Chore(deps): Bump mlflow from 2.14.1 to 2.19.0

[dependabot]

Bumps mlflow from 2.14.1 to 2.19.0.

Release notes

Sourced from mlflow's releases.

MLflow 2.19.0

We are excited to announce the release of MLflow 2.19.0! This release includes a number of significant features, enhancements, and bug fixes.

Major New Features

• ChatModel enhancements - ChatModel now adopts ChatCompletionRequest and ChatCompletionResponse as its new schema. The predict_stream interface uses ChatCompletionChunk to deliver true streaming responses. Additionally, the custom_inputs and custom_outputs fields in ChatModel now utilize AnyType, enabling support for a wider variety of data types. Note: In a future version of MLflow, ChatParams (and by extension, ChatCompletionRequest) will have the default values for n, temperature, and stream removed. (#13782, #13857, @​stevenchen-db)

• Tracing improvements - MLflow Tracing now supports both automatic and manual tracing for DSPy, LlamaIndex and Langchain flavors. Tracing feature is also auto-enabled for mlflow evaluation for all supported flavors. (#13790, #13793, #13795, #13897, @​B-Step62)

• New Tracing Integrations - MLflow Tracing now supports CrewAI and Anthropic, enabling a one-line, fully automated tracing experience. (#13903, @​TomeHirata, #13851, @​gabrielfu)

• Any Type in model signature - MLflow now supports AnyType in model signature. It can be used to host any data types that were not supported before. (#13766, @​serena-ruan)

Other Features:

• [Tracking] Add update_current_trace API for adding tags to an active trace. (#13828, @​B-Step62)
• [Deployments] Update databricks deployments to support AI gateway & additional update endpoints (#13513, @​djliden)
• [Models] Support uv in mlflow.models.predict (#13824, @​serena-ruan)
• [Models] Add type hints support including pydantic models (#13924, @​serena-ruan)
• [Tracking] Add the trace.search_spans() method for searching spans within traces (#13984, @​B-Step62)

Bug fixes:

• [Tracking] Allow passing in spark connect dataframes in mlflow evaluate API (#13889, @​WeichenXu123)
• [Tracking] Fix mlflow.end_run inside a MLflow run context manager (#13888, @​WeichenXu123)
• [Scoring] Fix spark_udf conditional check on remote spark-connect client or Databricks Serverless (#13827, @​WeichenXu123)
• [Models] Allow changing max_workers for built-in LLM-as-a-Judge metrics (#13858, @​B-Step62)
• [Models] Support saving all langchain runnables using code-based logging (#13821, @​serena-ruan)
• [Model Registry] return empty array when DatabricksSDKModelsArtifactRepository.list_artifacts is called on a file (#14027, @​shichengzhou-db)
• [Tracking] Stringify param values in client.log_batch() (#14015, @​B-Step62)
• [Tracking] Remove deprecated squared parameter (#14028, @​B-Step62)
• [Tracking] Fix request/response field in the search_traces output (#13985, @​B-Step62)

Documentation updates:

• [Docs] Add Ollama and Instructor examples in tracing doc (#13937, @​B-Step62)

Small bug fixes and documentation updates:

#13972, #13968, #13917, #13912, #13906, #13846, @​serena-ruan; #13969, #13959, #13957, #13958, #13925, #13882, #13879, #13881, #13869, #13870, #13868, #13854, #13849, #13847, #13836, #13823, #13811, #13820, #13775, #13768, #13764, @​harupy; #13960, #13914, #13862, #13892, #13916, #13918, #13915, #13878, #13891, #13863, #13859, #13850, #13844, #13835, #13818, #13762, @​B-Step62; #13913, #13848, #13774, @​TomeHirata; #13936, #13954, #13883, @​daniellok-db; #13947, @​AHB102; #13929, #13922, @​Ajay-Satish-01; #13857, @​stevenchen-db; #13773, @​BenWilson2; #13705, @​williamjamir; #13745, #13743, @​WeichenXu123; #13895, @​chenmoneygithub; #14023, @​theBeginner86

MLflow 2.19.0rc0

We are excited to announce the release of MLflow 2.19.0rc0! This release includes a number of significant features, enhancements, and bug fixes.

Major New Features

• ChatModel enhancements - ChatModel now adopts ChatCompletionRequest and ChatCompletionResponse as its new schema. The predict_stream interface uses ChatCompletionChunk to deliver true streaming responses. Additionally, the custom_inputs and custom_outputs fields in ChatModel now utilize AnyType, enabling support for a wider variety of data types. (#13782, #13857, @​stevenchen-db)
• Any Type in model signature - MLflow now supports AnyType in model signature. It can be used to host any data types that were not supported before. (#13766, @​serena-ruan)
• Tracing improvements - MLflow Tracing now supports both automatic and manual tracing for DSPy, LlamaIndex and Langchain flavors. Tracing feature is also auto-enabled for mlflow evaluation for all supported flavors. (#13790, #13793, #13795, #13897, @​B-Step62)
• New Tracing Integrations - MLflow Tracing now supports CrewAI and Anthropic, enabling a one-line, fully automated tracing experience. (#13903, @​TomeHirata, #13851, @​gabrielfu)

... (truncated)

Changelog

Sourced from mlflow's changelog.

2.19.0 (2024-12-11)

We are excited to announce the release of MLflow 2.19.0! This release includes a number of significant features, enhancements, and bug fixes.

Major New Features

• ChatModel enhancements - ChatModel now adopts ChatCompletionRequest and ChatCompletionResponse as its new schema. The predict_stream interface uses ChatCompletionChunk to deliver true streaming responses. Additionally, the custom_inputs and custom_outputs fields in ChatModel now utilize AnyType, enabling support for a wider variety of data types. Note: In a future version of MLflow, ChatParams (and by extension, ChatCompletionRequest) will have the default values for n, temperature, and stream removed. (#13782, #13857, @​stevenchen-db)

• Tracing improvements - MLflow Tracing now supports both automatic and manual tracing for DSPy, LlamaIndex and Langchain flavors. Tracing feature is also auto-enabled for mlflow evaluation for all supported flavors. (#13790, #13793, #13795, #13897, @​B-Step62)

• New Tracing Integrations - MLflow Tracing now supports CrewAI and Anthropic, enabling a one-line, fully automated tracing experience. (#13903, @​TomeHirata, #13851, @​gabrielfu)

• Any Type in model signature - MLflow now supports AnyType in model signature. It can be used to host any data types that were not supported before. (#13766, @​serena-ruan)

Other Features:

• [Tracking] Add update_current_trace API for adding tags to an active trace. (#13828, @​B-Step62)
• [Deployments] Update databricks deployments to support AI gateway & additional update endpoints (#13513, @​djliden)
• [Models] Support uv in mlflow.models.predict (#13824, @​serena-ruan)
• [Models] Add type hints support including pydantic models (#13924, @​serena-ruan)
• [Tracking] Add the trace.search_spans() method for searching spans within traces (#13984, @​B-Step62)

Bug fixes:

• [Tracking] Allow passing in spark connect dataframes in mlflow evaluate API (#13889, @​WeichenXu123)
• [Tracking] Fix mlflow.end_run inside a MLflow run context manager (#13888, @​WeichenXu123)
• [Scoring] Fix spark_udf conditional check on remote spark-connect client or Databricks Serverless (#13827, @​WeichenXu123)
• [Models] Allow changing max_workers for built-in LLM-as-a-Judge metrics (#13858, @​B-Step62)
• [Models] Support saving all langchain runnables using code-based logging (#13821, @​serena-ruan)
• [Model Registry] return empty array when DatabricksSDKModelsArtifactRepository.list_artifacts is called on a file (#14027, @​shichengzhou-db)
• [Tracking] Stringify param values in client.log_batch() (#14015, @​B-Step62)
• [Tracking] Remove deprecated squared parameter (#14028, @​B-Step62)
• [Tracking] Fix request/response field in the search_traces output (#13985, @​B-Step62)

Documentation updates:

• [Docs] Add Ollama and Instructor examples in tracing doc (#13937, @​B-Step62)

Small bug fixes and documentation updates:

#13972, #13968, #13917, #13912, #13906, #13846, @​serena-ruan; #13969, #13959, #13957, #13958, #13925, #13882, #13879, #13881, #13869, #13870, #13868, #13854, #13849, #13847, #13836, #13823, #13811, #13820, #13775, #13768, #13764, @​harupy; #13960, #13914, #13862, #13892, #13916, #13918, #13915, #13878, #13891, #13863, #13859, #13850, #13844, #13835, #13818, #13762, @​B-Step62; #13913, #13848, #13774, @​TomeHirata; #13936, #13954, #13883, @​daniellok-db; #13947, @​AHB102; #13929, #13922, @​Ajay-Satish-01; #13857, @​stevenchen-db; #13773, @​BenWilson2; #13705, @​williamjamir; #13745, #13743, @​WeichenXu123; #13895, @​chenmoneygithub; #14023, @​theBeginner86

2.18.0 (2024-11-18)

We are excited to announce the release of MLflow 2.18.0! This release includes a number of significant features, enhancements, and bug fixes.

Python Version Update

Python 3.8 is now at an end-of-life point. With official support being dropped for this legacy version, MLflow now requires Python 3.9 as a minimum supported version.

... (truncated)

Commits

• ad17101 Run python3 dev/update_mlflow_versions.py pre-release ... (#14055)
• 31962bf Drop support for LightGBM < 3.3 (#14032)
• 0e0b689 Use mean_squared_error if root_mean_squared_error is unavailable (#14040)
• a3dc2e0 Add warning for removing default values in ChatParams (#14043)
• c95c4a4 Temporarily pin sklearn to <1.6 in test requirements (#14038)
• dabf714 Fix eval test failure (#14036)
• c94bfc0 Make the source in Assessment optional & add CODE as a new AssessmentSourceTy...
• 168a859 Remove deprecated squared parameter (#14028)
• 16f550e return empty array when DatabricksSDKModelsArtifactRepository.list_artifacts ...
• 84579de [tracing] Fix Tracing section (#14023)
• Additional commits viewable in compare view

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