adding remaining samples

sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_analyze_workflow_app.py

@@ -19,11 +19,10 @@
     Set the environment variables with your own values before running the sample:
     1) AZURE_CONVERSATIONS_ENDPOINT - the endpoint to your CLU resource.
     2) AZURE_CONVERSATIONS_KEY - your CLU API key.
-    3) AZURE_CONVERSATIONS_PROJECT - the name of your CLU conversations project.
-    4) AZURE_CONVERSATIONS_WORKFLOW_PROJECT - the name of your CLU workflow project.
+    3) AZURE_CONVERSATIONS_WORKFLOW_PROJECT - the name of your CLU workflow project.
 """
 
-def sample_analyze_workflow_app(self, conv_account, conv_key, workflow_project):
+def sample_analyze_workflow_app():
     # [START analyze_workflow_app]
     # import libraries
     import os

sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_analyze_workflow_app_direct.py

@@ -0,0 +1,81 @@
+# coding=utf-8
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+
+"""
+FILE: sample_analyze_workflow_app_direct.py
+
+DESCRIPTION:
+    This sample demonstrates how to analyze user query using an orchestration/workflow project.
+    In this sample, we direct the orchestrator project to use a specifc subproject using the "direct_target" parameter.
+    The "direct_target" in our case will be a Qna project.
+    
+    For more info about how to setup a CLU workflow project, see the README.
+
+USAGE:
+    python sample_analyze_workflow_app_direct.py
+
+    Set the environment variables with your own values before running the sample:
+    1) AZURE_CONVERSATIONS_ENDPOINT - the endpoint to your CLU resource.
+    2) AZURE_CONVERSATIONS_KEY - your CLU API key.
+    3) AZURE_CONVERSATIONS_WORKFLOW_PROJECT - the name of your CLU workflow project.
+"""
+
+def sample_analyze_workflow_app_direct():
+    # [START analyze_workflow_app_direct]
+    # import libraries
+    import os
+    from azure.core.credentials import AzureKeyCredential
+
+    from azure.ai.language.conversations import ConversationAnalysisClient
+    from azure.ai.language.conversations.models import AnalyzeConversationOptions
+
+    # get secrets
+    conv_endpoint = os.environ.get("AZURE_CONVERSATIONS_ENDPOINT"),
+    conv_key = os.environ.get("AZURE_CONVERSATIONS_KEY"),
+    workflow_project = os.environ.get("AZURE_CONVERSATIONS_WORKFLOW_PROJECT")
+
+    # prepare data
+    query = "How do you make sushi rice?",
+    target_intent = "SushiMaking"
+    input = AnalyzeConversationOptions(
+        query=query,
+        direct_target=target_intent,
+        parameters={
+            "SushiMaking": QuestionAnsweringParameters(
+                calling_options={
+                    "question": query,
+                    "top": 1,
+                    "confidenceScoreThreshold": 0.1
+                }
+            )
+        }
+    )
+
+    # analyze query
+    client = ConversationAnalysisClient(conv_endpoint, AzureKeyCredential(conv_key))
+    with client:
+        result = client.analyze_conversations(
+            input,
+            project_name=workflow_project,
+            deployment_name='production',
+        )
+
+    # view result
+    print("query: {}".format(result.query))
+    print("project kind: {}\n".format(result.prediction.project_kind))
+
+    print("view top intent:")
+    print("top intent: {}".format(result.prediction.top_intent))
+    print("\tcategory: {}".format(result.prediction.intents[0].category))
+    print("\tconfidence score: {}\n".format(result.prediction.intents[0].confidence_score))
+
+    print("view qna result:")
+    print("\tresult: {}\n".format(result.prediction.intents[0].result))
+    # [START analyze_workflow_app_direct]
+
+
+if __name__ == '__main__':
+    sample_analyze_workflow_app_direct()

sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_analyze_workflow_app_with_parms.py

@@ -19,11 +19,10 @@
     Set the environment variables with your own values before running the sample:
     1) AZURE_CONVERSATIONS_ENDPOINT - the endpoint to your CLU resource.
     2) AZURE_CONVERSATIONS_KEY - your CLU API key.
-    3) AZURE_CONVERSATIONS_PROJECT - the name of your CLU conversations project.
-    4) AZURE_CONVERSATIONS_WORKFLOW_PROJECT - the name of your CLU workflow project.
+    3) AZURE_CONVERSATIONS_WORKFLOW_PROJECT - the name of your CLU workflow project.
 """
 
-def sample_analyze_workflow_app_with_parms(self, conv_account, conv_key, workflow_project):
+def sample_analyze_workflow_app_with_parms():
     # [START analyze_workflow_app_with_parms]
     # import libraries
     import os

sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_authentication.py

@@ -50,7 +50,7 @@ def sample_authentication_api_key():
 
 
 def sample_authentication_with_azure_active_directory():
-    # [START create_dt_client_with_aad]
+    # [START create_clu_client_with_aad]
     """DefaultAzureCredential will use the values from these environment
     variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET
     """
@@ -61,7 +61,7 @@ def sample_authentication_with_azure_active_directory():
     credential = DefaultAzureCredential()
 
     clu_client = ConversationAnalysisClient(endpoint, credential)
-    # [END create_dt_client_with_aad]
+    # [END create_clu_client_with_aad]
 
 
 if __name__ == '__main__':