Fix-Sample-Code-Agents - Github Issue 44431

sdk/ai/azure-ai-projects/.env.template

@@ -17,7 +17,7 @@
 #  `https://<your-ai-services-account-name>.services.ai.azure.com/api/projects/<your-project-name>`
 AZURE_AI_PROJECT_ENDPOINT=
 AZURE_AI_MODEL_DEPLOYMENT_NAME=
-AGENT_NAME=
+AZURE_AI_AGENT_NAME=
 CONVERSATION_ID=
 CONNECTION_NAME=
 AZURE_AI_PROJECTS_AZURE_SUBSCRIPTION_ID=

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

@@ -122,19 +122,20 @@ See the "responses" folder in the [package samples][samples] for additional samp
 <!-- SNIPPET:sample_responses_basic.responses -->
 
 ```python
-with project_client.get_openai_client() as openai_client:
-    response = openai_client.responses.create(
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-        input="What is the size of France in square miles?",
-    )
-    print(f"Response output: {response.output_text}")
+openai_client = project_client.get_openai_client()
 
-    response = openai_client.responses.create(
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-        input="And what is the capital city?",
-        previous_response_id=response.id,
-    )
-    print(f"Response output: {response.output_text}")
+response = openai_client.responses.create(
+    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+    input="What is the size of France in square miles?",
+)
+print(f"Response output: {response.output_text}")
+
+response = openai_client.responses.create(
+    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+    input="And what is the capital city?",
+    previous_response_id=response.id,
+)
+print(f"Response output: {response.output_text}")
 ```
 
 <!-- END SNIPPET -->
@@ -150,43 +151,44 @@ See the "agents" folder in the [package samples][samples] for an extensive set o
 <!-- SNIPPET:sample_agent_basic.prompt_agent_basic -->
 
 ```python
-with project_client.get_openai_client() as openai_client:
-    agent = project_client.agents.create_version(
-        agent_name="MyAgent",
-        definition=PromptAgentDefinition(
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-            instructions="You are a helpful assistant that answers general questions",
-        ),
-    )
-    print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")
+openai_client = project_client.get_openai_client()
 
-    conversation = openai_client.conversations.create(
-        items=[{"type": "message", "role": "user", "content": "What is the size of France in square miles?"}],
-    )
-    print(f"Created conversation with initial user message (id: {conversation.id})")
+agent = project_client.agents.create_version(
+    agent_name="MyAgent",
+    definition=PromptAgentDefinition(
+        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        instructions="You are a helpful assistant that answers general questions",
+    ),
+)
+print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")
 
-    response = openai_client.responses.create(
-        conversation=conversation.id,
-        extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
-        input="",
-    )
-    print(f"Response output: {response.output_text}")
+conversation = openai_client.conversations.create(
+    items=[{"type": "message", "role": "user", "content": "What is the size of France in square miles?"}],
+)
+print(f"Created conversation with initial user message (id: {conversation.id})")
 
-    openai_client.conversations.items.create(
-        conversation_id=conversation.id,
-        items=[{"type": "message", "role": "user", "content": "And what is the capital city?"}],
-    )
-    print(f"Added a second user message to the conversation")
+response = openai_client.responses.create(
+    conversation=conversation.id,
+    extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
+    input="",
+)
+print(f"Response output: {response.output_text}")
 
-    response = openai_client.responses.create(
-        conversation=conversation.id,
-        extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
-        input="",
-    )
-    print(f"Response output: {response.output_text}")
+openai_client.conversations.items.create(
+    conversation_id=conversation.id,
+    items=[{"type": "message", "role": "user", "content": "And what is the capital city?"}],
+)
+print(f"Added a second user message to the conversation")
+
+response = openai_client.responses.create(
+    conversation=conversation.id,
+    extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
+    input="",
+)
+print(f"Response output: {response.output_text}")
 
-    openai_client.conversations.delete(conversation_id=conversation.id)
-    print("Conversation deleted")
+openai_client.conversations.delete(conversation_id=conversation.id)
+print("Conversation deleted")
 
 project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
 print("Agent deleted")
@@ -632,7 +634,7 @@ with (
     agent = project_client.agents.create_version(
         agent_name=os.environ["AZURE_AI_AGENT_NAME"],
         definition=PromptAgentDefinition(
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=model_deployment_name,
             instructions="You are a helpful assistant that answers general questions",
         ),
     )
@@ -643,12 +645,29 @@ with (
         item_schema={"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]},
         include_sample_schema=True,
     )
+    # Notes: for data_mapping:
+    # sample.output_text is the string output of the agent
+    # sample.output_items is the structured JSON output of the agent, including tool calls information
     testing_criteria = [
         {
             "type": "azure_ai_evaluator",
             "name": "violence_detection",
             "evaluator_name": "builtin.violence",
-            "data_mapping": {"query": "{{item.query}}", "response": "{{item.response}}"},
+            "data_mapping": {"query": "{{item.query}}", "response": "{{sample.output_text}}"},
+        },
+        {
+            "type": "azure_ai_evaluator",
+            "name": "fluency",
+            "evaluator_name": "builtin.fluency",
+            "initialization_parameters": {"deployment_name": f"{model_deployment_name}"},
+            "data_mapping": {"query": "{{item.query}}", "response": "{{sample.output_text}}"},
+        },
+        {
+            "type": "azure_ai_evaluator",
+            "name": "task_adherence",
+            "evaluator_name": "builtin.task_adherence",
+            "initialization_parameters": {"deployment_name": f"{model_deployment_name}"},
+            "data_mapping": {"query": "{{item.query}}", "response": "{{sample.output_items}}"},
         }
     ]
     eval_object = openai_client.evals.create(
@@ -825,7 +844,9 @@ folder in the [package samples][samples].
 <!-- SNIPPET:sample_indexes.indexes_sample-->
 
 ```python
-print(f"Create Index `{index_name}` with version `{index_version}`, referencing an existing AI Search resource:")
+print(
+    f"Create Index `{index_name}` with version `{index_version}`, referencing an existing AI Search resource:"
+)
 index = project_client.indexes.create_or_update(
     name=index_name,
     version=index_version,
@@ -863,24 +884,20 @@ with open(file_path, "rb") as f:
     uploaded_file = openai_client.files.create(file=f, purpose="fine-tune")
 print(uploaded_file)
 
-print("Waits for the given file to be processed, default timeout is 30 mins")
-processed_file = openai_client.files.wait_for_processing(uploaded_file.id)
-print(processed_file)
-
-print(f"Retrieving file metadata with ID: {processed_file.id}")
-retrieved_file = openai_client.files.retrieve(processed_file.id)
+print(f"Retrieving file metadata with ID: {uploaded_file.id}")
+retrieved_file = openai_client.files.retrieve(uploaded_file.id)
 print(retrieved_file)
 
-print(f"Retrieving file content with ID: {processed_file.id}")
-file_content = openai_client.files.content(processed_file.id)
+print(f"Retrieving file content with ID: {uploaded_file.id}")
+file_content = openai_client.files.content(uploaded_file.id)
 print(file_content.content)
 
 print("Listing all files:")
 for file in openai_client.files.list():
     print(file)
 
-print(f"Deleting file with ID: {processed_file.id}")
-deleted_file = openai_client.files.delete(processed_file.id)
+print(f"Deleting file with ID: {uploaded_file.id}")
+deleted_file = openai_client.files.delete(uploaded_file.id)
 print(f"Successfully deleted file: {deleted_file.id}")
 ```
 

sdk/ai/azure-ai-projects/samples/agents/sample_agent_retrieve_basic.py

@@ -23,7 +23,7 @@
     Set these environment variables with your own values:
     1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
        page of your Microsoft Foundry portal.
-    2) AGENT_NAME - The name of an existing Agent in your Microsoft Foundry project.
+    2) AZURE_AI_AGENT_NAME - The name of an existing Agent in your Microsoft Foundry project.
     3) CONVERSATION_ID - The ID of an existing Conversation associated with the Agent
 """
 
@@ -34,7 +34,7 @@
 
 load_dotenv()
 
-agent_name = os.environ["AGENT_NAME"]
+agent_name = os.environ["AZURE_AI_AGENT_NAME"]
 conversation_id = os.environ["CONVERSATION_ID"]
 
 endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]

sdk/ai/azure-ai-projects/samples/agents/sample_agent_retrieve_basic_async.py

@@ -23,7 +23,7 @@
     Set these environment variables with your own values:
     1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
        page of your Microsoft Foundry portal.
-    2) AGENT_NAME - The name of an existing Agent in your Microsoft Foundry project.
+    2) AZURE_AI_AGENT_NAME - The name of an existing Agent in your Microsoft Foundry project.
     3) CONVERSATION_ID - The ID of an existing Conversation associated with the Agent
 """
 
@@ -36,7 +36,7 @@
 load_dotenv()
 
 endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
-agent_name = os.environ["AGENT_NAME"]
+agent_name = os.environ["AZURE_AI_AGENT_NAME"]
 conversation_id = os.environ["CONVERSATION_ID"]
 
 

sdk/ai/azure-ai-projects/samples/evaluations/sample_agent_evaluation.py

@@ -40,6 +40,7 @@
 
 load_dotenv()
 endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
+model_deployment_name = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", "")  # Sample : gpt-4o-mini
 
 # [START agent_evaluation_basic]
 with (
@@ -50,7 +51,7 @@
     agent = project_client.agents.create_version(
         agent_name=os.environ["AZURE_AI_AGENT_NAME"],
         definition=PromptAgentDefinition(
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=model_deployment_name,
             instructions="You are a helpful assistant that answers general questions",
         ),
     )
@@ -61,12 +62,29 @@
         item_schema={"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]},
         include_sample_schema=True,
     )
+    # Notes: for data_mapping:
+    # sample.output_text is the string output of the agent
+    # sample.output_items is the structured JSON output of the agent, including tool calls information
     testing_criteria = [
         {
             "type": "azure_ai_evaluator",
             "name": "violence_detection",
             "evaluator_name": "builtin.violence",
-            "data_mapping": {"query": "{{item.query}}", "response": "{{item.response}}"},
+            "data_mapping": {"query": "{{item.query}}", "response": "{{sample.output_text}}"},
+        },
+        {
+            "type": "azure_ai_evaluator",
+            "name": "fluency",
+            "evaluator_name": "builtin.fluency",
+            "initialization_parameters": {"deployment_name": f"{model_deployment_name}"},
+            "data_mapping": {"query": "{{item.query}}", "response": "{{sample.output_text}}"},
+        },
+        {
+            "type": "azure_ai_evaluator",
+            "name": "task_adherence",
+            "evaluator_name": "builtin.task_adherence",
+            "initialization_parameters": {"deployment_name": f"{model_deployment_name}"},
+            "data_mapping": {"query": "{{item.query}}", "response": "{{sample.output_items}}"},
         }
     ]
     eval_object = openai_client.evals.create(

sdk/ai/azure-ai-projects/samples/evaluations/sample_model_evaluation.py

@@ -51,16 +51,18 @@
         item_schema={"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]},
         include_sample_schema=True,
     )
+    # Notes: for data_mapping:
+    # {{sample.output_text}} is the string output of the provide model target for the given input in {{item.query}}
     testing_criteria = [
         {
             "type": "azure_ai_evaluator",
             "name": "violence_detection",
             "evaluator_name": "builtin.violence",
-            "data_mapping": {"query": "{{item.query}}", "response": "{{item.response}}"},
+            "data_mapping": {"query": "{{item.query}}", "response": "{{sample.output_text}}"},
         }
     ]
     eval_object = openai_client.evals.create(
-        name="Agent Evaluation",
+        name="Model Evaluation",
         data_source_config=data_source_config,
         testing_criteria=testing_criteria,  # type: ignore
     )