improve validation of llm_config

autogen/agentchat/conversable_agent.py

@@ -65,7 +65,7 @@ class ConversableAgent(LLMAgent):
     `run_code`, and `execute_function` methods respectively.
     """
 
-    DEFAULT_CONFIG = {}  # An empty configuration
+    DEFAULT_CONFIG = None  # None or dict, the default config for llm inference
     MAX_CONSECUTIVE_AUTO_REPLY = 100  # maximum number of consecutive auto replies (subject to future change)
 
     DEFAULT_SUMMARY_PROMPT = "Summarize the takeaway from the conversation. Do not add any introductory phrases."
@@ -123,6 +123,7 @@ def __init__(
             llm_config (dict or False or None): llm inference configuration.
                 Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create)
                 for available options.
+                When using OpenAI or Azure OpenAI endpoints, please specify a non-empty 'model' either in `llm_config` or in each config of 'config_list' in `llm_config`.
                 To disable llm-based auto reply, set to False.
             default_auto_reply (str or dict): default auto reply when no code execution or llm-based reply is generated.
             description (str): a short description of the agent. This description is used by other agents
@@ -139,20 +140,14 @@ def __init__(
             else (lambda x: content_str(x.get("content")) == "TERMINATE")
         )
 
-        if llm_config is False:
+        if llm_config is False or llm_config is None and self.DEFAULT_CONFIG is None:
             self.llm_config = False
             self.client = None
         else:
-            self.llm_config = self.DEFAULT_CONFIG.copy()
+            self.llm_config = {} if self.DEFAULT_CONFIG is None else self.DEFAULT_CONFIG.copy()
             if isinstance(llm_config, dict):
                 self.llm_config.update(llm_config)
-            if "model" not in self.llm_config and (
-                not self.llm_config.get("config_list")
-                or any(not config.get("model") for config in self.llm_config["config_list"])
-            ):
-                raise ValueError(
-                    "Please either set llm_config to False, or specify a non-empty 'model' either in 'llm_config' or in each config of 'config_list'."
-                )
+            self._validate_llm_config()
             self.client = OpenAIWrapper(**self.llm_config)
 
         if logging_enabled():
@@ -246,6 +241,13 @@ def __init__(
             "process_message_before_send": [],
         }
 
+    def _validate_llm_config(self):
+        # TODO: more complete validity check
+        if self.llm_config in [{}, {"config_list": []}, {"config_list": [{"model": ""}]}]:
+            raise ValueError(
+                "When using OpenAI or Azure OpenAI endpoints, specify a non-empty 'model' either in 'llm_config' or in each config of 'config_list'."
+            )
+
     @property
     def name(self) -> str:
         """Get the name of the agent."""

autogen/agentchat/user_proxy_agent.py

@@ -70,7 +70,7 @@ def __init__(
                 - timeout (Optional, int): The maximum execution time in seconds.
                 - last_n_messages (Experimental, Optional, int): The number of messages to look back for code execution. Default to 1.
             default_auto_reply (str or dict or None): the default auto reply message when no code execution or llm based reply is generated.
-            llm_config (dict or False): llm inference configuration.
+            llm_config (dict or False or None): llm inference configuration.
                 Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create)
                 for available options.
                 Default to false, which disables llm-based auto reply.

autogen/oai/client.py

@@ -356,6 +356,7 @@ def __init__(self, *, config_list: Optional[List[Dict[str, Any]]] = None, **base
 
             base_config: base config. It can contain both keyword arguments for openai client
                 and additional kwargs.
+                When using OpenAI or Azure OpenAI endpoints, please specify a non-empty 'model' either in `base_config` or in each config of `config_list`.
         """
 
         if logging_enabled():

autogen/version.py

@@ -1 +1 @@
-__version__ = "0.2.17"
+__version__ = "0.2.18"

test/agentchat/test_conversable_agent.py

@@ -817,25 +817,21 @@ def exec_python(cell: Annotated[str, "Valid Python cell to execute."]) -> str:
 
 
 def test_register_for_llm_without_LLM():
-    with pytest.raises(
-        ValueError,
-        match="Please either set llm_config to False, or specify a non-empty 'model' either in 'llm_config' or in each config of 'config_list'.",
-    ):
-        ConversableAgent(name="agent", llm_config=None)
+    ConversableAgent(name="agent", llm_config=None)
 
 
 def test_register_for_llm_without_configuration():
     with pytest.raises(
         ValueError,
-        match="Please either set llm_config to False, or specify a non-empty 'model' either in 'llm_config' or in each config of 'config_list'.",
+        match="When using OpenAI or Azure OpenAI endpoints, specify a non-empty 'model' either in 'llm_config' or in each config of 'config_list'.",
     ):
         ConversableAgent(name="agent", llm_config={"config_list": []})
 
 
 def test_register_for_llm_without_model_name():
     with pytest.raises(
         ValueError,
-        match="Please either set llm_config to False, or specify a non-empty 'model' either in 'llm_config' or in each config of 'config_list'.",
+        match="When using OpenAI or Azure OpenAI endpoints, specify a non-empty 'model' either in 'llm_config' or in each config of 'config_list'.",
     ):
         ConversableAgent(name="agent", llm_config={"config_list": [{"model": ""}]})