fix: code formatting

autogen/agentchat/groupchat.py

@@ -7,12 +7,12 @@
 from dataclasses import dataclass, field
 from typing import Callable, Dict, List, Literal, Optional, Tuple, Union
 
-from ..oai.client import ModelClient
 from ..code_utils import content_str
 from ..exception_utils import AgentNameConflict, NoEligibleSpeaker, UndefinedNextAgent
 from ..formatting_utils import colored
 from ..graph_utils import check_graph_validity, invert_disallowed_to_allowed
 from ..io.base import IOStream
+from ..oai.client import ModelClient
 from ..runtime_logging import log_new_agent, logging_enabled
 from .agent import Agent
 from .conversable_agent import ConversableAgent
@@ -103,15 +103,15 @@ def custom_speaker_selection_func(
     messages: List[Dict]
     llm_config: Optional[Union[Dict, Literal[False]]] = None
     model_client_cls: Optional[Union[ModelClient, List[ModelClient]]] = None
-    max_round: Optional[int] = 10
-    admin_name: Optional[str] = "Admin"
-    func_call_filter: Optional[bool] = True
+    max_round: int = 10
+    admin_name: str = "Admin"
+    func_call_filter: bool = True
     speaker_selection_method: Union[Literal["auto", "manual", "random", "round_robin"], Callable] = "auto"
-    max_retries_for_selecting_speaker: Optional[int] = 2
+    max_retries_for_selecting_speaker: int = 2
     allow_repeat_speaker: Optional[Union[bool, List[Agent]]] = None
     allowed_or_disallowed_speaker_transitions: Optional[Dict] = None
     speaker_transitions_type: Literal["allowed", "disallowed", None] = None
-    enable_clear_history: Optional[bool] = False
+    enable_clear_history: bool = False
     send_introductions: bool = False
     select_speaker_message_template: str = """You are in a role play game. The following roles are available:
                 {roles}.
@@ -564,7 +564,7 @@ def _finalize_speaker(self, last_speaker: Agent, final: bool, name: str, agents:
         return agent if agent else self.next_agent(last_speaker, agents)
 
     def _register_client_from_config(self, agent: Agent, config: Dict):
-        model_client_cls_to_match = config.get('model_client_cls')
+        model_client_cls_to_match = config.get("model_client_cls")
         if model_client_cls_to_match:
             if not self.model_client_cls:
                 raise ValueError(
@@ -599,14 +599,13 @@ def _register_custom_model_clients(self, agent: ConversableAgent):
         if not self.llm_config:
             return
 
-        config_format_is_list = 'config_list' in self.llm_config.keys()
+        config_format_is_list = "config_list" in self.llm_config.keys()
         if config_format_is_list:
-            for config in self.llm_config['config_list']:
+            for config in self.llm_config["config_list"]:
                 self._register_client_from_config(agent, config)
         elif not config_format_is_list:
             self._register_client_from_config(agent, self.llm_config)
 
-
     def _auto_select_speaker(
         self,
         last_speaker: Agent,

website/docs/topics/groupchat/using_custom_models.md

@@ -1,11 +1,11 @@
 # Using Custom Models
 
-When using `GroupChatManager` we need to pass a `GroupChat` object in the constructor, a dataclass responsible for 
-gathering agents, preparing messages from prompt templates and selecting speakers 
+When using `GroupChatManager` we need to pass a `GroupChat` object in the constructor, a dataclass responsible for
+gathering agents, preparing messages from prompt templates and selecting speakers
 (eventually using `speaker_selection_method` as described [here](customized_speaker_selection)).
 
 To do so GroupChat internally initializes two instances of ConversableAgent.
-In order to control the model clients used by the agents instantiated within the GroupChat, which already receives the 
+In order to control the model clients used by the agents instantiated within the GroupChat, which already receives the
 `llm_config` passed to GroupChatManager, the optional `model_client_cls` attribute can be set.
 
 
@@ -49,13 +49,13 @@ user_proxy = UserProxyAgent(name="user", llm_config=llm_config, code_execution_c
 user_proxy.register_model_client(MyModelClient)
 ```
 
-Note that the agents definition illustrated here is minimal and might not suit your needs. The only aim is to show a 
+Note that the agents definition illustrated here is minimal and might not suit your needs. The only aim is to show a
 basic setup for a group chat scenario.
 
 We then create a `GroupChat` and, if we want the underlying agents used by GroupChat to use our
- custom client, we will pass it in the `model_client_cls` attribute. 
+custom client, we will pass it in the `model_client_cls` attribute.
 
-Finally we create an instance of `GroupChatManager` and pass the config to it. This same config will be forwarded to 
+Finally we create an instance of `GroupChatManager` and pass the config to it. This same config will be forwarded to
 the GroupChat, that (if needed) will automatically handle registration of custom models only.
 
 ```python
@@ -71,9 +71,9 @@ user_proxy.initiate_chat(chat_manager, initial_message="Suggest me the most tren
 
 ```
 
-This attribute can either be a class or a list of classes which adheres to the `ModelClient` protocol (see 
-[this link](../non-openai-models/about-using-nonopenai-models) for more info about defining a custom model client 
+This attribute can either be a class or a list of classes which adheres to the `ModelClient` protocol (see
+[this link](../non-openai-models/about-using-nonopenai-models) for more info about defining a custom model client
 class).
 
-Note that it is not necessary to define a `model_client_cls` when working with Azure OpenAI, OpenAI or other non-custom 
+Note that it is not necessary to define a `model_client_cls` when working with Azure OpenAI, OpenAI or other non-custom
 models natively supported by the library.