Allow to submit job results #541
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WalkthroughThe pull request introduces modifications to the Changes
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Actionable comments posted: 2
🧹 Outside diff range and nitpick comments (3)
prediction_market_agent_tooling/jobs/jobs_models.py (2)
51-53: Enhance the docstring for better clarity.The docstring could be more descriptive to help implementers understand:
- The purpose and expected behavior
- Parameter descriptions
- Return value details
- Any constraints or requirements
Consider expanding the docstring like this:
- """Submit the completed result for this job.""" + """Submit the completed result for this job and process associated trades. + + Args: + max_bond (float): The maximum amount to be used for trading + result (str): The result/output of the completed job + + Returns: + ProcessedTradedMarket: The processed market trade details + + Raises: + Should document any expected exceptions + """
64-68: Add docstring to document the method's behavior.The method lacks documentation explaining its purpose and behavior.
Add a descriptive docstring:
def get_job_answer(self, result: str) -> ProbabilisticAnswer: + """Convert a job result into a probabilistic answer. + + Args: + result (str): The result/output of the completed job + + Returns: + ProbabilisticAnswer: Answer with probability and confidence values + """ # Just return 100% yes with 100% confidence, because we assume the job is completed correctly.prediction_market_agent_tooling/jobs/omen/omen_jobs.py (1)
3-3: Review the use ofAPIKeysThe
APIKeysclass is imported and instantiated to obtainkeysfor storing trades. Ensure that sensitive information is securely managed and consider ifAPIKeysshould be passed as a parameter or managed by a configuration handler to promote better security practices.Also applies to: 74-75
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prediction_market_agent_tooling/jobs/jobs_models.py(3 hunks)prediction_market_agent_tooling/jobs/omen/omen_jobs.py(3 hunks)
🔇 Additional comments (4)
prediction_market_agent_tooling/jobs/jobs_models.py (2)
6-11: LGTM: Import statements are properly organized.
The new imports are necessary for the added functionality and are well-organized.
64-68: Consider making confidence values configurable.
The method currently assumes perfect execution (100% probability and confidence) for all jobs. This might not be suitable for:
- Jobs with varying degrees of success
- Jobs that might fail partially
- Jobs where confidence level depends on the result quality
Consider:
- Making these values configurable per job type
- Deriving confidence from the job result
- Adding validation logic to assess result quality
Let's check if there are any job types that might need different confidence levels:
prediction_market_agent_tooling/jobs/omen/omen_jobs.py (2)
38-51: Verify handling of empty result in get_reward
In the get_reward method, you're passing an empty string as the result parameter to get_job_trade. Please ensure that get_job_trade and any subsequent methods can handle an empty string without causing errors or unintended behavior.
65-77:
Ensure error handling in submit_job_result
In the submit_job_result method, after executing the trade and storing it, please verify that any exceptions or errors during these processes are properly handled to prevent unexpected failures.
| answer=self.get_job_answer(result), | ||
| trades=[PlacedTrade.from_trade(trade, id=buy_id)], |
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Implement the get_job_answer method
The method get_job_answer is called but not implemented in OmenJobAgentMarket. Since it's an abstract method in the base class JobAgentMarket, you need to provide an implementation in this subclass to prevent runtime errors.
Consider adding the following implementation:
def get_job_answer(self, result: str) -> bool:
# Convert the result string to the expected answer format
return result.lower() == "yes"Also applies to: 84-84
| assert trade.trade_type == TradeType.BUY, "Should only buy on job markets." | ||
| assert trade.outcome, "Should buy only YES on job markets." | ||
| assert ( | ||
| trade.amount.currency == Currency.xDai |
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🛠️ Refactor suggestion
Replace assertions with explicit exception handling
The assertions in get_job_trade check for specific conditions but will raise generic AssertionErrors if they fail. It's better to use explicit exceptions to provide clearer error messages and improve code robustness.
Consider replacing the assertions with exceptions:
if trade.trade_type != TradeType.BUY:
raise ValueError("Only 'BUY' trade types are supported on job markets.")
if not trade.outcome:
raise ValueError("Trade outcome must be 'YES' on job markets.")
if trade.amount.currency != Currency.xDai:
raise ValueError("Only xDai currency is supported for trades.")
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (2)
prediction_market_agent_tooling/jobs/omen/omen_jobs.py (2)
38-51: Add documentation to clarify the empty result parameter.The empty result string in
get_job_tradecall could be confusing. Consider adding a docstring to explain why an empty result is used when calculating potential rewards.def get_reward(self, max_bond: float) -> float: + """ + Calculate potential reward for the job without submitting a result. + + Args: + max_bond: Maximum amount willing to bond/bet + + Returns: + float: Potential reward based on current market conditions + """ trade = self.get_job_trade( max_bond, result="", # Pass empty result, as we are computing only potential reward at this point. )
85-87: Convert comment to proper docstring format.The explanation about job liquidity is valuable but should be formatted as a proper docstring.
def get_job_trade(self, max_bond: float, result: str) -> Trade: - # Because jobs are powered by prediction markets, potentional reward depends on job's liquidity and our will to bond (bet) our xDai into our job completion. + """ + Calculate the trade required for job result submission. + + Because jobs are powered by prediction markets, potential reward depends on: + - Job's liquidity in the market + - Our willingness to bond (bet) xDai into job completion + + Args: + max_bond: Maximum amount willing to bond + result: Job result to submit + + Returns: + Trade: Calculated trade parameters + """ strategy = KellyBettingStrategy(max_bet_amount=max_bond)
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📒 Files selected for processing (3)
prediction_market_agent_tooling/jobs/jobs.py(0 hunks)prediction_market_agent_tooling/jobs/omen/omen_jobs.py(3 hunks)prediction_market_agent_tooling/markets/markets.py(3 hunks)
💤 Files with no reviewable changes (1)
- prediction_market_agent_tooling/jobs/jobs.py
🔇 Additional comments (6)
prediction_market_agent_tooling/markets/markets.py (3)
6-7: LGTM: Imports are well-structured.
The new imports for job-related functionality are properly organized and follow the existing import style.
51-55: LGTM: Well-implemented property with proper error handling.
The implementation is consistent with the existing market_class property pattern and includes appropriate error handling.
Let's verify that all market types used with job functionality are properly mapped:
#!/bin/bash
# Description: Check for any potential unmapped market types in job-related code
# Look for uses of MarketType that might need job market mapping
rg -A 2 "MarketType\." --type py | grep -v "MarketType\.OMEN"69-71: Verify if single market type mapping is intentional.
The dictionary currently only maps MarketType.OMEN to its job agent market implementation. While this aligns with the PR's focus on OMEN functionality, we should verify if other market types will need job agent implementations in the future.
Let's check if there are any TODO comments or plans for other market types:
✅ Verification successful
Based on the results, I can see that there are other market types (MANIFOLD, POLYMARKET, METACULUS) used throughout the codebase, but they don't have corresponding job agent market implementations. Let me verify if this is a new feature specific to OMEN by checking the JobAgentMarket interface and its usage.
Let me try a different approach to understand the JobAgentMarket implementation and its intended usage.
Single market type mapping is intentional - job markets are an OMEN-specific feature
The code search reveals that job markets are a specialized feature specifically designed for OMEN. This is evidenced by:
JobAgentMarketis only implemented byOmenJobAgentMarket- The implementation includes OMEN-specific job submission logic with comments about "jobs being powered by prediction markets"
- No other market types (Manifold, Polymarket, Metaculus) have job-related implementations
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Look for indicators of planned job market implementations
# Search for TODOs or FIXMEs related to job markets
rg -i "todo|fixme|hack|xxx" --type py | grep -i "job.*market"
# Search for abstract job market references that might indicate needed implementations
ast-grep --pattern 'class $_ implements JobAgentMarket'
Length of output: 6788
Script:
#!/bin/bash
# Search for JobAgentMarket interface/class definition and its implementations
ast-grep --pattern 'class JobAgentMarket'
# Look for any references to job markets in recent commits or documentation
rg -l "job.*market" --type md
Length of output: 75
Script:
#!/bin/bash
# Search for JobAgentMarket definition and usage
rg -A 5 "class JobAgentMarket" --type py
# Search for files containing JobAgentMarket
rg -l "JobAgentMarket" --type py
# Look for any job market related code
rg "job.*market" -i --type py
Length of output: 4156
prediction_market_agent_tooling/jobs/omen/omen_jobs.py (3)
3-10: LGTM! Imports are well-organized and necessary.
The new imports support the job result submission functionality.
65-69: LGTM! Job retrieval implementation is clean and well-typed.
The static method provides a clear way to fetch individual jobs.
80-81: Verify APIKeys access control.
The code uses APIKeys for trade storage. Let's verify that proper access controls are in place.
✅ Verification successful
Let me try a different search strategy to gather information about APIKeys usage and implementation.
Let me check the implementation of store_trades method to understand how the APIKeys are used.
Let me check the BaseSettings implementation and security measures in the config file since APIKeys inherits from it.
Based on the gathered information, I can now provide the final response:
APIKeys implementation follows secure practices - no issues found
The APIKeys class is properly implemented using Pydantic's BaseSettings with appropriate security measures:
- Sensitive data like API keys and private keys are stored using SecretStr type
- Environment variables are properly loaded with secure encoding
- The class inherits from BaseSettings which provides secure parameter handling
- All sensitive fields are properly typed as Optional[SecretStr] or similar secure types
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for APIKeys usage and access control patterns
echo "Checking APIKeys usage patterns..."
rg -A 5 "APIKeys" --type python
echo "Checking for access control decorators..."
ast-grep --pattern 'class APIKeys {
$$$
}'
Length of output: 281
Script:
#!/bin/bash
# Search for APIKeys in all files
echo "Searching for APIKeys class and usage..."
rg -l "APIKeys"
echo "Looking for specific files with APIKeys content..."
rg -A 10 "class APIKeys"
echo "Checking imports and usage..."
rg "from.*APIKeys|import.*APIKeys"
Length of output: 11244
Script:
#!/bin/bash
# Search for store_trades implementation
echo "Looking for store_trades implementation..."
rg -A 10 "def store_trades" prediction_market_agent_tooling/jobs/omen/omen_jobs.py
# Check how APIKeys are used in the file
echo "Checking APIKeys usage in omen_jobs.py..."
rg -B 5 -A 10 "APIKeys" prediction_market_agent_tooling/jobs/omen/omen_jobs.py
Length of output: 1622
Script:
#!/bin/bash
# Check the APIKeys class implementation and security measures
echo "Checking APIKeys implementation and security measures..."
rg -B 10 -A 20 "class APIKeys" prediction_market_agent_tooling/config.py
# Check if there are any access control decorators or security measures
echo "Checking for security-related imports in config.py..."
rg "^import|^from.*import" prediction_market_agent_tooling/config.py
Length of output: 1992
| def submit_job_result(self, max_bond: float, result: str) -> ProcessedTradedMarket: | ||
| trade = self.get_job_trade(max_bond, result) | ||
| buy_id = self.buy_tokens(outcome=trade.outcome, amount=trade.amount) | ||
|
|
||
| processed_traded_market = ProcessedTradedMarket( | ||
| answer=self.get_job_answer(result), | ||
| trades=[PlacedTrade.from_trade(trade, id=buy_id)], | ||
| ) | ||
|
|
||
| keys = APIKeys() | ||
| self.store_trades(processed_traded_market, keys) | ||
|
|
||
| return processed_traded_market | ||
|
|
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Add error handling for token purchase operation.
The buy_tokens call could fail due to network issues or insufficient funds. Consider adding try-catch block to handle potential errors gracefully.
def submit_job_result(self, max_bond: float, result: str) -> ProcessedTradedMarket:
trade = self.get_job_trade(max_bond, result)
- buy_id = self.buy_tokens(outcome=trade.outcome, amount=trade.amount)
+ try:
+ buy_id = self.buy_tokens(outcome=trade.outcome, amount=trade.amount)
+ except Exception as e:
+ raise ValueError(f"Failed to purchase tokens: {str(e)}") from e📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def submit_job_result(self, max_bond: float, result: str) -> ProcessedTradedMarket: | |
| trade = self.get_job_trade(max_bond, result) | |
| buy_id = self.buy_tokens(outcome=trade.outcome, amount=trade.amount) | |
| processed_traded_market = ProcessedTradedMarket( | |
| answer=self.get_job_answer(result), | |
| trades=[PlacedTrade.from_trade(trade, id=buy_id)], | |
| ) | |
| keys = APIKeys() | |
| self.store_trades(processed_traded_market, keys) | |
| return processed_traded_market | |
| def submit_job_result(self, max_bond: float, result: str) -> ProcessedTradedMarket: | |
| trade = self.get_job_trade(max_bond, result) | |
| try: | |
| buy_id = self.buy_tokens(outcome=trade.outcome, amount=trade.amount) | |
| except Exception as e: | |
| raise ValueError(f"Failed to purchase tokens: {str(e)}") from e | |
| processed_traded_market = ProcessedTradedMarket( | |
| answer=self.get_job_answer(result), | |
| trades=[PlacedTrade.from_trade(trade, id=buy_id)], | |
| ) | |
| keys = APIKeys() | |
| self.store_trades(processed_traded_market, keys) | |
| return processed_traded_market |
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (2)
prediction_market_agent_tooling/jobs/jobs_models.py (1)
54-57: Enhance docstring with error handling detailsConsider expanding the docstring to include information about possible exceptions when the job ID doesn't exist or is invalid.
@staticmethod @abstractmethod def get_job(id: str) -> "JobAgentMarket": - """Get a single job by its id.""" + """Get a single job by its id. + + Args: + id: The unique identifier of the job + + Returns: + JobAgentMarket: The job market instance + + Raises: + ValueError: If the job ID doesn't exist or is invalid + """prediction_market_agent_tooling/jobs/omen/omen_jobs.py (1)
38-51: Add docstring explaining empty result parameter.The empty result string in
get_job_tradecall should be documented to explain why it's valid for reward calculation.Consider adding a docstring:
def get_reward(self, max_bond: float) -> float: """Calculate potential reward for the job. Args: max_bond: Maximum amount willing to bond Returns: float: Potential reward amount Note: Uses empty result string as we're only computing potential reward, not submitting an actual result. """
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📒 Files selected for processing (2)
prediction_market_agent_tooling/jobs/jobs_models.py(3 hunks)prediction_market_agent_tooling/jobs/omen/omen_jobs.py(3 hunks)
🧰 Additional context used
🪛 Ruff
prediction_market_agent_tooling/jobs/omen/omen_jobs.py
77-77: f-string without any placeholders
Remove extraneous f prefix
(F541)
🔇 Additional comments (8)
prediction_market_agent_tooling/jobs/jobs_models.py (3)
6-11: LGTM: Imports are properly organized
The new imports are necessary for the added functionality and are well-organized.
47-50: LGTM: Improved method signature with sensible defaults
The addition of default values for filter_by and sort_by parameters improves usability while maintaining backward compatibility.
59-61: Enhance docstring and consider parameter validation
- The docstring should provide more details about the parameters and return value.
- Consider adding validation for the
max_bondparameter.
@abstractmethod
def submit_job_result(self, max_bond: float, result: str) -> ProcessedTradedMarket:
- """Submit the completed result for this job."""
+ """Submit the completed result for this job.
+
+ Args:
+ max_bond: The maximum amount to bond for the submission
+ result: The result/output of the completed job
+
+ Returns:
+ ProcessedTradedMarket: The processed market after submitting the result
+
+ Raises:
+ ValueError: If max_bond is negative or result is empty
+ """Let's check if any implementations already validate the parameters:
✅ Verification successful
Docstring improvement suggestion is valid and necessary
The search results show that:
- The abstract method is defined in
jobs_models.pywith minimal documentation - There is only one implementation in
omen_jobs.pywhich focuses on IPFS upload validation but doesn't validate the input parameters
The suggestion to improve the docstring is valid since:
- Current documentation is minimal ("Submit the completed result for this job")
- No implementations validate max_bond or result parameters
- Better documentation would help implementers understand parameter requirements
The proposed docstring enhancement with parameter descriptions and potential error cases would improve code maintainability and guide future implementations.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for implementations of submit_job_result to check parameter validation
ast-grep --pattern 'def submit_job_result(self, max_bond: float, result: str)'
Length of output: 80
Script:
#!/bin/bash
# Let's try a broader search pattern to find implementations
ast-grep --pattern 'def submit_job_result'
# Also search using ripgrep for any submit_job_result implementations
rg "submit_job_result" -A 5
Length of output: 1232
prediction_market_agent_tooling/jobs/omen/omen_jobs.py (5)
3-10: LGTM: Imports are well-organized and necessary.
The new imports support the job result submission functionality.
Line range hint 54-72: LGTM: Well-structured job retrieval methods.
The default parameters and static method implementation look good.
93-110: Previous review comment about replacing assertions with explicit exceptions is still applicable.
84-85: Previous review comment about implementing the get_job_answer method is still applicable.
74-91:
Previous review comment about error handling is still applicable.
Remove unnecessary f-string prefix.
The error message doesn't contain any placeholders, so the f-string prefix can be removed.
- raise RuntimeError(
- f"ENABLE_IPFS_UPLOAD must be set to True to upload job results."
- )
+ raise RuntimeError(
+ "ENABLE_IPFS_UPLOAD must be set to True to upload job results."
+ )🧰 Tools
🪛 Ruff
77-77: f-string without any placeholders
Remove extraneous f prefix
(F541)
| def get_job_answer(self, result: str) -> ProbabilisticAnswer: | ||
| # Just return 100% yes with 100% confidence, because we assume the job is completed correctly. | ||
| return ProbabilisticAnswer( | ||
| p_yes=Probability(1.0), confidence=1.0, reasoning=result | ||
| ) |
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Add basic validation for job result
The method assumes 100% confidence without validating the result. Consider adding basic validation to ensure the result is not empty or malformed before assuming perfect confidence.
def get_job_answer(self, result: str) -> ProbabilisticAnswer:
+ if not result or not result.strip():
+ raise ValueError("Job result cannot be empty")
+
# Just return 100% yes with 100% confidence, because we assume the job is completed correctly.
return ProbabilisticAnswer(
p_yes=Probability(1.0), confidence=1.0, reasoning=result
)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def get_job_answer(self, result: str) -> ProbabilisticAnswer: | |
| # Just return 100% yes with 100% confidence, because we assume the job is completed correctly. | |
| return ProbabilisticAnswer( | |
| p_yes=Probability(1.0), confidence=1.0, reasoning=result | |
| ) | |
| def get_job_answer(self, result: str) -> ProbabilisticAnswer: | |
| if not result or not result.strip(): | |
| raise ValueError("Job result cannot be empty") | |
| # Just return 100% yes with 100% confidence, because we assume the job is completed correctly. | |
| return ProbabilisticAnswer( | |
| p_yes=Probability(1.0), confidence=1.0, reasoning=result | |
| ) |
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