Built in Models

Built-in Models

The models, publishers, and evaluators that ship with ccflow. Use these off the shelf or as references for your own. For usage, see the How-to Guides; for the base classes, see Core Types.

Note

Some entries are marked Coming Soon! — they are part of the design and in the process of being open-sourced.

Models

Beyond the CallableModel and BaseModel subclasses you write yourself, ccflow provides composition models used throughout the framework, including:

• PublisherModel — runs a CallableModel and hands its result to a publisher in one step.
• RetryModel — wraps a CallableModel with a retry policy as a first-class graph node (see Retry on Failure).
• Narwhals-based models for cross-dataframe-library operations.

Additional data-reading models are being open-sourced over time.

Publishers

Publishers (ccflow.publishers) are models that write or send data. A common interface lets one be substituted for another purely through configuration. See Bind Logic to Configs to write your own.

Name	Path	Description
DictTemplateFilePublisher	ccflow.publishers	Publish to a file after populating a Jinja template.
GenericFilePublisher	ccflow.publishers	Publish using a generic "dump" callable. Uses smart_open, so local and cloud paths work.
JSONPublisher	ccflow.publishers	Publish to a file in JSON format.
PandasFilePublisher	ccflow.publishers	Publish a pandas DataFrame using an appropriate pd.DataFrame method.
NarwhalsFilePublisher	ccflow.publishers	Publish a Narwhals DataFrame using an appropriate nw.DataFrame method.
PicklePublisher	ccflow.publishers	Publish data to a pickle file.
PydanticJSONPublisher	ccflow.publishers	Publish a pydantic model to a JSON file.
YAMLPublisher	ccflow.publishers	Publish to a file in YAML format.
CompositePublisher	ccflow.publishers	Decompose a model or dict into pieces and publish each separately.
PrintPublisher	ccflow.publishers	Print data using standard print.
LogPublisher	ccflow.publishers	Print data using standard logging.
PrintJSONPublisher	ccflow.publishers	Print data in JSON format.
PrintYAMLPublisher	ccflow.publishers	Print data in YAML format.
PrintPydanticJSONPublisher	ccflow.publishers	Print a pydantic model as JSON.
ArrowDatasetPublisher	Coming Soon!
PandasParquetPublisher	Coming Soon!
PandasDeltaPublisher	Coming Soon!
EmailPublisher	Coming Soon!
MatplotlibFilePublisher	Coming Soon!
PlotlyFilePublisher	Coming Soon!
XArrayPublisher	Coming Soon!
MLFlowArtifactPublisher	Coming Soon!
MLFlowPublisher	Coming Soon!

Evaluators

Evaluators control how a CallableModel runs. Set one through FlowOptions (see Core Types). Usage is in Cache Results and Retry on Failure.

Name	Path	Description
LazyEvaluator	ccflow.evaluators	Runs the callable only once an attribute of the result is queried.
LoggingEvaluator	ccflow.evaluators	Logs information about evaluating the callable (the default).
MemoryCacheEvaluator	ccflow.evaluators	Caches results in memory.
MultiEvaluator	ccflow.evaluators	Combines multiple evaluators.
GraphEvaluator	ccflow.evaluators	Evaluates the dependency graph in topologically sorted order.
RetryEvaluator	ccflow.evaluators	Retries evaluation on failure with exponential backoff and jitter.
ChunkedDateRangeEvaluator	Coming Soon!
ChunkedDateRangeResultsAggregator	Coming Soon!
DependencyTrackingEvaluator	Coming Soon!
DiskCacheEvaluator	Coming Soon!
ParquetCacheEvaluator	Coming Soon!
RayChunkedDateRangeEvaluator	Coming Soon!
RayCacheEvaluator	Coming Soon!
RayGraphEvaluator	Coming Soon!
RayDelayedDistributedEvaluator	Coming Soon!

How cache keys are built

MemoryCacheEvaluator uses ccflow.evaluators.cache_key(), which delegates to ccflow.utils.compute_cache_token(). A key combines a data token (for the serialized model/context payload) and a behavior token (for each callable/evaluator class that can affect the result).

• For a direct CallableModel call, the key depends on model.model_dump(mode="python") and compute_behavior_token(type(model)).
• For a ModelEvaluationContext, the structural key depends on the context payload plus the function name (__call__ vs __deps__), the behavior token of the model class, and the data/behavior tokens of any non-transparent evaluators in the chain.

Transparent evaluators (those whose is_transparent returns True) are skipped, so wrapping a model in logging or timing does not change its cache identity.

compute_behavior_token() hashes the class's Python-defined methods and also consults __ccflow_tokenizer_deps__ for behavior defined outside the class body:

class MyModel(CallableModel):
    __ccflow_tokenizer_deps__ = [helper, SharedLogic]

Entries may be functions or classes; class dependencies are tokenized recursively. Recursive class dependency graphs raise a TypeError.

RetryEvaluator parameters

RetryEvaluator (and RetryModel, which share a RetryPolicy) support:

Parameter	Meaning
max_attempts	Maximum number of attempts.
wait_initial	Wait before the first retry.
wait_multiplier	Backoff multiplier between retries.
wait_max	Cap on any single wait.
wait_jitter	Maximum random jitter added to a wait.
max_delay	Cap on cumulative waiting; no retry once the next backoff would exceed it.
retry_exceptions	Exceptions (classes or importable path strings) that trigger a retry.
no_retry_exceptions	Exceptions that must not be retried.

See Retry on Failure for usage.

See also

• Core Types — the base classes these extend.
• Flow Model — the @Flow.model API.