Feature extractor response model for API responses.
| Name | Type | Description | Notes |
|---|---|---|---|
| feature_extractor_name | str | ||
| version | str | ||
| feature_extractor_id | str | ||
| description | str | ||
| icon | str | ||
| source | ExtractorSource | The origin/source of this extractor: 'builtin' (shipped with Mixpeek), 'custom' (user-created), or 'community' (marketplace). | [optional] |
| input_schema | Dict[str, object] | ||
| output_schema | Dict[str, object] | ||
| parameter_schema | Dict[str, object] | ||
| supported_input_types | List[str] | ||
| max_inputs | Dict[str, int] | ||
| default_parameters | Dict[str, object] | ||
| costs | CostsInfo | Credit cost information for this extractor | [optional] |
| required_vector_indexes | List[VectorIndexDefinition] | ||
| required_payload_indexes | List[PayloadIndexConfigOutput] | ||
| position_fields | List[str] | Output fields that uniquely identify each document within a source object. Enables idempotent reprocessing: rerunning a batch produces the same document IDs, so existing documents are updated instead of creating duplicates. Works with bucket `unique_key` to enable fully deterministic document IDs. Empty list means single-output extractor (one document per source). Read-only (set by extractor). | [optional] |
from mixpeek.models.feature_extractor_response_model import FeatureExtractorResponseModel
# TODO update the JSON string below
json = "{}"
# create an instance of FeatureExtractorResponseModel from a JSON string
feature_extractor_response_model_instance = FeatureExtractorResponseModel.from_json(json)
# print the JSON string representation of the object
print(FeatureExtractorResponseModel.to_json())
# convert the object into a dict
feature_extractor_response_model_dict = feature_extractor_response_model_instance.to_dict()
# create an instance of FeatureExtractorResponseModel from a dict
feature_extractor_response_model_from_dict = FeatureExtractorResponseModel.from_dict(feature_extractor_response_model_dict)