[Core] Fix update_usage_summary when response doesn't have usage attribute

autogen/oai/client.py

@@ -351,6 +351,15 @@ def _update_usage_summary(self, response: ChatCompletion | Completion, use_cache
 
         Usage is calculated no matter filter is passed or not.
         """
+        try:
+            usage = response.usage
+            assert usage is not None
+            usage.prompt_tokens = 0 if usage.prompt_tokens is None else usage.prompt_tokens
+            usage.completion_tokens = 0 if usage.completion_tokens is None else usage.completion_tokens
+            usage.total_tokens = 0 if usage.total_tokens is None else usage.total_tokens
+        except (AttributeError, AssertionError):
+            logger.debug("Usage attribute is not found in the response.", exc_info=1)
+            return
 
         def update_usage(usage_summary):
             if usage_summary is None:
@@ -360,12 +369,10 @@ def update_usage(usage_summary):
 
             usage_summary[response.model] = {
                 "cost": usage_summary.get(response.model, {}).get("cost", 0) + response.cost,
-                "prompt_tokens": usage_summary.get(response.model, {}).get("prompt_tokens", 0)
-                + response.usage.prompt_tokens,
+                "prompt_tokens": usage_summary.get(response.model, {}).get("prompt_tokens", 0) + usage.prompt_tokens,
                 "completion_tokens": usage_summary.get(response.model, {}).get("completion_tokens", 0)
-                + response.usage.completion_tokens,
-                "total_tokens": usage_summary.get(response.model, {}).get("total_tokens", 0)
-                + response.usage.total_tokens,
+                + usage.completion_tokens,
+                "total_tokens": usage_summary.get(response.model, {}).get("total_tokens", 0) + usage.total_tokens,
             }
             return usage_summary
 
@@ -435,6 +442,7 @@ def cost(self, response: Union[ChatCompletion, Completion]) -> float:
         model = response.model
         if model not in oai_price1k:
             # TODO: add logging to warn that the model is not found
+            logger.debug(f"Model {model} is not found. The cost will be 0.", exc_info=1)
             return 0
 
         n_input_tokens = response.usage.prompt_tokens

notebook/agentchat_function_call_async.ipynb

@@ -316,7 +316,11 @@
     "    llm_config=llm_config,\n",
     ")\n",
     "groupchat = autogen.GroupChat(agents=[user_proxy, coder, markdownagent], messages=[], max_round=12)\n",
-    "manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config,\n",
+    "\n",
+    "llm_config_manager = llm_config.copy()\n",
+    "llm_config_manager.pop(\"functions\", None)\n",
+    "\n",
+    "manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config_manager,\n",
     "                                is_termination_msg=lambda x: \"GROUPCHAT_TERMINATE\" in x.get(\"content\", \"\"),\n",
     "                                )"
    ]

setup.py

@@ -14,7 +14,7 @@
 __version__ = version["__version__"]
 
 install_requires = [
-    "openai~=1.3",
+    "openai<=1.4.0",
     "diskcache",
     "termcolor",
     "flaml",