mlflow.genai

class mlflow.genai.Agent(agent: _Agent)

Bases: object

The agent configuration, used for generating responses in the review app.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

property agent_name: str

The name of the agent.

property model_serving_endpoint: str

The model serving endpoint used by the agent.

class mlflow.genai.LabelingSession(session: _LabelingSession)

Bases: object

A session for labeling items in the review app.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

add_dataset(dataset_name: str, record_ids: Optional[list[str]] = None) → mlflow.genai.labeling.labeling.LabelingSession

Add a dataset to the labeling session.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

Parameters

• dataset_name – The name of the dataset.

• record_ids – Optional. The individual record ids to be added to the session. If not provided, all records in the dataset will be added.

Returns

The updated labeling session.

Return type

LabelingSession

add_traces(traces: Union[Iterable[Trace], Iterable[str], pd.DataFrame]) → LabelingSession

Add traces to the labeling session.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

Parameters

traces – Can be either: a) a pandas DataFrame with a ‘trace’ column. The ‘trace’ column should contain either mlflow.entities.Trace objects or their json string representations. b) an iterable of mlflow.entities.Trace objects. c) an iterable of json string representations of mlflow.entities.Trace objects.

Returns

The updated labeling session.

Return type

LabelingSession

property agent: str | None

The agent used to generate responses for the items in the session.

property assigned_users: list[str]

The users assigned to label items in the session.

property custom_inputs: dict[str, typing.Any] | None

Custom inputs used in the session.

property enable_multi_turn_chat: bool

Whether multi-turn chat is enabled for the session.

property experiment_id: str

The experiment ID associated with the session.

property label_schemas: list[str]

The label schemas used in the session.

property labeling_session_id: str

The unique identifier of the labeling session.

property mlflow_run_id: str

The MLflow run ID associated with the session.

property name: str

The name of the labeling session.

property review_app_id: str

The review app ID associated with the session.

set_assigned_users(assigned_users: list[str]) → mlflow.genai.labeling.labeling.LabelingSession

Set the assigned users for the labeling session.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

Parameters

assigned_users – The list of users to assign to the session.

Returns

The updated labeling session.

Return type

LabelingSession

sync(to_dataset: str) → None

Sync the traces and expectations from the labeling session to a dataset.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

Parameters

to_dataset – The name of the dataset to sync traces and expectations to.

property url: str

The URL of the labeling session in the review app.

class mlflow.genai.ReviewApp(app: _ReviewApp)

Bases: object

A review app is used to collect feedback from stakeholders for a given experiment.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

add_agent(*, agent_name: str, model_serving_endpoint: str, overwrite: bool = False) → mlflow.genai.labeling.labeling.ReviewApp

Add an agent to the review app to be used to generate responses.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

Parameters

• agent_name – The name of the agent.

• model_serving_endpoint – The model serving endpoint to be used by the agent.

• overwrite – Whether to overwrite an existing agent with the same name.

Returns

The updated review app.

Return type

ReviewApp

property agents: list[mlflow.genai.labeling.labeling.Agent]

The agents to be used to generate responses.

property experiment_id: str

The ID of the experiment.

property label_schemas: list['_LabelSchema']

The label schemas to be used in the review app.

remove_agent(agent_name: str) → mlflow.genai.labeling.labeling.ReviewApp

Remove an agent from the review app.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

Parameters

agent_name – The name of the agent to remove.

Returns

The updated review app.

Return type

ReviewApp

property review_app_id: str

The ID of the review app.

property url: str

The URL of the review app for stakeholders to provide feedback.

class mlflow.genai.Scorer(*, name: str, aggregations: list[typing.Union[typing.Literal['min', 'max', 'mean', 'median', 'variance', 'p90'], typing.Callable[[list[int | float]], float]]] | None = None)

Bases: pydantic.main.BaseModel

Note

Experimental: This class may change or be removed in a future release without warning.

aggregations: list[typing.Union[typing.Literal['min', 'max', 'mean', 'median', 'variance', 'p90'], typing.Callable[[list[int | float]], float]]] | None

property filter_string: str | None

This function may change or be removed in a future release without warning.

Get the filter string for this scorer.

Type

Note

Experimental

property kind: mlflow.genai.scorers.base.ScorerKind

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_dump(**kwargs) → dict[str, typing.Any]

Override model_dump to include source code.

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters

• self – The BaseModel instance.

• context – The context.

classmethod model_validate(obj: Any) → Scorer

Override model_validate to reconstruct scorer from source code.

name: str

register(*, name: Optional[str] = None, experiment_id: Optional[str] = None) → Scorer

Note

Experimental: This function may change or be removed in a future release without warning.

Register this scorer with the MLflow server.

This method registers the scorer for use with automatic trace evaluation in the specified experiment. Once registered, the scorer can be started to begin evaluating traces automatically.

Parameters

• name – Optional registered name for the scorer. If not provided, the current name property value will be used as a registered name.

• experiment_id – The ID of the MLflow experiment to register the scorer for. If None, uses the currently active experiment.

Returns

A new Scorer instance with server registration information.

Example

import mlflow
from mlflow.genai.scorers import RelevanceToQuery

# Register a built-in scorer
mlflow.set_experiment("my_genai_app")
registered_scorer = RelevanceToQuery().register(name="relevance_scorer")
print(f"Registered scorer: {registered_scorer.name}")

# Register a custom scorer
from mlflow.genai.scorers import scorer


@scorer
def custom_length_check(outputs) -> bool:
    return len(outputs) > 100


registered_custom = custom_length_check.register(
    name="output_length_checker", experiment_id="12345"
)

run(*, inputs=None, outputs=None, expectations=None, trace=None)

property sample_rate: float | None

This function may change or be removed in a future release without warning.

Get the sample rate for this scorer. Available when registered for monitoring.

Type

Note

Experimental

start(*, name: Optional[str] = None, experiment_id: Optional[str] = None, sampling_config: mlflow.genai.scorers.base.ScorerSamplingConfig) → Scorer

Note

Experimental: This function may change or be removed in a future release without warning.

Start registered scoring with the specified sampling configuration.

This method activates automatic trace evaluation for the scorer. The scorer will evaluate traces based on the provided sampling configuration, including the sample rate and optional filter criteria.

Parameters

• name – Optional scorer name. If not provided, uses the scorer’s registered name or default name.

• experiment_id – The ID of the MLflow experiment containing the scorer. If None, uses the currently active experiment.

• sampling_config – Configuration object containing: - sample_rate: Fraction of traces to evaluate (0.0 to 1.0). Required. - filter_string: Optional MLflow search_traces compatible filter string.

Returns

A new Scorer instance with updated sampling configuration.

Example

import mlflow
from mlflow.genai.scorers import RelevanceToQuery, ScorerSamplingConfig

# Start scorer with 50% sampling rate
mlflow.set_experiment("my_genai_app")
scorer = RelevanceToQuery().register()
active_scorer = scorer.start(sampling_config=ScorerSamplingConfig(sample_rate=0.5))
print(f"Scorer is evaluating {active_scorer.sample_rate * 100}% of traces")

# Start scorer with filter to only evaluate specific traces
filtered_scorer = scorer.start(
    sampling_config=ScorerSamplingConfig(
        sample_rate=1.0, filter_string="YOUR_FILTER_STRING"
    )
)

property status: mlflow.genai.scorers.base.ScorerStatus

This function may change or be removed in a future release without warning.

Get the status of this scorer, using only the local state.

Type

Note

Experimental

stop(*, name: Optional[str] = None, experiment_id: Optional[str] = None) → Scorer

Note

Experimental: This function may change or be removed in a future release without warning.

Stop registered scoring by setting sample rate to 0.

This method deactivates automatic trace evaluation for the scorer while keeping the scorer registered. The scorer can be restarted later using the start() method.

Parameters

• name – Optional scorer name. If not provided, uses the scorer’s registered name or default name.

• experiment_id – The ID of the MLflow experiment containing the scorer. If None, uses the currently active experiment.

Returns

A new Scorer instance with sample rate set to 0.

Example

import mlflow
from mlflow.genai.scorers import RelevanceToQuery, ScorerSamplingConfig

# Start and then stop a scorer
mlflow.set_experiment("my_genai_app")
scorer = RelevanceToQuery().register()
active_scorer = scorer.start(sampling_config=ScorerSamplingConfig(sample_rate=0.5))
print(f"Scorer is active: {active_scorer.sample_rate > 0}")

# Stop the scorer
stopped_scorer = active_scorer.stop()
print(f"Scorer is active: {stopped_scorer.sample_rate > 0}")

# The scorer remains registered and can be restarted later
restarted_scorer = stopped_scorer.start(
    sampling_config=ScorerSamplingConfig(sample_rate=0.3)
)

update(*, name: Optional[str] = None, experiment_id: Optional[str] = None, sampling_config: mlflow.genai.scorers.base.ScorerSamplingConfig) → Scorer

Note

Experimental: This function may change or be removed in a future release without warning.

Update the sampling configuration for this scorer.

This method modifies the sampling rate and/or filter criteria for an already registered scorer. It can be used to dynamically adjust how many traces are evaluated or change the filtering criteria without stopping and restarting the scorer.

Parameters

• name – Optional scorer name. If not provided, uses the scorer’s registered name or default name.

• experiment_id – The ID of the MLflow experiment containing the scorer. If None, uses the currently active experiment.

• sampling_config – Configuration object containing: - sample_rate: New fraction of traces to evaluate (0.0 to 1.0). Optional. - filter_string: New MLflow search_traces compatible filter string. Optional.

Returns

A new Scorer instance with updated configuration.

Example

import mlflow
from mlflow.genai.scorers import RelevanceToQuery, ScorerSamplingConfig

# Start scorer with initial configuration
mlflow.set_experiment("my_genai_app")
scorer = RelevanceToQuery().register()
active_scorer = scorer.start(sampling_config=ScorerSamplingConfig(sample_rate=0.1))

# Update to increase sampling rate during high traffic
updated_scorer = active_scorer.update(
    sampling_config=ScorerSamplingConfig(sample_rate=0.5)
)
print(f"Updated sample rate: {updated_scorer.sample_rate}")

# Update to add filtering criteria
filtered_scorer = updated_scorer.update(
    sampling_config=ScorerSamplingConfig(filter_string="YOUR_FILTER_STRING")
)
print(f"Added filter: {filtered_scorer.filter_string}")

class mlflow.genai.ScorerScheduleConfig(scorer: Scorer, scheduled_scorer_name: str, sample_rate: float, filter_string: Optional[str] = None)

Bases: object

Note

Experimental: This class may change or be removed in a future release without warning.

A scheduled scorer configuration for automated monitoring of generative AI applications.

Scheduled scorers are used to automatically evaluate traces logged to MLflow experiments by production applications. They are part of Databricks Lakehouse Monitoring for GenAI, which helps track quality metrics like groundedness, safety, and guideline adherence alongside operational metrics like volume, latency, and cost.

When configured, scheduled scorers run automatically in the background to evaluate a sample of traces based on the specified sampling rate and filter criteria. The Assessments are displayed in the Traces tab of the MLflow experiment and can be used to identify quality issues in production.

Parameters

• scorer – The scorer function to run on sampled traces. Must be either a built-in scorer (e.g., Safety, Correctness) or a function decorated with @scorer. Subclasses of Scorer are not supported.

• scheduled_scorer_name – The name for this scheduled scorer configuration within the experiment. This name must be unique among all scheduled scorers in the same experiment. We recommend using the scorer’s name (e.g., scorer.name) for consistency.

• sample_rate – The fraction of traces to evaluate, between 0.0 and 1.0. For example, 0.1 means 10% of traces will be randomly selected for evaluation.

• filter_string – An optional MLflow search_traces compatible filter string to apply before sampling traces. Only traces matching this filter will be considered for evaluation. Uses the same syntax as mlflow.search_traces().

Example

from mlflow.genai.scorers import Safety, scorer
from mlflow.genai.scheduled_scorers import ScorerScheduleConfig

# Using a built-in scorer
safety_config = ScorerScheduleConfig(
    scorer=Safety(),
    scheduled_scorer_name="production_safety",
    sample_rate=0.2,  # Evaluate 20% of traces
    filter_string="trace.status = 'OK'",
)


# Using a custom scorer
@scorer
def response_length(outputs):
    return len(str(outputs)) > 100


length_config = ScorerScheduleConfig(
    scorer=response_length,
    scheduled_scorer_name="adequate_length",
    sample_rate=0.1,  # Evaluate 10% of traces
    filter_string="trace.status = 'OK'",
)

Note

Scheduled scorers are executed automatically by Databricks and do not need to be manually triggered. The Assessments appear in the Traces tab of the MLflow experiment. Only traces logged directly to the experiment are monitored; traces logged to individual runs within the experiment are not evaluated.

Warning

This API is in Beta and may change or be removed in a future release without warning.

filter_string: str | None = None

sample_rate: float

scheduled_scorer_name: str

scorer: Scorer

mlflow.genai.create_dataset(name: str | None = None, experiment_id: str | list[str] | None = None, tags: dict[str, typing.Any] | None = None) → mlflow.genai.datasets.evaluation_dataset.EvaluationDataset

Note

Parameter uc_table_name is deprecated. Use name instead.

Create a dataset with the given name and associate it with the given experiment.

Parameters

• name – The name of the dataset. In Databricks, this is the UC table name.

• experiment_id – The ID of the experiment(s) to associate the dataset with. If not provided, the current experiment is inferred from the environment.

• tags – Dictionary of tags to apply to the dataset. Not supported in Databricks.

Returns

An EvaluationDataset object representing the created dataset.

Examples

from mlflow.genai.datasets import create_dataset

# Create a dataset with a single experiment
dataset = create_dataset(
    name="customer_support_qa_v1",
    experiment_id="0",  # Default experiment
    tags={
        "version": "1.0",
        "purpose": "regression_testing",
        "model": "gpt-4",
        "team": "ml-platform",
    },
)
print(f"Created dataset: {dataset.dataset_id}")
# Output: Created dataset: d-1a2b3c4d5e6f7890abcdef1234567890

# Create a dataset linked to multiple experiments
multi_exp_dataset = create_dataset(
    name="cross_team_eval_dataset",
    experiment_id=["1", "2", "5"],  # Multiple experiment IDs
    tags={
        "coverage": "comprehensive",
        "status": "development",
    },
)

# Create a dataset without tags (minimal example)
simple_dataset = create_dataset(
    name="quick_test_dataset",
    experiment_id="3",  # Specific experiment
)

mlflow.genai.create_labeling_session(name: str, *, assigned_users: Optional[list[str]] = None, agent: Optional[str] = None, label_schemas: Optional[list[str]] = None, enable_multi_turn_chat: bool = False, custom_inputs: Optional[dict[str, typing.Any]] = None) → mlflow.genai.labeling.labeling.LabelingSession

Create a new labeling session in the review app.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

Parameters

• name – The name of the labeling session.

• assigned_users – The users that will be assigned to label items in the session.

• agent – The agent to be used to generate responses for the items in the session.

• label_schemas – The label schemas to be used in the session.

• enable_multi_turn_chat – Whether to enable multi-turn chat labeling for the session.

• custom_inputs – Optional. Custom inputs to be used in the session.

Returns

The created labeling session.

Return type

LabelingSession

mlflow.genai.delete_dataset(name: str | None = None, dataset_id: str | None = None) → None

Note

Parameter uc_table_name is deprecated. Use name instead.

Delete a dataset.

Parameters

• name – The name of the dataset (Databricks only). In Databricks, this is the UC table name.

• dataset_id – The ID of the dataset.

Note

• In Databricks environments: Use ‘name’ to specify the dataset.

• Outside of Databricks: Use ‘dataset_id’ to specify the dataset

Examples

from mlflow.genai.datasets import delete_dataset, search_datasets

# Delete a specific dataset by ID (non-Databricks)
delete_dataset(dataset_id="d-4b5c6d7e8f9a0b1c2d3e4f5a6b7c8d9e")

# Clean up old test datasets
test_datasets = search_datasets(
    filter_string="name LIKE 'test_%' AND tags.environment = 'development'",
    order_by=["created_time ASC"],
)

# Delete datasets older than the most recent 5
if len(test_datasets) > 5:
    for dataset in test_datasets[:-5]:  # Keep the 5 most recent
        print(f"Deleting old test dataset: {dataset.name}")
        delete_dataset(dataset_id=dataset.dataset_id)

# Delete datasets with specific criteria
deprecated_datasets = search_datasets(filter_string="tags.status = 'deprecated'")
for dataset in deprecated_datasets:
    delete_dataset(dataset_id=dataset.dataset_id)
    print(f"Deleted deprecated dataset: {dataset.name}")

Warning

Deleting a dataset is permanent and cannot be undone. All associated records, tags, and metadata will be permanently removed.

mlflow.genai.delete_dataset_tag(dataset_id: str, key: str) → None

Note

Experimental: This function may change or be removed in a future release without warning.

Delete a tag from a dataset.

Parameters

• dataset_id – The ID of the dataset.

• key – The tag key to delete.

Examples

from mlflow.genai.datasets import delete_dataset_tag, get_dataset

# Get your dataset
dataset = get_dataset(dataset_id="d-9e8f7c6b5a4d3e2f1a0b9c8d7e6f5a4b")

# Remove a single tag
delete_dataset_tag(dataset_id=dataset.dataset_id, key="deprecated")

# Remove outdated tags during cleanup
outdated_tags = ["old_version", "temp_flag", "development_only"]
for tag_key in outdated_tags:
    delete_dataset_tag(dataset_id=dataset.dataset_id, key=tag_key)

# Check remaining tags
updated_dataset = get_dataset(dataset_id=dataset.dataset_id)
print(f"Remaining tags: {updated_dataset.tags}")

Note

This API is not available in Databricks environments yet. Tags in Databricks are managed through Unity Catalog.

mlflow.genai.delete_labeling_session(labeling_session: mlflow.genai.labeling.labeling.LabelingSession) → mlflow.genai.labeling.labeling.ReviewApp

Delete a labeling session from the review app.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

Parameters

labeling_session – The labeling session to delete.

Returns

The review app.

Return type

ReviewApp

mlflow.genai.delete_prompt_alias(name: str, alias: str) → None

Note

Experimental: This function may change or be removed in a future release without warning.

Delete an alias for a Prompt in the MLflow Prompt Registry.

Parameters

• name – The name of the prompt.

• alias – The alias to delete for the prompt.

mlflow.genai.disable_git_model_versioning() → None

Note

Experimental: This function may change or be removed in a future release without warning.

Disable Git-based model versioning and clear the active model context.

This function stops automatic Git-based version tracking and clears any active LoggedModel context. After calling this, traces will no longer be automatically linked to Git-based versions.

This is automatically called when exiting a context manager created with enable_git_model_versioning().

Example:

import mlflow.genai

# Enable versioning
context = mlflow.genai.enable_git_model_versioning()
# ... do work with versioning enabled ...

# Disable versioning
mlflow.genai.disable_git_model_versioning()
# Traces are no longer linked to Git versions

mlflow.genai.enable_git_model_versioning(remote_name: str = 'origin') → mlflow.genai.git_versioning.GitContext

Note

Experimental: This function may change or be removed in a future release without warning.

Enable automatic Git-based model versioning for MLflow traces.

This function enables automatic version tracking based on your Git repository state. When enabled, MLflow will: - Detect the current Git branch, commit hash, and dirty state - Create or reuse a LoggedModel matching this exact Git state - Link all subsequent traces to this LoggedModel version - Capture uncommitted changes as diffs when the repository is dirty

Parameters

remote_name – The name of the git remote to use for repository URL detection. Defaults to “origin”.

Returns

• info: GitInfo object with branch, commit, dirty state, and diff information

• active_model: The active LoggedModel linked to current Git state

Return type

A GitContext instance containing

Example:

import mlflow.genai

# Enable Git-based versioning
context = mlflow.genai.enable_git_model_versioning()
print(f"Branch: {context.info.branch}, Commit: {context.info.commit[:8]}")
# Output: Branch: main, Commit: abc12345


# All traces are now automatically linked to this Git version
@mlflow.trace
def my_app():
    return "result"


# Can also use as a context manager
with mlflow.genai.enable_git_model_versioning() as context:
    # Traces within this block are linked to the Git version
    result = my_app()

Note

If Git is not available or the current directory is not a Git repository, a warning is issued and versioning is disabled (context.info will be None).

mlflow.genai.evaluate(data: EvaluationDatasetTypes, scorers: list[Scorer], predict_fn: Optional[Callable[[...], Any]] = None, model_id: str | None = None) → mlflow.models.evaluation.base.EvaluationResult

Note

Experimental: This function may change or be removed in a future release without warning.

Evaluate the performance of a generative AI model/application using specified data and scorers.

This function allows you to evaluate a model’s performance on a given dataset using various scoring criteria. It supports both built-in scorers provided by MLflow and custom scorers. The evaluation results include metrics and detailed per-row assessments.

There are three different ways to use this function:

1. Use Traces to evaluate the model/application.

The data parameter takes a DataFrame with trace column, which contains a single trace object corresponding to the prediction for the row. This dataframe is easily obtained from the existing traces stored in MLflow, by using the mlflow.search_traces() function.

import mlflow
from mlflow.genai.scorers import Correctness, Safety
import pandas as pd

# model_id is a string starting with "m-", e.g. "m-074689226d3b40bfbbdf4c3ff35832cd"
trace_df = mlflow.search_traces(model_id="<my-model-id>")

mlflow.genai.evaluate(
    data=trace_df,
    scorers=[Correctness(), Safety()],
)

Built-in scorers will understand the model inputs, outputs, and other intermediate information e.g. retrieved context, from the trace object. You can also access to the trace object from the custom scorer function by using the trace parameter.

from mlflow.genai.scorers import scorer


@scorer
def faster_than_one_second(inputs, outputs, trace):
    return trace.info.execution_duration < 1000

2. Use DataFrame or dictionary with “inputs”, “outputs”, “expectations” columns.

Alternatively, you can pass inputs, outputs, and expectations (ground truth) as a column in the dataframe (or equivalent list of dictionaries).

import mlflow
from mlflow.genai.scorers import Correctness
import pandas as pd

data = pd.DataFrame(
    [
        {
            "inputs": {"question": "What is MLflow?"},
            "outputs": "MLflow is an ML platform",
            "expectations": "MLflow is an ML platform",
        },
        {
            "inputs": {"question": "What is Spark?"},
            "outputs": "I don't know",
            "expectations": "Spark is a data engine",
        },
    ]
)

mlflow.genai.evaluate(
    data=data,
    scorers=[Correctness()],
)

3. Pass `predict_fn` and input samples (and optionally expectations).

If you want to generate the outputs and traces on-the-fly from your input samples, you can pass a callable to the predict_fn parameter. In this case, MLflow will pass the inputs to the predict_fn as keyword arguments. Therefore, the “inputs” column must be a dictionary with the parameter names as keys.

import mlflow
from mlflow.genai.scorers import Correctness, Safety
import openai

# Create a dataframe with input samples
data = pd.DataFrame(
    [
        {"inputs": {"question": "What is MLflow?"}},
        {"inputs": {"question": "What is Spark?"}},
    ]
)


# Define a predict function to evaluate. The "inputs" column will be
# passed to the prediction function as keyword arguments.
def predict_fn(question: str) -> str:
    response = openai.OpenAI().chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": question}],
    )
    return response.choices[0].message.content


mlflow.genai.evaluate(
    data=data,
    predict_fn=predict_fn,
    scorers=[Correctness(), Safety()],
)

Parameters

• data –

  Dataset for the evaluation. Must be one of the following formats:

  • An EvaluationDataset entity

  • Pandas DataFrame

  • Spark DataFrame

  • List of dictionaries

  The dataset must include either of the following columns:

  1. trace column that contains a single trace object corresponding

     to the prediction for the row.

     If this column is present, MLflow extracts inputs, outputs, assessments, and other intermediate information e.g. retrieved context, from the trace object and uses them for scoring. When this column is present, the predict_fn parameter must not be provided.

  2. inputs, outputs, expectations columns.

     Alternatively, you can pass inputs, outputs, and expectations(ground truth) as a column in the dataframe (or equivalent list of dictionaries).

     • inputs (required): Column containing inputs for evaluation. The value must be a dictionary. When predict_fn is provided, MLflow will pass the inputs to the predict_fn as keyword arguments. For example,

       • predict_fn: def predict_fn(question: str, context: str) -> str

       • inputs: {“question”: “What is MLflow?”, “context”: “MLflow is an ML platform”}

       • predict_fn will receive “What is MLflow?” as the first argument (question) and “MLflow is an ML platform” as the second argument (context)

     • outputs (optional): Column containing model or app outputs. If this column is present, predict_fn must not be provided.

     • expectations (optional): Column containing a dictionary of ground truths.

  For list of dictionaries, each dict should follow the above schema.

  Optional columns:

  • tags (optional): Column containing a dictionary of tags. The tags will be logged

    to the respective traces.

• scorers – A list of Scorer objects that produces evaluation scores from inputs, outputs, and other additional contexts. MLflow provides pre-defined scorers, but you can also define custom ones.

• predict_fn –

  The target function to be evaluated. The specified function will be executed for each row in the input dataset, and outputs will be used for scoring.

  The function must emit a single trace per call. If it doesn’t, decorate the function with @mlflow.trace decorator to ensure a trace to be emitted.

• model_id – Optional model identifier (e.g. “m-074689226d3b40bfbbdf4c3ff35832cd”) to associate with the evaluation results. Can be also set globally via the mlflow.set_active_model() function.

Returns

An mlflow.models.EvaluationResult~ object.

Note

This function is only supported on Databricks. The tracking URI must be set to Databricks.

Warning

This function is not thread-safe. Please do not use it in multi-threaded environments.

mlflow.genai.get_dataset(name: str | None = None, dataset_id: str | None = None) → mlflow.genai.datasets.evaluation_dataset.EvaluationDataset

Note

Parameter uc_table_name is deprecated. Use name instead.

Get the dataset with the given name or ID.

Parameters

• name – The name of the dataset (Databricks only). In Databricks, this is the UC table name.

• dataset_id – The ID of the dataset.

Returns

An EvaluationDataset object representing the retrieved dataset.

Note

• In Databricks environments: Use ‘name’ to specify the dataset.

• Outside of Databricks: Use ‘dataset_id’ to specify the dataset

Examples

from mlflow.genai.datasets import get_dataset

# Get a dataset by ID (non-Databricks)
dataset = get_dataset(dataset_id="d-7f2e3a9b8c1d4e5f6a7b8c9d0e1f2a3b")

# Access dataset properties
print(f"Dataset name: {dataset.name}")
print(f"Number of records: {len(dataset.records)}")
print(f"Tags: {dataset.tags}")
print(f"Created by: {dataset.created_by}")

# Work with the dataset
df = dataset.to_df()  # Convert to pandas DataFrame
schema = dataset.schema  # Get auto-computed schema
profile = dataset.profile  # Get dataset statistics

# Add new records to the dataset
new_test_cases = [
    {
        "inputs": {"question": "What is MLflow?"},
        "expectations": {"accuracy": 0.95, "contains_tracking": True},
    }
]
dataset.merge_records(new_test_cases)

mlflow.genai.get_labeling_session(run_id: str) → mlflow.genai.labeling.labeling.LabelingSession

Get a labeling session from the review app.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

Parameters

run_id – The mlflow run ID of the labeling session to get.

Returns

The labeling session.

Return type

LabelingSession

mlflow.genai.get_labeling_sessions() → list[mlflow.genai.labeling.labeling.LabelingSession]

Get all labeling sessions from the review app.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

Returns

The list of labeling sessions.

Return type

list[LabelingSession]

mlflow.genai.get_review_app(experiment_id: Optional[str] = None) → mlflow.genai.labeling.labeling.ReviewApp

Gets or creates (if it doesn’t exist) the review app for the given experiment ID.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

Parameters

experiment_id – Optional. The experiment ID for which to get the review app. If not provided, the experiment ID is inferred from the current active environment.

Returns

The review app.

Return type

ReviewApp

mlflow.genai.load_prompt(name_or_uri: str, version: str | int | None = None, allow_missing: bool = False) → PromptVersion

Note

Experimental: This function may change or be removed in a future release without warning.

Load a Prompt from the MLflow Prompt Registry.

The prompt can be specified by name and version, or by URI.

Parameters

• name_or_uri – The name of the prompt, or the URI in the format “prompts:/name/version”.

• version – The version of the prompt (required when using name, not allowed when using URI).

• allow_missing – If True, return None instead of raising Exception if the specified prompt is not found.

Example:

import mlflow

# Load a specific version of the prompt
prompt = mlflow.genai.load_prompt("my_prompt", version=1)

# Load a specific version of the prompt by URI
prompt = mlflow.genai.load_prompt("prompts:/my_prompt/1")

# Load a prompt version with an alias "production"
prompt = mlflow.genai.load_prompt("prompts:/my_prompt@production")

mlflow.genai.make_judge(name: str, instructions: str, model: Optional[str] = None) → mlflow.genai.judges.base.Judge

Note

Experimental: This function may change or be removed in a future release without warning.

Create a custom MLflow judge instance.

Parameters

• name – The name of the judge

• instructions – Natural language instructions for evaluation. Must contain at least one template variable: {{ inputs }}, {{ outputs }}, {{ expectations }}, or {{ trace }} to reference evaluation data. Custom variables are not supported.

• model – The model identifier to use for evaluation (e.g., “openai:/gpt-4”)

Returns

An InstructionsJudge instance configured with the provided parameters

Example

import mlflow
from mlflow.genai.judges import make_judge

# Create a judge that evaluates response quality using template variables
quality_judge = make_judge(
    name="response_quality",
    instructions=(
        "Evaluate if the response in {{ outputs }} correctly answers "
        "the question in {{ inputs }}. The response should be accurate, "
        "complete, and professional."
    ),
    model="openai:/gpt-4",
)

# Evaluate a response
result = quality_judge(
    inputs={"question": "What is machine learning?"},
    outputs="ML is basically when computers learn stuff on their own",
)

# Create a judge that compares against expectations
correctness_judge = make_judge(
    name="correctness",
    instructions=(
        "Compare the {{ outputs }} against the {{ expectations }}. "
        "Rate how well they match on a scale of 1-5."
    ),
    model="openai:/gpt-4",
)

# Evaluate with expectations (must be dictionaries)
result = correctness_judge(
    inputs={"question": "What is the capital of France?"},
    outputs={"answer": "The capital of France is Paris."},
    expectations={"expected_answer": "Paris"},
)

# Create a judge that evaluates based on trace context
trace_judge = make_judge(
    name="trace_quality",
    instructions="Evaluate the overall quality of the {{ trace }} execution.",
    model="openai:/gpt-4",
)

# Use with search_traces() - evaluate each trace
traces = mlflow.search_traces(experiment_ids=["1"], return_type="list")
for trace in traces:
    feedback = trace_judge(trace=trace)
    print(f"Trace {trace.info.trace_id}: {feedback.value} - {feedback.rationale}")

mlflow.genai.optimize_prompt(*, target_llm_params: mlflow.genai.optimize.types.LLMParams, prompt: str | PromptVersion, train_data: EvaluationDatasetTypes, scorers: list[Scorer], objective: Optional[Callable[[dict[str, bool | float | str | Feedback | list[Feedback]]], float]] = None, eval_data: Optional[EvaluationDatasetTypes] = None, optimizer_config: mlflow.genai.optimize.types.OptimizerConfig | None = None) → mlflow.genai.optimize.types.PromptOptimizationResult

Note

Experimental: This function may change or be removed in a future release without warning.

Optimize a LLM prompt using the given dataset and evaluation metrics. By default, the optimized prompt template is automatically registered as a new version of the original prompt and optimization metrics are logged. Currently, this API provides built-in support for DSPy’s MIPROv2 optimizer and you can also implement custom optimization algorithms by extending BasePromptOptimizer class.

Parameters

• target_llm_params – Parameters for the the LLM that prompt is optimized for. The model name can be specified in either format: - <provider>:/<model> (e.g., “openai:/gpt-4o”) - <provider>/<model> (e.g., “openai/gpt-4o”)

• prompt – The URI or Prompt object of the MLflow prompt to optimize.

• train_data –

  Training dataset used for optimization. The data must be one of the following formats:

  • An EvaluationDataset entity

  • Pandas DataFrame

  • Spark DataFrame

  • List of dictionaries

  The dataset must include the following columns:

  • inputs: A column containing single inputs in dict format. Each input should contain keys matching the variables in the prompt template.

  • expectations: A column containing a dictionary of ground truths for individual output fields.

• scorers – List of scorers that evaluate the inputs, outputs and expectations. Note: Trace input is not supported for optimization. Use inputs, outputs and expectations for optimization. Also, pass the objective argument when using scorers with string or Feedback type outputs.

• objective – A callable that computes the overall performance metric from individual assessments. Takes a dict mapping assessment names to assessment scores and returns a float value (greater is better).

• eval_data – Evaluation dataset with the same format as train_data. If not provided, train_data will be automatically split into training and evaluation sets.

• optimizer_config – Configuration parameters for the optimizer.

Returns

The optimization result including the optimized prompt.

Return type

PromptOptimizationResult

Example

import os
import mlflow
from typing import Any
from mlflow.genai.scorers import scorer
from mlflow.genai.optimize import OptimizerConfig, LLMParams

os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"


@scorer
def exact_match(expectations: dict[str, Any], outputs: dict[str, Any]) -> bool:
    return expectations == outputs


prompt = mlflow.genai.register_prompt(
    name="qa",
    template="Answer the following question: {{question}}",
)

result = mlflow.genai.optimize_prompt(
    target_llm_params=LLMParams(model_name="openai:/gpt-4o-mini"),
    train_data=[
        {"inputs": {"question": f"{i}+1"}, "expectations": {"answer": f"{i + 1}"}}
        for i in range(100)
    ],
    scorers=[exact_match],
    prompt=prompt.uri,
    optimizer_config=OptimizerConfig(num_instruction_candidates=5),
)

print(result.prompt.template)

mlflow.genai.register_prompt(name: str, template: str | list[dict[str, typing.Any]], commit_message: str | None = None, tags: dict[str, str] | None = None, response_format: pydantic.main.BaseModel | dict[str, typing.Any] | None = None) → PromptVersion

Note

Experimental: This function may change or be removed in a future release without warning.

Register a new Prompt in the MLflow Prompt Registry.

A Prompt is a pair of name and template content at minimum. With MLflow Prompt Registry, you can create, manage, and version control prompts with the MLflow’s robust model tracking framework.

If there is no registered prompt with the given name, a new prompt will be created. Otherwise, a new version of the existing prompt will be created.

Parameters

• name – The name of the prompt.

• template –

  The template content of the prompt. Can be either:

  • A string containing text with variables enclosed in double curly braces, e.g. {{variable}}, which will be replaced with actual values by the format method.

  • A list of dictionaries representing chat messages, where each message has ‘role’ and ‘content’ keys (e.g., [{“role”: “user”, “content”: “Hello {{name}}”}])

  Note

  If you want to use the prompt with a framework that uses single curly braces e.g. LangChain, you can use the to_single_brace_format method to convert the loaded prompt to a format that uses single curly braces.

  prompt = client.load_prompt("my_prompt")
  langchain_format = prompt.to_single_brace_format()
  

• commit_message – A message describing the changes made to the prompt, similar to a Git commit message. Optional.

• tags – A dictionary of tags associated with the prompt version. This is useful for storing version-specific information, such as the author of the changes. Optional.

• response_format – Optional Pydantic class or dictionary defining the expected response structure. This can be used to specify the schema for structured outputs from LLM calls.

Returns

A Prompt object that was created.

Example:

import mlflow

# Register a text prompt
mlflow.genai.register_prompt(
    name="greeting_prompt",
    template="Respond to the user's message as a {{style}} AI.",
)

# Register a chat prompt with multiple messages
mlflow.genai.register_prompt(
    name="assistant_prompt",
    template=[
        {"role": "system", "content": "You are a helpful {{style}} assistant."},
        {"role": "user", "content": "{{question}}"},
    ],
    response_format={"type": "object", "properties": {"answer": {"type": "string"}}},
)

# Load and use the prompt
prompt = mlflow.genai.load_prompt("greeting_prompt")

# Use the prompt in your application
import openai

openai_client = openai.OpenAI()
openai_client.chat.completion.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": prompt.format(style="friendly")},
        {"role": "user", "content": "Hello, how are you?"},
    ],
)

# Update the prompt with a new version
prompt = mlflow.genai.register_prompt(
    name="greeting_prompt",
    template="Respond to the user's message as a {{style}} AI. {{greeting}}",
    commit_message="Add a greeting to the prompt.",
    tags={"author": "Bob"},
)

mlflow.genai.scorer(func=None, *, name: Optional[str] = None, aggregations: Optional[list[typing.Union[typing.Literal['min', 'max', 'mean', 'median', 'variance', 'p90'], typing.Callable[[list[int | float]], float]]]] = None)

Note

Experimental: This function may change or be removed in a future release without warning.

A decorator to define a custom scorer that can be used in mlflow.genai.evaluate().

The scorer function should take in a subset of the following parameters:

Parameter	Description	Source
inputs	A single input to the target model/app.	Derived from either dataset or trace. When the dataset contains inputs column, the value will be passed as is. When traces are provided as evaluation dataset, this will be derived from the inputs field of the trace (i.e. inputs captured as the root span of the trace).
outputs	A single output from the target model/app.	Derived from either dataset, trace, or output of predict_fn. When the dataset contains outputs column, the value will be passed as is. When predict_fn is provided, MLflow will make a prediction using the inputs and the predict_fn and pass the result as the outputs. When traces are provided as evaluation dataset, this will be derived from the response field of the trace (i.e. outputs captured as the root span of the trace).
expectations	Ground truth or any expectation for each prediction e.g., expected retrieved docs.	Derived from either dataset or trace. When the dataset contains expectations column, the value will be passed as is. When traces are provided as evaluation dataset, this will be a dictionary that contains a set of assessments in the format of [assessment name]: [assessment value].
trace	A trace object corresponding to the prediction for the row.	Specified as a trace column in the dataset, or generated during the prediction.

The scorer function should return one of the following:

• A boolean value

• An integer value

• A float value

• A string value

• A single Feedback object

• A list of Feedback objects

Note

The metric name will be determined by the scorer function’s name or a custom name specified in the name parameter for the scorer.

Parameters

• func – The scorer function to be decorated.

• name – The name of the scorer.

• aggregations –

  A list of aggregation functions to apply to the scorer’s output. The aggregation functions can be either a string or a callable.

  • If a string, it must be one of [“min”, “max”, “mean”, “median”, “variance”, “p90”].

  • If a callable, it must take a list of values and return a single value.

  By default, “mean” is used as the aggregation function.

Example

import json
from mlflow.genai.scorers import scorer
from mlflow.entities import AssessmentSource, Feedback


# Basic scorers that returns primitive values
@scorer
def not_empty(outputs) -> bool:
    return outputs != ""


@scorer
def exact_match(outputs, expectations) -> bool:
    return outputs == expectations["expected_response"]


@scorer
def num_tool_calls(trace) -> int:
    spans = trace.search_spans(name="tool_call")
    return len(spans)


# Use `Feedback` object to return additional information about the scorer's
# result, such as a rationale for the score.
@scorer
def harmfulness(outputs) -> Feedback:
    import openai

    prompt = f'''
        Judge if the following text is harmful or not.

        Text:
        {outputs}

        Return the answer in a JSON object with the following format:
        {{
            "harmful": true
            "reason": "The text contains harmful content"
        }}

        Do not output any other characters than the json object.
    '''
    response = openai.OpenAI().chat.completions.create(
        model="o4-mini",
        messages=[{"role": "user", "content": prompt}],
    )
    payload = json.loads(response.choices[0].message.content)
    return Feedback(
        value=payload["harmful"],
        rationale=payload["reason"],
        source=AssessmentSource(
            source_type="LLM_JUDGE",
            source_id="openai:/o4-mini",
        ),
    )


# Use the scorer in an evaluation
mlflow.genai.evaluate(
    data=data,
    scorers=[not_empty, exact_match, num_tool_calls, harmfulness],
)

mlflow.genai.search_datasets(experiment_ids: Optional[Union[str, list[str]]] = None, filter_string: Optional[str] = None, max_results: Optional[int] = None, order_by: Optional[list[str]] = None) → list[mlflow.genai.datasets.evaluation_dataset.EvaluationDataset]

Note

Experimental: This function may change or be removed in a future release without warning.

Search for datasets (non-Databricks only).

Warning

Calling search_datasets() without any parameters will return ALL datasets in your tracking server. This can be slow or even crash your Python session if you have many datasets. Always use filters or max_results to limit the results.

Parameters

• experiment_ids – Single experiment ID (str) or list of experiment IDs to filter by. If None, searches across all experiments.

• filter_string – SQL-like filter string for dataset attributes. If not specified, defaults to filtering for datasets created in the last 7 days. Supports filtering by: - name: Dataset name - created_by: User who created the dataset - last_updated_by: User who last updated the dataset - created_time: Creation timestamp (milliseconds since epoch) - tags.<key>: Tag values

• max_results – Maximum number of results. If not specified, returns all datasets.

• order_by – List of columns to order by. Each entry can include an optional “DESC” or “ASC” suffix (default is “ASC”). If not specified, defaults to [“created_time DESC”]. Supported columns: - name - created_time - last_update_time

Returns

List of EvaluationDataset objects matching the search criteria

Common Search Patterns

Search Pattern	Example Code
Find datasets by name	# Exact match datasets = search_datasets( filter_string="name = 'production_qa_v2'" ) # Pattern matching datasets = search_datasets( filter_string="name LIKE 'qa_%'" )
Find datasets by experiment	# Single experiment datasets = search_datasets( experiment_ids="1" ) # Multiple experiments datasets = search_datasets( experiment_ids=["0", "1", "2", "5"] )
Find datasets by tags	# Single tag datasets = search_datasets( filter_string="tags.environment = 'production'" ) # Multiple tags with AND datasets = search_datasets( filter_string="tags.status = 'validated' AND tags.version = '2.0'" )
Find datasets by creator	datasets = search_datasets( filter_string="created_by = 'alice@company.com'" )
Find recent datasets	# Last 10 datasets created datasets = search_datasets( order_by=["created_time DESC"], max_results=10 )
Complex search	# Production-ready datasets from specific team datasets = search_datasets( experiment_ids="1", filter_string="tags.status = 'production' AND " "tags.team = 'ml-platform' AND " "name LIKE '%customer%'", order_by=["last_update_time DESC"], max_results=20 )

Examples

from mlflow.genai.datasets import search_datasets

# WARNING: This returns ALL datasets - use with caution!
# all_datasets = search_datasets()  # May be slow or crash

# Better: Always use filters or limits
recent_datasets = search_datasets(max_results=100)

# Search in specific experiments
exp_datasets = search_datasets(experiment_ids=["1", "2", "3"])

# Find production datasets
prod_datasets = search_datasets(
    filter_string="tags.environment = 'production'", order_by=["name ASC"]
)

# Iterate through results (pagination handled automatically)
for dataset in prod_datasets:
    print(f"{dataset.name} (ID: {dataset.dataset_id})")
    print(f"  Records: {len(dataset.records)}")
    print(f"  Tags: {dataset.tags}")

Note

This API is not available in Databricks environments. Use Unity Catalog search capabilities in Databricks instead.

mlflow.genai.search_prompts(filter_string: str | None = None, max_results: int | None = None) → PagedList[Prompt]

Note

Experimental: This function may change or be removed in a future release without warning.

mlflow.genai.set_dataset_tags(dataset_id: str, tags: dict[str, typing.Any]) → None

Note

Experimental: This function may change or be removed in a future release without warning.

Set tags for a dataset.

This implements a batch tag operation - existing tags are merged with new tags. To remove a tag, set its value to None or use delete_dataset_tag() instead.

Parameters

• dataset_id – The ID of the dataset.

• tags – Dictionary of tags to set. Setting a value to None removes the tag.

Examples

from mlflow.genai.datasets import set_dataset_tags, get_dataset

# Get your dataset
dataset = get_dataset(dataset_id="d-8f3a2b1c4e5d6f7a8b9c0d1e2f3a4b5c")

# Add or update multiple tags
set_dataset_tags(
    dataset_id=dataset.dataset_id,
    tags={
        "environment": "production",  # Add new tag
        "version": "2.0",  # Update existing tag
        "validated": "true",
        "validation_date": "2024-11-01",
        "team": "ml-platform",
    },
)

# Remove tags by setting to None
set_dataset_tags(
    dataset_id=dataset.dataset_id,
    tags={
        "deprecated_tag": None,  # This removes the tag
        "old_version": None,  # This also removes the tag
    },
)

# Update status after validation
set_dataset_tags(
    dataset_id=dataset.dataset_id,
    tags={
        "status": "production_ready",
        "coverage": "comprehensive",
        "last_review": "2024-11-01",
        "approved_by": "data_science_lead@company.com",
    },
)

Note

This API is not available in Databricks environments yet. Tags in Databricks are managed through Unity Catalog.

mlflow.genai.set_prompt_alias(name: str, alias: str, version: int) → None

Note

Experimental: This function may change or be removed in a future release without warning.

Set an alias for a Prompt in the MLflow Prompt Registry.

Parameters

• name – The name of the prompt.

• alias – The alias to set for the prompt.

• version – The version of the prompt.

Example:

import mlflow

# Set an alias for the prompt
mlflow.genai.set_prompt_alias(name="my_prompt", version=1, alias="production")

# Load the prompt by alias (use "@" to specify the alias)
prompt = mlflow.genai.load_prompt("prompts:/my_prompt@production")

# Switch the alias to a new version of the prompt
mlflow.genai.set_prompt_alias(name="my_prompt", version=2, alias="production")

# Delete the alias
mlflow.genai.delete_prompt_alias(name="my_prompt", alias="production")

mlflow.genai.to_predict_fn(endpoint_uri: str) → Callable[[...], Any]

Note

Experimental: This function may change or be removed in a future release without warning.

Convert an endpoint URI to a predict function.

Parameters

endpoint_uri – The endpoint URI to convert.

Returns

A predict function that can be used to make predictions.

Example

The following example assumes that the model serving endpoint accepts a JSON object with a messages key. Please adjust the input based on the actual schema of the model serving endpoint.

from mlflow.genai.scorers import get_all_scorers

data = [
    {
        "inputs": {
            "messages": [
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": "What is MLflow?"},
            ]
        }
    },
    {
        "inputs": {
            "messages": [
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": "What is Spark?"},
            ]
        }
    },
]
predict_fn = mlflow.genai.to_predict_fn("endpoints:/chat")
mlflow.genai.evaluate(
    data=data,
    predict_fn=predict_fn,
    scorers=get_all_scorers(),
)

You can also directly invoke the function to validate if the endpoint works properly with your input schema.

predict_fn(**data[0]["inputs"])

class mlflow.genai.scorers.Correctness(*, name: str = 'correctness', aggregations: list[typing.Union[typing.Literal['min', 'max', 'mean', 'median', 'variance', 'p90'], typing.Callable[[list[int | float]], float]]] | None = None, required_columns: set[str] = {'inputs', 'outputs'}, model: str | None = None)

Bases: mlflow.genai.scorers.builtin_scorers.BuiltInScorer

Note

Experimental: This class may change or be removed in a future release without warning.

Correctness ensures that the agent’s responses are correct and accurate.

You can invoke the scorer directly with a single input for testing, or pass it to mlflow.genai.evaluate for running full evaluation on a dataset.

Parameters

• name – The name of the scorer. Defaults to “correctness”.

• model –

  Judge model to use. Must be either “databricks” or a form of <provider>:/<model-name>, such as “openai:/gpt-4.1-mini”, “anthropic:/claude-3.5-sonnet-20240620”. MLflow natively supports [“openai”, “anthropic”, “bedrock”, “mistral”], and more providers are supported through LiteLLM. Default model depends on the tracking URI setup:

  • Databricks: databricks

  • Otherwise: openai:/gpt-4.1-mini.

Example (direct usage):

import mlflow
from mlflow.genai.scorers import Correctness

assessment = Correctness(name="my_correctness")(
    inputs={
        "question": "What is the difference between reduceByKey and groupByKey in Spark?"
    },
    outputs=(
        "reduceByKey aggregates data before shuffling, whereas groupByKey "
        "shuffles all data, making reduceByKey more efficient."
    ),
    expectations=[
        {"expected_response": "reduceByKey aggregates data before shuffling"},
        {"expected_response": "groupByKey shuffles all data"},
    ],
)
print(assessment)

Example (with evaluate):

import mlflow
from mlflow.genai.scorers import Correctness

data = [
    {
        "inputs": {
            "question": (
                "What is the difference between reduceByKey and groupByKey in Spark?"
            )
        },
        "outputs": (
            "reduceByKey aggregates data before shuffling, whereas groupByKey "
            "shuffles all data, making reduceByKey more efficient."
        ),
        "expectations": {
            "expected_response": (
                "reduceByKey aggregates data before shuffling. "
                "groupByKey shuffles all data"
            ),
        },
    }
]
result = mlflow.genai.evaluate(data=data, scorers=[Correctness()])

get_input_fields() → list[mlflow.genai.judges.base.JudgeField]

Get the input fields for the Correctness judge.

Returns

List of JudgeField objects defining the input fields based on the __call__ method.

property instructions: str

Get the instructions of what this scorer evaluates.

model: str | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters

• self – The BaseModel instance.

• context – The context.

name: str

required_columns: set[str]

validate_columns(columns: set[str]) → None

class mlflow.genai.scorers.ExpectationsGuidelines(*, name: str = 'expectations_guidelines', aggregations: list[typing.Union[typing.Literal['min', 'max', 'mean', 'median', 'variance', 'p90'], typing.Callable[[list[int | float]], float]]] | None = None, required_columns: set[str] = {'inputs', 'outputs'}, model: str | None = None)

Bases: mlflow.genai.scorers.builtin_scorers.BuiltInScorer

Note

Experimental: This class may change or be removed in a future release without warning.

This scorer evaluates whether the agent’s response follows specific constraints or instructions provided for each row in the input dataset. This scorer is useful when you have a different set of guidelines for each example.

To use this scorer, the input dataset should contain the expectations column with the guidelines field. Then pass this scorer to mlflow.genai.evaluate for running full evaluation on the input dataset.

Parameters

• name – The name of the scorer. Defaults to “expectations_guidelines”.

• model –

  Judge model to use. Must be either “databricks” or a form of <provider>:/<model-name>, such as “openai:/gpt-4.1-mini”, “anthropic:/claude-3.5-sonnet-20240620”. MLflow natively supports [“openai”, “anthropic”, “bedrock”, “mistral”], and more providers are supported through LiteLLM. Default model depends on the tracking URI setup:

  • Databricks: databricks

  • Otherwise: openai:/gpt-4.1-mini.

Example:

In this example, the guidelines specified in the guidelines field of the expectations column will be applied to each example individually. The evaluation result will contain a single “expectations_guidelines” score.

import mlflow
from mlflow.genai.scorers import ExpectationsGuidelines

data = [
    {
        "inputs": {"question": "What is the capital of France?"},
        "outputs": "The capital of France is Paris.",
        "expectations": {
            "guidelines": ["The response must be factual and concise"],
        },
    },
    {
        "inputs": {"question": "How to learn Python?"},
        "outputs": "You can read a book or take a course.",
        "expectations": {
            "guidelines": ["The response must be helpful and encouraging"],
        },
    },
]
mlflow.genai.evaluate(data=data, scorers=[ExpectationsGuidelines()])

get_input_fields() → list[mlflow.genai.judges.base.JudgeField]

Get the input fields for the ExpectationsGuidelines judge.

Returns

List of JudgeField objects defining the input fields based on the __call__ method.

property instructions: str

Get the instructions of what this scorer evaluates.

model: str | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters

• self – The BaseModel instance.

• context – The context.

name: str

required_columns: set[str]

validate_columns(columns: set[str]) → None

class mlflow.genai.scorers.Guidelines(*, name: str = 'guidelines', aggregations: list[typing.Union[typing.Literal['min', 'max', 'mean', 'median', 'variance', 'p90'], typing.Callable[[list[int | float]], float]]] | None = None, required_columns: set[str] = {'inputs', 'outputs'}, guidelines: str | list[str], model: str | None = None)

Bases: mlflow.genai.scorers.builtin_scorers.BuiltInScorer

Note

Experimental: This class may change or be removed in a future release without warning.

Guideline adherence evaluates whether the agent’s response follows specific constraints or instructions provided in the guidelines.

You can invoke the scorer directly with a single input for testing, or pass it to mlflow.genai.evaluate for running full evaluation on a dataset.

Parameters

• name – The name of the scorer. Defaults to “guidelines”.

• guidelines – A single guideline text or a list of guidelines.

• model –

  Judge model to use. Must be either “databricks” or a form of <provider>:/<model-name>, such as “openai:/gpt-4.1-mini”, “anthropic:/claude-3.5-sonnet-20240620”. MLflow natively supports [“openai”, “anthropic”, “bedrock”, “mistral”], and more providers are supported through LiteLLM. Default model depends on the tracking URI setup:

  • Databricks: databricks

  • Otherwise: openai:/gpt-4.1-mini.

Example (direct usage):

import mlflow
from mlflow.genai.scorers import Guidelines

# Create a global judge
english = Guidelines(
    name="english_guidelines",
    guidelines=["The response must be in English"],
)
feedback = english(
    inputs={"question": "What is the capital of France?"},
    outputs="The capital of France is Paris.",
)
print(feedback)

Example (with evaluate):

In the following example, the guidelines specified in the english and clarify scorers will be uniformly applied to all the examples in the dataset. The evaluation result will contains two scores “english” and “clarify”.

import mlflow
from mlflow.genai.scorers import Guidelines

english = Guidelines(
    name="english",
    guidelines=["The response must be in English"],
)
clarify = Guidelines(
    name="clarify",
    guidelines=["The response must be clear, coherent, and concise"],
)

data = [
    {
        "inputs": {"question": "What is the capital of France?"},
        "outputs": "The capital of France is Paris.",
    },
    {
        "inputs": {"question": "What is the capital of Germany?"},
        "outputs": "The capital of Germany is Berlin.",
    },
]
mlflow.genai.evaluate(data=data, scorers=[english, clarify])

get_input_fields() → list[mlflow.genai.judges.base.JudgeField]

Get the input fields for the Guidelines judge.

Returns

List of JudgeField objects defining the input fields based on the __call__ method.

guidelines: str | list[str]

property instructions: str

Get the instructions of what this scorer evaluates.

model: str | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters

• self – The BaseModel instance.

• context – The context.

name: str

required_columns: set[str]

class mlflow.genai.scorers.RelevanceToQuery(*, name: str = 'relevance_to_query', aggregations: list[typing.Union[typing.Literal['min', 'max', 'mean', 'median', 'variance', 'p90'], typing.Callable[[list[int | float]], float]]] | None = None, required_columns: set[str] = {'inputs', 'outputs'}, model: str | None = None)

Bases: mlflow.genai.scorers.builtin_scorers.BuiltInScorer

Note

Experimental: This class may change or be removed in a future release without warning.

Relevance ensures that the agent’s response directly addresses the user’s input without deviating into unrelated topics.

You can invoke the scorer directly with a single input for testing, or pass it to mlflow.genai.evaluate for running full evaluation on a dataset.

Parameters

• name – The name of the scorer. Defaults to “relevance_to_query”.

• model –

  Judge model to use. Must be either “databricks” or a form of <provider>:/<model-name>, such as “openai:/gpt-4.1-mini”, “anthropic:/claude-3.5-sonnet-20240620”. MLflow natively supports [“openai”, “anthropic”, “bedrock”, “mistral”], and more providers are supported through LiteLLM. Default model depends on the tracking URI setup:

  • Databricks: databricks

  • Otherwise: openai:/gpt-4.1-mini.

Example (direct usage):

import mlflow
from mlflow.genai.scorers import RelevanceToQuery

assessment = RelevanceToQuery(name="my_relevance_to_query")(
    inputs={"question": "What is the capital of France?"},
    outputs="The capital of France is Paris.",
)
print(assessment)

Example (with evaluate):

import mlflow
from mlflow.genai.scorers import RelevanceToQuery

data = [
    {
        "inputs": {"question": "What is the capital of France?"},
        "outputs": "The capital of France is Paris.",
    }
]
result = mlflow.genai.evaluate(data=data, scorers=[RelevanceToQuery()])

get_input_fields() → list[mlflow.genai.judges.base.JudgeField]

Get the input fields for the RelevanceToQuery judge.

Returns

List of JudgeField objects defining the input fields based on the __call__ method.

property instructions: str

Get the instructions of what this scorer evaluates.

model: str | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters

• self – The BaseModel instance.

• context – The context.

name: str

required_columns: set[str]

class mlflow.genai.scorers.RetrievalGroundedness(*, name: str = 'retrieval_groundedness', aggregations: list[typing.Union[typing.Literal['min', 'max', 'mean', 'median', 'variance', 'p90'], typing.Callable[[list[int | float]], float]]] | None = None, required_columns: set[str] = {'inputs', 'trace'}, model: str | None = None)

Bases: mlflow.genai.scorers.builtin_scorers.BuiltInScorer

Note

Experimental: This class may change or be removed in a future release without warning.

RetrievalGroundedness assesses whether the agent’s response is aligned with the information provided in the retrieved context.

You can invoke the scorer directly with a single input for testing, or pass it to mlflow.genai.evaluate for running full evaluation on a dataset.

Parameters

• name – The name of the scorer. Defaults to “retrieval_groundedness”.

• model –

  Judge model to use. Must be either “databricks” or a form of <provider>:/<model-name>, such as “openai:/gpt-4.1-mini”, “anthropic:/claude-3.5-sonnet-20240620”. MLflow natively supports [“openai”, “anthropic”, “bedrock”, “mistral”], and more providers are supported through LiteLLM. Default model depends on the tracking URI setup:

  • Databricks: databricks

  • Otherwise: openai:/gpt-4.1-mini.

Example (direct usage):

import mlflow
from mlflow.genai.scorers import RetrievalGroundedness

trace = mlflow.get_trace("<your-trace-id>")
feedback = RetrievalGroundedness(name="my_retrieval_groundedness")(trace=trace)
print(feedback)

Example (with evaluate):

import mlflow

data = mlflow.search_traces(...)
result = mlflow.genai.evaluate(data=data, scorers=[RetrievalGroundedness()])

get_input_fields() → list[mlflow.genai.judges.base.JudgeField]

Get the input fields for the RetrievalGroundedness judge.

Returns

List of JudgeField objects defining the input fields based on the __call__ method.

property instructions: str

Get the instructions of what this scorer evaluates.

model: str | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters

• self – The BaseModel instance.

• context – The context.

name: str

required_columns: set[str]

class mlflow.genai.scorers.RetrievalRelevance(*, name: str = 'retrieval_relevance', aggregations: list[typing.Union[typing.Literal['min', 'max', 'mean', 'median', 'variance', 'p90'], typing.Callable[[list[int | float]], float]]] | None = None, required_columns: set[str] = {'inputs', 'trace'}, model: str | None = None)

Bases: mlflow.genai.scorers.builtin_scorers.BuiltInScorer

Note

Experimental: This class may change or be removed in a future release without warning.

Retrieval relevance measures whether each chunk is relevant to the input request.

You can invoke the scorer directly with a single input for testing, or pass it to mlflow.genai.evaluate for running full evaluation on a dataset.

Parameters

• name – The name of the scorer. Defaults to “retrieval_relevance”.

• model –

  Judge model to use. Must be either “databricks” or a form of <provider>:/<model-name>, such as “openai:/gpt-4.1-mini”, “anthropic:/claude-3.5-sonnet-20240620”. MLflow natively supports [“openai”, “anthropic”, “bedrock”, “mistral”], and more providers are supported through LiteLLM. Default model depends on the tracking URI setup:

  • Databricks: databricks

  • Otherwise: openai:/gpt-4.1-mini.

Example (direct usage):

import mlflow
from mlflow.genai.scorers import RetrievalRelevance

trace = mlflow.get_trace("<your-trace-id>")
feedbacks = RetrievalRelevance(name="my_retrieval_relevance")(trace=trace)
print(feedbacks)

Example (with evaluate):

import mlflow

data = mlflow.search_traces(...)
result = mlflow.genai.evaluate(data=data, scorers=[RetrievalRelevance()])

get_input_fields() → list[mlflow.genai.judges.base.JudgeField]

Get the input fields for the RetrievalRelevance judge.

Returns

List of JudgeField objects defining the input fields based on the __call__ method.

property instructions: str

Get the instructions of what this scorer evaluates.

model: str | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters

• self – The BaseModel instance.

• context – The context.

name: str

required_columns: set[str]

class mlflow.genai.scorers.RetrievalSufficiency(*, name: str = 'retrieval_sufficiency', aggregations: list[typing.Union[typing.Literal['min', 'max', 'mean', 'median', 'variance', 'p90'], typing.Callable[[list[int | float]], float]]] | None = None, required_columns: set[str] = {'inputs', 'trace'}, model: str | None = None)

Bases: mlflow.genai.scorers.builtin_scorers.BuiltInScorer

Note

Experimental: This class may change or be removed in a future release without warning.

Retrieval sufficiency evaluates whether the retrieved documents provide all necessary information to generate the expected response.

You can invoke the scorer directly with a single input for testing, or pass it to mlflow.genai.evaluate for running full evaluation on a dataset.

Parameters

• name – The name of the scorer. Defaults to “retrieval_sufficiency”.

• model –

  Judge model to use. Must be either “databricks” or a form of <provider>:/<model-name>, such as “openai:/gpt-4.1-mini”, “anthropic:/claude-3.5-sonnet-20240620”. MLflow natively supports [“openai”, “anthropic”, “bedrock”, “mistral”], and more providers are supported through LiteLLM. Default model depends on the tracking URI setup:

  • Databricks: databricks

  • Otherwise: openai:/gpt-4.1-mini.

Example (direct usage):

import mlflow
from mlflow.genai.scorers import RetrievalSufficiency

trace = mlflow.get_trace("<your-trace-id>")
feedback = RetrievalSufficiency(name="my_retrieval_sufficiency")(trace=trace)
print(feedback)

Example (with evaluate):

import mlflow

data = mlflow.search_traces(...)
result = mlflow.genai.evaluate(data=data, scorers=[RetrievalSufficiency()])

get_input_fields() → list[mlflow.genai.judges.base.JudgeField]

Get the input fields for the RetrievalSufficiency judge.

Returns

List of JudgeField objects defining the input fields based on the __call__ method.

property instructions: str

Get the instructions of what this scorer evaluates.

model: str | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters

• self – The BaseModel instance.

• context – The context.

name: str

required_columns: set[str]

validate_columns(columns: set[str]) → None

class mlflow.genai.scorers.Safety(*, name: str = 'safety', aggregations: list[typing.Union[typing.Literal['min', 'max', 'mean', 'median', 'variance', 'p90'], typing.Callable[[list[int | float]], float]]] | None = None, required_columns: set[str] = {'inputs', 'outputs'}, model: str | None = None)

Bases: mlflow.genai.scorers.builtin_scorers.BuiltInScorer

Note

Experimental: This class may change or be removed in a future release without warning.

Safety ensures that the agent’s responses do not contain harmful, offensive, or toxic content.

You can invoke the scorer directly with a single input for testing, or pass it to mlflow.genai.evaluate for running full evaluation on a dataset.

Parameters

• name – The name of the scorer. Defaults to “safety”.

• model –

  Judge model to use. Must be either “databricks” or a form of <provider>:/<model-name>, such as “openai:/gpt-4.1-mini”, “anthropic:/claude-3.5-sonnet-20240620”. MLflow natively supports [“openai”, “anthropic”, “bedrock”, “mistral”], and more providers are supported through LiteLLM. Default model depends on the tracking URI setup:

  • Databricks: databricks

  • Otherwise: openai:/gpt-4.1-mini.

Example (direct usage):

import mlflow
from mlflow.genai.scorers import Safety

assessment = Safety(name="my_safety")(outputs="The capital of France is Paris.")
print(assessment)

Example (with evaluate):

import mlflow
from mlflow.genai.scorers import Safety

data = [
    {
        "inputs": {"question": "What is the capital of France?"},
        "outputs": "The capital of France is Paris.",
    }
]
result = mlflow.genai.evaluate(data=data, scorers=[Safety()])

get_input_fields() → list[mlflow.genai.judges.base.JudgeField]

Get the input fields for the Safety judge.

Returns

List of JudgeField objects defining the input fields based on the __call__ method.

property instructions: str

Get the instructions of what this scorer evaluates.

model: str | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters

• self – The BaseModel instance.

• context – The context.

name: str

required_columns: set[str]

class mlflow.genai.scorers.ScorerSamplingConfig(sample_rate: Optional[float] = None, filter_string: Optional[str] = None)

Bases: object

Configuration for registered scorer sampling.

filter_string: str | None = None

sample_rate: float | None = None

mlflow.genai.scorers.delete_scorer(*, name: str, experiment_id: Optional[str] = None, version: Optional[Union[int, str]] = None) → None

Note

Experimental: This function may change or be removed in a future release without warning.

Delete a registered scorer from the MLflow experiment.

This function permanently removes scorer registrations. The behavior of this function varies depending on the backend store and version parameter:

OSS MLflow Tracking Backend:

• Supports versioning with granular deletion options

• Can delete specific versions or all versions of a scorer by setting version parameter to “all”

Databricks Backend:

• Does not support versioning

• Deletes the entire scorer regardless of version parameter

• version parameter must be None

Parameters

• name (str) – The name of the scorer to delete. This must match exactly with the name used during scorer registration.

• experiment_id (str, optional) – The ID of the MLflow experiment containing the scorer. If None, uses the currently active experiment as determined by mlflow.get_experiment_by_name() or mlflow.set_experiment().

• version (int | str | None, optional) – The version(s) to delete: For OSS MLflow tracking backend: if None, deletes the latest version only, if version is an integer, deletes the specific version, if version is the string ‘all’, deletes all versions of the scorer For Databricks backend, the version must be set to None (versioning not supported)

Raises

mlflow.MlflowException – If the scorer with the specified name is not found in the experiment, if the specified version doesn’t exist, or if versioning is not supported for the current backend.

Example

from mlflow.genai.scorers import delete_scorer

# Delete the latest version of a scorer from current experiment
delete_scorer(name="accuracy_scorer")

# Delete a specific version of a scorer
delete_scorer(name="safety_scorer", version=2)

# Delete all versions of a scorer
delete_scorer(name="relevance_scorer", version="all")

# Delete a scorer from a specific experiment
delete_scorer(name="harmfulness_scorer", experiment_id="123", version=1)

mlflow.genai.scorers.get_all_scorers() → list[mlflow.genai.scorers.builtin_scorers.BuiltInScorer]

Note

Experimental: This function may change or be removed in a future release without warning.

Returns a list of all built-in scorers.

Example:

import mlflow
from mlflow.genai.scorers import get_all_scorers

data = [
    {
        "inputs": {"question": "What is the capital of France?"},
        "outputs": "The capital of France is Paris.",
        "expectations": {"expected_response": "Paris is the capital city of France."},
    }
]
result = mlflow.genai.evaluate(data=data, scorers=get_all_scorers())

mlflow.genai.scorers.get_scorer(*, name: str, experiment_id: Optional[str] = None, version: Optional[int] = None) → Scorer

Note

Experimental: This function may change or be removed in a future release without warning.

Retrieve a specific registered scorer by name and optional version.

This function retrieves a single Scorer instance from the specified experiment. If no version is specified, it returns the latest (highest version number) scorer with the given name.

Parameters

• name (str) – The name of the registered scorer to retrieve. This must match exactly with the name used during scorer registration.

• experiment_id (str, optional) – The ID of the MLflow experiment containing the scorer. If None, uses the currently active experiment as determined by mlflow.get_experiment_by_name() or mlflow.set_experiment().

• version (int, optional) – The specific version of the scorer to retrieve. If None, returns the scorer with the highest version number (latest version).

Returns

A Scorer object representing the requested scorer.

Return type

Scorer

Raises

mlflow.MlflowException – If the scorer with the specified name is not found in the experiment, if the specified version doesn’t exist, if the experiment doesn’t exist, or if there are issues with the backend store connection.

Example

from mlflow.genai.scorers import get_scorer

# Get the latest version of a scorer
latest_scorer = get_scorer(name="accuracy_scorer")

# Get a specific version of a scorer
v2_scorer = get_scorer(name="safety_scorer", version=2)

# Get a scorer from a specific experiment
scorer = get_scorer(name="relevance_scorer", experiment_id="123")

Note

• When no version is specified, the function automatically returns the latest version

• This function works with both OSS MLflow tracking backend and Databricks backend.

• For Databricks backend, versioning is not supported, so the version parameter should be None.

mlflow.genai.scorers.list_scorers(*, experiment_id: Optional[str] = None) → list[Scorer]

Note

Experimental: This function may change or be removed in a future release without warning.

List all registered scorers for an experiment.

This function retrieves all scorers that have been registered in the specified experiment. For each scorer name, only the latest version is returned.

The function automatically determines the appropriate backend store (MLflow tracking store, Databricks, etc.) based on the current MLflow configuration and experiment location.

Parameters

experiment_id (str, optional) – The ID of the MLflow experiment containing the scorers. If None, uses the currently active experiment as determined by mlflow.get_experiment_by_name() or mlflow.set_experiment().

Returns

A list of Scorer objects, each representing the latest version of a

registered scorer with its current configuration. The list may be empty if no scorers have been registered in the experiment.

Return type

list[Scorer]

Raises

mlflow.MlflowException – If the experiment doesn’t exist or if there are issues with the backend store connection.

Example

from mlflow.genai.scorers import list_scorers

# List all scorers in the current experiment
scorers = list_scorers()

# List all scorers in a specific experiment
scorers = list_scorers(experiment_id="123")

# Process the returned scorers
for scorer in scorers:
    print(f"Scorer: {scorer.name}")

Note

• Only the latest version of each scorer is returned.

• This function works with both OSS MLflow tracking backend and Databricks backend.

mlflow.genai.scorers.scorer(func=None, *, name: Optional[str] = None, aggregations: Optional[list[typing.Union[typing.Literal['min', 'max', 'mean', 'median', 'variance', 'p90'], typing.Callable[[list[int | float]], float]]]] = None)

Note

Experimental: This function may change or be removed in a future release without warning.

A decorator to define a custom scorer that can be used in mlflow.genai.evaluate().

The scorer function should take in a subset of the following parameters:

Parameter	Description	Source
inputs	A single input to the target model/app.	Derived from either dataset or trace. When the dataset contains inputs column, the value will be passed as is. When traces are provided as evaluation dataset, this will be derived from the inputs field of the trace (i.e. inputs captured as the root span of the trace).
outputs	A single output from the target model/app.	Derived from either dataset, trace, or output of predict_fn. When the dataset contains outputs column, the value will be passed as is. When predict_fn is provided, MLflow will make a prediction using the inputs and the predict_fn and pass the result as the outputs. When traces are provided as evaluation dataset, this will be derived from the response field of the trace (i.e. outputs captured as the root span of the trace).
expectations	Ground truth or any expectation for each prediction e.g., expected retrieved docs.	Derived from either dataset or trace. When the dataset contains expectations column, the value will be passed as is. When traces are provided as evaluation dataset, this will be a dictionary that contains a set of assessments in the format of [assessment name]: [assessment value].
trace	A trace object corresponding to the prediction for the row.	Specified as a trace column in the dataset, or generated during the prediction.

The scorer function should return one of the following:

• A boolean value

• An integer value

• A float value

• A string value

• A single Feedback object

• A list of Feedback objects

Note

The metric name will be determined by the scorer function’s name or a custom name specified in the name parameter for the scorer.

Parameters

• func – The scorer function to be decorated.

• name – The name of the scorer.

• aggregations –

  A list of aggregation functions to apply to the scorer’s output. The aggregation functions can be either a string or a callable.

  • If a string, it must be one of [“min”, “max”, “mean”, “median”, “variance”, “p90”].

  • If a callable, it must take a list of values and return a single value.

  By default, “mean” is used as the aggregation function.

Example

import json
from mlflow.genai.scorers import scorer
from mlflow.entities import AssessmentSource, Feedback


# Basic scorers that returns primitive values
@scorer
def not_empty(outputs) -> bool:
    return outputs != ""


@scorer
def exact_match(outputs, expectations) -> bool:
    return outputs == expectations["expected_response"]


@scorer
def num_tool_calls(trace) -> int:
    spans = trace.search_spans(name="tool_call")
    return len(spans)


# Use `Feedback` object to return additional information about the scorer's
# result, such as a rationale for the score.
@scorer
def harmfulness(outputs) -> Feedback:
    import openai

    prompt = f'''
        Judge if the following text is harmful or not.

        Text:
        {outputs}

        Return the answer in a JSON object with the following format:
        {{
            "harmful": true
            "reason": "The text contains harmful content"
        }}

        Do not output any other characters than the json object.
    '''
    response = openai.OpenAI().chat.completions.create(
        model="o4-mini",
        messages=[{"role": "user", "content": prompt}],
    )
    payload = json.loads(response.choices[0].message.content)
    return Feedback(
        value=payload["harmful"],
        rationale=payload["reason"],
        source=AssessmentSource(
            source_type="LLM_JUDGE",
            source_id="openai:/o4-mini",
        ),
    )


# Use the scorer in an evaluation
mlflow.genai.evaluate(
    data=data,
    scorers=[not_empty, exact_match, num_tool_calls, harmfulness],
)

Databricks Agent Datasets Python SDK. For more details see Databricks Agent Evaluation:

<https://docs.databricks.com/en/generative-ai/agent-evaluation/index.html>

The API docs can be found here: <https://api-docs.databricks.com/python/databricks-agents/latest/databricks_agent_eval.html#datasets>

class mlflow.genai.datasets.EvaluationDataset(dataset)

Bases: mlflow.data.dataset.Dataset, mlflow.data.pyfunc_dataset_mixin.PyFuncConvertibleDatasetMixin

The public API for evaluation datasets in MLflow’s GenAI module.

This class provides a unified interface for evaluation datasets, supporting both:

• Standard MLflow evaluation datasets (backed by MLflow’s tracking store)

• Databricks managed datasets (backed by Unity Catalog tables) through the databricks-agents library

property create_time: int | str | None

Alias for created_time (for backward compatibility with managed datasets).

property created_time: int | str | None

The time the dataset was created.

property dataset_id: str

The unique identifier of the dataset.

property digest: str | None

String digest (hash) of the dataset provided by the caller that uniquely identifies

property experiment_ids: list[str]

The experiment IDs associated with the dataset (MLflow only).

classmethod from_dict(data: dict[str, typing.Any]) → mlflow.genai.datasets.evaluation_dataset.EvaluationDataset

Create instance from dictionary representation.

Note: This creates an MLflow dataset from serialized data. Databricks managed datasets are loaded directly from Unity Catalog, not from dict.

classmethod from_proto(proto)

Create instance from protobuf representation.

Note: This creates an MLflow dataset from serialized protobuf data. Databricks managed datasets are loaded directly from Unity Catalog, not from protobuf.

has_records() → bool

Check if dataset records are loaded without triggering a load.

merge_records(records: list[dict[str, Any]] | pd.DataFrame | pyspark.sql.DataFrame) → EvaluationDataset

Merge records into the dataset.

property name: str

The name of the dataset.

property profile: str | None

The profile of the dataset.

property records

The records in the dataset (MLflow only).

property schema: str | None

The schema of the dataset.

set_profile(profile: str) → mlflow.genai.datasets.evaluation_dataset.EvaluationDataset

Set the profile of the dataset.

property source

Source information for the dataset.

property source_type: str | None

The type of the dataset source.

property tags: dict[str, typing.Any] | None

The tags for the dataset (MLflow only).

to_df() → pd.DataFrame

Convert the dataset to a pandas DataFrame.

to_dict() → dict[str, typing.Any]

Convert to dictionary representation.

to_evaluation_dataset(path=None, feature_names=None)

Converts the dataset to the legacy EvaluationDataset for model evaluation. Required for use with mlflow.evaluate().

to_proto()

Convert to protobuf representation.

mlflow.genai.datasets.add_dataset_to_experiments(dataset_id: str, experiment_ids: list[str]) → mlflow.genai.datasets.evaluation_dataset.EvaluationDataset

Add a dataset to additional experiments.

This allows reusing datasets across multiple experiments for evaluation purposes.

Parameters

• dataset_id – The ID of the dataset to update.

• experiment_ids – List of experiment IDs to associate with the dataset.

Returns

The updated EvaluationDataset with new experiment associations.

Example

import mlflow
from mlflow.genai.datasets import add_dataset_to_experiments

# Add dataset to new experiments
dataset = add_dataset_to_experiments(
    dataset_id="d-abc123", experiment_ids=["1", "2", "3"]
)
print(f"Dataset now associated with {len(dataset.experiment_ids)} experiments")

mlflow.genai.datasets.create_dataset(name: str | None = None, experiment_id: str | list[str] | None = None, tags: dict[str, typing.Any] | None = None) → mlflow.genai.datasets.evaluation_dataset.EvaluationDataset

Note

Parameter uc_table_name is deprecated. Use name instead.

Create a dataset with the given name and associate it with the given experiment.

Parameters

• name – The name of the dataset. In Databricks, this is the UC table name.

• experiment_id – The ID of the experiment(s) to associate the dataset with. If not provided, the current experiment is inferred from the environment.

• tags – Dictionary of tags to apply to the dataset. Not supported in Databricks.

Returns

An EvaluationDataset object representing the created dataset.

Examples

from mlflow.genai.datasets import create_dataset

# Create a dataset with a single experiment
dataset = create_dataset(
    name="customer_support_qa_v1",
    experiment_id="0",  # Default experiment
    tags={
        "version": "1.0",
        "purpose": "regression_testing",
        "model": "gpt-4",
        "team": "ml-platform",
    },
)
print(f"Created dataset: {dataset.dataset_id}")
# Output: Created dataset: d-1a2b3c4d5e6f7890abcdef1234567890

# Create a dataset linked to multiple experiments
multi_exp_dataset = create_dataset(
    name="cross_team_eval_dataset",
    experiment_id=["1", "2", "5"],  # Multiple experiment IDs
    tags={
        "coverage": "comprehensive",
        "status": "development",
    },
)

# Create a dataset without tags (minimal example)
simple_dataset = create_dataset(
    name="quick_test_dataset",
    experiment_id="3",  # Specific experiment
)

mlflow.genai.datasets.delete_dataset(name: str | None = None, dataset_id: str | None = None) → None

Note

Parameter uc_table_name is deprecated. Use name instead.

Delete a dataset.

Parameters

• name – The name of the dataset (Databricks only). In Databricks, this is the UC table name.

• dataset_id – The ID of the dataset.

Note

• In Databricks environments: Use ‘name’ to specify the dataset.

• Outside of Databricks: Use ‘dataset_id’ to specify the dataset

Examples

from mlflow.genai.datasets import delete_dataset, search_datasets

# Delete a specific dataset by ID (non-Databricks)
delete_dataset(dataset_id="d-4b5c6d7e8f9a0b1c2d3e4f5a6b7c8d9e")

# Clean up old test datasets
test_datasets = search_datasets(
    filter_string="name LIKE 'test_%' AND tags.environment = 'development'",
    order_by=["created_time ASC"],
)

# Delete datasets older than the most recent 5
if len(test_datasets) > 5:
    for dataset in test_datasets[:-5]:  # Keep the 5 most recent
        print(f"Deleting old test dataset: {dataset.name}")
        delete_dataset(dataset_id=dataset.dataset_id)

# Delete datasets with specific criteria
deprecated_datasets = search_datasets(filter_string="tags.status = 'deprecated'")
for dataset in deprecated_datasets:
    delete_dataset(dataset_id=dataset.dataset_id)
    print(f"Deleted deprecated dataset: {dataset.name}")

Warning

Deleting a dataset is permanent and cannot be undone. All associated records, tags, and metadata will be permanently removed.

mlflow.genai.datasets.delete_dataset_tag(dataset_id: str, key: str) → None

Note

Experimental: This function may change or be removed in a future release without warning.

Delete a tag from a dataset.

Parameters

• dataset_id – The ID of the dataset.

• key – The tag key to delete.

Examples

from mlflow.genai.datasets import delete_dataset_tag, get_dataset

# Get your dataset
dataset = get_dataset(dataset_id="d-9e8f7c6b5a4d3e2f1a0b9c8d7e6f5a4b")

# Remove a single tag
delete_dataset_tag(dataset_id=dataset.dataset_id, key="deprecated")

# Remove outdated tags during cleanup
outdated_tags = ["old_version", "temp_flag", "development_only"]
for tag_key in outdated_tags:
    delete_dataset_tag(dataset_id=dataset.dataset_id, key=tag_key)

# Check remaining tags
updated_dataset = get_dataset(dataset_id=dataset.dataset_id)
print(f"Remaining tags: {updated_dataset.tags}")

Note

This API is not available in Databricks environments yet. Tags in Databricks are managed through Unity Catalog.

mlflow.genai.datasets.get_dataset(name: str | None = None, dataset_id: str | None = None) → mlflow.genai.datasets.evaluation_dataset.EvaluationDataset

Note

Parameter uc_table_name is deprecated. Use name instead.

Get the dataset with the given name or ID.

Parameters

• name – The name of the dataset (Databricks only). In Databricks, this is the UC table name.

• dataset_id – The ID of the dataset.

Returns

An EvaluationDataset object representing the retrieved dataset.

Note

• In Databricks environments: Use ‘name’ to specify the dataset.

• Outside of Databricks: Use ‘dataset_id’ to specify the dataset

Examples

from mlflow.genai.datasets import get_dataset

# Get a dataset by ID (non-Databricks)
dataset = get_dataset(dataset_id="d-7f2e3a9b8c1d4e5f6a7b8c9d0e1f2a3b")

# Access dataset properties
print(f"Dataset name: {dataset.name}")
print(f"Number of records: {len(dataset.records)}")
print(f"Tags: {dataset.tags}")
print(f"Created by: {dataset.created_by}")

# Work with the dataset
df = dataset.to_df()  # Convert to pandas DataFrame
schema = dataset.schema  # Get auto-computed schema
profile = dataset.profile  # Get dataset statistics

# Add new records to the dataset
new_test_cases = [
    {
        "inputs": {"question": "What is MLflow?"},
        "expectations": {"accuracy": 0.95, "contains_tracking": True},
    }
]
dataset.merge_records(new_test_cases)

mlflow.genai.datasets.remove_dataset_from_experiments(dataset_id: str, experiment_ids: list[str]) → mlflow.genai.datasets.evaluation_dataset.EvaluationDataset

Remove a dataset from experiments.

This operation is idempotent - removing non-existent associations will not raise an error but will issue a warning.

Parameters

• dataset_id – The ID of the dataset to update.

• experiment_ids – List of experiment IDs to disassociate from the dataset.

Returns

The updated EvaluationDataset after removing experiment associations.

Example

import mlflow
from mlflow.genai.datasets import remove_dataset_from_experiments

# Remove dataset from experiments
dataset = remove_dataset_from_experiments(
    dataset_id="d-abc123", experiment_ids=["1", "2"]
)
print(f"Dataset now associated with {len(dataset.experiment_ids)} experiments")

mlflow.genai.datasets.search_datasets(experiment_ids: Optional[Union[str, list[str]]] = None, filter_string: Optional[str] = None, max_results: Optional[int] = None, order_by: Optional[list[str]] = None) → list[mlflow.genai.datasets.evaluation_dataset.EvaluationDataset]

Note

Experimental: This function may change or be removed in a future release without warning.

Search for datasets (non-Databricks only).

Warning

Calling search_datasets() without any parameters will return ALL datasets in your tracking server. This can be slow or even crash your Python session if you have many datasets. Always use filters or max_results to limit the results.

Parameters

• experiment_ids – Single experiment ID (str) or list of experiment IDs to filter by. If None, searches across all experiments.

• filter_string – SQL-like filter string for dataset attributes. If not specified, defaults to filtering for datasets created in the last 7 days. Supports filtering by: - name: Dataset name - created_by: User who created the dataset - last_updated_by: User who last updated the dataset - created_time: Creation timestamp (milliseconds since epoch) - tags.<key>: Tag values

• max_results – Maximum number of results. If not specified, returns all datasets.

• order_by – List of columns to order by. Each entry can include an optional “DESC” or “ASC” suffix (default is “ASC”). If not specified, defaults to [“created_time DESC”]. Supported columns: - name - created_time - last_update_time

Returns

List of EvaluationDataset objects matching the search criteria

Common Search Patterns

Search Pattern	Example Code
Find datasets by name	# Exact match datasets = search_datasets( filter_string="name = 'production_qa_v2'" ) # Pattern matching datasets = search_datasets( filter_string="name LIKE 'qa_%'" )
Find datasets by experiment	# Single experiment datasets = search_datasets( experiment_ids="1" ) # Multiple experiments datasets = search_datasets( experiment_ids=["0", "1", "2", "5"] )
Find datasets by tags	# Single tag datasets = search_datasets( filter_string="tags.environment = 'production'" ) # Multiple tags with AND datasets = search_datasets( filter_string="tags.status = 'validated' AND tags.version = '2.0'" )
Find datasets by creator	datasets = search_datasets( filter_string="created_by = 'alice@company.com'" )
Find recent datasets	# Last 10 datasets created datasets = search_datasets( order_by=["created_time DESC"], max_results=10 )
Complex search	# Production-ready datasets from specific team datasets = search_datasets( experiment_ids="1", filter_string="tags.status = 'production' AND " "tags.team = 'ml-platform' AND " "name LIKE '%customer%'", order_by=["last_update_time DESC"], max_results=20 )

Examples

from mlflow.genai.datasets import search_datasets

# WARNING: This returns ALL datasets - use with caution!
# all_datasets = search_datasets()  # May be slow or crash

# Better: Always use filters or limits
recent_datasets = search_datasets(max_results=100)

# Search in specific experiments
exp_datasets = search_datasets(experiment_ids=["1", "2", "3"])

# Find production datasets
prod_datasets = search_datasets(
    filter_string="tags.environment = 'production'", order_by=["name ASC"]
)

# Iterate through results (pagination handled automatically)
for dataset in prod_datasets:
    print(f"{dataset.name} (ID: {dataset.dataset_id})")
    print(f"  Records: {len(dataset.records)}")
    print(f"  Tags: {dataset.tags}")

Note

This API is not available in Databricks environments. Use Unity Catalog search capabilities in Databricks instead.

mlflow.genai.datasets.set_dataset_tags(dataset_id: str, tags: dict[str, typing.Any]) → None

Note

Experimental: This function may change or be removed in a future release without warning.

Set tags for a dataset.

This implements a batch tag operation - existing tags are merged with new tags. To remove a tag, set its value to None or use delete_dataset_tag() instead.

Parameters

• dataset_id – The ID of the dataset.

• tags – Dictionary of tags to set. Setting a value to None removes the tag.

Examples

from mlflow.genai.datasets import set_dataset_tags, get_dataset

# Get your dataset
dataset = get_dataset(dataset_id="d-8f3a2b1c4e5d6f7a8b9c0d1e2f3a4b5c")

# Add or update multiple tags
set_dataset_tags(
    dataset_id=dataset.dataset_id,
    tags={
        "environment": "production",  # Add new tag
        "version": "2.0",  # Update existing tag
        "validated": "true",
        "validation_date": "2024-11-01",
        "team": "ml-platform",
    },
)

# Remove tags by setting to None
set_dataset_tags(
    dataset_id=dataset.dataset_id,
    tags={
        "deprecated_tag": None,  # This removes the tag
        "old_version": None,  # This also removes the tag
    },
)

# Update status after validation
set_dataset_tags(
    dataset_id=dataset.dataset_id,
    tags={
        "status": "production_ready",
        "coverage": "comprehensive",
        "last_review": "2024-11-01",
        "approved_by": "data_science_lead@company.com",
    },
)

Note

This API is not available in Databricks environments yet. Tags in Databricks are managed through Unity Catalog.

Databricks Agent Label Schemas Python SDK. For more details see Databricks Agent Evaluation: <https://docs.databricks.com/en/generative-ai/agent-evaluation/index.html>

The API docs can be found here: <https://api-docs.databricks.com/python/databricks-agents/latest/databricks_agent_eval.html#review-app>

class mlflow.genai.label_schemas.InputCategorical(options: list[str])

Bases: mlflow.genai.label_schemas.label_schemas.InputType

A single-select dropdown for collecting assessments from stakeholders.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

options: list[str]

List of available options for the categorical selection.

class mlflow.genai.label_schemas.InputCategoricalList(options: list[str])

Bases: mlflow.genai.label_schemas.label_schemas.InputType

A multi-select dropdown for collecting assessments from stakeholders.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

options: list[str]

List of available options for the multi-select categorical (dropdown).

class mlflow.genai.label_schemas.InputNumeric(min_value: Optional[float] = None, max_value: Optional[float] = None)

Bases: mlflow.genai.label_schemas.label_schemas.InputType

A numeric input for collecting assessments from stakeholders.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

max_value: float | None = None

Maximum allowed numeric value. None means no maximum limit.

min_value: float | None = None

Minimum allowed numeric value. None means no minimum limit.

class mlflow.genai.label_schemas.InputText(max_length: Optional[int] = None)

Bases: mlflow.genai.label_schemas.label_schemas.InputType

A free-form text box for collecting assessments from stakeholders.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

max_length: int | None = None

Maximum character length for the text input. None means no limit.

class mlflow.genai.label_schemas.InputTextList(max_length_each: Optional[int] = None, max_count: Optional[int] = None)

Bases: mlflow.genai.label_schemas.label_schemas.InputType

Like Text, but allows multiple entries.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

max_count: int | None = None

Maximum number of text entries allowed. None means no limit.

max_length_each: int | None = None

Maximum character length for each individual text entry. None means no limit.

class mlflow.genai.label_schemas.LabelSchema(name: str, type: mlflow.genai.label_schemas.label_schemas.LabelSchemaType, title: str, input: mlflow.genai.label_schemas.label_schemas.InputCategorical | mlflow.genai.label_schemas.label_schemas.InputCategoricalList | mlflow.genai.label_schemas.label_schemas.InputText | mlflow.genai.label_schemas.label_schemas.InputTextList | mlflow.genai.label_schemas.label_schemas.InputNumeric, instruction: Optional[str] = None, enable_comment: bool = False)

Bases: object

A label schema for collecting input from stakeholders.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

enable_comment: bool = False

Whether to enable additional comment functionality for reviewers.

input: mlflow.genai.label_schemas.label_schemas.InputCategorical | mlflow.genai.label_schemas.label_schemas.InputCategoricalList | mlflow.genai.label_schemas.label_schemas.InputText | mlflow.genai.label_schemas.label_schemas.InputTextList | mlflow.genai.label_schemas.label_schemas.InputNumeric

Input type specification that defines how stakeholders will provide their assessment (e.g., dropdown, text box, numeric input)

instruction: str | None = None

Optional detailed instructions shown to stakeholders for guidance.

name: str

Unique name identifier for the label schema.

title: str

Display title shown to stakeholders in the labeling review UI.

type: mlflow.genai.label_schemas.label_schemas.LabelSchemaType

Type of the label schema, either ‘feedback’ or ‘expectation’.

class mlflow.genai.label_schemas.LabelSchemaType(value)

Bases: mlflow.genai.utils.enum_utils.StrEnum

Type of label schema.

EXPECTATION = 'expectation'

FEEDBACK = 'feedback'

mlflow.genai.label_schemas.create_label_schema(name: str, *, type: Literal['feedback', 'expectation'], title: str, input: mlflow.genai.label_schemas.label_schemas.InputCategorical | mlflow.genai.label_schemas.label_schemas.InputCategoricalList | mlflow.genai.label_schemas.label_schemas.InputText | mlflow.genai.label_schemas.label_schemas.InputTextList | mlflow.genai.label_schemas.label_schemas.InputNumeric, instruction: Optional[str] = None, enable_comment: bool = False, overwrite: bool = False) → mlflow.genai.label_schemas.label_schemas.LabelSchema

Create a new label schema for the review app.

A label schema defines the type of input that stakeholders will provide when labeling items in the review app.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

Parameters

• name – The name of the label schema. Must be unique across the review app.

• type – The type of the label schema. Either “feedback” or “expectation”.

• title – The title of the label schema shown to stakeholders.

• input – The input type of the label schema.

• instruction – Optional. The instruction shown to stakeholders.

• enable_comment – Optional. Whether to enable comments for the label schema.

• overwrite – Optional. Whether to overwrite the existing label schema with the same name.

Returns

The created label schema.

Return type

LabelSchema

mlflow.genai.label_schemas.delete_label_schema(name: str) → mlflow.genai.labeling.labeling.ReviewApp

Delete a label schema from the review app.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

Parameters

name – The name of the label schema to delete.

Returns

The review app.

Return type

ReviewApp

mlflow.genai.label_schemas.get_label_schema(name: str) → mlflow.genai.label_schemas.label_schemas.LabelSchema

Get a label schema from the review app.

Note

This functionality is only available in Databricks. Please run pip install mlflow[databricks] to use it.

Parameters

name – The name of the label schema to get.

Returns

The label schema.

Return type

LabelSchema

class mlflow.genai.optimize.BasePromptOptimizer(optimizer_config: mlflow.genai.optimize.types.OptimizerConfig)

Bases: abc.ABC

Note

Experimental: This class may change or be removed in a future release without warning.

abstract optimize(prompt: PromptVersion, target_llm_params: mlflow.genai.optimize.types.LLMParams, train_data: pd.DataFrame, scorers: list[Scorer], objective: Optional[Callable[[dict[str, bool | float | str | Feedback | list[Feedback]]], float]] = None, eval_data: Optional[pd.DataFrame] = None) → mlflow.genai.optimize.types.OptimizerOutput

Optimize the given prompt using the specified configuration.

Parameters

• prompt – The prompt to optimize.

• target_llm_params – Parameters for the agent LLM.

• train_data – Training dataset for optimization.

• scorers – List of scorers to evaluate the optimization.

• objective – Optional function to compute overall performance metric.

• eval_data – Optional evaluation dataset.

Returns

The optimized prompt version registered in the prompt registry as a new version.

property optimizer_config: mlflow.genai.optimize.types.OptimizerConfig

class mlflow.genai.optimize.DSPyPromptOptimizer(optimizer_config: mlflow.genai.optimize.types.OptimizerConfig)

Bases: mlflow.genai.optimize.optimizers.base_optimizer.BasePromptOptimizer

Note

Experimental: This class may change or be removed in a future release without warning.

optimize(prompt: PromptVersion, target_llm_params: mlflow.genai.optimize.types.LLMParams, train_data: pd.DataFrame, scorers: list[Scorer], objective: Optional[Callable[[dict[str, bool | float | str | Feedback | list[Feedback]]], float]] = None, eval_data: Optional[pd.DataFrame] = None) → mlflow.genai.optimize.types.OptimizerOutput

Optimize the given prompt using the specified configuration.

Parameters

• prompt – The prompt to optimize.

• target_llm_params – Parameters for the agent LLM.

• train_data – Training dataset for optimization.

• scorers – List of scorers to evaluate the optimization.

• objective – Optional function to compute overall performance metric.

• eval_data – Optional evaluation dataset.

Returns

The optimized prompt version registered in the prompt registry as a new version.

run_optimization(prompt: PromptVersion, program: dspy.Module, metric: Callable[[dspy.Example], float], train_data: list['dspy.Example'], eval_data: list['dspy.Example']) → mlflow.genai.optimize.types.OptimizerOutput

Run the optimization process for the given prompt and program.

Parameters

• prompt (PromptVersion) – The prompt version to optimize.

• program (dspy.Module) – The DSPy program/module to optimize.

• metric (Callable[[dspy.Example], float]) – A callable that computes a metric score for a given example.

• train_data (list[dspy.Example]) – List of training examples for optimization.

• eval_data (list[dspy.Example]) – List of evaluation examples for validation.

Returns

The result of the optimization, including the optimized prompt and metrics.

Return type

OptimizerOutput

Raises

NotImplementedError – This method must be implemented by subclasses.

class mlflow.genai.optimize.LLMParams(model_name: str, base_uri: Optional[str] = None, temperature: Optional[float] = None)

Bases: object

Note

Experimental: This class may change or be removed in a future release without warning.

Parameters for configuring a LLM Model.

Parameters

• model_name – Name of the model in the format <provider>:/<model name> or <provider>/<model name>. For example, “openai:/gpt-4o”, “anthropic:/claude-4”, or “openai/gpt-4o”.

• base_uri – Optional base URI for the API endpoint. If not provided, the default endpoint for the provider will be used.

• temperature – Optional sampling temperature for the model’s outputs. Higher values (e.g., 0.8) make the output more random, while lower values (e.g., 0.2) make it more deterministic.

base_uri: str | None = None

model_name: str

temperature: float | None = None

class mlflow.genai.optimize.OptimizerConfig(num_instruction_candidates: int = 6, max_few_show_examples: int = 6, num_threads: int = <factory>, optimizer_llm: Optional[mlflow.genai.optimize.types.LLMParams] = None, algorithm: str | type['BasePromptOptimizer'] = 'DSPy/MIPROv2', verbose: bool = False, autolog: bool = True, convert_to_single_text: bool = True, extract_instructions: bool = True)

Bases: object

Note

Experimental: This class may change or be removed in a future release without warning.

Configuration for prompt optimization.

Parameters

• num_instruction_candidates – Number of candidate instructions to generate during each optimization iteration. Higher values may lead to better results but increase optimization time. Default: 6

• max_few_show_examples – Maximum number of examples to show in few-shot demonstrations. Default: 6

• num_threads – Number of threads to use for parallel optimization. Default: (number of CPU cores * 2 + 1)

• optimizer_llm – Optional LLM parameters for the teacher model. If not provided, the target LLM will be used as the teacher.

• algorithm – The optimization algorithm to use. When a string is provided, it must be one of the supported algorithms: “DSPy/MIPROv2”. When a BasePromptOptimizer is provided, it will be used as the optimizer. Default: “DSPy/MIPROv2”

• verbose – Whether to show optimizer logs during optimization. Default: False

• autolog – Whether to enable automatic logging and prompt registration. If set to True, a MLflow run is automatically created to store optimization parameters, datasets and metrics, and the optimized prompt is registered. If set to False, the raw optimized template is returned without registration. Default: True

• convert_to_single_text – Whether to convert the optimized prompt to a single prompt. Default: True

• extract_instructions – Whether to extract instructions from the initial prompt. Default: True

algorithm: str | type['BasePromptOptimizer'] = 'DSPy/MIPROv2'

autolog: bool = True

convert_to_single_text: bool = True

extract_instructions: bool = True

max_few_show_examples: int = 6

num_instruction_candidates: int = 6

num_threads: int

optimizer_llm: mlflow.genai.optimize.types.LLMParams | None = None

verbose: bool = False

class mlflow.genai.optimize.OptimizerOutput(*, optimized_prompt: str | dict[str, typing.Any], optimizer_name: str, final_eval_score: Optional[float] = None, initial_eval_score: Optional[float] = None)

Bases: object

Note

Experimental: This class may change or be removed in a future release without warning.

Output of the optimize method of mlflow.genai.optimize.BasePromptOptimizer.

Parameters

• optimized_prompt – The optimized prompt version entity.

• optimizer_name – The name of the optimizer.

• final_eval_score – The final evaluation score of the optimized prompt.

• initial_eval_score – The initial evaluation score of the optimized prompt.

final_eval_score: float | None = None

initial_eval_score: float | None = None

optimized_prompt: str | dict[str, typing.Any]

optimizer_name: str

class mlflow.genai.optimize.PromptOptimizationResult(prompt: str | dict[str, typing.Any] | PromptVersion, initial_prompt: PromptVersion, optimizer_name: str, final_eval_score: float | None, initial_eval_score: float | None)

Bases: object

Note

Experimental: This class may change or be removed in a future release without warning.

Result of the mlflow.genai.optimize_prompt() API.

Parameters

• prompt – The optimized prompt. When autolog=True (default), this is a PromptVersion entity containing the registered optimized template. When autolog=False, this is the raw optimized template (str or dict).

• initial_prompt – A prompt version entity containing the initial template.

• optimizer_name – The name of the optimizer.

• final_eval_score – The final evaluation score of the optimized prompt.

• initial_eval_score – The initial evaluation score of the optimized prompt.

final_eval_score: float | None

initial_eval_score: float | None

initial_prompt: PromptVersion

optimizer_name: str

prompt: str | dict[str, typing.Any] | PromptVersion

mlflow.genai.optimize.format_dspy_prompt(program: dspy.Predict, convert_to_single_text: bool) → dict[str, typing.Any] | str

Note

Experimental: This function may change or be removed in a future release without warning.

mlflow.genai.optimize.optimize_prompt(*, target_llm_params: mlflow.genai.optimize.types.LLMParams, prompt: str | PromptVersion, train_data: EvaluationDatasetTypes, scorers: list[Scorer], objective: Optional[Callable[[dict[str, bool | float | str | Feedback | list[Feedback]]], float]] = None, eval_data: Optional[EvaluationDatasetTypes] = None, optimizer_config: mlflow.genai.optimize.types.OptimizerConfig | None = None) → mlflow.genai.optimize.types.PromptOptimizationResult

Note

Experimental: This function may change or be removed in a future release without warning.

Optimize a LLM prompt using the given dataset and evaluation metrics. By default, the optimized prompt template is automatically registered as a new version of the original prompt and optimization metrics are logged. Currently, this API provides built-in support for DSPy’s MIPROv2 optimizer and you can also implement custom optimization algorithms by extending BasePromptOptimizer class.

Parameters

• target_llm_params – Parameters for the the LLM that prompt is optimized for. The model name can be specified in either format: - <provider>:/<model> (e.g., “openai:/gpt-4o”) - <provider>/<model> (e.g., “openai/gpt-4o”)

• prompt – The URI or Prompt object of the MLflow prompt to optimize.

• train_data –

  Training dataset used for optimization. The data must be one of the following formats:

  • An EvaluationDataset entity

  • Pandas DataFrame

  • Spark DataFrame

  • List of dictionaries

  The dataset must include the following columns:

  • inputs: A column containing single inputs in dict format. Each input should contain keys matching the variables in the prompt template.

  • expectations: A column containing a dictionary of ground truths for individual output fields.

• scorers – List of scorers that evaluate the inputs, outputs and expectations. Note: Trace input is not supported for optimization. Use inputs, outputs and expectations for optimization. Also, pass the objective argument when using scorers with string or Feedback type outputs.

• objective – A callable that computes the overall performance metric from individual assessments. Takes a dict mapping assessment names to assessment scores and returns a float value (greater is better).

• eval_data – Evaluation dataset with the same format as train_data. If not provided, train_data will be automatically split into training and evaluation sets.

• optimizer_config – Configuration parameters for the optimizer.

Returns

The optimization result including the optimized prompt.

Return type

PromptOptimizationResult

Example

import os
import mlflow
from typing import Any
from mlflow.genai.scorers import scorer
from mlflow.genai.optimize import OptimizerConfig, LLMParams

os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"


@scorer
def exact_match(expectations: dict[str, Any], outputs: dict[str, Any]) -> bool:
    return expectations == outputs


prompt = mlflow.genai.register_prompt(
    name="qa",
    template="Answer the following question: {{question}}",
)

result = mlflow.genai.optimize_prompt(
    target_llm_params=LLMParams(model_name="openai:/gpt-4o-mini"),
    train_data=[
        {"inputs": {"question": f"{i}+1"}, "expectations": {"answer": f"{i + 1}"}}
        for i in range(100)
    ],
    scorers=[exact_match],
    prompt=prompt.uri,
    optimizer_config=OptimizerConfig(num_instruction_candidates=5),
)

print(result.prompt.template)

class mlflow.genai.judges.AlignmentOptimizer

Bases: abc.ABC

Note

Experimental: This class may change or be removed in a future release without warning.

Abstract base class for judge alignment optimizers.

Alignment optimizers improve judge accuracy by learning from traces that contain human feedback.

abstract align(judge: mlflow.genai.judges.base.Judge, traces: list[Trace]) → mlflow.genai.judges.base.Judge

Align a judge using the provided traces.

Parameters

• judge – The judge to be optimized

• traces – List of traces containing alignment data (feedback)

Returns

A new Judge instance that is better aligned with the input traces.

class mlflow.genai.judges.CategoricalRating(value)

Bases: mlflow.genai.utils.enum_utils.StrEnum

A categorical rating for an assessment.

Example

from mlflow.genai.judges import CategoricalRating
from mlflow.entities import Feedback

# Create feedback with categorical rating
feedback = Feedback(
    name="my_metric", value=CategoricalRating.YES, rationale="The metric is passing."
)

NO = 'no'

UNKNOWN = 'unknown'

YES = 'yes'

class mlflow.genai.judges.Judge(*, name: str, aggregations: list[typing.Union[typing.Literal['min', 'max', 'mean', 'median', 'variance', 'p90'], typing.Callable[[list[int | float]], float]]] | None = None)

Bases: Scorer

Note

Experimental: This class may change or be removed in a future release without warning.

Base class for LLM-as-a-judge scorers that can be aligned with human feedback.

Judges are specialized scorers that use LLMs to evaluate outputs based on configurable criteria and the results of human-provided feedback alignment.

align(traces: list[Trace], optimizer: Optional[mlflow.genai.judges.base.AlignmentOptimizer] = None) → mlflow.genai.judges.base.Judge

Note

Experimental: This function may change or be removed in a future release without warning.

Align this judge with human preferences using the provided optimizer and traces.

Parameters

• traces – Training traces for alignment

• optimizer – The alignment optimizer to use. If None, uses the default SIMBA optimizer.

Returns

A new Judge instance that is better aligned with the input traces.

abstract get_input_fields() → list[mlflow.genai.judges.base.JudgeField]

Get the input fields for this judge.

Returns

List of JudgeField objects defining the input fields.

classmethod get_output_fields() → list[mlflow.genai.judges.base.JudgeField]

Get the standard output fields used by all judges. This is the source of truth for judge output field definitions.

Returns

List of JudgeField objects defining the standard output fields.

abstract property instructions: str

Plain text instructions of what this judge evaluates.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters

• self – The BaseModel instance.

• context – The context.

mlflow.genai.judges.custom_prompt_judge(*, name: str, prompt_template: str, numeric_values: Optional[dict[str, float]] = None, model: Optional[str] = None) → Callable[[...], Feedback]

Note

Experimental: This function may change or be removed in a future release without warning.

Create a custom prompt judge that evaluates inputs using a template.

Parameters

• name – Name of the judge, used as the name of returned mlflow.entities.Feedback object.

• prompt_template – Template string with {{var_name}} placeholders for variable substitution. Should be prompted with choices as outputs.

• numeric_values – Optional mapping from categorical values to numeric scores. Useful if you want to create a custom judge that returns continuous valued outputs. Defaults to None.

• model –

  Judge model to use. Must be either “databricks” or a form of <provider>:/<model-name>, such as “openai:/gpt-4.1-mini”, “anthropic:/claude-3.5-sonnet-20240620”. MLflow natively supports [“openai”, “anthropic”, “bedrock”, “mistral”], and more providers are supported through LiteLLM. Default model depends on the tracking URI setup:

  • Databricks: databricks

  • Otherwise: openai:/gpt-4.1-mini.

Returns

A callable that takes keyword arguments mapping to the template variables and returns an mlflow mlflow.entities.Feedback.

Example prompt template:

You will look at the response and determine the formality of the response.

<request>{{request}}</request>
<response>{{response}}</response>

You must choose one of the following categories.

[[formal]]: The response is very formal.
[[semi_formal]]: The response is somewhat formal. The response is somewhat formal if the
response mentions friendship, etc.
[[not_formal]]: The response is not formal.

Variable names in the template should be enclosed in double curly braces, e.g., {{request}}, {{response}}. They should be alphanumeric and can include underscores, but should not contain spaces or special characters.

It is required for the prompt template to request choices as outputs, with each choice enclosed in square brackets. Choice names should be alphanumeric and can include underscores and spaces.

mlflow.genai.judges.is_context_relevant(*, request: str, context: Any, name: Optional[str] = None, model: Optional[str] = None) → Feedback

LLM judge determines whether the given context is relevant to the input request.

Parameters

• request – Input to the application to evaluate, user’s question or query.

• context – Context to evaluate the relevance to the request. Supports any JSON-serializable object.

• name – Optional name for overriding the default name of the returned feedback.

• model –

  Judge model to use. Must be either “databricks” or a form of <provider>:/<model-name>, such as “openai:/gpt-4.1-mini”, “anthropic:/claude-3.5-sonnet-20240620”. MLflow natively supports [“openai”, “anthropic”, “bedrock”, “mistral”], and more providers are supported through LiteLLM. Default model depends on the tracking URI setup:

  • Databricks: databricks

  • Otherwise: openai:/gpt-4.1-mini.

Returns

A mlflow.entities.assessment.Feedback~ object with a “yes” or “no” value indicating whether the context is relevant to the request.

Example

The following example shows how to evaluate whether a document retrieved by a retriever is relevant to the user’s question.

from mlflow.genai.judges import is_context_relevant

feedback = is_context_relevant(
    request="What is the capital of France?",
    context="Paris is the capital of France.",
)
print(feedback.value)  # "yes"

feedback = is_context_relevant(
    request="What is the capital of France?",
    context="Paris is known for its Eiffel Tower.",
)
print(feedback.value)  # "no"

mlflow.genai.judges.is_context_sufficient(*, request: str, context: Any, expected_facts: list[str], expected_response: Optional[str] = None, name: Optional[str] = None, model: Optional[str] = None) → Feedback

LLM judge determines whether the given context is sufficient to answer the input request.

Parameters

• request – Input to the application to evaluate, user’s question or query.

• context – Context to evaluate the sufficiency of. Supports any JSON-serializable object.

• expected_facts – A list of expected facts that should be present in the context. Optional.

• expected_response – The expected response from the application. Optional.

• name – Optional name for overriding the default name of the returned feedback.

• model –

  Judge model to use. Must be either “databricks” or a form of <provider>:/<model-name>, such as “openai:/gpt-4.1-mini”, “anthropic:/claude-3.5-sonnet-20240620”. MLflow natively supports [“openai”, “anthropic”, “bedrock”, “mistral”], and more providers are supported through LiteLLM. Default model depends on the tracking URI setup:

  • Databricks: databricks

  • Otherwise: openai:/gpt-4.1-mini.

Returns

A mlflow.entities.assessment.Feedback~ object with a “yes” or “no” value indicating whether the context is sufficient to answer the request.

Example

The following example shows how to evaluate whether the documents returned by a retriever gives sufficient context to answer the user’s question.

from mlflow.genai.judges import is_context_sufficient

feedback = is_context_sufficient(
    request="What is the capital of France?",
    context=[
        {"content": "Paris is the capital of France."},
        {"content": "Paris is known for its Eiffel Tower."},
    ],
    expected_facts=["Paris is the capital of France."],
)
print(feedback.value)  # "yes"

feedback = is_context_sufficient(
    request="What is the capital of France?",
    context={"content": "France is a country in Europe."},
    expected_response="Paris is the capital of France.",
)
print(feedback.value)  # "no"

mlflow.genai.judges.is_correct(*, request: str, response: str, expected_facts: Optional[list[str]] = None, expected_response: Optional[str] = None, name: Optional[str] = None, model: Optional[str] = None) → Feedback

LLM judge determines whether the given response is correct for the input request.

Parameters

• request – Input to the application to evaluate, user’s question or query.

• response – The response from the application to evaluate.

• expected_facts – A list of expected facts that should be present in the response. Optional.

• expected_response – The expected response from the application. Optional.

• name – Optional name for overriding the default name of the returned feedback.

• model –

  Judge model to use. Must be either “databricks” or a form of <provider>:/<model-name>, such as “openai:/gpt-4.1-mini”, “anthropic:/claude-3.5-sonnet-20240620”. MLflow natively supports [“openai”, “anthropic”, “bedrock”, “mistral”], and more providers are supported through LiteLLM. Default model depends on the tracking URI setup:

  • Databricks: databricks

  • Otherwise: openai:/gpt-4.1-mini.

Returns

A mlflow.entities.assessment.Feedback~ object with a “yes” or “no” value indicating whether the response is correct for the request.

Example

The following example shows how to evaluate whether the response is correct.

from mlflow.genai.judges import is_correct

feedback = is_correct(
    request="What is the capital of France?",
    response="Paris is the capital of France.",
    expected_response="Paris",
)
print(feedback.value)  # "yes"

feedback = is_correct(
    request="What is the capital of France?",
    response="London is the capital of France.",
    expected_facts=["Paris is the capital of France"],
)
print(feedback.value)  # "no"

mlflow.genai.judges.is_grounded(*, request: str, response: str, context: Any, name: Optional[str] = None, model: Optional[str] = None) → Feedback

LLM judge determines whether the given response is grounded in the given context.

Parameters

• request – Input to the application to evaluate, user’s question or query.

• response – The response from the application to evaluate.

• context – Context to evaluate the response against. Supports any JSON-serializable object.

• name – Optional name for overriding the default name of the returned feedback.

• model –

  Judge model to use. Must be either “databricks” or a form of <provider>:/<model-name>, such as “openai:/gpt-4.1-mini”, “anthropic:/claude-3.5-sonnet-20240620”. MLflow natively supports [“openai”, “anthropic”, “bedrock”, “mistral”], and more providers are supported through LiteLLM. Default model depends on the tracking URI setup:

  • Databricks: databricks

  • Otherwise: openai:/gpt-4.1-mini.

Returns

A mlflow.entities.assessment.Feedback~ object with a “yes” or “no” value indicating whether the response is grounded in the context.

Example

The following example shows how to evaluate whether the response is grounded in the context.

from mlflow.genai.judges import is_grounded

feedback = is_grounded(
    request="What is the capital of France?",
    response="Paris",
    context=[
        {"content": "Paris is the capital of France."},
        {"content": "Paris is known for its Eiffel Tower."},
    ],
)
print(feedback.value)  # "yes"

feedback = is_grounded(
    request="What is the capital of France?",
    response="London is the capital of France.",
    context=[
        {"content": "Paris is the capital of France."},
        {"content": "Paris is known for its Eiffel Tower."},
    ],
)
print(feedback.value)  # "no"

mlflow.genai.judges.is_safe(*, content: str, name: Optional[str] = None, model: Optional[str] = None) → Feedback

LLM judge determines whether the given response is safe.

Parameters

• content – Text content to evaluate for safety.

• name – Optional name for overriding the default name of the returned feedback.

• model –

  Judge model to use. Must be either “databricks” or a form of <provider>:/<model-name>, such as “openai:/gpt-4.1-mini”, “anthropic:/claude-3.5-sonnet-20240620”. MLflow natively supports [“openai”, “anthropic”, “bedrock”, “mistral”], and more providers are supported through LiteLLM. Default model depends on the tracking URI setup:

  • Databricks: databricks

  • Otherwise: openai:/gpt-4.1-mini.

Returns

A mlflow.entities.assessment.Feedback~ object with a “yes” or “no” value indicating whether the response is safe.

Example

from mlflow.genai.judges import is_safe

feedback = is_safe(content="I am a happy person.")
print(feedback.value)  # "yes"

mlflow.genai.judges.make_judge(name: str, instructions: str, model: Optional[str] = None) → mlflow.genai.judges.base.Judge

Note

Experimental: This function may change or be removed in a future release without warning.

Create a custom MLflow judge instance.

Parameters

• name – The name of the judge

• instructions – Natural language instructions for evaluation. Must contain at least one template variable: {{ inputs }}, {{ outputs }}, {{ expectations }}, or {{ trace }} to reference evaluation data. Custom variables are not supported.

• model – The model identifier to use for evaluation (e.g., “openai:/gpt-4”)

Returns

An InstructionsJudge instance configured with the provided parameters

Example

import mlflow
from mlflow.genai.judges import make_judge

# Create a judge that evaluates response quality using template variables
quality_judge = make_judge(
    name="response_quality",
    instructions=(
        "Evaluate if the response in {{ outputs }} correctly answers "
        "the question in {{ inputs }}. The response should be accurate, "
        "complete, and professional."
    ),
    model="openai:/gpt-4",
)

# Evaluate a response
result = quality_judge(
    inputs={"question": "What is machine learning?"},
    outputs="ML is basically when computers learn stuff on their own",
)

# Create a judge that compares against expectations
correctness_judge = make_judge(
    name="correctness",
    instructions=(
        "Compare the {{ outputs }} against the {{ expectations }}. "
        "Rate how well they match on a scale of 1-5."
    ),
    model="openai:/gpt-4",
)

# Evaluate with expectations (must be dictionaries)
result = correctness_judge(
    inputs={"question": "What is the capital of France?"},
    outputs={"answer": "The capital of France is Paris."},
    expectations={"expected_answer": "Paris"},
)

# Create a judge that evaluates based on trace context
trace_judge = make_judge(
    name="trace_quality",
    instructions="Evaluate the overall quality of the {{ trace }} execution.",
    model="openai:/gpt-4",
)

# Use with search_traces() - evaluate each trace
traces = mlflow.search_traces(experiment_ids=["1"], return_type="list")
for trace in traces:
    feedback = trace_judge(trace=trace)
    print(f"Trace {trace.info.trace_id}: {feedback.value} - {feedback.rationale}")

mlflow.genai.judges.meets_guidelines(*, guidelines: str | list[str], context: dict[str, typing.Any], name: Optional[str] = None, model: Optional[str] = None) → Feedback

LLM judge determines whether the given response meets the given guideline(s).

Parameters

• guidelines – A single guideline or a list of guidelines.

• context – Mapping of context to be evaluated against the guidelines. For example, pass {“response”: “<response text>”} to evaluate whether the response meets the given guidelines.

• name – Optional name for overriding the default name of the returned feedback.

• model –

  Judge model to use. Must be either “databricks” or a form of <provider>:/<model-name>, such as “openai:/gpt-4.1-mini”, “anthropic:/claude-3.5-sonnet-20240620”. MLflow natively supports [“openai”, “anthropic”, “bedrock”, “mistral”], and more providers are supported through LiteLLM. Default model depends on the tracking URI setup:

  • Databricks: databricks

  • Otherwise: openai:/gpt-4.1-mini.

Returns

A mlflow.entities.assessment.Feedback~ object with a “yes” or “no” value indicating whether the response meets the guideline(s).

Example

The following example shows how to evaluate whether the response meets the given guideline(s).

from mlflow.genai.judges import meets_guidelines

feedback = meets_guidelines(
    guidelines="Be polite and respectful.",
    context={"response": "Hello, how are you?"},
)
print(feedback.value)  # "yes"

feedback = meets_guidelines(
    guidelines=["Be polite and respectful.", "Must be in English."],
    context={"response": "Hola, ¿cómo estás?"},
)
print(feedback.value)  # "no"