[Endpoints] [1/x] Add backend DB tables for Endpoints#19002
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BenWilson2 merged 1 commit intomlflow:masterfrom Dec 4, 2025
Merged
[Endpoints] [1/x] Add backend DB tables for Endpoints#19002BenWilson2 merged 1 commit intomlflow:masterfrom
BenWilson2 merged 1 commit intomlflow:masterfrom
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This was referenced Nov 24, 2025
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B-Step62
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Dec 3, 2025
| Provider identifier: `String` (limit 64 characters). Optional. | ||
| E.g., "anthropic", "openai", "cohere", "vertex_ai", "bedrock", "databricks". | ||
| """ | ||
| credential_name = Column(String(255), nullable=True) |
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Do we plan to make this configurable by users? If not, wondering this is effectively same as hard coding the mapping between provider -> key somewhere in the code base.
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Yes, this is configurable by users (but supplied by default in the UI). The mutability of this is needed to provide flexibility for custom key names (and just in case there is a bug in litellm for a provider's config)
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| __tablename__ = "endpoint_bindings" | ||
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| binding_id = Column(String(36), nullable=False) |
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Can we use a composite key of endpoint_id+resouce_type+resource_id?
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great call out. Will update
Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com>
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Related Issues/PRs
#xxxWhat changes are proposed in this pull request?
Add the secret storage and endpoint definition tables.
How is this PR tested?
Does this PR require documentation update?
Release Notes
Is this a user-facing change?
What component(s), interfaces, languages, and integrations does this PR affect?
Components
area/tracking: Tracking Service, tracking client APIs, autologgingarea/models: MLmodel format, model serialization/deserialization, flavorsarea/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registryarea/scoring: MLflow Model server, model deployment tools, Spark UDFsarea/evaluation: MLflow model evaluation features, evaluation metrics, and evaluation workflowsarea/gateway: MLflow AI Gateway client APIs, server, and third-party integrationsarea/prompts: MLflow prompt engineering features, prompt templates, and prompt managementarea/tracing: MLflow Tracing features, tracing APIs, and LLM tracing functionalityarea/projects: MLproject format, project running backendsarea/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev serverarea/build: Build and test infrastructure for MLflowarea/docs: MLflow documentation pagesHow 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 notesShould this PR be included in the next patch release?
Yesshould be selected for bug fixes, documentation updates, and other small changes.Noshould be selected for new features and larger changes. If you're unsure about the release classification of this PR, leave this unchecked to let the maintainers decide.What is a minor/patch release?
Bug fixes, doc updates and new features usually go into minor releases.
Bug fixes and doc updates usually go into patch releases.