7.1.9

group_chat_example.py

@@ -0,0 +1,36 @@
+from swarms.structs.agent import Agent
+from swarms.structs.groupchat import GroupChat
+
+
+if __name__ == "__main__":
+
+    # Example agents
+    agent1 = Agent(
+        agent_name="Financial-Analysis-Agent",
+        system_prompt="You are a financial analyst specializing in investment strategies.",
+        model_name="gpt-4o",
+        max_loops=1,
+        dynamic_temperature_enabled=True,
+    )
+
+    agent2 = Agent(
+        agent_name="Tax-Adviser-Agent",
+        system_prompt="You are a tax adviser who provides clear and concise guidance on tax-related queries.",
+        model_name="gpt-4o",
+        max_loops=1,
+        dynamic_temperature_enabled=True,
+    )
+
+    agents = [agent1, agent2]
+
+    chat = GroupChat(
+        name="Investment Advisory",
+        description="Financial and tax analysis group",
+        agents=agents,
+        max_loops=1,
+    )
+
+    history = chat.run(
+        "How to optimize tax strategy for investments?"
+    )
+    print(history)

majority_voting_example.py

@@ -7,22 +7,22 @@
         agent_description="Personal finance advisor focused on market analysis",
         system_prompt="You are a financial advisor specializing in market analysis and investment opportunities.",
         max_loops=1,
-        model_name="gpt-4o"
+        model_name="gpt-4o",
     ),
     Agent(
-        agent_name="Risk-Assessment-Agent", 
+        agent_name="Risk-Assessment-Agent",
         agent_description="Risk analysis and portfolio management expert",
         system_prompt="You are a risk assessment expert focused on evaluating investment risks and portfolio diversification.",
         max_loops=1,
-        model_name="gpt-4o"
+        model_name="gpt-4o",
     ),
     Agent(
         agent_name="Tech-Investment-Agent",
         agent_description="Technology sector investment specialist",
         system_prompt="You are a technology investment specialist focused on AI, emerging tech, and growth opportunities.",
         max_loops=1,
-        model_name="gpt-4o"
-    )
+        model_name="gpt-4o",
+    ),
 ]
 
 
@@ -31,7 +31,7 @@
     agent_description="Consensus agent focused on analyzing investment advice",
     system_prompt="You are a consensus agent focused on analyzing investment advice and providing a final answer.",
     max_loops=1,
-    model_name="gpt-4o"
+    model_name="gpt-4o",
 )
 
 # Create majority voting system
@@ -40,13 +40,12 @@
     description="Multi-agent system for investment advice",
     agents=agents,
     verbose=True,
-    consensus_agent=consensus_agent
+    consensus_agent=consensus_agent,
 )
 
 # Run the analysis with majority voting
 result = majority_voting.run(
     task="Create a table of super high growth opportunities for AI. I have $40k to invest in ETFs, index funds, and more. Please create a table in markdown.",
-    correct_answer=""  # Optional evaluation metric
 )
 
 print(result)

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "7.1.7"
+version = "7.1.9"
 description = "Swarms - TGSC"
 license = "MIT"
 authors = ["Kye Gomez <[email protected]>"]
@@ -73,21 +73,9 @@ docstring_parser = "0.16" # TODO:
 tiktoken = "*"
 networkx = "*"
 aiofiles = "*"
-# chromadb = "*"
 rich = "*"
 numpy = "*"
 litellm = "*"
-# sentence-transformers = "*"
-
-
-# # All optional dependencies for convenience
-# all = [
-#     "torch",
-#     "transformers",
-#     "litellm"
-# ]
-
-
 
 [tool.poetry.scripts]
 swarms = "swarms.cli.main:main"

swarms/structs/__init__.py

@@ -14,9 +14,6 @@
     NodeType,
 )
 from swarms.structs.groupchat import (
-    AgentResponse,
-    ChatHistory,
-    ChatTurn,
     GroupChat,
     expertise_based,
 )
@@ -138,9 +135,6 @@
     "run_agents_with_tasks_concurrently",
     "showcase_available_agents",
     "GroupChat",
-    "ChatHistory",
-    "ChatTurn",
-    "AgentResponse",
     "expertise_based",
     "MultiAgentRouter",
     "MemeAgentGenerator",

swarms/structs/conversation.py

@@ -405,10 +405,23 @@ def get_visible_messages(self, agent: "Agent", turn: int):
                 visible_messages.append(message)
         return visible_messages
 
+    def get_last_message_as_string(self):
+        # fetch the last message from the conversation history with the agent name and the message of the agent
+        return f"{self.conversation_history[-1]['role']}: {self.conversation_history[-1]['content']}"
 
-# # Example usage
-# conversation = Conversation()
-# conversation.add("user", "Hello, how are you?")
+    def return_messages_as_list(self):
+        # we must concat the role and the content of the message
+        return [
+            f"{message['role']}: {message['content']}"
+            for message in self.conversation_history
+        ]
+
+
+# Example usage
+conversation = Conversation()
+conversation.add("user", "Hello, how are you?")
+# print(conversation.get_last_message_as_string())
+# print(conversation.return_messages_as_list())
 # conversation.add("assistant", "I am doing well, thanks.")
 # # print(conversation.to_json())
 # print(type(conversation.to_dict()))