespec/rest";
|
||||
import "@typespec/http";
|
||||
|
||||
using TypeSpec.Rest;
|
||||
using TypeSpec.Rest.Resource;
|
||||
|
||||
namespace Azure.OpenAI;
|
||||
|
||||
@doc("""
|
||||
Deployments manage the reserved quota for Azure OpenAI models and make them
|
||||
available for inference requests.
|
||||
""")
|
||||
@resource("deployments")
|
||||
model Deployment {
|
||||
@doc("Defines the type of an object.")
|
||||
@visibility("read")
|
||||
object?: TypeDiscriminator;
|
||||
|
||||
@doc("The state of a job or item.")
|
||||
@visibility("read")
|
||||
status: State;
|
||||
|
||||
@doc("A timestamp when this job or item was created (in unix epochs).")
|
||||
@visibility("read")
|
||||
created_at: int32;
|
||||
|
||||
@doc("A timestamp when this job or item was modified last (in unix epochs).")
|
||||
@visibility("read")
|
||||
updated_at: int32;
|
||||
|
||||
@doc("The identifier of the deployment")
|
||||
@key("deploymentId")
|
||||
@visibility("read")
|
||||
id: string;
|
||||
|
||||
@doc("The OpenAI model to deploy. Can be a base model or a fine tune.")
|
||||
"model": string;
|
||||
|
||||
@doc("""
|
||||
The owner of this deployment. For Azure OpenAI only \"organization-owner\" is
|
||||
supported.
|
||||
""")
|
||||
@visibility("read")
|
||||
owner?: string;
|
||||
|
||||
@doc("""
|
||||
The scale settings of a deployment. It defines the modes for scaling and the
|
||||
reserved capacity.
|
||||
""")
|
||||
scale_settings: ScaleSettings;
|
||||
}
|
||||
|
||||
@doc("""
|
||||
The scale settings of a deployment. It defines the modes for scaling and the
|
||||
reserved capacity.
|
||||
""")
|
||||
@discriminator("scale_type")
|
||||
model ScaleSettings {}
|
||||
|
|
@ -1,92 +0,0 @@
|
|||
import "@typespec/rest";
|
||||
import "@typespec/http";
|
||||
import "@azure-tools/typespec-azure-core";
|
||||
|
||||
using TypeSpec.Rest;
|
||||
using Azure.Core;
|
||||
|
||||
namespace Azure.OpenAI;
|
||||
|
||||
@doc("""
|
||||
A file is a document usable for training and validation. It can also be a
|
||||
service generated document with result details.
|
||||
""")
|
||||
@resource("files")
|
||||
model File {
|
||||
@doc("Defines the type of an object.")
|
||||
@visibility("read")
|
||||
object?: TypeDiscriminator;
|
||||
|
||||
@doc("The state of a job or item.")
|
||||
@visibility("read")
|
||||
status?: State;
|
||||
|
||||
@doc("A timestamp when this job or item was created (in unix epochs).")
|
||||
@visibility("read")
|
||||
created_at?: int32;
|
||||
|
||||
@doc("A timestamp when this job or item was modified last (in unix epochs).")
|
||||
@visibility("read")
|
||||
updated_at?: int32;
|
||||
|
||||
@doc("The identity of this item.")
|
||||
@visibility("read")
|
||||
@key("fileId")
|
||||
id: string;
|
||||
|
||||
@doc("""
|
||||
The size of this file when available (can be null). File sizes larger than
|
||||
2^53-1 are not supported to ensure compatibility
|
||||
with JavaScript integers.
|
||||
""")
|
||||
@visibility("read")
|
||||
bytes?: int32;
|
||||
|
||||
@doc("""
|
||||
The intended purpose of the uploaded documents. Use \"fine-tune\" for
|
||||
fine-tuning. This allows us to validate the format of the uploaded file.
|
||||
""")
|
||||
purpose: Purpose;
|
||||
|
||||
@doc("The name of the file.")
|
||||
filename: string;
|
||||
}
|
||||
|
||||
@doc("""
|
||||
Defines a document to import from an external content url to be usable with
|
||||
Azure OpenAI.
|
||||
""")
|
||||
model FileImport {
|
||||
@doc("""
|
||||
The intended purpose of the uploaded documents. Use \"fine-tune\" for
|
||||
fine-tuning. This allows us to validate the format of the uploaded file.
|
||||
""")
|
||||
purpose: Purpose;
|
||||
|
||||
@doc("""
|
||||
The name of the [JSON Lines](https://jsonlines.readthedocs.io/en/latest/) file
|
||||
to be uploaded.
|
||||
If the `purpose` is set to \"fine-tune\", each line is a JSON
|
||||
record with \"prompt\" and \"completion\" fields representing your training
|
||||
examples.
|
||||
""")
|
||||
filename: string;
|
||||
|
||||
@doc("""
|
||||
The url to download the document from (can be SAS url of a blob or any other
|
||||
external url accessible with a GET request).
|
||||
""")
|
||||
content_url: string;
|
||||
}
|
||||
|
||||
@doc("content of uploaded file")
|
||||
model FileContent {
|
||||
@doc("""
|
||||
The intended purpose of the uploaded documents. Use \"fine-tune\" for
|
||||
fine-tuning. This allows us to validate the format of the uploaded file.
|
||||
""")
|
||||
purpose: Purpose;
|
||||
|
||||
@doc("Gets or sets the file to upload into Azure OpenAI.")
|
||||
file: bytes;
|
||||
}
|
||||
|
|
@ -1,205 +0,0 @@
|
|||
import "@typespec/rest";
|
||||
import "@typespec/http";
|
||||
import "@azure-tools/typespec-azure-core";
|
||||
|
||||
using TypeSpec.Http;
|
||||
using TypeSpec.Rest;
|
||||
using Azure.Core;
|
||||
|
||||
namespace Azure.OpenAI;
|
||||
|
||||
@doc("Fine tuning is a job to tailor a model to specific training data.")
|
||||
@resource("fine-tunes")
|
||||
model FineTune {
|
||||
@doc("Defines the type of an object.")
|
||||
@visibility("read")
|
||||
object?: TypeDiscriminator;
|
||||
|
||||
@doc("The state of a job or item.")
|
||||
@visibility("read")
|
||||
status?: State;
|
||||
|
||||
@doc("A timestamp when this job or item was created (in unix epochs).")
|
||||
@visibility("read")
|
||||
created_at?: int32;
|
||||
|
||||
@doc("A timestamp when this job or item was modified last (in unix epochs).")
|
||||
@visibility("read")
|
||||
updated_at?: int32;
|
||||
|
||||
@doc("The identity of this item.")
|
||||
@visibility("read")
|
||||
@key("fineTuneId")
|
||||
id: string;
|
||||
|
||||
@doc("The identifier of the base model used for the fine-tune.")
|
||||
"model": string;
|
||||
|
||||
@doc("""
|
||||
The identifier of the resulting fine tuned model. This property is only
|
||||
populated for successfully completed fine-tune runs.
|
||||
Use this identifier to
|
||||
create a deployment for inferencing.
|
||||
""")
|
||||
@visibility("read")
|
||||
fine_tuned_model?: string;
|
||||
|
||||
@doc("The files that are used for training the fine tuned model.")
|
||||
training_files: File[];
|
||||
|
||||
@doc("The files that are used to evaluate the fine tuned model during training.")
|
||||
validation_files?: File[];
|
||||
|
||||
@doc("""
|
||||
The result files containing training and evaluation metrics in csv format.
|
||||
The
|
||||
file is only available for successfully completed fine-tune runs.
|
||||
""")
|
||||
@visibility("read")
|
||||
result_files?: File[];
|
||||
|
||||
@doc("""
|
||||
The events that show the progress of the fine-tune run including queued,
|
||||
running and completed.
|
||||
""")
|
||||
@visibility("read")
|
||||
events?: Event[];
|
||||
|
||||
@doc("""
|
||||
The organisation id of this fine tune job. Unused on Azure OpenAI;
|
||||
compatibility for OpenAI only.
|
||||
""")
|
||||
@visibility("read")
|
||||
organisation_id?: string;
|
||||
|
||||
@doc("""
|
||||
The user id of this fine tune job. Unused on Azure OpenAI; compatibility for
|
||||
OpenAI only.
|
||||
""")
|
||||
@visibility("read")
|
||||
user_id?: string;
|
||||
|
||||
@doc("The hyper parameter settings used in a fine tune job.")
|
||||
hyperparams?: HyperParameters;
|
||||
}
|
||||
|
||||
@doc("Event")
|
||||
model Event {
|
||||
@doc("Defines the type of an object.")
|
||||
@visibility("read")
|
||||
object?: TypeDiscriminator;
|
||||
|
||||
@doc("A timestamp when this event was created (in unix epochs).")
|
||||
@visibility("read")
|
||||
created_at?: int32;
|
||||
|
||||
@doc("The verbosity level of an event.")
|
||||
@visibility("read")
|
||||
level?: LogLevel;
|
||||
|
||||
@doc("""
|
||||
The message describing the event. This can be a change of state, e.g.,
|
||||
enqueued, started, failed or completed, or other events like uploaded results.
|
||||
""")
|
||||
@visibility("read")
|
||||
message?: string;
|
||||
}
|
||||
|
||||
@doc("The hyper parameter settings used in a fine tune job.")
|
||||
model HyperParameters {
|
||||
@doc("""
|
||||
The batch size to use for training. The batch size is the number of training
|
||||
examples used to train a single forward and backward pass.
|
||||
In general, we've
|
||||
found that larger batch sizes tend to work better for larger datasets.
|
||||
The
|
||||
default value as well as the maximum value for this property are specific to a
|
||||
base model.
|
||||
""")
|
||||
batch_size?: int32;
|
||||
|
||||
@doc("""
|
||||
The learning rate multiplier to use for training. The fine-tuning learning rate
|
||||
is the original learning rate used for pre-training multiplied by this
|
||||
value.
|
||||
Larger learning rates tend to perform better with larger batch
|
||||
sizes.
|
||||
We recommend experimenting with values in the range 0.02 to 0.2 to see
|
||||
what produces the best results.
|
||||
""")
|
||||
learning_rate_multiplier?: float32;
|
||||
|
||||
@doc("""
|
||||
The number of epochs to train the model for. An epoch refers to one full cycle
|
||||
through the training dataset.
|
||||
""")
|
||||
n_epochs?: int32;
|
||||
|
||||
@doc("""
|
||||
The weight to use for loss on the prompt tokens. This controls how much the
|
||||
model tries to learn to generate the prompt
|
||||
(as compared to the completion
|
||||
which always has a weight of 1.0), and can add a stabilizing effect to training
|
||||
when completions are short.
|
||||
If prompts are extremely long (relative to
|
||||
completions), it may make sense to reduce this weight so as to avoid
|
||||
over-prioritizing learning the prompt.
|
||||
""")
|
||||
prompt_loss_weight?: float32;
|
||||
|
||||
@doc("""
|
||||
A value indicating whether to compute classification metrics.
|
||||
If set, we
|
||||
calculate classification-specific metrics such as accuracy and F-1 score using
|
||||
the validation set at the end of every epoch.
|
||||
These metrics can be viewed in
|
||||
the results file. In order to compute classification metrics, you must provide
|
||||
a validation_file.Additionally,
|
||||
you must specify classification_n_classes for
|
||||
multiclass classification or classification_positive_class for binary
|
||||
classification.
|
||||
""")
|
||||
compute_classification_metrics?: boolean;
|
||||
|
||||
@doc("""
|
||||
The number of classes in a classification task.
|
||||
This parameter is required for
|
||||
multiclass classification.
|
||||
""")
|
||||
classification_n_classes?: int32;
|
||||
|
||||
@doc("""
|
||||
The positive class in binary classification.
|
||||
This parameter is needed to
|
||||
generate precision, recall, and F1 metrics when doing binary classification.
|
||||
""")
|
||||
classification_positive_class?: string;
|
||||
|
||||
@doc("""
|
||||
The classification beta values. If this is provided, we calculate F-beta scores
|
||||
at the specified beta values.
|
||||
The F-beta score is a generalization of F-1
|
||||
score. This is only used for binary classification.
|
||||
With a beta of 1 (i.e.the
|
||||
F-1 score), precision and recall are given the same weight.
|
||||
A larger beta
|
||||
score puts more weight on recall and less on precision. A smaller beta score
|
||||
puts more weight on precision and less on recall.
|
||||
""")
|
||||
classification_betas?: float32[];
|
||||
}
|
||||
|
||||
@doc("Defines the values of a fine tune job.")
|
||||
model FineTuneCreation {
|
||||
@doc("The identifier of the base model used for this fine-tune.")
|
||||
"model": string;
|
||||
|
||||
@doc("The file that is used for training this fine tuned model.")
|
||||
training_file: string;
|
||||
|
||||
@doc("The file that is used to evaluate the fine tuned model during training.")
|
||||
validation_file?: string;
|
||||
|
||||
@doc("The hyper parameter settings used in a fine tune job.")
|
||||
hyperparams?: HyperParameters;
|
||||
}
|
||||
|
|
@ -1,114 +0,0 @@
|
|||
import "@typespec/rest";
|
||||
import "@typespec/http";
|
||||
import "@azure-tools/typespec-azure-core";
|
||||
|
||||
using TypeSpec.Http;
|
||||
using TypeSpec.Rest;
|
||||
using Azure.Core;
|
||||
|
||||
namespace Azure.OpenAI;
|
||||
|
||||
@doc("A model is either a base model or the result of a successful fine tune job.")
|
||||
@resource("models")
|
||||
model Model {
|
||||
@doc("Defines the type of an object.")
|
||||
@visibility("read")
|
||||
object?: TypeDiscriminator;
|
||||
|
||||
@doc("The state of a job or item.")
|
||||
@visibility("read")
|
||||
status?: State;
|
||||
|
||||
@doc("A timestamp when this job or item was created (in unix epochs).")
|
||||
@visibility("read")
|
||||
created_at?: int32;
|
||||
|
||||
@doc("A timestamp when this job or item was modified last (in unix epochs).")
|
||||
@visibility("read")
|
||||
updated_at?: int32;
|
||||
|
||||
@doc("The identity of this item.")
|
||||
@visibility("read")
|
||||
@key("model_id")
|
||||
id: string;
|
||||
|
||||
@doc("The base model ID if this is a fine tune model; otherwise `null`.")
|
||||
@visibility("read")
|
||||
"model"?: string;
|
||||
|
||||
@doc("The fine tune job ID if this is a fine tune model; otherwise `null`.")
|
||||
@visibility("read")
|
||||
fine_tune?: string;
|
||||
|
||||
@doc("The capabilities of a base or fine tune model.")
|
||||
capabilities?: Capabilities;
|
||||
|
||||
@doc("""
|
||||
Defines the dates of deprecation for the different use cases of a
|
||||
model.
|
||||
Usually base models support 1 year of fine tuning after creation.
|
||||
Inference is typically supported 2 years after creation of base or
|
||||
fine tuned
|
||||
models. The exact dates are specified in the properties.
|
||||
""")
|
||||
deprecation?: Deprecation;
|
||||
}
|
||||
|
||||
@doc("The capabilities of a base or fine tune model.")
|
||||
model Capabilities {
|
||||
@doc("A value indicating whether a model can be used for fine tuning.")
|
||||
@visibility("read")
|
||||
fine_tune?: boolean;
|
||||
|
||||
@doc("A value indicating whether a model can be deployed.")
|
||||
@visibility("read")
|
||||
inference?: boolean;
|
||||
|
||||
@doc("A value indicating whether a model supports completion.")
|
||||
@visibility("read")
|
||||
completion?: boolean;
|
||||
|
||||
@doc("A value indicating whether a model supports embeddings.")
|
||||
@visibility("read")
|
||||
embeddings?: boolean;
|
||||
|
||||
@doc("The supported scale types for deployments of this model.")
|
||||
@visibility("read")
|
||||
scale_types?: ScaleType[];
|
||||
}
|
||||
|
||||
@doc("""
|
||||
Defines the dates of deprecation for the different use cases of a
|
||||
model.
|
||||
Usually base models support 1 year of fine tuning after creation.
|
||||
Inference is typically supported 2 years after creation of base or
|
||||
fine tuned
|
||||
models. The exact dates are specified in the properties.
|
||||
""")
|
||||
model Deprecation {
|
||||
@doc("""
|
||||
The end date of fine tune support of this model. Will be `null` for fine tune
|
||||
models.
|
||||
""")
|
||||
@visibility("read")
|
||||
fine_tune_end_date?: int32;
|
||||
|
||||
@doc("The end date of inference support of this model.")
|
||||
@visibility("read")
|
||||
inference?: int32;
|
||||
}
|
||||
|
||||
@doc("Settings for the manual scale type.")
|
||||
model ManualScaleSettings extends ScaleSettings {
|
||||
@doc("The constant reserved capacity of the inference endpoint for this deployment.")
|
||||
capacity: int32;
|
||||
|
||||
@doc("Defines how scaling operations will be executed.")
|
||||
scale_type: "manual";
|
||||
}
|
||||
|
||||
@doc("Settings for the standard scale type.")
|
||||
model StandardScaleSettings extends ScaleSettings {
|
||||
@doc("Defines how scaling operations will be executed.")
|
||||
scale_type: "standard";
|
||||
}
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
import "@azure-tools/typespec-azure-core";
|
||||
import "./common.tsp";
|
||||
|
||||
namespace Azure.OpenAI.ServiceCustomizations;
|
||||
|
||||
@friendlyName("{name}List", T)
|
||||
model OpenAiList<T extends object, TType extends Azure.OpenAI.TypeDiscriminator> {
|
||||
@doc("Defines the type of this object")
|
||||
object?: TType;
|
||||
@doc("The list of {name}s", T)
|
||||
data?: T[];
|
||||
}
|
||||
|
||||
// TODO: enum value types not supported in csharp emitter, change TypeDiscriminator to TypeDiscriminatorKnownValues enum when fixed
|
||||
#suppress "@azure-tools/typespec-azure-core/documentation-required" "Internal template - no need for public documentation"
|
||||
op OpenAIListOperation<T extends object, TType extends Azure.OpenAI.TypeDiscriminator> is Azure.Core.Foundations.ResourceList<T, {}, OpenAiList<T, TType>>;
|
||||
|
|
@ -1,3 +0,0 @@
|
|||
#!/bin/bash
|
||||
autorest --reset
|
||||
autorest --require=README.md --cadl-init --use=https://aka.ms/autorest.cadl --output-folder="." --input-file="https://raw.githubusercontent.com/Azure/azure-rest-api-specs/main/specification/cognitiveservices/data-plane/AzureOpenAI/authoring/preview/2022-06-01-preview/azureopenai.json"
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
emit:
|
||||
- "@azure-tools/typespec-autorest"
|
||||
- "@azure-tools/cadl-apiview"
|
||||
# options:
|
||||
# Uncomment this line and add "@azure-tools/cadl-python" to your package.json to generate Python code
|
||||
# "@azure-tools/cadl-python":
|
||||
# "basic-setup-py": true
|
||||
# "package-version":
|
||||
# "package-name":
|
||||
# "output-path":
|
||||
# Uncomment this line and add "@azure-tools/cadl-java" to your package.json to generate Java code
|
||||
# "@azure-tools/cadl-java": true
|
||||
# Uncomment this line and add "@azure-tools/cadl-csharp" to your package.json to generate C# code
|
||||
# "@azure-tools/cadl-csharp": true
|
||||
# Uncomment this line and add "@azure-tools/cadl-typescript" to your package.json to generate Typescript code
|
||||
# "@azure-tools/cadl-typescript": true
|
||||
Загрузка…
Ссылка в новой задаче