uAbility to update_model on conversable agents

autogen/agentchat/conversable_agent.py

@@ -129,6 +129,7 @@ def __init__(
         self._name = name
         # a dictionary of conversations, default value is list
         self._oai_messages = defaultdict(list)
+        self._update_model_metadata = defaultdict(list)
         self._oai_system_message = [{"content": system_message, "role": "system"}]
         self._description = description if description is not None else system_message
         self._is_termination_msg = (
@@ -1066,6 +1067,31 @@ def clear_history(self, recipient: Optional[Agent] = None, nr_messages_to_preser
                     flush=True,
                 )
 
+    def update_model(self, preference_data: List[Dict[str, Any]], agent: Agent, **kwargs) -> Dict[str, Any]:
+        """Update the model using the preference data and the conversation history.
+        
+        Args:
+            preference_data (List[Dict]): a list of dictionaries containing the preference data.
+            agent (Agent): the agent to update the model.
+            **kwargs: additional keyword arguments for the update model function.
+
+        Returns:
+            Dict: a dictionary containing the update model statistics.
+
+        Raises:
+            ValueError: If no OpenAIWrapper client is found.
+            ValueError: If multiple model clients are registered.
+            NotImplementedError: If update_model is not implemented for the underlying client.
+        """
+        if self.client is None:
+            raise ValueError("No OpenAIWrapper client is found.")
+        messages = self._oai_messages[agent]
+        update_model_stats = self.client.update_model(preference_data, messages, **kwargs)
+        self._update_model_metadata[agent].append(
+            {"messages": messages, "preference_data": preference_data, "update_stats": update_model_stats}
+        )
+        return update_model_stats
+
     def generate_oai_reply(
         self,
         messages: Optional[List[Dict]] = None,

autogen/oai/client.py

@@ -83,8 +83,7 @@ class Message(Protocol):
         choices: List[Choice]
         model: str
 
-    def create(self, **params: Any) -> ModelClientResponseProtocol:
-        ...  # pragma: no cover
+    def create(self, **params: Any) -> ModelClientResponseProtocol: ...  # pragma: no cover
 
     def message_retrieval(
         self, response: ModelClientResponseProtocol
@@ -97,14 +96,30 @@ def message_retrieval(
         """
         ...  # pragma: no cover
 
-    def cost(self, response: ModelClientResponseProtocol) -> float:
-        ...  # pragma: no cover
+    def cost(self, response: ModelClientResponseProtocol) -> float: ...  # pragma: no cover
 
     @staticmethod
     def get_usage(response: ModelClientResponseProtocol) -> Dict:
         """Return usage summary of the response using RESPONSE_USAGE_KEYS."""
         ...  # pragma: no cover
 
+    def update_model(
+        self, preference_data: List[Dict[str, Any]], inference_messages: List[Dict[str, Any]], **kwargs: Any
+    ) -> Dict[str, Any]:
+        """Optional method to learn from the preference data, if the model supports learning. Can be missing.
+
+        Learn from the preference data.
+
+        Args:
+            preference_data: The preference data.
+            inference_messages: The messages used for inference.
+            **kwargs: other arguments.
+
+        Returns:
+            Learning stats.
+        """
+        ...  # pragma: no cover
+
 
 class PlaceHolderClient:
     def __init__(self, config):
@@ -503,6 +518,33 @@ def _construct_create_params(self, create_config: Dict[str, Any], extra_kwargs:
             ]
         return params
 
+    def update_model(
+        self, preference_data: List[Any], inference_messages: List[Dict[str, Any]], **kwargs: Any
+    ) -> Dict[str, Any]:
+        """Learn from the preference data.
+
+        update_model is not supported for multiple model clients as it would be ambiguous which client was responsible for the inference messages.
+
+        Args:
+            preference_data: The preference data.
+            inference_messages: The messages used for inference.
+            **kwargs: other arguments.
+
+        Returns:
+            Learning stats.
+
+        Raises:
+            ValueError: If multiple model clients are registered.
+            NotImplementedError: If update_model is not implemented for the client.
+        """
+        if len(self._clients) != 1:
+            raise ValueError("update_model is not supported for multiple model clients.")
+        client = self._clients[0]
+        if hasattr(client, "update_model") and callable(getattr(client, "update_model")):
+            return client.update_model(preference_data, inference_messages, **kwargs)
+        else:
+            raise NotImplementedError(f"update_model is not implemented for {client.__class__.__name__}.")
+
     def create(self, **config: Any) -> ModelClient.ModelClientResponseProtocol:
         """Make a completion for a given config using available clients.
         Besides the kwargs allowed in openai's [or other] client, we allow the following additional kwargs.