[PAPER IMPLEMENTATION] Self-MoA

2 months ago · 9d7dd9c1a2
parent b8955fbf7e
commit 9d7dd9c1a2
1 changed files with 107 additions and 0 deletions
--- a/examples/multi_agent/paper_implementations/self_moa.py
+++ b/examples/multi_agent/paper_implementations/self_moa.py
@ -0,0 +1,107 @@
+from swarms import Agent
+
+class SelfMoA:
+    def __init__(self, query):
+        # Model specification for classification
+        self.model_spec = """
+1. Multi-step logical problem solving and complex planning - claude opus 3
+2. Mathematical problem solving and abstract reasoning - gpt-oss-120b
+3. Code generation, refactoring, and debugging - gpt-5-codex
+4. Creative writing/content generation and text summarization and question answering - claude-haiku-3
+5. Conversational AI and virtual assistants - gpt-4.1
+6. Research into specialized topics - o3-deep-research
+7. Extraction of documents - claude-sonnet-3.5
+8. Analysis of specialized information - gpt-5
+9. Autonomous workflow orchestration/agentic tasks - claude-sonnet-4.5
+"""
+        self.model_choice = [
+            "claude-3-opus-20240229",         # 1. Multi-step logical problem solving and complex planning
+            "gpt-4.1",                        # 2. Mathematical problem solving and abstract reasoning
+            "gpt-5-codex",                    # 3. Code generation, refactoring, and debugging
+            "claude-3-haiku-20240307",        # 4. Creative writing/content generation and text summarization and question answering
+            "gpt-4.1",                        # 5. Conversational AI and virtual assistants
+            "gpt-5",                          # 6. Research into specialized topics
+            "claude-3-5-sonnet-20240620",     # 7. Extraction of documents
+            "gpt-5",                          # 8. Analysis of specialized information
+            "claude-3-opus-20240229"          # 9. Autonomous workflow orchestration/agentic tasks
+        ]
+        self.query = query
+        print("Developing Self-MoA Flow Response to '"+ query +"'")
+        self.classifier_agent = Agent(
+            agent_name="ProposerAgent",
+            agent_description="Determines best model for the use-case/application",
+            model_name="claude-3-opus-20240229",
+            system_prompt=f"""You are a model classification agent. Your task is to analyze a user query and classify it into one of the following categories based on the provided model_spec.
+
+{self.model_spec}
+
+You MUST output ONLY the number corresponding to the best-fit model. Output a SINGLE INTEGER 1-9, nothing else: NO ADDITIONAL TEXT, NO EXPLANATION, NO PUNCTUATION.
+""",
+            max_loops=1,
+            verbose=True,
+        )
+        self.selected_model_name = self._propose_model()
+        self.main_agent_outputs = self._run_main_agents()
+        self.final_answer = self._aggregate_outputs()
+
+    def _propose_model(self):
+        classification_result = self.classifier_agent.run(self.query)
+        model_index = int(classification_result) - 1
+        return self.model_choice[model_index]
+
+    def _run_main_agents(self):
+        main_agent_outputs = []
+        num_runs = 6
+        min_temp = 0
+        temperature_step = 0.15
+        for i in range(num_runs):
+            temp = min_temp + i * temperature_step
+            main_agent = Agent(
+                agent_name="MainAgent",
+                agent_description="Executes the task using the chosen model",
+                model_name=self.selected_model_name,
+                system_prompt="You are a helpful assistant. Please provide a detailed response to the user's query. Do it under 1500 characters.",
+                max_loops=1,
+                verbose=True,
+                temperature=temp,
+                max_tokens=2000,
+            )
+            print(f"\n\n\nRun {i+1} - Temperature: {temp:.2f} | Output saved.")
+            response = main_agent.run(self.query)
+            output_entry = {
+                "temperature": temp,
+                "response": response
+            }
+            main_agent_outputs.append(output_entry)
+        return main_agent_outputs
+
+    def _aggregate_outputs(self):
+        aggregator_prompt = (
+            "You are AggregatorAgent. You are given multiple outputs (potentially variations on detail, clarity, or order) produced by an assistant in response to the same query. "
+            "Your task is to carefully examine all these outputs and synthesize them into a single, clear, best possible answer. "
+            "Avoid unnecessary repetition, contradictions, and merge the best points from all responses.\n\n"
+            "USER QUESTION:\n"
+            f"{self.query}\n\n"
+            "OUTPUTS:\n"
+        )
+        for entry in self.main_agent_outputs:
+            aggregator_prompt += f"- (temperature={entry['temperature']:.2f}) {entry['response']}\n"
+        aggregator_prompt += "\nFinal, best single answer:"
+        aggregator_agent = Agent(
+            agent_name="AggregatorAgent",
+            agent_description="Aggregates and refines multiple agent outputs into a single, superior final answer.",
+            model_name=self.selected_model_name,
+            system_prompt=aggregator_prompt,
+            max_loops=1,
+            verbose=True,
+        )
+        print("\n\n\nRefined Aggregated Final Answer")
+        final_answer = aggregator_agent.run("Please aggregate all outputs above into a clear, best possible answer to the user's question.")
+        return final_answer
+
+    def get_final_answer(self):
+        return self.final_answer
+
+# Example usage:
+query = "Help me understand everything about Quantum Computing algorithms like the Shor's Algorithm. I need to do a school project on it."
+SelfMoA(query)