snorkelflow.client.nodes.add_node

warning

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snorkelflow.client.nodes.add_node(application, input_node_uids=None, expected_op_type=None, node_config=None, output_node_uid=None, output_node_uids=None, node_cls='ApplicationNode', op_type=None, op_config=None, add_to_parent_block=False)

Adds a new node to an application’s data processing pipeline.

Creates a node in the application’s directed acyclic graph (DAG), optionally with a committed operator. When you add a node, you add to your data processing pipeline. Each node is a single step in your sequence of data transformation operations. Each node can have input/output connections and the operation to perform on the data at that step.

When creating a node, you must specify input_node_uids and optionally output_node_uids to define the node’s connections in the pipeline. Use input_node_uids when building the pipeline forward from source to target, and output_node_uids when building backward from target to source.

You must specify either op_type (to commit an operator immediately) or expected_op_type (to create a placeholder node that will have an operator committed later). Use op_type when you know the exact operator and configuration, and expected_op_type when you want to reserve a spot in the pipeline for a specific type of operation.

Parameters

Parameters

Name	Type	Default	Info
application	Union[str, int]		Name or UID of the application where you want to add the node.
input_node_uids	Optional[List[int]]	None	List of input node UIDs that feed data into this node. Use [-1] to connect to the initial dataset node. Required.
expected_op_type	Optional[str]	None	The expected type of operator for this node (e.g., “Featurizer”, “Filter”, “Model”). Required if not providing op_type, otherwise can be omitted. See the operators reference for a comprehensive list of operator types.
node_config	Optional[Dict[str, Any]]	None	Dictionary with configuration for the node. For model nodes, this can include label_map containing class-to-index mappings.
output_node_uids	Optional[List[int]]	None	List of node UIDs that receive data from this node.
output_node_uid	Optional[int]	None	DEPRECATED. Use output_node_uids instead.
node_cls	str	'ApplicationNode'	The node class type. Valid node class types include: ApplicationNode: Default node class for general purposes. ClassificationNode: Node specific to text classification tasks. SequenceTaggingNode: Node specific to sequence tagging tasks. WordClassificationNode: Node for word-level classification tasks. EntityClassificationNode: Node for entity classification tasks.
op_type	Optional[str]	None	Type of operator to commit to the node (e.g., “TruncatePreprocessor”, “RegexFilter”). Required if providing op_config. See the operators reference for a comprehensive list of operator types.
op_config	Optional[Dict[str, Any]]	None	Dictionary with configuration specific to the operator type. For example, a TruncatePreprocessor requires field, target_field, length, and by parameters.
add_to_parent_block	Optional[bool]	False	When True, adds the node to the parent block of the output node. This affects node nesting in the application structure. When using with a single block application with input_node_uids=[-1] and add_to_parent_block=True, the node will be added ahead of the block, not within it.

Raises

Raises

• ValueError – When op_config is provided without op_type.

• ValueError – When neither input_node_uids nor output_node_uids is specified.

Return type

Return type

Dict[str, Any]

Examples

Example 1

Example 1

Adds a placeholder node with an expected operator type of "Featurizer".

# Add a placeholder Featurizer node
placeholder_featurizer = sf.add_node(
    application="your-app-name",
    input_node_uids=[-1],
    expected_op_type="Featurizer",
    node_config={
        "description": "Text feature extraction node"
    }
)

Example 1 return

Example 1 return

Returns information about the newly created node.

{
    "node_uid": 100,
    "op_version_uid": 200
}

Example 2

Example 2

Adds a node with a committed operator, inserting it into the pipeline ahead of the placeholder node.

preprocessing_node = sf.add_node(
    application="your-app-name",
    input_node_uids=[-1],  # Connect to dataset node
    op_type="TruncatePreprocessor",  # Specific operator type
    op_config={
        "field": "text",
        "target_field": "text_truncated",
        "by": "chars",
        "length": 512
    },
    output_node_uids=[100]
)

Example 2 return

Example 2 return

Returns information about the newly created preprocessing node.

{
    "node_uid": 101,
    "op_version_uid": 201
}

Example 3

Example 3

Adds a model node with a label map in the node configuration and adds it to the parent block.

# Add a model node with a classification label map
model_node = sf.add_node(
    application="classification-example-app",
    input_node_uids=[101],
    expected_op_type="Model",
    node_cls="ClassificationNode",
    node_config={
        "label_map": {
            "negative": -1,
            "neutral": 0,
            "positive": 1
        }
    },
    add_to_parent_block=True
)

Example 3 return

Example 3 return

Returns information about the newly created model node.

{
    "node_uid": 102,
    "op_version_uid": 202
}