Deployable PredictionProphetAgents provide reasoning with their predictions #488
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (2)
prediction_market_agent/agents/prophet_agent/deploy.py (2)
77-77: LGTM! Consider refactoring to reduce duplication.The addition of
include_reasoning=Trueis consistent with the changes in other agent classes, which is good. However, given that this parameter is being added to multiple similar classes, consider refactoring to reduce code duplication.You might want to create a base class or a factory method that sets common parameters like
include_reasoning. This would make future changes easier and reduce the risk of inconsistencies. For example:class BasePredictionProphetAgent(DeployableTraderAgentER): def create_agent(self, model: str, **kwargs) -> None: self.agent = PredictionProphetAgent( model=model, include_reasoning=True, tavily_storage=TavilyStorage(agent_id=self.__class__.__name__), logger=logger, **kwargs ) class DeployablePredictionProphetGPT4TurboFinalAgent(BasePredictionProphetAgent): def load(self) -> None: super().load() self.create_agent("gpt-4-turbo-2024-04-09")This approach would centralize the common configuration and make it easier to update all agents simultaneously in the future.
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1-122: Summary: Consistent implementation of reasoning across all agent classesThe changes in this file consistently add
include_reasoning=Trueto allPredictionProphetAgentinitializations across different model configurations. This aligns well with the PR objective of providing reasoning with predictions.Key points:
- The implementation is consistent across all agent classes.
- Model-specific settings (like temperature) are properly maintained alongside the new parameter.
- The changes should enable all agents to provide explanations for their predictions.
Suggestions for improvement:
- Consider refactoring to reduce code duplication, as mentioned in a previous comment.
- Ensure that the
PredictionProphetAgentclass properly handles this new parameter.- Update tests and documentation to reflect this new capability.
To further improve the architecture:
- Implement a base class or factory method for creating agents with common parameters.
- Consider using a configuration file or environment variables for model-specific settings to make updates easier in the future.
- Ensure that the reasoning provided by the agents is properly logged or returned in a way that can be utilized by the system consuming these predictions.
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🔇 Additional comments (4)
prediction_market_agent/agents/prophet_agent/deploy.py (4)
64-64: LGTM! Consistent implementation across agent classes.The addition of
include_reasoning=Trueis consistent with the changes in other agent classes. This uniformity is good for code maintainability and ensures that all agent configurations provide reasoning with their predictions.
103-103: LGTM! Model-specific settings properly maintained.The addition of
include_reasoning=Trueis consistent with other agent classes. It's good to see that the model-specific temperature settings (1.0 for both research and prediction) are maintained alongside this new parameter.
119-119: LGTM! Consistent implementation across all agent classes.The addition of
include_reasoning=Trueis consistent across all agent classes, including this final instance. The model-specific temperature settings are correctly maintained.To ensure the changes have been applied consistently and correctly across all agent classes, please run the following verification script:
#!/bin/bash # Description: Verify consistent application of include_reasoning across all agent classes echo "Checking for consistent application of include_reasoning parameter:" rg --type python "PredictionProphetAgent\(" prediction_market_agent/agents/prophet_agent/deploy.py echo "Checking for any instances where include_reasoning might be missing:" rg --type python "PredictionProphetAgent\(" prediction_market_agent/agents/prophet_agent/deploy.py | grep -v "include_reasoning" echo "Verifying that all DeployablePredictionProphet*Agent classes have been updated:" rg --type python "class DeployablePredictionProphet" prediction_market_agent/agents/prophet_agent/deploy.pyThis script will help ensure that the
include_reasoningparameter has been added consistently to all relevant agent classes and that no instances have been missed.
51-51: LGTM! Verify the impact of including reasoning.The addition of
include_reasoning=Truealigns with the PR objective of providing reasoning with predictions. This change should enable the agent to explain its decision-making process.To ensure this change is properly implemented and doesn't have unintended side effects, please run the following verification script:
Comparison ReportMarket Results
Agent ResultsSummary Statistics
Markets |
Comparison ReportMarket Results
Agent ResultsSummary Statistics
Markets |
evangriffiths
deleted the
evan/PredictionProphetAgent-provides-reasoning-with-prediction
branch
See comparison reports below - 'prophet with reasoning' appears to be a little bit better (i.e. lower MSE vs crowd prediction) than without reasoning. Not the motivation of this feature, but still a small bonus!
To reproduce: