refactor

chatflock/base.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional, Sequence, TypeVar
+from typing import Any, Callable, List, Optional, Sequence, TypeVar
 
 import abc
 import dataclasses
@@ -36,9 +36,6 @@ def on_participant_joined_chat(self, chat: "Chat", participant: "ChatParticipant
     def on_participant_left_chat(self, chat: "Chat", participant: "ChatParticipant") -> None:
         pass
 
-    def initialize(self) -> None:
-        pass
-
     def __str__(self) -> str:
         return self.name
 
@@ -49,7 +46,7 @@ def detailed_str(self, level: int = 0) -> str:
 
 class ActiveChatParticipant(ChatParticipant):
     symbol: str
-    messages_hidden: bool
+    messages_hidden: bool = False
 
     def __init__(self, name: str, symbol: str = "👤", messages_hidden: bool = False):
         super().__init__(name=name)
@@ -77,6 +74,9 @@ class ChatMessage(BaseModel):
 
 
 class ChatConductor(abc.ABC):
+    def __init__(self, composition_generator: "ChatCompositionGenerator"):
+        self.composition_generator = composition_generator
+
     @abc.abstractmethod
     def select_next_speaker(self, chat: "Chat") -> Optional[ActiveChatParticipant]:
         raise NotImplementedError()
@@ -90,22 +90,17 @@ def get_chat_result(self, chat: "Chat") -> str:
 
         return last_message.content
 
-    def initialize_chat(self, chat: "Chat", **kwargs: Any) -> None:
-        # Make sure all participants are initialized.
-        for active_participant in chat.get_active_participants():
-            active_participant.initialize()
-
-        for non_active_participant in chat.get_non_active_participants():
-            non_active_participant.initialize()
+    def prepare_chat(self, chat: "Chat", **kwargs: Any) -> None:
+        pass
 
-    def initiate_chat_with_result(
+    def initiate_dialog(
         self,
         chat: "Chat",
         initial_message: Optional[str] = None,
         from_participant: Optional[ChatParticipant] = None,
         **kwargs: Any,
     ) -> str:
-        self.initialize_chat(chat=chat, **kwargs)
+        self.prepare_chat(chat=chat, **kwargs)
 
         active_participants = chat.get_active_participants()
         if len(active_participants) <= 1:

chatflock/conductors/langchain.py

@@ -124,7 +124,7 @@ def create_next_speaker_first_human_prompt(self, chat: "Chat") -> str:
 
         return str(prompt)
 
-    def initialize_chat(self, chat: "Chat", **kwargs: Any) -> None:
+    def prepare_chat(self, chat: "Chat", **kwargs: Any) -> None:
         # If a composition generator is provided, generate a new composition for the chat before starting.
         if self.composition_generator is not None and not self.composition_initialized:
             composition_suggestion = kwargs.get("composition_suggestion", None)
@@ -154,7 +154,7 @@ def initialize_chat(self, chat: "Chat", **kwargs: Any) -> None:
 
             self.composition_initialized = True
 
-        super().initialize_chat(chat=chat, **kwargs)
+        super().prepare_chat(chat=chat, **kwargs)
 
     def select_next_speaker(self, chat: Chat) -> Optional[ActiveChatParticipant]:
         participants = chat.get_active_participants()

chatflock/parsing_utils.py

@@ -80,7 +80,7 @@ def chat_messages_to_pydantic(
     )
     conductor = RoundRobinChatConductor()
 
-    _ = conductor.initiate_chat_with_result(chat=parser_chat)
+    _ = conductor.initiate_dialog(chat=parser_chat)
 
     if json_parser.output is None:
         raise MessageCouldNotBeParsedError("An output could not be parsed from the chat messages.")

chatflock/participants/internal_group.py

@@ -36,10 +36,9 @@ def __init__(
 
         super().__init__(name=group_name, **kwargs)
 
-    def initialize(self):
         # Make sure the chat & conductor are initialized, as it may be a dynamic chat with
         # no participants yet.
-        self.inner_chat_conductor.initialize_chat(chat=self.inner_chat)
+        self.inner_chat_conductor.prepare_chat(chat=self.inner_chat)
 
     def respond_to_chat(self, chat: "Chat") -> str:
         if self.clear_inner_chat_before_responding:
@@ -64,7 +63,7 @@ def respond_to_chat(self, chat: "Chat") -> str:
             answerer=leader,
         )
 
-        group_response = self.inner_chat_conductor.initiate_chat_with_result(
+        group_response = self.inner_chat_conductor.initiate_dialog(
             chat=self.inner_chat, initial_message=request_for_group
         )
 

chatflock/use_cases/bshr.py

@@ -165,11 +165,11 @@ def generate_queries(
         if spinner is not None:
             spinner.stop()
 
-        _ = chat_conductor.initiate_chat_with_result(
+        _ = chat_conductor.initiate_dialog(
             chat=chat, initial_message=f"What is your information need or query?", from_participant=query_generator
         )
     else:
-        _ = chat_conductor.initiate_chat_with_result(
+        _ = chat_conductor.initiate_dialog(
             chat=chat,
             initial_message=str(
                 StructuredString(

chatflock/use_cases/request_response.py

@@ -24,6 +24,6 @@ def get_response(
     )
 
     chat_conductor = RoundRobinChatConductor()
-    answer = chat_conductor.initiate_chat_with_result(chat=chat, initial_message=query, from_participant=user)
+    answer = chat_conductor.initiate_dialog(chat=chat, initial_message=query, from_participant=user)
 
     return answer, chat

chatflock/web_research/web_research.py

@@ -201,7 +201,7 @@ def get_answer(
             max_total_messages=2,
         )
         chat_conductor = RoundRobinChatConductor()
-        final_answer = chat_conductor.initiate_chat_with_result(
+        final_answer = chat_conductor.initiate_dialog(
             chat=chat,
             initial_message=str(
                 StructuredString(

examples/automatic_chat_simple_composition.py

@@ -36,11 +36,11 @@ def automatic_simple_chat_composition(model: str = "gpt-4-1106-preview", tempera
 
     # Not necessary in practice since initiation is done automatically when calling `initiate_chat_with_result`.
     # However, this is needed to eagerly generate the composition. Default is lazy.
-    chat_conductor.initialize_chat(chat=chat)
+    chat_conductor.prepare_chat(chat=chat)
 
     print(f"Generated Composition:\n=================\n{chat.active_participants_str}\n=================\n\n")
 
-    result = chat_conductor.initiate_chat_with_result(chat=chat)
+    result = chat_conductor.initiate_dialog(chat=chat)
     print(result)
 
 

examples/automatic_hierarchical_chat_composition.py

@@ -54,7 +54,7 @@ def create_chat(**kwargs: Any) -> Chat:
     # It's not necessary in practice to manually call `initialize_chat` since initiation is done automatically
     # when calling `initiate_chat_with_result`. However, this is needed to eagerly generate the composition.
     # Default is lazy and will happen when the chat is initiated.
-    chat_conductor.initialize_chat(
+    chat_conductor.prepare_chat(
         chat=chat,
         # Only relevant when passing in a composition generator
         composition_suggestion="DevCompany: Includes a CEO, Product Team, Marketing Team, and a Development "
@@ -64,7 +64,7 @@ def create_chat(**kwargs: Any) -> Chat:
     print(f"\nGenerated composition:\n=================\n{chat.active_participants_str}\n=================\n\n")
 
     # You can also pass in a composition suggestion here.
-    result = chat_conductor.initiate_chat_with_result(chat=chat)
+    result = chat_conductor.initiate_dialog(chat=chat)
     print(result)
 
 

examples/automatic_internal_group_participant.py

@@ -43,11 +43,11 @@ def automatic_internal_group_participant(model: str = "gpt-4-1106-preview", temp
 
     # Not necessary in practice since initiation is done automatically when calling `initiate_chat_with_result`.
     # However, this is needed to eagerly generate the composition. Default is lazy.
-    chat_conductor.initialize_chat(chat=chat)
+    chat_conductor.prepare_chat(chat=chat)
     print(f"\nGenerated composition:\n=================\n{chat.active_participants_str}\n=================\n\n")
 
     # You can also pass in a composition suggestion here.
-    result = chat_conductor.initiate_chat_with_result(chat=chat)
+    result = chat_conductor.initiate_dialog(chat=chat)
     print(result)
 
 

examples/chatgpt_clone.py

@@ -24,7 +24,7 @@ def chatgpt_clone(model: str = "gpt-4-1106-preview", temperature: float = 0.0) -
     )
 
     chat_conductor = RoundRobinChatConductor()
-    chat_conductor.initiate_chat_with_result(chat=chat)
+    chat_conductor.initiate_dialog(chat=chat)
 
 
 if __name__ == "__main__":

examples/chatgpt_clone_with_additional_tools.py

@@ -66,7 +66,7 @@ def chatgpt_clone_with_additional_tools(
     )
 
     chat_conductor = RoundRobinChatConductor()
-    chat_conductor.initiate_chat_with_result(chat=chat)
+    chat_conductor.initiate_dialog(chat=chat)
 
     page_retriever.close()
 

examples/chatgpt_clone_with_langchain_memory.py

@@ -31,7 +31,7 @@ def chatgpt_clone_with_langchain_memory(model: str = "gpt-4-1106-preview", tempe
     chat = Chat(backing_store=backing_store, renderer=TerminalChatRenderer(), initial_participants=participants)
 
     chat_conductor = RoundRobinChatConductor()
-    chat_conductor.initiate_chat_with_result(chat=chat)
+    chat_conductor.initiate_dialog(chat=chat)
 
 
 if __name__ == "__main__":

examples/chatgpt_clone_with_langchain_retrieval.py

@@ -45,7 +45,7 @@ def chatgpt_clone_with_langchain_retrieval(model: str = "gpt-4-1106-preview", te
     )
 
     chat_conductor = RoundRobinChatConductor()
-    chat_conductor.initiate_chat_with_result(chat=chat)
+    chat_conductor.initiate_dialog(chat=chat)
 
 
 if __name__ == "__main__":

examples/manual_internal_group_participant.py

@@ -59,7 +59,7 @@ def manual_internal_group_participant(model: str = "gpt-4-1106-preview", tempera
 
     chat_conductor = RoundRobinChatConductor()
 
-    chat_conductor.initiate_chat_with_result(chat=chat)
+    chat_conductor.initiate_dialog(chat=chat)
 
 
 if __name__ == "__main__":

examples/three_way_ai_conductor.py

@@ -56,7 +56,7 @@ def three_way_ai_conductor(model: str = "gpt-4-1106-preview", temperature: float
         termination_condition="When the user finds the food satisfactory.",
     )
 
-    chat_conductor.initiate_chat_with_result(chat=chat)
+    chat_conductor.initiate_dialog(chat=chat)
 
 
 if __name__ == "__main__":