469 missing information in ipfsagentresult #521
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…ing-information-in-ipfsagentresult # Conflicts: # prediction_market_agent_tooling/deploy/agent.py
| reasoning = ( | ||
| processed_market.answer.reasoning | ||
| if processed_market.answer.reasoning | ||
| else "" | ||
| ) | ||
| agent_result = IPFSAgentResult(reasoning=reasoning, agent_name=self.agent_name) |
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I moved this logic from OmenAgentMarket -> DeployableTraderAgent because the property agent_name belongs to DeployableTraderAgent.
One could simply pass the property as arg, but I would argue that the responsibility to build a IPFSAgentResult should be handled by the agent itself.
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IPFSAgentResult is specific to Omen; for example, Metaculus wants only a comment without any IPFS upload.
That refactoring was made based on Evan's comment because there were a lot of Omen-uniquess in the DeployableTraderAgent.
Maybe it could be named IPFSOmenAgentResult or something to make it clearer in the code. 🤔
Wdyt just passing agent_name into store_prediction?
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No strong preference here - passing agent_name is fine
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Maybe it could be named IPFSOmenAgentResult or something to make it clearer in the code. 🤔
I would prefer to keep it as IPFSAgentResult - we can rename it once we have other agents uploading results as well.
Co-authored-by: Peter Jung <[email protected]>
WalkthroughThe pull request introduces several modifications across multiple files, primarily focusing on enhancing the Changes
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🚧 Files skipped from review as they are similar to previous changes (3)
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Actionable comments posted: 2
🧹 Outside diff range and nitpick comments (2)
prediction_market_agent_tooling/markets/agent_market.py (1)
232-235: LGTM: Method signature updated correctly.The
store_predictionmethod signature has been appropriately updated to include theagent_resultparameter. This change aligns with the PR objectives and maintains the abstract nature of the method.Consider updating the method's docstring to reflect the new
agent_resultparameter and its purpose.prediction_market_agent_tooling/markets/omen/data_models.py (1)
801-801: LGTM! Consider reordering fields for better readability.The addition of the
agent_namefield is correct and aligns with the PR objectives. However, for better code organization, consider moving theagent_namefield above themodel_config. This keeps all the fields together and separates them from the configuration.Here's a suggested reordering:
class IPFSAgentResult(BaseModel): reasoning: str - agent_name: str + agent_name: str model_config = ConfigDict( extra="forbid", )
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📒 Files selected for processing (4)
- prediction_market_agent_tooling/deploy/agent.py (3 hunks)
- prediction_market_agent_tooling/markets/agent_market.py (2 hunks)
- prediction_market_agent_tooling/markets/omen/data_models.py (1 hunks)
- prediction_market_agent_tooling/markets/omen/omen.py (1 hunks)
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🔇 Additional comments (4)
prediction_market_agent_tooling/markets/agent_market.py (1)
22-22: LGTM: New import statement is correctly placed and follows best practices.The import of
IPFSAgentResultfrom the Omen-specific module is appropriate and aligns with the PR objectives.prediction_market_agent_tooling/markets/omen/omen.py (1)
Line range hint
420-438: LGTM! The changes improve thestore_predictionmethod.The updated implementation correctly incorporates the
agent_resultparameter, which now includes theagent_nameas mentioned in the PR objectives. The code is more streamlined by directly using theagent_resultin thestore_agent_resultcall, avoiding unnecessary object creation.prediction_market_agent_tooling/deploy/agent.py (2)
338-340: Addition ofagent_nameproperty enhances metadata handlingThe
agent_nameproperty provides a standardized way to access the agent's class name, improving metadata consistency across different agents.
500-508:⚠️ Potential issueRefactor to remove Omen-specific logic from
DeployableTraderAgentCreating an
IPFSAgentResultand using it inafter_process_marketintroduces Omen-specific functionality intoDeployableTraderAgent. This violates the separation of concerns and reduces the agent's applicability to other markets that do not use IPFS, such as Metaculus.As previously suggested, consider the following refactoring:
- Pass
agent_nameas an argument tostore_prediction, allowing market-specific implementations to handle the result appropriately.- Define an abstract method or hook in
DeployableTraderAgentfor result handling that can be overridden by subclasses or specific market implementations.- Move the creation of
IPFSAgentResultinto the Omen-specific market class to maintain a clean separation between generic agent logic and market-specific details.
| @@ -19,6 +19,7 @@ | |||
| TokenAmount, | |||
| ) | |||
| from prediction_market_agent_tooling.markets.market_fees import MarketFees | |||
| from prediction_market_agent_tooling.markets.omen.data_models import IPFSAgentResult | |||
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💡 Codebase verification
Missing IPFSAgentResult Parameter in Subclasses and Method Call
Several areas in the codebase have not been updated to include the IPFSAgentResult parameter:
-
Subclasses missing
IPFSAgentResultinstore_prediction:prediction_market_agent_tooling/markets/omen/omen.py
-
Method call missing
IPFSAgentResultargument:prediction_market_agent_tooling/deploy/agent.py
Please update the store_prediction methods in the affected subclasses and ensure all calls to store_prediction include the IPFSAgentResult parameter.
🔗 Analysis chain
Verify implementation in subclasses and related components.
The changes to the AgentMarket class, while minimal, have a significant impact on the codebase:
- All subclasses implementing
store_predictionwill need to be updated to handle the newagent_resultparameter. - Any code calling
store_predictionwill need to provide theIPFSAgentResultargument.
Please run the following script to identify potential areas that need updates:
Ensure that all identified areas are updated to accommodate the new agent_result parameter.
Also applies to: 232-235
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Identify classes that inherit from AgentMarket and places where store_prediction is called.
# Find subclasses of AgentMarket
echo "Subclasses of AgentMarket:"
rg "class \w+\(.*AgentMarket.*\):" -g '*.py'
# Find calls to store_prediction
echo "\nCalls to store_prediction:"
rg "\.store_prediction\(" -g '*.py'
Length of output: 902
Script:
#!/bin/bash
# Description: Verify that each subclass of AgentMarket has an updated store_prediction method with the IPFSAgentResult parameter.
# List of subclass files
subclasses=(
"prediction_market_agent_tooling/markets/polymarket/polymarket.py"
"prediction_market_agent_tooling/markets/omen/omen.py"
"prediction_market_agent_tooling/markets/metaculus/metaculus.py"
"prediction_market_agent_tooling/jobs/jobs_models.py"
"prediction_market_agent_tooling/jobs/omen/omen_jobs.py"
"prediction_market_agent_tooling/markets/manifold/manifold.py"
)
for file in "${subclasses[@]}"; do
echo "Checking store_prediction method in $file:"
rg "def store_prediction\s*\(" "$file" | while read -r line; do
# Check if the method includes 'IPFSAgentResult' as a parameter
if [[ "$line" =~ store_prediction\([^\)]*IPFSAgentResult[^\)]*\) ]]; then
echo " ✅ Updated store_prediction method found."
else
echo " ❌ store_prediction method missing IPFSAgentResult parameter."
fi
done
done
# Check the call in deploy/agent.py
echo -e "\nChecking store_prediction call in deploy/agent.py:"
rg "\.store_prediction\s*\(" prediction_market_agent_tooling/deploy/agent.py | while read -r line; do
# Check if the call includes 'IPFSAgentResult' argument
if [[ "$line" =~ store_prediction\([^\)]*IPFSAgentResult[^\)]*\) ]]; then
echo " ✅ store_prediction call includes IPFSAgentResult argument."
else
echo " ❌ store_prediction call is missing IPFSAgentResult argument."
fi
done
Length of output: 3078
| @@ -48,6 +48,7 @@ | |||
| MarketType, | |||
| have_bet_on_market_since, | |||
| ) | |||
| from prediction_market_agent_tooling.markets.omen.data_models import IPFSAgentResult | |||
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Avoid importing Omen-specific classes in a generic agent module
Importing IPFSAgentResult from markets.omen.data_models introduces Omen-specific dependencies into the generic DeployableAgent class hierarchy. This reduces modularity and reusability, potentially causing issues for other markets like Metaculus that do not require IPFS uploads.
Consider abstracting the result handling to be market-agnostic. You could define a generic interface or base class for agent results and let market-specific implementations extend it. This way, DeployableTraderAgent remains decoupled from any specific market logic.
-> Adding agent_name to structure being uploaded to IPFS