Added LLM call as a way to save Tavily credits #41
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WalkthroughThe update enhances the Changes
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Actionable comments generated: 1
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Files selected for processing (2)
- prediction_market_agent/agents/known_outcome_agent/deploy.py (4 hunks)
- prediction_market_agent/agents/known_outcome_agent/known_outcome_agent.py (3 hunks)
Additional comments: 5
prediction_market_agent/agents/known_outcome_agent/deploy.py (4)
- 12-12: The import of
has_question_event_happened_in_the_pastis correctly added to enable its usage within theDeployableKnownOutcomeAgentclass. This aligns with the PR's objective to enhance decision-making capabilities.- 21-21: Updating the model to "gpt-3.5-turbo" from "gpt-4-1106-preview" is a significant change. Ensure that this model change has been thoroughly tested for accuracy and efficiency in the context of the agent's operations. It's also important to verify that the new model version is compatible with all existing functionalities and integrations.
- 33-37: The addition of the
has_question_event_happened_in_the_pastcheck within thepick_marketsmethod is crucial for filtering out markets related to events that have already occurred. This logic helps in optimizing the decision-making process. However, it's important to ensure that the methodhas_question_event_happened_in_the_pasthandles edge cases and errors gracefully to avoid unintended behavior.Verification successful
The
has_question_event_happened_in_the_pastmethod in theprediction_market_agent/agents/known_outcome_agent/known_outcome_agent.pyfile is designed to determine if the event referenced by a question has already occurred, based on the current date. It uses a language model to interpret the question and return a boolean value indicating whether the event has happened in the past.Key Observations:
Error Handling: The method includes a try-except block around the conversion of the language model's answer from string to integer. This is a basic form of error handling that catches exceptions that could arise from this conversion process, such as if the model's response is not a valid integer string.
Exception Logging: In the case of an exception, the method logs the exception message with a print statement. This provides some insight into why the method might fail to determine if an event has happened in the past.
Return Value in Exception Cases: If an exception occurs or if the model's response does not indicate that the event has definitely happened in the past (i.e., the response is not
1), the method returnsFalse. This conservative approach ensures that the method does not falsely claim an event has occurred without clear confirmation.Use of Language Model: The method relies on a language model to interpret the question and determine if the event has already happened. This approach is flexible and can handle a wide range of questions but also introduces a dependency on the model's accuracy and interpretation capabilities.
Conclusion:
The method
has_question_event_happened_in_the_pastincorporates basic error handling and logging mechanisms to manage exceptions. It takes a conservative approach by returningFalsein cases where it cannot confidently assert that an event has occurred in the past. While this method includes fundamental error handling, its robustness and effectiveness in edge cases largely depend on the language model's performance and the clarity of the questions it processes.Given the information reviewed, the original comment about ensuring the method handles edge cases and errors gracefully is aligned with the observed implementation. The method does attempt to handle errors, but its robustness is inherently tied to the external language model's responses.
* 64-66: The use of `load_dotenv()` and the subsequent call to `agent.deploy_local` with specific parameters are standard practices for initializing and deploying agents locally. Ensure that the `sleep_time` and `timeout` values are appropriately configured for the operational environment and that they align with the expected behavior and performance requirements.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify the robustness and error handling of the has_question_event_happened_in_the_past method echo "Review the implementation of 'has_question_event_happened_in_the_past' for error handling and edge case management."Length of output: 246
Script:
#!/bin/bash # Search for the definition of `has_question_event_happened_in_the_past` across the codebase rg "def has_question_event_happened_in_the_past"Length of output: 204
Script:
#!/bin/bash # Display the implementation of `has_question_event_happened_in_the_past` method cat prediction_market_agent/agents/known_outcome_agent/known_outcome_agent.pyLength of output: 7554
prediction_market_agent/agents/known_outcome_agent/known_outcome_agent.py (1)
- 46-60: The definition of
HAS_QUESTION_HAPPENED_IN_THE_PAST_PROMPTis well-structured and clearly outlines the expected behavior and response format. This prompt is essential for the new functionality introduced to assess the timing of events. Ensure that the prompt's wording is clear and unambiguous to minimize potential misunderstandings when processed by the model.
prediction_market_agent/agents/known_outcome_agent/known_outcome_agent.py
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Files selected for processing (1)
- prediction_market_agent/agents/known_outcome_agent/deploy.py (2 hunks)
Files skipped from review as they are similar to previous changes (1)
- prediction_market_agent/agents/known_outcome_agent/deploy.py
prediction_market_agent/agents/known_outcome_agent/known_outcome_agent.py
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Actionable comments generated: 1
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Files selected for processing (2)
- prediction_market_agent/agents/known_outcome_agent/deploy.py (2 hunks)
- prediction_market_agent/agents/known_outcome_agent/known_outcome_agent.py (3 hunks)
Files skipped from review as they are similar to previous changes (1)
- prediction_market_agent/agents/known_outcome_agent/known_outcome_agent.py
Additional comments: 2
prediction_market_agent/agents/known_outcome_agent/deploy.py (2)
- 20-20: The function
has_question_event_happened_in_the_pastis correctly imported and used. This addition should enhance the agent's decision-making process by filtering out markets based on past events.- 40-44: The integration of
has_question_event_happened_in_the_pastwithinpick_marketsmethod is well-implemented. It uses the function to filter markets where the event has already occurred, which aligns with the PR's objectives.
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