Updating langfuse integration #345
Conversation
|
Important Review skippedReview was skipped due to path filters Files ignored due to path filters (2)
You can disable this status message by setting the WalkthroughThe recent updates significantly enhance the integration of Langfuse within the prediction market agent tooling. Key changes include modifications to type annotations for better flexibility, the introduction of new methods and properties to support conditional Langfuse integration, and improved observability of agent activities and market processing. These changes streamline functionality, allowing easier configuration management and monitoring across various modules. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Agent
participant Langfuse
User->>Agent: Initialize with enable_langfuse
Agent->>Langfuse: Check keys (PUBLIC, SECRET, HOST)
Langfuse-->>Agent: Return status (enabled/disabled)
Agent->>User: Return initialization status
sequenceDiagram
participant User
participant ImageGen
participant Langfuse
User->>ImageGen: Request image generation with question
ImageGen->>Langfuse: Utilize langfuse if enabled
Langfuse-->>ImageGen: Provide context or logging
ImageGen->>User: Return generated image
Thank you for using CodeRabbit. We offer it for free to the OSS community and would appreciate your support in helping us grow. If you find it useful, would you consider giving us a shout-out on your favorite social media? TipsChatThere are 3 ways to chat with CodeRabbit:
Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments. CodeRabbit Commands (invoked as PR comments)
Additionally, you can add CodeRabbit Configuration File (
|
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 1
Outside diff range, codebase verification and nitpick comments (1)
prediction_market_agent_tooling/tools/image_gen/market_thumbnail_gen.py (1)
Line range hint
8-30:
Enhance ImportError handling.The function correctly integrates Langfuse conditionally, and the try-except block is a good practice. However, consider using
raise ... from Noneto clarify the source of the ImportError.- except ImportError: + except ImportError as e: + raise ImportError( + "openai not installed, please install extras `langchain` to use this function." + ) from None
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files ignored due to path filters (3)
mypy.iniis excluded by!**/*.inipoetry.lockis excluded by!**/*.lock,!**/*.lockpyproject.tomlis excluded by!**/*.toml
Files selected for processing (7)
- prediction_market_agent_tooling/config.py (3 hunks)
- prediction_market_agent_tooling/deploy/agent.py (7 hunks)
- prediction_market_agent_tooling/markets/categorize.py (3 hunks)
- prediction_market_agent_tooling/monitor/monitor.py (1 hunks)
- prediction_market_agent_tooling/tools/image_gen/market_thumbnail_gen.py (2 hunks)
- prediction_market_agent_tooling/tools/is_predictable.py (4 hunks)
- prediction_market_agent_tooling/tools/langfuse_.py (1 hunks)
Files skipped from review due to trivial changes (1)
- prediction_market_agent_tooling/monitor/monitor.py
Additional context used
Ruff
prediction_market_agent_tooling/markets/categorize.py
17-19: Within an
exceptclause, raise exceptions withraise ... from errorraise ... from Noneto distinguish them from errors in exception handling(B904)
prediction_market_agent_tooling/deploy/agent.py
356-359: Return the condition
is_predictable_binary_observed(market.question)directlyReplace with
return is_predictable_binary_observed(market.question)(SIM103)
Additional comments not posted (20)
prediction_market_agent_tooling/tools/langfuse_.py (1)
12-26: LGTM!The
observefunction is well-implemented, providing a flexible wrapper aroundoriginal_observewith clear type annotations and customizable parameters.prediction_market_agent_tooling/tools/image_gen/market_thumbnail_gen.py (2)
36-39: LGTM!The addition of the
enable_langfuseparameter enhances the flexibility of thegenerate_image_for_marketfunction.
43-45: LGTM!The
generate_image_for_market_observedfunction is well-implemented, leveraging theobservedecorator to enable monitoring.prediction_market_agent_tooling/markets/categorize.py (1)
51-57: LGTM!The
infer_category_observedfunction is well-implemented, leveraging theobservedecorator to enable monitoring.prediction_market_agent_tooling/config.py (3)
138-144: The addition ofdefault_enable_langfuseis well-implemented.This property method provides a clear and efficient way to determine if Langfuse integration should be enabled based on key presence.
127-129: Verify the impact of changing the return type oflangfuse_public_keytostr.The return type change may affect how the public key is used in the codebase. Ensure that all usages are compatible with this change.
Verification successful
The change to
langfuse_public_keyreturn type is compatible with its usage.The method
langfuse_public_keyis used directly as apublic_keyargument without any secret-specific operations, confirming that changing the return type tostrdoes not introduce any issues.
- File:
prediction_market_agent_tooling/deploy/agent.py
- Line:
public_key=keys.langfuse_public_keyScripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of `langfuse_public_key` to ensure compatibility with the new return type. # Test: Search for the usage of `langfuse_public_key`. Expect: Compatibility with `str` type. rg --type python -A 5 $'langfuse_public_key'Length of output: 74
Script:
#!/bin/bash # Description: Verify the usage of `langfuse_public_key` to ensure compatibility with the new return type. # Test: Search for the usage of `langfuse_public_key`. Expect: Compatibility with `str` type. rg -A 5 'langfuse_public_key'Length of output: 965
43-43: Consider the security implications of changingLANGFUSE_PUBLIC_KEYtostr.The change from
SecretStrtostrmay reduce the security of handling the public key as it is no longer treated as a secret. Ensure that this change aligns with your security requirements.Verification successful
Verify security requirements for
LANGFUSE_PUBLIC_KEYhandling.The
LANGFUSE_PUBLIC_KEYis not treated as sensitive data in the current codebase, which might justify changing its type fromSecretStrtostr. Ensure that this change aligns with your security policies and requirements.
LANGFUSE_PUBLIC_KEYis used inprediction_market_agent_tooling/config.pywithout special handling for sensitive data.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of `LANGFUSE_PUBLIC_KEY` to ensure it aligns with security requirements. # Test: Search for the usage of `LANGFUSE_PUBLIC_KEY`. Expect: Proper handling as non-sensitive data. rg --type python -A 5 $'LANGFUSE_PUBLIC_KEY'Length of output: 74
Script:
#!/bin/bash # Description: Verify the usage of `LANGFUSE_PUBLIC_KEY` to ensure it aligns with security requirements. # Test: Search for the usage of `LANGFUSE_PUBLIC_KEY`. Expect: Proper handling as non-sensitive data. rg --type py -A 5 $'LANGFUSE_PUBLIC_KEY'Length of output: 1371
prediction_market_agent_tooling/tools/is_predictable.py (4)
110-124: The addition ofis_predictable_binary_observedis well-implemented.This function offers a straightforward way to observe predictions with Langfuse enabled, enhancing observability.
167-183: The addition ofis_predictable_without_description_observedis well-implemented.This function offers a straightforward way to observe predictions with Langfuse enabled, enhancing observability.
Line range hint
81-105: The addition ofenable_langfuseinis_predictable_binaryis well-implemented.This change allows for optional Langfuse integration, enhancing the function's flexibility.
Ensure that all calls to
is_predictable_binaryare updated to accommodate the new parameter.
Line range hint
133-162: The addition ofenable_langfuseinis_predictable_without_descriptionis well-implemented.This change allows for optional Langfuse integration, enhancing the function's flexibility.
Ensure that all calls to
is_predictable_without_descriptionare updated to accommodate the new parameter.prediction_market_agent_tooling/deploy/agent.py (9)
116-125: The addition ofinitialize_langfuseis well-implemented.This method provides a centralized setup for Langfuse integration, enhancing maintainability.
127-156: The addition oflangfuse_update_current_traceis well-implemented.This method provides flexibility in updating trace information with Langfuse, enhancing observability.
287-298: The addition ofupdate_langfuse_trace_by_marketis well-implemented.This method provides a clear way to update Langfuse trace information based on market data, enhancing observability.
300-313: The addition ofupdate_langfuse_trace_by_processed_marketis well-implemented.This method provides a structured approach to updating Langfuse trace information after market processing, enhancing observability.
408-444: The addition ofprocess_marketis well-implemented.This method integrates market processing with Langfuse observability enhancements, improving traceability.
468-486: The refactoring ofprocess_betstoprocess_marketsis well-implemented.This change aligns with the new processing logic, enhancing clarity and maintainability.
328-372: The observability enhancements using@observeare well-implemented.The use of the
@observedecorator enhances the monitoring of agent interactions, providing valuable insights.Also applies to: 397-403
Tools
Ruff
356-359: Return the condition
is_predictable_binary_observed(market.question)directlyReplace with
return is_predictable_binary_observed(market.question)(SIM103)
107-115: The addition ofenable_langfuseinDeployableAgentis well-implemented.This change enhances the configurability of the agent with Langfuse integration.
Ensure that all instantiations of
DeployableAgentare updated to accommodate the new parameter.
279-284: The addition ofenable_langfuseinDeployableTraderAgentis well-implemented.This change enhances the configurability of the trader agent with Langfuse integration.
Ensure that all instantiations of
DeployableTraderAgentare updated to accommodate the new parameter.
There was a problem hiding this comment.
Actionable comments posted: 1
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files ignored due to path filters (1)
poetry.lockis excluded by!**/*.lock,!**/*.lock
Files selected for processing (1)
- prediction_market_agent_tooling/deploy/agent.py (7 hunks)
Additional context used
Ruff
prediction_market_agent_tooling/deploy/agent.py
359-362: Return the condition
is_predictable_binary_observed(market.question)directlyReplace with
return is_predictable_binary_observed(market.question)(SIM103)
Additional comments not posted (5)
prediction_market_agent_tooling/deploy/agent.py (5)
96-98: LGTM!The
ProcessedMarketclass is well-defined and uses Pydantic for data validation.
101-103: LGTM!The
AnsweredEnumclass provides clear and concise states for market answers.
107-114: LGTM!The constructor of
DeployableAgenteffectively integrates Langfuse with conditional initialization.
116-128: LGTM!The
initialize_langfusemethod is well-structured and handles configuration based on theenable_langfuseflag.
130-159: LGTM!The
langfuse_update_current_tracemethod effectively updates the Langfuse trace with relevant metadata and provides useful default arguments.
| LANGFUSE_HOST: t.Optional[str] = None | ||
| LANGFUSE_DEPLOYMENT_VERSION: t.Optional[str] = None |
There was a problem hiding this comment.
This is a new env variable that is populated by git commit sha in PMA's docker building pipeline; it will show us, in the UI, what version of PMA was used.
| @@ -134,6 +135,14 @@ def langfuse_host(self) -> str: | |||
| self.LANGFUSE_HOST, "LANGFUSE_HOST missing in the environment." | |||
| ) | |||
|
|
|||
| @property | |||
| def default_enable_langfuse(self) -> bool: | |||
There was a problem hiding this comment.
This is used as the default argument in DeployableAgent init method
There was a problem hiding this comment.
and self.LANGFUSE_DEPLOYMENT_VERSION is not None?
There was a problem hiding this comment.
That's not necessary for langfuse to work, it can be None
| """ | ||
| Provide some useful default arguments when updating the current trace in our agents. | ||
| """ | ||
| langfuse_context.update_current_trace( |
There was a problem hiding this comment.
This is a wrapper around update_current_trace, see the default arguments below. The reason is to always include these in our PMAT-based agents.
| def have_bet_on_market_since_observed( | ||
| self, market: AgentMarket, since: timedelta | ||
| ) -> bool: | ||
| return self.have_bet_on_market_since(market, since) |
There was a problem hiding this comment.
This is the pattern I was talking about last time, I don't like it so much but I also don't see any clearer way to proceed.
This pattern is used on all observed methods of DeployableAgent.
The reason is, that if some method is @observed() and the agent class needs to implement that method (answer_binary_market is a clear example, but I think it will be usable for others as well), then the overridden method would not be @observed() anymore.
There was a problem hiding this comment.
I don't love this pattern either, this means every time you add a new method, you need to add an observed one (to avoid people overriding the observed one and only overriding the "pure" one).
One solution I came across is adding a class decorator to DeployableAgent, which "observes" the a set of functions without annotating them. Hence you could avoid having to manually decorate them, instead only decorating the base class (see snippet below).
What this would imply:
-> Annotate DeployableAgent with decorator, define that methods [have_bet_on_market, ...] should be observed. Add logic to observe function (e.g. call decorator(my_function) or similar)
-> If subclass (e.g. CoinflipDeployableAgent) overwrites the method, it doesn't matter, it will keep getting observed (due to class decorator being in the base class).
-> This is nice because you avoid having duplicate methods everywhere.
There was a problem hiding this comment.
That could work, but based on the gist, it would decorate all the methods, which is not needed (and maybe including methods as __init__ and similar?)
So we would need something like
@prepend_foo_to_methods(allowed_methods=["have_bet_on_market", ...])
class MyClass:
which isn't type-safe and can be easily forgotten.
Another approach I was experimenting with was something like
def initialize_langfuse(self) -> None:
...
self.have_bet_on_market = observe()(self.have_bet_on_market) # type: ignore[method-assign]
It works similarly to your suggestion, but mypy should warn us if for example have_bet_on_market gets deleted or something (but I didn't test it), although it still needs type: ignore comment, because changing methods like this isn't allowed.
I didn't use that approach because it needs type: ignore, but I don't feel strongly about it.
Do you have preferences?
There was a problem hiding this comment.
@prepend_foo_to_methods(allowed_methods=["have_bet_on_market", ...])
class MyClass:
What I had in mind was:
-> Add the decorator to the base class (DeployableTraderAgent). Note that subclasses (e.g. DeployableCoinFlipTraderAgent) and similar do not get decorated. Hence we need to decorate only once (or maybe more, but only on base classes), so not very hard to remember.
-> The decorator does not modify all functions, only a subset (either hardcoded or even better, as you suggested it, provided as an argument).
-> Not sure how to satisfy mypy here, maybe using a FrozenSet but not sure
Regarding the other approach (overriding methods with observe()(self.have_bet_on_market) # type: ignore[method-assign], it's a similar approach to the decorator, but I like the decorator a bit better because I find it more elegant (no real reason).
The point I'm trying to make is: I don't love the duplicated-function pattern (observed + pure). If we can find any solution to this (be it decorator or non-decorator), I'm happy with any option.
There was a problem hiding this comment.
Personally, I don't care for duplicated *_observer method or doing some magic, but what I'm afraid of is that
@magic(["predict"])
class Agent:
def predictt(self):
print("Predicting")
will never trigger mypy as a mistake, but
from prediction_market_agent_tooling.tools.langfuse_ import observe
class Agent:
def init_langfuse(self) -> None:
self.predict = observe()(self.predict)
def predictt(self) -> None:
print("Predicting")
will. And it also works with the auto-refactoring features of VSCode.
In any case, we will get rid of _observed methods, I'll change it.
| return have_bet_on_market_since(keys=APIKeys(), market=market, since=since) | ||
|
|
||
| @observe() | ||
| def verify_market_observed( |
There was a problem hiding this comment.
I had to refactor the logic of these methods, before it was:
- Fetch markets
- Filter markets
- Predict+Bet on them one by one
Now it is
- Fetch markets
- One by one check if the market is ok to predict and if so, predict + bet on it.
The reason is that we need the whole process end-to-end saved in Langfuse, this is how it looks like:
| ) -> t.Sequence[AgentMarket]: | ||
| return random.sample(markets, 1) | ||
| def verify_market(self, market_type: MarketType, market: AgentMarket) -> bool: | ||
| return True |
There was a problem hiding this comment.
After refactoring, it's no longer possible to randomly pick one of the markets, but hopefully, that's not a problem.
| @@ -17,6 +26,10 @@ def infer_category( | |||
| """ | |||
| ) | |||
|
|
|||
| config: RunnableConfig = {} | |||
| if enable_langfuse: | |||
| config["callbacks"] = [langfuse_context.get_current_langchain_handler()] | |||
There was a problem hiding this comment.
This is again a pattern that I don't like so much, but it's required.
One would probably expect a solution as:
def infer_category(
...,
callbacks: list[...] | None = None,
):And then simply use the callbacks in the invoke method.
However, when langfuse_context.get_current_langchain_handler() is called, it will pick up the latest observed function (see in the previous screenshot that observation can be nested), and because of that, we need to initialize this callback right before calling the invoke method, because otherwise, it could be linked to a different observation.
There was a problem hiding this comment.
But I will think some more about this, if you don't have some clear refactor idea for this, I'd merge it and change in another PR if I think of something 🙏
There was a problem hiding this comment.
Could you at least turn this into a util function, like:
def get_langchain_runnable_config(enable_langfuse: bool) -> RunnableConfig:
config: RunnableConfig = {}
if enable_langfuse:
config["callbacks"] = [langfuse_context.get_current_langchain_handler()]
return config
?
There was a problem hiding this comment.
I guess that's what you propose as (1) in #346, except existing_config is always None in PMAT.
There was a problem hiding this comment.
Any reason not to do that? Just to save lines I guess
There was a problem hiding this comment.
I don't think so, I can do it. I just also need to think about the suggestion below. But now I'm just responding in-between time when I don't know what to write into the paper 😄
I'll look in more detail into all comments tomorrow 👌
| capture_output: bool = True, | ||
| transform_to_string: Optional[Callable[[Iterable[Any]], str]] = None, | ||
| ) -> Callable[[Callable[P, R]], Callable[P, R]]: | ||
| casted: Callable[[Callable[P, R]], Callable[P, R]] = original_observe( |
There was a problem hiding this comment.
This is described at langfuse/langfuse-python#862
There was a problem hiding this comment.
Could we also override their decorator to avoid having to have this pattern
@observe
def foo_observed()
return foo(enable_langfuse=True)
by doing something like:
from prediction_market_agent.utils import APIKeys
class APIKeysTest(APIKeys):
FOO: str | None = None
@property
def default_enable_langfuse(self):
return self.FOO is not None
def original_observe(func):
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
return result + "foo"
return wrapper
def observe(func):
def noop_decorator(func):
return func
enable = APIKeysTest().default_enable_langfuse
if enable:
return original_observe(func)
else:
return noop_decorator(func)
@observe
def hello_world():
return "Hello, world! "
print(hello_world())
i.e. you get
% python test.py
Hello, world!
% FOO=1 python test.py
Hello, world! foo
The drawback to this is that now you can't do:
agent0 = DeployableFooAgent(enable_langfuse=True)
agent1 = DeployableFooAgent(enable_langfuse=False)
There was a problem hiding this comment.
I don't think it's a good idea to add additional logic into their decorator, because what can happen is that someone uses both PMAT and also observes some of his method manually (like we do in PMA), and then they could receive different behaviours on observed methods.
However, it seems like this is fine even without modifying the observer decorator:
So we can just @observe everything by default. I thought it's a good idea to have both observed and non-observed functions, but now I don't feel strong about it.
And in combination with #345 (comment), code will be simpler to follow.
There was a problem hiding this comment.
someone uses both PMAT and also observes some of his method manually (like we do in PMA), and then they could receive different behaviours on observed methods.
Sorry I don't understand this bit.
So we can just @observe everything by default.
But anyway, this sounds good
| def verify_market(self, market_type: MarketType, market: AgentMarket) -> bool: | ||
| return True |
There was a problem hiding this comment.
Consider implementing verification logic in verify_market.
The verify_market method currently acts as a placeholder by always returning True. If market verification is intended to be a part of the agent's functionality, consider implementing the necessary logic to validate the market_type and market.
There was a problem hiding this comment.
Actionable comments posted: 3
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (5)
- prediction_market_agent_tooling/deploy/agent.py (7 hunks)
- prediction_market_agent_tooling/markets/categorize.py (2 hunks)
- prediction_market_agent_tooling/tools/image_gen/market_thumbnail_gen.py (2 hunks)
- prediction_market_agent_tooling/tools/is_predictable.py (4 hunks)
- prediction_market_agent_tooling/tools/langfuse_.py (1 hunks)
Files skipped from review as they are similar to previous changes (2)
- prediction_market_agent_tooling/tools/image_gen/market_thumbnail_gen.py
- prediction_market_agent_tooling/tools/langfuse_.py
Additional context used
Ruff
prediction_market_agent_tooling/markets/categorize.py
19-21: Within an
exceptclause, raise exceptions withraise ... from errorraise ... from Noneto distinguish them from errors in exception handling(B904)
prediction_market_agent_tooling/deploy/agent.py
354-357: Return the condition
is_predictable_binary(market.question)directlyReplace with
return is_predictable_binary(market.question)(SIM103)
Additional comments not posted (7)
prediction_market_agent_tooling/markets/categorize.py (2)
8-8:infer_category_observedfunction looks good.The function correctly wraps
infer_categoryto enable Langfuse by default.
Line range hint
14-42:
LGTM! But verify the function usage in the codebase.The code changes are approved.
However, ensure that all function calls to
infer_categorymatch the new signature.Verification successful
No usage of
infer_categoryfound in the codebase. Theinfer_categoryfunction is defined but not called anywhere else in the current codebase. No further verification of function calls is required.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify all function calls to `infer_category` match the new signature. # Test: Search for the function usage. Expect: Only occurrences of the new signature. rg --type python -A 5 $'infer_category'Length of output: 69
Script:
#!/bin/bash # Correcting the file type specification and searching for the function definition and usage of `infer_category`. # Search for the definition of `infer_category` in Python files rg --type py -A 5 'def infer_category' # Search for the usage of `infer_category` in Python files rg --type py -A 5 'infer_category('Length of output: 606
Script:
#!/bin/bash # Correcting the regex pattern by escaping the parenthesis and searching for the usage of `infer_category`. # Search for the usage of `infer_category` in Python files rg --type py -A 5 'infer_category\('Length of output: 485
Tools
Ruff
19-21: Within an
exceptclause, raise exceptions withraise ... from errorraise ... from Noneto distinguish them from errors in exception handling(B904)
prediction_market_agent_tooling/tools/is_predictable.py (2)
Line range hint
79-108:
Integration with Langfuse looks good.The function correctly integrates Langfuse capabilities for enhanced functionality.
Line range hint
114-149:
Integration with Langfuse looks good.The function correctly integrates Langfuse capabilities for enhanced functionality.
prediction_market_agent_tooling/deploy/agent.py (3)
105-157: Langfuse integration inDeployableAgentlooks good.The class correctly integrates Langfuse for enhanced monitoring capabilities.
Line range hint
280-427:
Enhancements inDeployableTraderAgentlook good.The refactoring and new methods significantly improve functionality and observability.
450-475: Verify the impact of method renaming on the codebase.Ensure that all references to renamed methods are updated accordingly.
Verification successful
Method Renaming Verified Successfully
The codebase does not contain any references to the old method names
process_bets,before_process_bets, orafter_process_bets. This indicates that all references have been updated accordingly.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify all references to renamed methods are updated. # Test: Search for the old method names. Expect: No occurrences of old method names. rg --type python -A 5 $'process_bets|before|after'Length of output: 82
Script:
#!/bin/bash # Description: Verify all references to renamed methods are updated. # Test: Search for the old method names. Expect: No occurrences of old method names. rg --type py -A 5 'process_bets|before_process_bets|after_process_bets'Length of output: 73
| raise ImportError( | ||
| "openai not installed, please install extras `langchain` to use this function." | ||
| ) |
There was a problem hiding this comment.
Enhance ImportError handling.
Consider using raise ... from None to clarify the source of the ImportError.
- except ImportError:
+ except ImportError as e:
+ raise ImportError(
+ "openai not installed, please install extras `langchain` to use this function."
+ ) from NoneCommittable 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.
| raise ImportError( | |
| "openai not installed, please install extras `langchain` to use this function." | |
| ) | |
| except ImportError as e: | |
| raise ImportError( | |
| "openai not installed, please install extras `langchain` to use this function." | |
| ) from None |
Tools
Ruff
19-21: Within an
exceptclause, raise exceptions withraise ... from errorraise ... from Noneto distinguish them from errors in exception handling(B904)
| completion = str( | ||
| llm( | ||
| messages, max_tokens=max_tokens, config=get_langfuse_langchain_config() | ||
| ).content | ||
| ) |
There was a problem hiding this comment.
Simplify the return statement.
The return statement can be simplified by directly returning the condition.
- completion = str(
- llm(
- messages, max_tokens=max_tokens, config=get_langfuse_langchain_config()
- ).content
- )
+ completion = llm(
+ messages, max_tokens=max_tokens, config=get_langfuse_langchain_config()
+ ).contentCommittable 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.
| completion = str( | |
| llm( | |
| messages, max_tokens=max_tokens, config=get_langfuse_langchain_config() | |
| ).content | |
| ) | |
| completion = llm( | |
| messages, max_tokens=max_tokens, config=get_langfuse_langchain_config() | |
| ).content |
| completion = str( | ||
| llm.invoke( | ||
| messages, max_tokens=max_tokens, config=get_langfuse_langchain_config() | ||
| ).content | ||
| ) |
There was a problem hiding this comment.
Simplify the return statement.
The return statement can be simplified by directly returning the condition.
- completion = str(
- llm.invoke(
- messages, max_tokens=max_tokens, config=get_langfuse_langchain_config()
- ).content
- )
+ completion = llm.invoke(
+ messages, max_tokens=max_tokens, config=get_langfuse_langchain_config()
+ ).contentCommittable 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.
| completion = str( | |
| llm.invoke( | |
| messages, max_tokens=max_tokens, config=get_langfuse_langchain_config() | |
| ).content | |
| ) | |
| completion = llm.invoke( | |
| messages, max_tokens=max_tokens, config=get_langfuse_langchain_config() | |
| ).content |
There was a problem hiding this comment.
Actionable comments posted: 0
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files ignored due to path filters (2)
poetry.lockis excluded by!**/*.lock,!**/*.lockpyproject.tomlis excluded by!**/*.toml
Files selected for processing (1)
- prediction_market_agent_tooling/config.py (3 hunks)
Files skipped from review as they are similar to previous changes (1)
- prediction_market_agent_tooling/config.py
There was a problem hiding this comment.
Actionable comments posted: 1
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (1)
- prediction_market_agent_tooling/deploy/agent.py (8 hunks)
Additional context used
Ruff
prediction_market_agent_tooling/deploy/agent.py
358-361: Return the condition
is_predictable_binary(market.question)directlyReplace with
return is_predictable_binary(market.question)(SIM103)
Additional comments not posted (11)
prediction_market_agent_tooling/deploy/agent.py (11)
1-11: Imports look good.The import statements are appropriate and necessary for the file's functionality.
76-88: Langfuse initialization is well-implemented.The
initialize_langfusefunction correctly configures Langfuse based on theenable_langfuseflag.
109-117: New class and enum are well-structured.The
ProcessedMarketclass andAnsweredEnumenum enhance the structure and readability of market processing logic.
120-131: Constructor changes enhance configurability.The inclusion of the
enable_langfuseparameter and the call toinitialize_langfuseimprove flexibility and ensure proper initialization.
132-161: Trace update method is well-implemented.The
langfuse_update_current_tracemethod is well-documented and provides useful defaults for updating traces.
284-289: Constructor changes ensure proper initialization.The addition of the
enable_langfuseparameter and the call to the superclass constructor enhance configurability and ensure proper initialization.
292-299: Automated observability is well-implemented.The use of the
observedecorator to automatically observe methods reduces manual effort and enhances observability.
301-327: Trace update methods enhance traceability.The
update_langfuse_trace_by_marketandupdate_langfuse_trace_by_processed_marketmethods are well-structured and improve traceability of market interactions.
394-430: Market processing method is comprehensive.The
process_marketmethod effectively integrates Langfuse tracking and ensures comprehensive market processing.
454-471: Multiple market processing is well-structured.The
process_marketsmethod ensures appropriate balance checks and processes multiple markets efficiently.
477-479: Market processing flow is well-orchestrated.The
runmethod provides a clear and organized flow for processing markets.
| if not is_predictable_binary(market.question): | ||
| return False | ||
|
|
||
| return picked | ||
| return True |
There was a problem hiding this comment.
Simplify the return statement.
The return statement can be simplified by directly returning the condition.
- if not is_predictable_binary(market.question):
- return False
- return True
+ return is_predictable_binary(market.question)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.
| if not is_predictable_binary(market.question): | |
| return False | |
| return picked | |
| return True | |
| return is_predictable_binary(market.question) |
Tools
Ruff
358-361: Return the condition
is_predictable_binary(market.question)directlyReplace with
return is_predictable_binary(market.question)(SIM103)
| def initialize_langfuse(self) -> None: | ||
| super().initialize_langfuse() | ||
| # Auto-observe all the methods where it makes sense, so that subclassses don't need to do it manually. | ||
| self.have_bet_on_market_since = observe()(self.have_bet_on_market_since) # type: ignore[method-assign] |
There was a problem hiding this comment.
Is there a reason you do @observe() in some places, but observe()(self.some_class_method) here?
There was a problem hiding this comment.
Yeah, if something is just a function:
@observe()
def research(...) -> ...
We can use the decorator, and no harm is done. Anyone who uses research will use the observed variant.
But in our agent:
class Agent:
@observe()
def xyz(...) -> <default strategy>
class ProphetAgent(Agent):
def xyz(...) -> <some more fancy strategy> <<<--- This would remove the decorator, so all deployable agent would have to manually @observe their methods.
There was a problem hiding this comment.
This is related to #345 (comment)
There was a problem hiding this comment.
Ah makes sense, thanks
No description provided.