HierarchicalTaxonomyConfigOutput.md

HierarchicalTaxonomyConfigOutput

Hybrid hierarchical taxonomy configuration supporting inference with manual additions. All hierarchical taxonomies are hybrid: - Base hierarchy can be inferred via schema, clustering, or LLM - Additional collections can be explicitly added with specific retrievers - Supports mixing inference strategies with manual additions/overrides Examples: 1. Pure inference: Set inference_strategy + inference_collections 2. Pure manual: Set hierarchical_nodes only 3. Hybrid: Set inference_strategy + inference_collections + hierarchical_nodes (infers base from collections, adds/overrides with explicit nodes)

Properties

Name	Type	Description	Notes
taxonomy_type	str	Discriminator identifying this as a hierarchical taxonomy.	[optional] [default to 'hierarchical']
retriever_id	str	Default retriever to use for all nodes unless overridden per-node.	[optional]
input_mappings	List[InputMapping]	Default input mappings for all nodes unless overridden per-node.	[optional]
inference_strategy	HierarchyInferenceStrategy	Strategy for inferring hierarchy structure from collections. Can be 'schema' (overlap-based), 'cluster' (clustering-based), or 'llm' (AI-based). When set, will infer relationships from inference_collections.	[optional]
inference_collections	List[str]	Collection IDs to use for hierarchy inference. The inference_strategy will analyze these collections to discover relationships. Can be combined with hierarchical_nodes for hybrid configuration.	[optional]
llm_provider	str	LLM provider to use for hierarchy inference (default openai_chat_v1)	[optional]
llm_model	str	LLM model name (e.g., gpt-4o-mini)	[optional]
llm_prompt_template	str	Optional prompt template. Variables available: {collection_id}, {collection_name}.	[optional]
llm_sample_size	int	Optional number of sample docs to include in prompts (0 = disabled).	[optional] [default to 0]
cluster_ids	List[str]	Cluster IDs to use for CLUSTER inference strategy	[optional]
cluster_overlap_threshold	float	Minimum overlap ratio to establish parent-child relationship between clusters	[optional] [default to 0.7]
hierarchical_nodes	List[HierarchicalNodeOutput]	Explicit node definitions that either: 1) Define the entire hierarchy (when inference_strategy is None), 2) Add additional nodes to an inferred hierarchy, or 3) Override specific relationships in an inferred hierarchy. Supports true hybrid: infer from some collections, manually add others.	[optional]
step_analytics	StepAnalyticsConfigOutput	Optional configuration for step transition analytics. Enables tracking how documents progress through hierarchical taxonomy nodes over time (e.g., content workflow tracking from 'draft' to 'published'). If not provided, only basic assignment events are logged.	[optional]

Example

from mixpeek.models.hierarchical_taxonomy_config_output import HierarchicalTaxonomyConfigOutput

# TODO update the JSON string below
json = "{}"
# create an instance of HierarchicalTaxonomyConfigOutput from a JSON string
hierarchical_taxonomy_config_output_instance = HierarchicalTaxonomyConfigOutput.from_json(json)
# print the JSON string representation of the object
print(HierarchicalTaxonomyConfigOutput.to_json())

# convert the object into a dict
hierarchical_taxonomy_config_output_dict = hierarchical_taxonomy_config_output_instance.to_dict()
# create an instance of HierarchicalTaxonomyConfigOutput from a dict
hierarchical_taxonomy_config_output_from_dict = HierarchicalTaxonomyConfigOutput.from_dict(hierarchical_taxonomy_config_output_dict)

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