Support completions endpoints #10577
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Signed-off-by: Prithvi Kannan <prithvi.kannan@databricks.com>
Signed-off-by: Prithvi Kannan <prithvi.kannan@databricks.com>
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Signed-off-by: Prithvi Kannan <prithvi.kannan@databricks.com>
prithvikannan
added
rn/breaking-change
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@mlflow-automation autoformat |
| elif prefix == "gateway": | ||
| return _call_gateway_api(suffix, payload) | ||
| return _call_gateway_api(suffix, payload, eval_parameters) |
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Is this for backward compatibility?
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Yes. Lmk if this is not needed and we can remove
mlflow/metrics/genai/model_utils.py
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| except (KeyError, IndexError, TypeError): | ||
| text = None | ||
| return text | ||
| elif endpoint["task"] == "llm/v1/chat": |
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If MLflow deployments server (previously known as gateway) is used, this line would throw because endpoint doesn't have a task field.
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If this needs to work with MLflow deployments server, we need to define an endpoint object and make MlflowDeploymentClient and DatabricksDeploymentClient (and other deployments client) return the endpoint object for get_endpoint.
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MLflowDeploymentClient return the endpoint_task -- can we use this?
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Solved this by checking for task or endpoint_type
…ry-pick-endpoints-prs
Signed-off-by: Prithvi Kannan <prithvi.kannan@databricks.com>
Signed-off-by: Prithvi Kannan <prithvi.kannan@databricks.com>
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#xxxWhat changes are proposed in this pull request?
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Is this a user-facing change?
What component(s), interfaces, languages, and integrations does this PR affect?
Components
area/artifacts: Artifact stores and artifact loggingarea/build: Build and test infrastructure for MLflowarea/docs: MLflow documentation pagesarea/examples: Example codearea/gateway: AI Gateway service, Gateway client APIs, third-party Gateway integrationsarea/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registryarea/models: MLmodel format, model serialization/deserialization, flavorsarea/recipes: Recipes, Recipe APIs, Recipe configs, Recipe Templatesarea/projects: MLproject format, project running backendsarea/scoring: MLflow Model server, model deployment tools, Spark UDFsarea/server-infra: MLflow Tracking server backendarea/tracking: Tracking Service, tracking client APIs, autologgingInterface
area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev serverarea/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Modelsarea/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registryarea/windows: Windows supportLanguage
language/r: R APIs and clientslanguage/java: Java APIs and clientslanguage/new: Proposals for new client languagesIntegrations
integrations/azure: Azure and Azure ML integrationsintegrations/sagemaker: SageMaker integrationsintegrations/databricks: Databricks integrationsHow should the PR be classified in the release notes? Choose one:
rn/none- No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" sectionrn/breaking-change- The PR will be mentioned in the "Breaking Changes" sectionrn/feature- A new user-facing feature worth mentioning in the release notesrn/bug-fix- A user-facing bug fix worth mentioning in the release notesrn/documentation- A user-facing documentation change worth mentioning in the release notes