fix(examples): detect incomplete WebSocket responses

README.md

@@ -396,7 +396,7 @@ Basic text based example:
 
 ```py
 import asyncio
-from openai import AsyncOpenAI
+from openai import AsyncOpenAI, OpenAIError
 
 
 async def main():
@@ -422,7 +422,13 @@ async def main():
                 print()
 
             elif event.type == "response.done":
+                if event.response.status != "completed":
+                    raise OpenAIError("The Realtime response did not complete")
                 break
+            elif event.type == "error":
+                raise OpenAIError("Realtime returned an error; inspect the event in your application")
+        else:
+            raise OpenAIError("Realtime disconnected before response.done")
 
 
 asyncio.run(main())

examples/live/audio_transcript.py

@@ -59,6 +59,7 @@ async def receive() -> None:
                     await transcript.push(event)
                     if event.type == "session.closed":
                         return
+                raise OpenAIError("Live disconnected before session.closed")
 
             receiver = asyncio.create_task(receive())
             try:

examples/realtime/azure_realtime.py

@@ -20,60 +20,64 @@ async def main() -> None:
     Enter "q" to quit the conversation.
     """
 
-    credential = DefaultAzureCredential()
-    token_provider = get_bearer_token_provider(credential, "https://cognitiveservices.azure.com/.default")
-    token = await token_provider()
-
-    # The endpoint of your Azure OpenAI resource is required. You can set it in the AZURE_OPENAI_ENDPOINT
-    # environment variable.
-    # You can find it in the Microsoft Foundry portal in the Overview page of your Azure OpenAI resource.
-    # Example: https://{your-resource}.openai.azure.com
-    endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
-
-    # The deployment name of the model you want to use is required. You can set it in the AZURE_OPENAI_DEPLOYMENT_NAME
-    # environment variable.
-    # You can find it in the Foundry portal in the "Models + endpoints" page of your Azure OpenAI resource.
-    # Example: gpt-realtime
-    deployment_name = os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"]
-
-    base_url = endpoint.replace("https://", "wss://").rstrip("/") + "/openai/v1"
-
-    # The APIs are compatible with the OpenAI client library.
-    # You can use the OpenAI client library to access the Azure OpenAI APIs.
-    # Make sure to set the baseURL and apiKey to use the Azure OpenAI endpoint and token.
-    client = AsyncOpenAI(websocket_base_url=base_url, api_key=token)
-    async with client.realtime.connect(
-        model=deployment_name,
-    ) as connection:
-        await connection.session.update(
-            session={
-                "output_modalities": ["text"],
-                "model": deployment_name,
-                "type": "realtime",
-            }
-        )
-        while True:
-            user_input = input("Enter a message: ")
-            if user_input == "q":
-                break
-
-            await connection.conversation.item.create(
-                item={
-                    "type": "message",
-                    "role": "user",
-                    "content": [{"type": "input_text", "text": user_input}],
-                }
+    async with DefaultAzureCredential() as credential:
+        token_provider = get_bearer_token_provider(credential, "https://cognitiveservices.azure.com/.default")
+        token = await token_provider()
+
+        # The endpoint of your Azure OpenAI resource is required. You can set it in the AZURE_OPENAI_ENDPOINT
+        # environment variable.
+        # You can find it in the Microsoft Foundry portal in the Overview page of your Azure OpenAI resource.
+        # Example: https://{your-resource}.openai.azure.com
+        endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
+
+        # The deployment name of the model you want to use is required. You can set it in the AZURE_OPENAI_DEPLOYMENT_NAME
+        # environment variable.
+        # You can find it in the Foundry portal in the "Models + endpoints" page of your Azure OpenAI resource.
+        # Example: gpt-realtime
+        deployment_name = os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"]
+
+        base_url = endpoint.replace("https://", "wss://").rstrip("/") + "/openai/v1"
+
+        # The APIs are compatible with the OpenAI client library.
+        # You can use the OpenAI client library to access the Azure OpenAI APIs.
+        # Make sure to set the baseURL and apiKey to use the Azure OpenAI endpoint and token.
+        async with (
+            AsyncOpenAI(websocket_base_url=base_url, api_key=token) as client,
+            client.realtime.connect(model=deployment_name) as connection,
+        ):
+            await connection.session.update(
+                session={
+                    "output_modalities": ["text"],
+                    "model": deployment_name,
+                    "type": "realtime",
+                },
             )
-            await connection.response.create()
-            async for event in connection:
-                if event.type == "response.output_text.delta":
-                    print(event.delta, flush=True, end="")
-                elif event.type == "response.output_text.done":
-                    print()
-                elif event.type == "response.done":
+            while True:
+                user_input = input("Enter a message: ")
+                if user_input == "q":
                     break
 
-    await credential.close()
+                await connection.conversation.item.create(
+                    item={
+                        "type": "message",
+                        "role": "user",
+                        "content": [{"type": "input_text", "text": user_input}],
+                    }
+                )
+                await connection.response.create()
+                async for event in connection:
+                    if event.type == "response.output_text.delta":
+                        print(event.delta, flush=True, end="")
+                    elif event.type == "response.output_text.done":
+                        print()
+                    elif event.type == "error":
+                        raise RuntimeError(f"Realtime error: {event.error.type} ({event.error.code})")
+                    elif event.type == "response.done":
+                        if event.response.status != "completed":
+                            raise RuntimeError(f"Response did not complete successfully: {event.response.status}")
+                        break
+                else:
+                    raise RuntimeError("WebSocket closed before the response completed.")
 
 
 asyncio.run(main())

examples/responses/multi_agent_websocket.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from openai import OpenAI
+from openai import OpenAI, OpenAIError
 from openai.types.beta import BetaResponseOutputItem
 
 PROMPT = """Compare these fictional proposals on speed, cost, reliability, and security.
@@ -42,5 +42,9 @@ def agent_name(item: BetaResponseOutputItem) -> str:
             print(event.delta, end="", flush=True)
         if event.type == "response.completed":
             break
+        if event.type in {"response.failed", "response.incomplete", "error"}:
+            raise OpenAIError(f"Multi-agent request ended with {event.type}")
+    else:
+        raise OpenAIError("Connection closed before the multi-agent response completed")
 
 print()