Support base64 images in chat blocks#16761
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daniellok-db merged 3 commits intoJul 16, 2025
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Signed-off-by: Fatih Altinok <fatihaltinok@live.com>
base64 urls can become pretty long and they look garbled when truncated. Image urls also can get pretty long, so this fixes both problems. Signed-off-by: Fatih Altinok <fatihaltinok@live.com>
| if (value.startsWith('data:image/png') || value.startsWith('data:image/jpeg')) { | ||
| return value; | ||
| } | ||
| return defaultUrlTransform(value); |
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what does this url transform do?
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Sanitizes the url: remarkjs/react-markdown#774
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ah makes sense, so it was blocking these data urls before
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lgtm but maybe we can add a comment explaining what the url transform does? from the docs it says it makes the url "safe"but i'm not clear on what that means
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Documentation preview for 1ee92d7 is available at: More info
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Signed-off-by: Fatih Altinok <fatihaltinok@live.com>
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@daniellok-db added a note about what the transform does, how it works, and why it's overridden. Feel free to initiate merge if the note is good at explaining it! |
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Related Issues/PRs
Followup to #16736What changes are proposed in this pull request?
Handle base64 encoded images in chat messages. Also make them exempt from truncation.
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Does this PR require documentation update?
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area/artifacts: Artifact stores and artifact loggingarea/build: Build and test infrastructure for MLflowarea/deployments: MLflow Deployments client APIs, server, and third-party Deployments integrationsarea/docs: MLflow documentation pagesarea/evaluation: MLflow model evaluation features, evaluation metrics, and evaluation workflowsarea/examples: Example codearea/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registryarea/models: MLmodel format, model serialization/deserialization, flavorsarea/projects: MLproject format, project running backendsarea/prompt: MLflow prompt engineering features, prompt templates, and prompt managementarea/scoring: MLflow Model server, model deployment tools, Spark UDFsarea/server-infra: MLflow Tracking server backendarea/tracing: MLflow Tracing features, tracing APIs, and LLM tracing functionalityarea/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:
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