black patch on outstanding files that were causing workflow fails on …

…PRs (#193)

memgpt/autogen/examples/agent_autoreply.py

@@ -34,7 +34,7 @@
     code_execution_config={"last_n_messages": 2, "work_dir": "groupchat"},
     human_input_mode="TERMINATE",  # needed?
     default_auto_reply="You are going to figure all out by your own. "
-                       "Work by yourself, the user won't reply until you output `TERMINATE` to end the conversation.",
+    "Work by yourself, the user won't reply until you output `TERMINATE` to end the conversation.",
 )
 
 if not USE_MEMGPT:
@@ -43,7 +43,7 @@
         name="Coder",
         llm_config=llm_config,
         system_message=f"I am a 10x engineer, trained in Python. I was the first engineer at Uber "
-                       f"(which I make sure to tell everyone I work with).",
+        f"(which I make sure to tell everyone I work with).",
         human_input_mode="TERMINATE",
     )
 
@@ -54,13 +54,12 @@
         "MemGPT_coder",
         llm_config=llm_config,
         system_message=f"I am a 10x engineer, trained in Python. I was the first engineer at Uber "
-                       f"(which I make sure to tell everyone I work with).",
+        f"(which I make sure to tell everyone I work with).",
         human_input_mode="TERMINATE",
     )
 
 # Begin the group chat with a message from the user
 user_proxy.initiate_chat(
     coder,
-    message="I want to design an app to make me one million dollars in one month. "
-            "Tell me all the details, then try out every steps.",
+    message="I want to design an app to make me one million dollars in one month. " "Tell me all the details, then try out every steps.",
 )

memgpt/autogen/examples/agent_groupchat.py

@@ -58,9 +58,9 @@
         coder = create_autogen_memgpt_agent(
             "MemGPT_coder",
             persona_description="I am a 10x engineer, trained in Python. I was the first engineer at Uber "
-                                "(which I make sure to tell everyone I work with).",
+            "(which I make sure to tell everyone I work with).",
             user_description=f"You are participating in a group chat with a user ({user_proxy.name}) "
-                             f"and a product manager ({pm.name}).",
+            f"and a product manager ({pm.name}).",
             # extra options
             # interface_kwargs={"debug": True},
         )
@@ -69,9 +69,9 @@
             "MemGPT_coder",
             llm_config=llm_config,
             system_message=f"I am a 10x engineer, trained in Python. I was the first engineer at Uber "
-                           f"(which I make sure to tell everyone I work with).\n"
-                           f"You are participating in a group chat with a user ({user_proxy.name}) "
-                           f"and a product manager ({pm.name}).",
+            f"(which I make sure to tell everyone I work with).\n"
+            f"You are participating in a group chat with a user ({user_proxy.name}) "
+            f"and a product manager ({pm.name}).",
         )
 
 # Initialize the group chat between the user and two LLM agents (PM and coder)
@@ -81,6 +81,5 @@
 # Begin the group chat with a message from the user
 user_proxy.initiate_chat(
     manager,
-    message="I want to design an app to make me one million dollars in one month. "
-            "Yes, your heard that right.",
+    message="I want to design an app to make me one million dollars in one month. " "Yes, your heard that right.",
 )

memgpt/autogen/memgpt_agent.py

@@ -75,7 +75,7 @@ def create_memgpt_autogen_agent_from_config(
         groupchat = GroupChat(
             agents=[autogen_memgpt_agent, coop_agent1, coop_agent2],
             messages=[],
-            max_round=12 if max_consecutive_auto_reply is None else max_consecutive_auto_reply
+            max_round=12 if max_consecutive_auto_reply is None else max_consecutive_auto_reply,
         )
         manager = GroupChatManager(name=name, groupchat=groupchat, llm_config=llm_config)
         return manager
@@ -146,9 +146,7 @@ def __init__(
         self.register_reply([Agent, None], MemGPTAgent._generate_reply_for_user_message)
         self.messages_processed_up_to_idx = 0
 
-        self._is_termination_msg = (
-            is_termination_msg if is_termination_msg is not None else (lambda x: x == "TERMINATE")
-        )
+        self._is_termination_msg = is_termination_msg if is_termination_msg is not None else (lambda x: x == "TERMINATE")
 
     def format_other_agent_message(self, msg):
         if "name" in msg:
@@ -220,7 +218,7 @@ async def _a_generate_reply_for_user_message(
                 break
 
         # Stop the conversation
-        if self._is_termination_msg(new_messages[-1]['content']):
+        if self._is_termination_msg(new_messages[-1]["content"]):
             return True, None
 
         # Pass back to AutoGen the pretty-printed calls MemGPT made to the interface

memgpt/config.py

@@ -121,7 +121,9 @@ async def config_init(cls: Type["Config"], config_file: str = None):
         ).ask_async()
 
         self.archival_storage_index = None
-        self.preload_archival = await questionary.confirm("Would you like to preload anything into MemGPT's archival memory?", default=False).ask_async()
+        self.preload_archival = await questionary.confirm(
+            "Would you like to preload anything into MemGPT's archival memory?", default=False
+        ).ask_async()
         if self.preload_archival:
             self.load_type = await questionary.select(
                 "What would you like to load?",

memgpt/memory.py

@@ -685,7 +685,7 @@ def search(self, query_string, count=None, start=None):
         # from pprint import pprint
         # pprint(results)
         return results, len(results)
-    
+
     async def a_search(self, query_string, count=None, start=None):
         return self.search(query_string, count, start)