Create 16_multi_model_consensus.py

Author: Pierre

examples/16_multi_model_consensus.py

@@ -0,0 +1,128 @@
+"""
+This example demonstrates how to ask the same question to multiple different LLMs
+and then combine their responses into a single coherent answer using another LLM.
+
+The example uses three different models for answering:
+- GPT-4O Mini
+- Gemini 2.0 Flash
+- Llama 3.3 70B
+
+Then uses O3 Mini (with medium reasoning effort) to analyze and combine their responses.
+"""
+
+import asyncio
+from typing import List
+
+from pydantic import BaseModel, Field
+
+import workflowai
+from workflowai import Model, Run
+
+
+class MultiModelInput(BaseModel):
+    """Input model containing the question to ask all models."""
+    question: str = Field(
+        description="The question to ask all models",
+    )
+
+
+class ModelResponse(BaseModel):
+    """Response from an individual model."""
+    model_name: str = Field(description="Name of the model that provided this response")
+    response: str = Field(description="The model's response to the question")
+
+
+class CombinerInput(BaseModel):
+    """Input for the response combiner."""
+    responses: List[ModelResponse] = Field(description="List of responses to combine")
+
+
+class CombinedOutput(BaseModel):
+    """Final output combining responses from all models."""
+    individual_responses: List[ModelResponse] = Field(
+        description="List of responses from each individual model",
+    )
+    combined_answer: str = Field(
+        description="Synthesized answer combining insights from all models",
+    )
+    explanation: str = Field(
+        description="Explanation of how the responses were combined and why",
+    )
+
+
+@workflowai.agent(
+    id="gpt4o-mini-agent",
+    model=Model.GPT_4O_MINI_LATEST,
+)
+async def get_gpt4_response(query: MultiModelInput) -> Run[ModelResponse]:
+    """Get response from GPT-4O Mini model."""
+    ...
+
+
+@workflowai.agent(
+    id="gemini-agent",
+    model=Model.GEMINI_2_0_FLASH_LATEST,
+)
+async def get_gemini_response(query: MultiModelInput) -> Run[ModelResponse]:
+    """Get response from Gemini 2.0 Flash model."""
+    ...
+
+
+@workflowai.agent(
+    id="llama-agent",
+    model=Model.LLAMA_3_3_70B,
+)
+async def get_llama_response(query: MultiModelInput) -> Run[ModelResponse]:
+    """Get response from Llama 3.3 70B model."""
+    ...
+
+
+@workflowai.agent(
+    id="response-combiner",
+    model=Model.O3_MINI_2025_01_31_MEDIUM_REASONING_EFFORT,
+
+)
+async def combine_responses(responses_input: CombinerInput) -> Run[CombinedOutput]:
+    """
+    Analyze and combine responses from multiple models into a single coherent answer.
+
+    You are an expert at analyzing and synthesizing information from multiple sources.
+    Your task is to:
+    1. Review the responses from different models
+    2. Identify key insights and unique perspectives from each
+    3. Create a comprehensive answer that combines the best elements
+    4. Explain your synthesis process
+
+    Please ensure the combined answer is:
+    - Accurate and well-reasoned
+    - Incorporates unique insights from each model
+    - Clear and coherent
+    - Properly attributed when using specific insights
+    """
+    ...
+
+
+async def main():
+    # Example: Scientific explanation
+    print("\nExample: Scientific Concept")
+    print("-" * 50)
+    question = "What is dark matter and why is it important for our understanding of the universe?"
+
+    # Get responses from all models
+    gpt4_run = await get_gpt4_response(MultiModelInput(question=question))
+    gemini_run = await get_gemini_response(MultiModelInput(question=question))
+    llama_run = await get_llama_response(MultiModelInput(question=question))
+
+    # Combine responses
+    combined = await combine_responses(CombinerInput(
+        responses=[
+            gpt4_run.output,
+            gemini_run.output,
+            llama_run.output,
+        ],
+    ))
+    print(combined)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())