inits

Former-commit-id: a5de07505c

example_godmode.py

@@ -1,6 +1,6 @@
-from langchain.llms import GooglePalm, OpenAIChat
 
-from swarms.swarms.god_mode import Anthropic, GodMode
+from swarms.models import Anthropic, GooglePalm, OpenAIChat
+from swarms.swarms.god_mode import GodMode
 
 claude = Anthropic(anthropic_api_key="")
 palm = GooglePalm(google_api_key="")

multi_agent_debate.py

@@ -1,47 +1,5 @@
 from typing import List, Callable
-from swarms import Worker
+from swarms import Worker, MultiAgentDebate, select_speaker
-
-
-class MultiAgentDebate:
-    def __init__(
-        self, 
-        agents: List[Worker], 
-        selection_func: Callable[[int, List[Worker]], int]
-    ):
-        self.agents = agents
-        self.selection_func = selection_func
-
-    def reset_agents(self):
-        for agent in self.agents:
-            agent.reset()
-
-    def inject_agent(self, agent: Worker):
-        self.agents.append(agent)
-
-    def run(self, task: str, max_iters: int = None):
-        self.reset_agents()
-        results = []
-        for i in range(max_iters or len(self.agents)):
-            speaker_idx = self.selection_func(i, self.agents)
-            speaker = self.agents[speaker_idx]
-            response = speaker.run(task)
-            results.append({
-                'agent': speaker.ai_name,
-                'response': response
-            })
-        return results
-
-    def update_task(self, task: str):
-        self.task = task
-
-    def format_results(self, results):
-        formatted_results = "\n".join([f"Agent {result['agent']} responded: {result['response']}" for result in results])
-        return formatted_results
-    
-# Define a selection function
-def select_speaker(step: int, agents: List[Worker]) -> int:
-    # This function selects the speaker in a round-robin fashion
-    return step % len(agents)
 
 # Initialize agents
 worker1 = Worker(openai_api_key="", ai_name="Optimus Prime")

swarms/__init__.py

@@ -12,23 +12,14 @@ from swarms.workers.worker import Worker
 # from swarms.boss.boss_node import Boss
 
 #models
-from swarms.models.anthropic import Anthropic
+import swarms.models
-from swarms.models.palm import GooglePalm
-from swarms.models.petals import Petals
-from swarms.models.openai import OpenAIChat
 
 #structs
 from swarms.structs.workflow import Workflow
 from swarms.structs.task import Task
 
 # swarms
-from swarms.swarms.dialogue_simulator import DialogueSimulator
+import swarms.swarms
-from swarms.swarms.autoscaler import AutoScaler
-from swarms.swarms.orchestrate import Orchestrator
-from swarms.swarms.god_mode import GodMode
-from swarms.swarms.simple_swarm import SimpleSwarm
-from swarms.swarms.multi_agent_debate import MultiAgentDebate
-
 
 #agents
 from swarms.swarms.profitpilot import ProfitPilot

swarms/models/__init__.py

@@ -1,7 +1,6 @@
 from swarms.models.anthropic import Anthropic
-# from swarms.models.palm import GooglePalm
+from swarms.models.palm import GooglePalm
 from swarms.models.petals import Petals
-#from swarms.models.openai import OpenAIChat
+from swarms.models.openai import OpenAIChat
-
 #prompts
-from swarms.models.prompts.debate import *
+from swarms.models.prompts.debate import *

swarms/swarms/__init__.py

@@ -1,10 +1,8 @@
-
 # swarms
 from swarms.swarms.dialogue_simulator import DialogueSimulator
 from swarms.swarms.autoscaler import AutoScaler
 from swarms.swarms.orchestrate import Orchestrator
 from swarms.swarms.god_mode import GodMode
 from swarms.swarms.simple_swarm import SimpleSwarm
-from swarms.swarms.multi_agent_debate import MultiAgentDebate
+from swarms.swarms.multi_agent_debate import MultiAgentDebate, select_speaker
-from swarms.swarms.groupchat import GroupChat
+from swarms.swarms.groupchat import GroupChat
-

swarms/swarms/multi_agent_debate.py

@@ -1,34 +1,45 @@
-from typing import List
+from typing import List, Callable
 from swarms import Worker
 
+    
+# Define a selection function
+def select_speaker(step: int, agents: List[Worker]) -> int:
+    # This function selects the speaker in a round-robin fashion
+    return step % len(agents)
+
+
 class MultiAgentDebate:
-    def __init__(self, agents: List[Worker]):
+    def __init__(
+        self, 
+        agents: List[Worker], 
+        selection_func: Callable[[int, List[Worker]], int]
+    ):
         self.agents = agents
+        self.selection_func = selection_func
 
-    def run(self, task: str):
+    def reset_agents(self):
-        results = []
         for agent in self.agents:
-            response = agent.run(task)
+            agent.reset()
+
+    def inject_agent(self, agent: Worker):
+        self.agents.append(agent)
+
+    def run(self, task: str, max_iters: int = None):
+        self.reset_agents()
+        results = []
+        for i in range(max_iters or len(self.agents)):
+            speaker_idx = self.selection_func(i, self.agents)
+            speaker = self.agents[speaker_idx]
+            response = speaker.run(task)
             results.append({
-                'agent': agent.ai_name,
+                'agent': speaker.ai_name,
                 'response': response
             })
         return results
 
-# Initialize agents
+    def update_task(self, task: str):
-agents = [
+        self.task = task
-    Worker(openai_api_key="", ai_name="Optimus Prime"),
-    Worker(openai_api_key="", ai_name="Bumblebee"),
-    Worker(openai_api_key="", ai_name="Megatron")
-]
-
-# Initialize multi-agent debate
-debate = MultiAgentDebate(agents)
-
-# Run task
-task = "What were the winning boston marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times."
-results = debate.run(task)
 
-# Print results
+    def format_results(self, results):
-for result in results:
+        formatted_results = "\n".join([f"Agent {result['agent']} responded: {result['response']}" for result in results])
-    print(f"Agent {result['agent']} responded: {result['response']}")
+        return formatted_results