ExecuteTaxonomyRequest.md

ExecuteTaxonomyRequest

Request model for on-demand taxonomy validation and testing ONLY. ⚠️ IMPORTANT: This endpoint is ONLY for testing taxonomy configuration with sample documents. DO NOT USE THIS FOR BATCH ENRICHMENT: ❌ Do NOT use this to enrich an entire collection ❌ Do NOT use source_collection_id expecting batch processing ❌ Do NOT use target_collection_id expecting persistence HOW TAXONOMY ENRICHMENT ACTUALLY WORKS: ✅ Automatic during ingestion: Attach taxonomies to collections via taxonomy_applications ✅ On-the-fly in retrieval: Add taxonomy_join stage to retriever pipelines This endpoint validates: - Taxonomy configuration is correct - Retriever can find matching taxonomy nodes - Enrichment fields are properly applied For production enrichment, see: - Collections API: attach taxonomies via taxonomy_applications field - Retrievers API: add taxonomy_join stage for on-the-fly enrichment

Properties

Name	Type	Description	Notes
taxonomy	TaxonomyModelInput	Full taxonomy model with configuration (fetched from DB by controller)
retriever	RetrieverModelInput	Optional retriever configuration override for testing. If omitted, uses the retriever configured in the taxonomy.	[optional]
source_documents	List[Dict[str, object]]	Sample documents to test enrichment (typically 1-5 docs). Results are returned immediately, not persisted. ⚠️ Do NOT pass collection_id expecting batch processing!	[optional]
source_collection_id	str	⚠️ IGNORED IN ON_DEMAND MODE. This field exists for legacy compatibility only. To enrich collections, use taxonomy_applications on the collection.	[optional]
target_collection_id	str	⚠️ IGNORED IN ON_DEMAND MODE. This field exists for legacy compatibility only. Results are never persisted via this endpoint.	[optional]
join_mode	JoinMode	Must be 'on_demand'. BATCH mode is NOT supported via API. Batch enrichment is automatic (triggered by engine during ingestion).	[optional]
batch_size	int	Batch size for the scroll iterator	[optional] [default to 1000]
scroll_filters	LogicalOperatorInput	Additional filters applied to the source collection prior to enrichment.	[optional]

Example

from mixpeek.models.execute_taxonomy_request import ExecuteTaxonomyRequest

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

# convert the object into a dict
execute_taxonomy_request_dict = execute_taxonomy_request_instance.to_dict()
# create an instance of ExecuteTaxonomyRequest from a dict
execute_taxonomy_request_from_dict = ExecuteTaxonomyRequest.from_dict(execute_taxonomy_request_dict)

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