groupchat

groupchat.py

@@ -1,109 +1,49 @@
-# from swarms.structs import Flow
-# from swarms.models import OpenAIChat
-# from swarms.swarms.groupchat import GroupChat
-# from swarms.agents import SimpleAgent
+from swarms import OpenAI, Flow
+from swarms.swarms.groupchat import GroupChatManager, GroupChat
 
-# api_key = ""
 
-# llm = OpenAIChat(
-#     openai_api_key=api_key,
-# )
+api_key = ""
 
-# agent1 = SimpleAgent("Captain Price", Flow(llm=llm, max_loops=4))
-# agent2 = SimpleAgent("John Mactavis", Flow(llm=llm, max_loops=4))
-
-# # Create a groupchat with the 2 agents
-# chat = GroupChat([agent1, agent2])
-
-# # Assign duties to the agents
-# chat.assign_duty(agent1.name, "Buy the groceries")
-# chat.assign_duty(agent2.name, "Clean the house")
-
-# # Initate a chat
-# response = chat.run("Captain Price", "Hello, how are you John?")
-# print(response)
-
-
-from swarms.models import OpenAIChat
-from swarms.structs import Flow
-import random
-
-api_key = ""  # Your API Key here
-
-
-class GroupChat:
-    """
-    GroupChat class that facilitates agent-to-agent communication using multiple instances of the Flow class.
-    """
-
-    def __init__(self, agents: list):
-        self.agents = {f"agent_{i}": agent for i, agent in enumerate(agents)}
-        self.message_log = []
-
-    def add_agent(self, agent: Flow):
-        agent_id = f"agent_{len(self.agents)}"
-        self.agents[agent_id] = agent
-
-    def remove_agent(self, agent_id: str):
-        if agent_id in self.agents:
-            del self.agents[agent_id]
-
-    def send_message(self, sender_id: str, recipient_id: str, message: str):
-        if sender_id not in self.agents or recipient_id not in self.agents:
-            raise ValueError("Invalid sender or recipient ID.")
-        formatted_message = f"{sender_id} to {recipient_id}: {message}"
-        self.message_log.append(formatted_message)
-        recipient_agent = self.agents[recipient_id]
-        recipient_agent.run(message)
-
-    def broadcast_message(self, sender_id: str, message: str):
-        for agent_id, agent in self.agents.items():
-            if agent_id != sender_id:
-                self.send_message(sender_id, agent_id, message)
-
-    def get_message_log(self):
-        return self.message_log
-
-
-class EnhancedGroupChatV2(GroupChat):
-    def __init__(self, agents: list):
-        super().__init__(agents)
-
-    def multi_round_conversation(self, rounds: int = 5):
-        """
-        Initiate a multi-round conversation between agents.
-
-        Args:
-            rounds (int): The number of rounds of conversation.
-        """
-        for _ in range(rounds):
-            # Randomly select a sender and recipient agent for the conversation
-            sender_id = random.choice(list(self.agents.keys()))
-            recipient_id = random.choice(list(self.agents.keys()))
-            while recipient_id == sender_id:  # Ensure the recipient is not the sender
-                recipient_id = random.choice(list(self.agents.keys()))
-
-            # Generate a message (for simplicity, a generic message is used)
-            message = f"Hello {recipient_id}, how are you today?"
-            self.send_message(sender_id, recipient_id, message)
-
-
-# Sample usage with EnhancedGroupChatV2
-# Initialize the language model
-llm = OpenAIChat(
+llm = OpenAI(
     openai_api_key=api_key,
     temperature=0.5,
     max_tokens=3000,
 )
 
-# Initialize two Flow agents
-agent1 = Flow(llm=llm, max_loops=5, dashboard=True)
-agent2 = Flow(llm=llm, max_loops=5, dashboard=True)
+# Initialize the flow
+flow1 = Flow(
+    llm=llm,
+    max_loops=1,
+    system_message="YOU ARE SILLY, YOU OFFER NOTHING OF VALUE",
+    name='silly',
+    dashboard=True,
+)
+flow2 = Flow(
+    llm=llm,
+    max_loops=1,
+    system_message="YOU ARE VERY SMART AND ANSWER RIDDLES",
+    name='detective',
+    dashboard=True,
+)
+flow3 = Flow(
+    llm=llm,
+    max_loops=1,
+    system_message="YOU MAKE RIDDLES",
+    name='riddler',
+    dashboard=True,
+)
+manager = Flow(
+    llm=llm,
+    max_loops=1,
+    system_message="YOU ARE A GROUP CHAT MANAGER",
+    name='manager',
+    dashboard=True,
+)
 
-# Create an enhanced group chat with the two agents
-enhanced_group_chat_v2 = EnhancedGroupChatV2(agents=[agent1, agent2])
 
-# Simulate multi-round agent to agent communication
-enhanced_group_chat_v2.multi_round_conversation(rounds=5)
+# Example usage:
+agents = [flow1, flow2, flow3]
 
-enhanced_group_chat_v2.get_message_log()  # Get the conversation log
+group_chat = GroupChat(agents=agents, messages=[], max_round=10)
+chat_manager = GroupChatManager(groupchat=group_chat, selector = manager)
+chat_history = chat_manager("Write me a riddle")

swarms/structs/flow.py

@@ -34,6 +34,7 @@
 This will enable you to leave the flow loop.
 """
 
+
 # Custome stopping condition
 def stop_when_repeats(response: str) -> bool:
     # Stop if the word stop appears in the response
@@ -100,6 +101,8 @@ def __init__(
         retry_interval: int = 1,
         interactive: bool = False,
         dashboard: bool = False,
+        name: str = "Flow agent",
+        system_message: str = FLOW_SYSTEM_PROMPT,
         # tools: List[BaseTool] = None,
         dynamic_temperature: bool = False,
         **kwargs: Any,
@@ -119,6 +122,8 @@ def __init__(
         self.dashboard = dashboard
         self.dynamic_temperature = dynamic_temperature
         # self.tools = tools
+        self.system_message = system_message
+        self.name = name
 
     def provide_feedback(self, feedback: str) -> None:
         """Allow users to provide feedback on the responses."""
@@ -131,11 +136,6 @@ def _check_stopping_condition(self, response: str) -> bool:
             return self.stopping_condition(response)
         return False
 
-    def __call__(self, prompt, **kwargs) -> str:
-        """Invoke the flow by providing a template and its variables."""
-        response = self.llm(prompt, **kwargs)
-        return response
-
     def dynamic_temperature(self):
         """
         1. Check the self.llm object for the temperature
@@ -282,6 +282,82 @@ def run(self, task: str, save: bool = True, **kwargs):
 
         return response  # , history
 
+    def __call__(self, task: str, save: bool = True, **kwargs):
+        """
+        Run the autonomous agent loop
+
+        Args:
+            task (str): The initial task to run
+
+        Flow:
+        1. Generate a response
+        2. Check stopping condition
+        3. If stopping condition is met, stop
+        4. If stopping condition is not met, generate a response
+        5. Repeat until stopping condition is met or max_loops is reached
+
+        Example:
+        >>> out = flow.run("Generate a 10,000 word blog on health and wellness.")
+
+        """
+        # Start with a new history or continue from the last saved state
+        if not self.memory or not self.memory[-1]:
+            history = [f"Human: {task}"]
+        else:
+            history = self.memory[-1]
+
+        response = task
+        history = [f"Human: {task}"]
+
+        # If dashboard = True then print the dashboard
+        if self.dashboard:
+            self.print_dashboard(task)
+
+        # Start or continue the loop process
+        for i in range(len(history), self.max_loops):
+            print(colored(f"\nLoop {i+1} of {self.max_loops}", "blue"))
+            print("\n")
+            response = history[-1].split(": ", 1)[-1]  # Get the last response
+
+            if self._check_stopping_condition(response) or parse_done_token(response):
+                break
+
+            # Adjust temperature, comment if no work
+            if self.dynamic_temperature:
+                self.dynamic_temperature()
+
+            attempt = 0
+            while attempt < self.retry_attempts:
+                try:
+                    response = self.llm(
+                        self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response)
+                        ** kwargs,
+                    )
+                    # print(f"Next query: {response}")
+                    # break
+                    if self.interactive:
+                        print(f"AI: {response}")
+                        history.append(f"AI: {response}")
+                        response = input("You: ")
+                        history.append(f"Human: {response}")
+                    else:
+                        print(f"AI: {response}")
+                        history.append(f"AI: {response}")
+                        print(response)
+                    break
+                except Exception as e:
+                    logging.error(f"Error generating response: {e}")
+                    attempt += 1
+                    time.sleep(self.retry_interval)
+            history.append(response)
+            time.sleep(self.loop_interval)
+        self.memory.append(history)
+
+        if save:
+            self.save_state("flow_history.json")
+
+        return response  # , history
+
     def _run(self, **kwargs: Any) -> str:
         """Generate a result using the provided keyword args."""
         task = self.format_prompt(**kwargs)
@@ -304,6 +380,7 @@ def agent_history_prompt(
         Returns:
             str: The agent history prompt
         """
+        system_prompt = system_prompt or self.system_message
         agent_history_prompt = f"""
             SYSTEM_PROMPT: {system_prompt}
 
@@ -608,3 +685,22 @@ def retry_on_failure(self, function, retries: int = 3, retry_delay: int = 1):
                 attempt += 1
                 time.sleep(retry_delay)
         raise Exception("All retry attempts failed")
+
+    def generate_reply(self, history: str, **kwargs) -> str:
+        """
+        Generate a response based on initial or task
+        """
+        prompt = f"""
+
+        SYSTEM_PROMPT: {self.system_message}
+
+        History: {history}
+
+        Your response:
+        """
+        response = self.llm(prompt, **kwargs)
+        return {"role": self.name, "content": response}
+
+    def update_system_message(self, system_message: str):
+        """Upddate the system message"""
+        self.system_message = system_message