name : detect-entities-v2.rst **Example 1: To detect entities directly from text** The following ``detect-entities-v2`` example shows the detected entities and labels them according to type, directly from input text. :: aws comprehendmedical detect-entities-v2 \ --text "Sleeping trouble on present dosage of Clonidine. Severe rash on face and leg, slightly itchy." Output:: { "Id": 0, "BeginOffset": 38, "EndOffset": 47, "Score": 0.9942955374717712, "Text": "Clonidine", "Category": "MEDICATION", "Type": "GENERIC_NAME", "Traits": [] } For more information, see `Detect Entities Version 2 <https://docs.aws.amazon.com/comprehend/latest/dg/extracted-med-info-V2.html>`__ in the *Amazon Comprehend Medical Developer Guide*. **Example 2: To detect entities from a file path** The following ``detect-entities-v2`` example shows the detected entities and labels them according to type from a file path. :: aws comprehendmedical detect-entities-v2 \ --text file://medical_entities.txt Contents of ``medical_entities.txt``:: { "Sleeping trouble on present dosage of Clonidine. Severe rash on face and leg, slightly itchy." } Output:: { "Id": 0, "BeginOffset": 38, "EndOffset": 47, "Score": 0.9942955374717712, "Text": "Clonidine", "Category": "MEDICATION", "Type": "GENERIC_NAME", "Traits": [] } For more information, see `Detect Entities Version 2 <https://docs.aws.amazon.com/comprehend-medical/latest/dev/textanalysis-entitiesv2.html>`__ in the *Amazon Comprehend Medical Developer Guide*.