Azure · howieleung · Dec 6, 2025 · Dec 3, 2025 · Dec 6, 2025 · Dec 6, 2025
@@ -2,5 +2,5 @@
   "AssetsRepo": "Azure/azure-sdk-assets",
   "AssetsRepoPrefixPath": "python",
   "TagPrefix": "python/ai/azure-ai-projects",
-  "Tag": "python/ai/azure-ai-projects_2204ef492a"
+  "Tag": "python/ai/azure-ai-projects_d20bfd0fdc"
 }
@@ -7,4 +7,4 @@ opentelemetry-sdk
 azure-core-tracing-opentelemetry
 azure-monitor-opentelemetry
 azure-mgmt-cognitiveservices
-
+jsonref
@@ -159,4 +159,4 @@ def print_final_output(response):
                 final_output += getattr(part, "text", None) or getattr(part, "refusal", None) or "" + "\n"
 
     print(f"Final status: {response.status}")
-    print(f"Final output: {final_output.strip()}")
+    print(f"==> Result: {final_output.strip()}")
@@ -24,6 +24,7 @@
     3) AI_SEARCH_PROJECT_CONNECTION_ID - The AI Search project connection ID,
        as found in the "Connections" tab in your Microsoft Foundry project.
     4) AI_SEARCH_INDEX_NAME - The name of the AI Search index to use for searching.
+    5) AI_SEARCH_USER_INPUT - (Optional) The question to ask. If not set, you will be prompted.
 """
 
 import os
@@ -74,7 +75,10 @@
     )
     print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")
 
-    user_input = input("Enter your question (e.g., 'Tell me about mental health services'): \n")
+    # Get user input from environment variable or prompt
+    user_input = os.environ.get("AI_SEARCH_USER_INPUT")
+    if not user_input:
+        user_input = input("Enter your question (e.g., 'Tell me about mental health services'): \n")
 
     stream_response = openai_client.responses.create(
         stream=True,
@@ -104,7 +108,7 @@
                             )
         elif event.type == "response.completed":
             print(f"\nFollow-up completed!")
-            print(f"Full response: {event.response.output_text}")
+            print(f"==> Result: {event.response.output_text}")
 
     print("\nCleaning up...")
     project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)

@@ -100,10 +100,12 @@
     # Download the generated file if available
     if file_id and filename:
         file_content = openai_client.containers.files.content.retrieve(file_id=file_id, container_id=container_id)
+        print(f"File ready for download: {filename}")
         with open(filename, "wb") as f:
             f.write(file_content.read())
-            print(f"File {filename} downloaded successfully.")
-        print(f"File ready for download: {filename}")
+
+        # Print result (should contain "file")
+        print(f"==> Result: file, {filename} downloaded successfully.")
     else:
         print("No file generated in response")
 

@@ -103,10 +103,12 @@ async def main() -> None:
                 file_content = await openai_client.containers.files.content.retrieve(
                     file_id=file_id, container_id=container_id
                 )
+                print(f"File ready for download: {filename}")
                 with open(filename, "wb") as f:
                     f.write(file_content.read())
-                    print(f"File {filename} downloaded successfully.")
-                print(f"File ready for download: {filename}")
+
+                # Print result (should contain "file")
+                print(f"==> Result: file, {filename} downloaded successfully.")
             else:
                 print("No file generated in response")
 

@@ -25,7 +25,7 @@
     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) AZURE_AI_MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
+    2) (Optional) COMPUTER_USE_MODEL_DEPLOYMENT_NAME - The deployment name of the computer-use-preview model, as found under the "Name" column in
        the "Models + endpoints" tab in your Microsoft Foundry project.
 """
 
@@ -70,7 +70,7 @@
     agent = project_client.agents.create_version(
         agent_name="ComputerUseAgent",
         definition=PromptAgentDefinition(
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=os.environ.get("COMPUTER_USE_MODEL_DEPLOYMENT_NAME", "computer-use-preview"),
             instructions="""
             You are a computer automation assistant. 
 

@@ -25,7 +25,7 @@
     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) AZURE_AI_MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
+    2) (Optional) COMPUTER_USE_MODEL_DEPLOYMENT_NAME - The deployment name of the computer-use-preview model, as found under the "Name" column in
        the "Models + endpoints" tab in your Microsoft Foundry project.
 """
 
@@ -72,7 +72,7 @@ async def main():
         agent = await project_client.agents.create_version(
             agent_name="ComputerUseAgent",
             definition=PromptAgentDefinition(
-                model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+                model=os.environ.get("COMPUTER_USE_MODEL_DEPLOYMENT_NAME", "computer-use-preview"),
                 instructions="""
                 You are a computer automation assistant. 
 

@@ -78,8 +78,7 @@
         input="Tell me about Contoso products",
         extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
     )
-    print(f"Response: {response.output_text}")
-
+    print(f"==> Result: {response.output_text}")
     print("\nCleaning up...")
     project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
     print("Agent deleted")
@@ -140,7 +140,7 @@
                             print(f"File Citation - Filename: {annotation.filename}, File ID: {annotation.file_id}")
         elif event.type == "response.completed":
             print(f"\nFollow-up completed!")
-            print(f"Full response: {event.response.output_text}")
+            print(f"==> Result: {event.response.output_text}")
 
     # Clean up resources
     print("\n" + "=" * 60)

@@ -145,7 +145,7 @@ async def main() -> None:
                                 print(f"File Citation - Filename: {annotation.filename}, File ID: {annotation.file_id}")
             elif event.type == "response.completed":
                 print(f"\nFollow-up completed!")
-                print(f"Full response: {event.response.output_text}")
+                print(f"==> Result: {event.response.output_text}")
 
         # Clean up resources
         print("\n" + "=" * 60)

@@ -108,6 +108,9 @@ def get_horoscope(sign: str) -> str:
         extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
     )
 
-    # The model should be able to give a response!
-    print("Final output:")
-    print("\n" + response.output_text)
+    # Print result (should contain "Tuesday")
+    print(f"==> Result: {response.output_text}")
+
+    print("\nCleaning up...")
+    project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
+    print("Agent deleted")
@@ -109,9 +109,8 @@ async def main():
             extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
         )
 
-        # The model should be able to give a response!
-        print("Final output:")
-        print("\n" + response.output_text)
+        # Print result (should contain "Tuesday")
+        print(f"==> Result: {response.output_text}")
 
 
 if __name__ == "__main__":

@@ -92,7 +92,9 @@
         with open(file_path, "wb") as f:
             f.write(base64.b64decode(image_data[0]))
         # [END download_image]
-        print(f"Image downloaded and saved to: {file_path}")
+
+        # Print result (should contain "file")
+        print(f"==> Result: Image downloaded and saved to file: {file_path}")
 
     print("\nCleaning up...")
     project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)

@@ -87,7 +87,8 @@ async def main():
             with open(file_path, "wb") as f:
                 f.write(base64.b64decode(image_data[0]))
 
-            print(f"Image downloaded and saved to: {file_path}")
+            # Print result (should contain "file")
+            print(f"==> Result: Image downloaded and saved to file: {file_path}")
 
         print("\nCleaning up...")
         await project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)

@@ -95,7 +95,8 @@
         extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
     )
 
-    print(f"Response: {response.output_text}")
+    # Print result (should contain "Azure")
+    print(f"==> Result: {response.output_text}")
 
     # Clean up resources by deleting the agent version
     # This prevents accumulation of unused agent versions in your project

@@ -99,7 +99,8 @@ async def main():
             extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
         )
 
-        print(f"Response: {response.output_text}")
+        # Print result (should contain "Azure")
+        print(f"==> Result: {response.output_text}")
 
         # Clean up resources by deleting the agent version
         # This prevents accumulation of unused agent versions in your project

@@ -76,8 +76,8 @@
         input="Use the OpenAPI tool to print out, what is the weather in Seattle, WA today.",
         extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
     )
-    # The response to the question may contain non ASCII letters. To avoid error, encode and re decode them.
-    print(f"Response created: {response.output_text.encode().decode('ascii', errors='ignore')}")
+    # Print result (should contain "\u00b0F")
+    print(f"==> Result: {response.output_text}")
 
     print("\nCleaning up...")
     project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)

@@ -24,6 +24,7 @@
        the "Models + endpoints" tab in your Microsoft Foundry project.
     3) SHAREPOINT_PROJECT_CONNECTION_ID - The SharePoint project connection ID,
        as found in the "Connections" tab in your Microsoft Foundry project.
+    4) SHAREPOINT_USER_INPUT - (Optional) The question to ask. If not set, you will be prompted.
 """
 
 import os
@@ -67,7 +68,10 @@
     )
     print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")
 
-    user_input = input("Enter your question corresponded to the documents in SharePoint:\n")
+    # Get user input from environment variable or prompt
+    user_input = os.environ.get("SHAREPOINT_USER_INPUT")
+    if not user_input:
+        user_input = input("Enter your question corresponded to the documents in SharePoint:\n")
 
     # Send initial request that will trigger the SharePoint tool
     stream_response = openai_client.responses.create(
@@ -97,7 +101,7 @@
                             )
         elif event.type == "response.completed":
             print(f"\nFollow-up completed!")
-            print(f"Full response: {event.response.output_text}")
+            print(f"==> Result: {event.response.output_text}")
 
     print("Cleaning up...")
     project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)

@@ -65,7 +65,7 @@
         input="Show me the latest London Underground service updates",
         extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
     )
-    print(f"Response: {response.output_text}")
+    print(f"==> Result: {response.output_text}")
 
     print("\nCleaning up...")
     project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)

@@ -11,6 +11,7 @@
     add_general_regex_sanitizer,
     add_body_key_sanitizer,
     add_remove_header_sanitizer,
+    add_general_string_sanitizer,
 )
 
 if not load_dotenv(find_dotenv(), override=True):
@@ -99,13 +100,21 @@ def sanitize_url_paths():
 
     sanitize_url_paths()
 
+    # Normalize Content-Type for CSV files in multipart form-data (varies by OS/Python version)
+    add_general_string_sanitizer(target="Content-Type: text/csv", value="Content-Type: application/vnd.ms-excel")
+
     # Sanitize API key from service response (this includes Application Insights connection string)
     add_body_key_sanitizer(json_path="credentials.key", value="sanitized-api-key")
 
     # Sanitize SAS URI from Datasets get credential response
     add_body_key_sanitizer(json_path="blobReference.credential.sasUri", value="sanitized-sas-uri")
     add_body_key_sanitizer(json_path="blobReferenceForConsumption.credential.sasUri", value="sanitized-sas-uri")
 
+    add_body_key_sanitizer(
+        json_path="$..project_connection_id",
+        value="/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/connections/connector-name",
+    )
+
     # Remove Stainless headers from OpenAI client requests, since they include platform and OS specific info, which we can't have in recorded requests.
     # Here is an example of all the `x-stainless` headers from a Responses call:
     #   x-stainless-arch: other:amd64