[text analytics] Fixed a few minor issues in a sample and the README (#17192)

Author: abhahn

sdk/textanalytics/azure-ai-textanalytics/README.md

@@ -503,7 +503,11 @@ Note: The Healthcare Entities Analysis service is currently available only in th
 ```python
 from azure.core.credentials import AzureKeyCredential
 from azure.ai.textanalytics import (
-    TextAnalyticsClient, RecognizeEntitiesAction, RecognizePiiEntitiesAction, ExtractKeyPhrasesAction
+    TextAnalyticsClient,
+    RecognizeEntitiesAction,
+    RecognizePiiEntitiesAction,
+    ExtractKeyPhrasesAction,
+    RecognizeLinkedEntitiesAction
 )
 
 credential = AzureKeyCredential("<api_key>")
@@ -520,6 +524,7 @@ poller = text_analytics_client.begin_analyze_batch_actions(
         RecognizeEntitiesAction(),
         RecognizePiiEntitiesAction(),
         ExtractKeyPhrasesAction(),
+        RecognizeLinkedEntitiesAction()
     ]
 )
 
@@ -559,6 +564,26 @@ for idx, doc in enumerate(docs):
     print("Document text: {}\n".format(documents[idx]))
     print("Key Phrases: {}\n".format(doc.key_phrases))
     print("------------------------------------------")
+
+fourth_action_result = next(result)
+print("Results of Linked Entities Recognition action:")
+docs = [doc for doc in fourth_action_result.document_results if not doc.is_error]
+
+for idx, doc in enumerate(docs):
+    print("Document text: {}\n".format(documents[idx]))
+    for linked_entity in doc.entities:
+        print("Entity name: {}".format(linked_entity.name))
+        print("...Data source: {}".format(linked_entity.data_source))
+        print("...Data source language: {}".format(linked_entity.language))
+        print("...Data source entity ID: {}".format(linked_entity.data_source_entity_id))
+        print("...Data source URL: {}".format(linked_entity.url))
+        print("...Document matches:")
+        for match in linked_entity.matches:
+            print("......Match text: {}".format(match.text))
+            print(".........Confidence Score: {}".format(match.confidence_score))
+            print(".........Offset: {}".format(match.offset))
+            print(".........Length: {}".format(match.length))
+    print("------------------------------------------")
 ```
 
 The returned response is an object encapsulating multiple iterables, each representing results of individual analyses.

sdk/textanalytics/azure-ai-textanalytics/samples/sample_analyze_healthcare_entities.py

@@ -116,8 +116,8 @@ def analyze_healthcare_entities(self):
         for relation in dosage_of_medication_relations:
             # The DosageOfMedication relation should only contain the dosage and medication roles
 
-            dosage_role = next(filter(lambda x: x.name == HealthcareEntityRelationRoleType.DOSAGE, relation.roles))
-            medication_role = next(filter(lambda x: x.name == HealthcareEntityRelationRoleType.MEDICATION, relation.roles))
+            dosage_role = next(iter(filter(lambda x: x.name == HealthcareEntityRelationRoleType.DOSAGE, relation.roles)))
+            medication_role = next(iter(filter(lambda x: x.name == HealthcareEntityRelationRoleType.MEDICATION, relation.roles)))
 
             try:
                 dosage_value = int(re.findall(r"\d+", dosage_role.entity.text)[0]) # we find the numbers in the dosage
@@ -126,12 +126,10 @@ def analyze_healthcare_entities(self):
                 # Error handling for if there's no dosage in numbers.
                 pass
 
-        [
+        for medication, dosage in medication_to_dosage.items():
             print("We have fulfilled '{}' total mg of '{}'".format(
                 dosage, medication
             ))
-            for medication, dosage in medication_to_dosage.items()
-        ]
 
 
 if __name__ == "__main__":