fix(wren-ai-service): langfuse cost display issue #1699
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cyyeh
added
module/ai-service
WalkthroughA new async decorator, Changes
Sequence Diagram(s)sequenceDiagram
participant Caller
participant PipelineFunction
participant Generator
participant LangfuseContext
Caller->>PipelineFunction: call with prompt, generator, generator_name
PipelineFunction->>Generator: generate(prompt, ...)
Generator-->>PipelineFunction: result
PipelineFunction->>LangfuseContext: trace_cost(result, generator_name)
PipelineFunction-->>Caller: (result, generator_name)
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Actionable comments posted: 4
🧹 Nitpick comments (1)
wren-ai-service/src/utils.py (1)
164-186: Well-implemented cost tracing decorator with proper async handling.The
trace_costdecorator correctly handles the expected tuple return format and appropriately extracts usage metadata for Langfuse tracking. The implementation properly handles both dict and list result types.However, consider adding error handling for potential edge cases:
def trace_cost(func): @functools.wraps(func) async def wrapper(*args, **kwargs): result, generator_name = await func(*args, **kwargs) + + try: if isinstance(result, dict): if meta := result.get("meta", []): langfuse_context.update_current_observation( model=generator_name, usage_details=meta[0].get("usage", {}), ) elif isinstance(result, list): for item in result: if meta := item.get("meta", []): langfuse_context.update_current_observation( model=generator_name, usage_details=meta[0].get("usage", {}), ) + except Exception as e: + # Log the error but don't break the main flow + logger.warning(f"Failed to update Langfuse observation: {e}") return result return wrapper
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📒 Files selected for processing (20)
wren-ai-service/src/pipelines/generation/chart_adjustment.py(3 hunks)wren-ai-service/src/pipelines/generation/chart_generation.py(3 hunks)wren-ai-service/src/pipelines/generation/data_assistance.py(3 hunks)wren-ai-service/src/pipelines/generation/followup_sql_generation.py(3 hunks)wren-ai-service/src/pipelines/generation/followup_sql_generation_reasoning.py(3 hunks)wren-ai-service/src/pipelines/generation/intent_classification.py(3 hunks)wren-ai-service/src/pipelines/generation/misleading_assistance.py(3 hunks)wren-ai-service/src/pipelines/generation/question_recommendation.py(3 hunks)wren-ai-service/src/pipelines/generation/relationship_recommendation.py(3 hunks)wren-ai-service/src/pipelines/generation/semantics_description.py(3 hunks)wren-ai-service/src/pipelines/generation/sql_answer.py(3 hunks)wren-ai-service/src/pipelines/generation/sql_correction.py(3 hunks)wren-ai-service/src/pipelines/generation/sql_generation.py(3 hunks)wren-ai-service/src/pipelines/generation/sql_generation_reasoning.py(3 hunks)wren-ai-service/src/pipelines/generation/sql_question.py(3 hunks)wren-ai-service/src/pipelines/generation/sql_regeneration.py(3 hunks)wren-ai-service/src/pipelines/generation/sql_tables_extraction.py(3 hunks)wren-ai-service/src/pipelines/generation/user_guide_assistance.py(3 hunks)wren-ai-service/src/pipelines/retrieval/db_schema_retrieval.py(3 hunks)wren-ai-service/src/utils.py(2 hunks)
🧰 Additional context used
🧬 Code Graph Analysis (4)
wren-ai-service/src/pipelines/generation/data_assistance.py (2)
wren-ai-service/src/utils.py (1)
trace_cost(164-185)wren-ai-service/src/core/provider.py (2)
get_model(11-12)get_model(30-31)
wren-ai-service/src/pipelines/generation/sql_generation.py (2)
wren-ai-service/src/utils.py (1)
trace_cost(164-185)wren-ai-service/src/core/provider.py (2)
get_model(11-12)get_model(30-31)
wren-ai-service/src/pipelines/generation/sql_tables_extraction.py (1)
wren-ai-service/src/utils.py (1)
trace_cost(164-185)
wren-ai-service/src/pipelines/generation/sql_generation_reasoning.py (2)
wren-ai-service/src/utils.py (1)
trace_cost(164-185)wren-ai-service/src/pipelines/generation/followup_sql_generation_reasoning.py (2)
generate_sql_reasoning(89-98)prompt(65-84)
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🔇 Additional comments (58)
wren-ai-service/src/utils.py (1)
222-223: Minor formatting improvement - LGTM!The change from single quotes to double quotes in the regex pattern is a minor formatting improvement with no functional impact.
wren-ai-service/src/pipelines/retrieval/db_schema_retrieval.py (3)
18-18: Import statement correctly added.The import of
trace_costfromsrc.utilsis properly placed with other utility imports.
319-328: Function correctly updated for cost tracing pattern.The
filter_columns_in_tablesfunction has been properly updated to:
- Accept the new
generator_nameparameter- Apply the
@trace_costdecorator- Return tuples in both conditional branches
The implementation correctly maintains the original logic while adapting to the new return format.
459-459: Generator name component properly added.The addition of
"generator_name": llm_provider.get_model()to the components dictionary correctly provides the model name for the pipeline.wren-ai-service/src/pipelines/generation/sql_answer.py (3)
13-13: Import statement correctly added.The import of
trace_costfromsrc.utilsis properly placed with other utility imports.
67-73: Function correctly updated for cost tracing pattern.The
generate_answerfunction has been properly updated to:
- Accept the new
generator_nameparameter- Apply the
@trace_costdecorator- Return a tuple of the generator result and generator name
The implementation maintains the original async generator call while adapting to the new return format.
94-94: Generator name component properly added.The addition of
"generator_name": llm_provider.get_model()to the components dictionary correctly provides the model name for the pipeline.wren-ai-service/src/pipelines/generation/chart_adjustment.py (3)
19-19: Import statement correctly added.The import of
trace_costfromsrc.utilsis properly placed with other utility imports.
112-118: Function correctly updated for cost tracing pattern.The
generate_chart_adjustmentfunction has been properly updated to:
- Accept the new
generator_nameparameter- Apply the
@trace_costdecorator- Return a tuple of the generator result and generator name
The implementation maintains the original async generator call while adapting to the new return format.
161-161: Generator name component properly added.The addition of
"generator_name": llm_provider.get_model()to the components dictionary correctly provides the model name for the pipeline.wren-ai-service/src/pipelines/generation/relationship_recommendation.py (3)
16-16: LGTM! Proper import of tracing utility.The import of
trace_costfromsrc.utilsis correctly added to support the cost tracing functionality.
58-60: LGTM! Consistent implementation of cost tracing pattern.The changes correctly implement the cost tracing pattern:
@trace_costdecorator is properly appliedgenerator_nameparameter is added to function signature- Return value follows the expected tuple format
(result, generator_name)The
trace_costdecorator will unwrap the tuple and return just the result, maintaining compatibility with downstream functions likenormalizedthat expect the generation result.
199-199: LGTM! Proper initialization of generator name component.The addition of
"generator_name": llm_provider.get_model()correctly provides the model name to the generation function, enabling proper cost tracking and model identification.wren-ai-service/src/pipelines/generation/sql_generation_reasoning.py (3)
17-17: LGTM! Proper import of tracing utility.The import of
trace_costfromsrc.utilsis correctly added to support the cost tracing functionality.
77-83: LGTM! Consistent implementation of cost tracing pattern.The changes correctly implement the cost tracing pattern:
@trace_costdecorator is properly appliedgenerator_nameparameter is added while preserving existingquery_idparameter- Return value follows the expected tuple format for the decorator
- The function maintains its original logic while adding the tracing capability
108-108: LGTM! Proper initialization of generator name component.The addition of
"generator_name": llm_provider.get_model()correctly provides the model name to the generation function for cost tracking purposes.wren-ai-service/src/pipelines/generation/intent_classification.py (3)
18-18: LGTM! Proper import of tracing utility.The import of
trace_costfromsrc.utilsis correctly added to support the cost tracing functionality.
284-286: LGTM! Consistent implementation of cost tracing pattern.The changes correctly implement the cost tracing pattern for the intent classification function:
@trace_costdecorator is properly appliedgenerator_nameparameter is added to the function signature- Return value follows the expected tuple format
(result, generator_name)The implementation is consistent with the pattern used across other generation functions.
353-353: LGTM! Proper initialization of generator name component.The addition of
"generator_name": llm_provider.get_model()correctly provides the model name to the classification function for cost tracking and model identification.wren-ai-service/src/pipelines/generation/question_recommendation.py (3)
14-14: LGTM! Proper import of tracing utility.The import of
trace_costfromsrc.utilsis correctly added to support the cost tracing functionality.
44-47: LGTM! Consistent implementation of cost tracing pattern.The changes correctly implement the cost tracing pattern:
@observedecorator maintains proper ordering before@trace_costgenerator_nameparameter is properly added to the function signature- Return value follows the expected tuple format for the decorator
- The implementation is consistent with the pattern used across all other generation functions
240-240: LGTM! Proper initialization of generator name component.The addition of
"generator_name": llm_provider.get_model()correctly provides the model name to the generation function, completing the cost tracking implementation for this pipeline.wren-ai-service/src/pipelines/generation/sql_correction.py (2)
20-20: LGTM: Consistent import additionThe import of
trace_costfromsrc.utilsaligns with the pattern applied across all generation pipeline modules.
116-116: LGTM: Proper component registrationThe addition of
generator_namecomponent correctly stores the model name for cost tracking purposes.wren-ai-service/src/pipelines/generation/chart_generation.py (2)
19-19: LGTM: Consistent import additionThe import follows the same pattern as other pipeline modules for enabling cost tracing.
130-130: LGTM: Consistent component registrationThe
generator_namecomponent is properly added to track the model used for cost attribution.wren-ai-service/src/pipelines/generation/sql_question.py (2)
15-15: LGTM: Consistent import additionThe
trace_costimport maintains consistency across the generation pipeline modules.
114-114: LGTM: Proper component registrationThe
generator_namecomponent addition is consistent with the cost tracking implementation.wren-ai-service/src/pipelines/generation/sql_regeneration.py (2)
22-22: LGTM: Consistent import additionThe
trace_costimport follows the established pattern for cost tracking integration.
162-162: LGTM: Consistent component registrationThe
generator_namecomponent addition completes the pattern for cost tracking across generation pipelines.wren-ai-service/src/pipelines/generation/semantics_description.py (3)
14-14: LGTM: Proper import of trace_cost decoratorThe import statement correctly adds the trace_cost decorator needed for cost tracking functionality.
69-71: LGTM: Correct implementation of cost tracingThe implementation properly:
- Applies the
@trace_costdecorator- Adds
generator_nameparameter to the function signature- Returns a tuple of
(result, generator_name)as expected by the decoratorThis follows the established pattern for enabling cost tracking in generation functions.
219-219: LGTM: Proper generator name propagationThe addition of
"generator_name": llm_provider.get_model()to the components dictionary correctly ensures the model name is available for cost tracking throughout the pipeline.wren-ai-service/src/pipelines/generation/sql_tables_extraction.py (3)
14-14: LGTM: Proper import of trace_cost decoratorThe import statement correctly adds the trace_cost decorator for cost tracking functionality.
67-69: LGTM: Correct cost tracing implementationThe implementation properly:
- Applies the
@trace_costdecorator- Adds
generator_nameparameter to function signature- Returns tuple format
(result, generator_name)as expected by the decoratorThis aligns with the established pattern for cost tracking in generation functions.
108-108: LGTM: Proper generator name setupAdding
"generator_name": llm_provider.get_model()to the components dictionary correctly propagates the model name for cost tracking.wren-ai-service/src/pipelines/generation/user_guide_assistance.py (3)
13-13: LGTM: Proper import of trace_cost decoratorThe import statement correctly adds the trace_cost decorator needed for cost tracking.
62-68: LGTM: Correct cost tracing implementationThe implementation properly:
- Applies the
@trace_costdecorator- Adds
generator_nameparameter to function signature- Returns tuple format
(result, generator_name)as expected by the decoratorThe multiline return statement maintains proper tuple structure for the decorator.
87-87: LGTM: Proper generator name setupAdding
"generator_name": llm_provider.get_model()to the components dictionary correctly enables model name tracking for cost tracing.wren-ai-service/src/pipelines/generation/followup_sql_generation_reasoning.py (3)
17-17: LGTM: Proper import of trace_cost decoratorThe import statement correctly adds the trace_cost decorator for cost tracking functionality.
88-98: LGTM: Correct cost tracing implementationThe implementation properly:
- Applies the
@trace_costdecorator- Adds
generator_nameparameter to function signature- Returns tuple format
(result, generator_name)as expected by the decoratorThis follows the established pattern for enabling cost tracking in generation functions.
123-123: LGTM: Proper generator name setupAdding
"generator_name": llm_provider.get_model()to the components dictionary correctly propagates the model name for cost tracking throughout the pipeline.wren-ai-service/src/pipelines/generation/data_assistance.py (4)
13-13: LGTM: Import addition for cost tracingThe import of
trace_costfromsrc.utilsis correctly added to support the new cost tracking functionality.
72-75: LGTM: Function signature enhanced for cost tracingThe addition of the
@trace_costdecorator andgenerator_name: strparameter correctly implements the cost tracking pattern. The decorator will handle extracting usage metadata and updating Langfuse observations.
79-79: LGTM: Return statement correctly modified for decorator compatibilityThe return statement now returns a tuple
(result, generator_name)as expected by the@trace_costdecorator. The decorator will unwrap this tuple and return only the result to maintain compatibility with existing callers.
97-97: LGTM: Generator name component properly addedThe addition of
"generator_name": llm_provider.get_model()to the components dictionary correctly provides the model identifier for cost tracking and tracing purposes.wren-ai-service/src/pipelines/generation/misleading_assistance.py (4)
13-13: LGTM: Consistent import additionThe import of
trace_costis consistently added across all pipeline files for uniform cost tracking implementation.
72-75: LGTM: Consistent decorator and parameter additionThe implementation follows the same pattern as other pipeline files, ensuring consistent cost tracking behavior across the codebase.
79-79: LGTM: Consistent return tuple implementationThe return statement correctly implements the tuple format expected by the
@trace_costdecorator, maintaining consistency with other pipeline modifications.
97-97: LGTM: Consistent component additionThe generator name component is added consistently with other pipeline classes, enabling uniform cost tracking across all generation pipelines.
wren-ai-service/src/pipelines/generation/sql_generation.py (4)
22-22: LGTM: Import addition for cost tracingThe
trace_costimport is correctly added to enable cost tracking functionality in the SQL generation pipeline.
110-114: LGTM: Function enhanced with cost tracingThe
@trace_costdecorator andgenerator_nameparameter are correctly added to thegenerate_sqlfunction, enabling cost tracking for SQL generation operations.
116-116: LGTM: Return statement properly modifiedThe return statement correctly returns the tuple
(result, generator_name)format expected by the@trace_costdecorator while maintaining the original function's output semantics.
149-149: LGTM: Component properly addedThe
"generator_name"component is correctly initialized withllm_provider.get_model(), ensuring the model identifier is available for cost tracking in the SQL generation pipeline.wren-ai-service/src/pipelines/generation/followup_sql_generation.py (4)
23-23: LGTM: Import addition for cost tracingThe
trace_costimport is correctly added to support cost tracking in the follow-up SQL generation pipeline.
114-119: LGTM: Function signature correctly enhancedThe
@trace_costdecorator andgenerator_nameparameter are properly added to thegenerate_sql_in_followupfunction. The function correctly handles the additionalhistoriesparameter alongside the new cost tracking functionality.
124-124: LGTM: Return statement properly implementedThe return statement correctly implements the tuple format
(result, generator_name)expected by the@trace_costdecorator, ensuring cost tracking data is properly captured.
157-157: LGTM: Component correctly addedThe
"generator_name"component is properly initialized in the follow-up SQL generation pipeline, maintaining consistency with other pipeline implementations for cost tracking.
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Actionable comments posted: 1
🧹 Nitpick comments (1)
wren-ai-service/src/utils.py (1)
169-169: Address the WIP comment for handling list of dicts.The current implementation only handles cases where
resultis a dict. The WIP comment indicates this needs to support cases whereresultis a list of dicts.Would you like me to help implement the handling for when
resultis a list of dicts? This would ensure comprehensive cost tracking across different result formats.
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📒 Files selected for processing (1)
wren-ai-service/src/utils.py(2 hunks)
🧰 Additional context used
🪛 Pylint (3.3.7)
wren-ai-service/src/utils.py
[convention] 164-164: Missing function or method docstring
(C0116)
🔇 Additional comments (1)
wren-ai-service/src/utils.py (1)
216-216: LGTM! Regex pattern quote change for consistency.The change from single quotes to double quotes in the regex pattern maintains the same functionality while improving code consistency.
| def trace_cost(func): | ||
| @functools.wraps(func) | ||
| async def wrapper(*args, **kwargs): | ||
| result, generator_name = await func(*args, **kwargs) | ||
|
|
||
| # WIP: deal with the case where the result is a list of dicts | ||
| if isinstance(result, dict): | ||
| if meta := result.get("meta", []): | ||
| langfuse_context.update_current_observation( | ||
| model=generator_name, | ||
| usage_details=meta[0].get("usage", {}), | ||
| ) | ||
|
|
||
| return result | ||
|
|
||
| return wrapper |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add docstring and improve error handling for the trace_cost decorator.
The trace_cost decorator implementation looks correct for its intended purpose, but needs documentation and better error handling.
Apply this diff to add proper documentation and error handling:
def trace_cost(func):
+ """
+ Decorator to trace cost and usage details for functions that return (result, generator_name).
+
+ This decorator expects the wrapped function to return a tuple of (result, generator_name).
+ It extracts usage metadata from the result and updates the current Langfuse observation
+ context with the model name and usage details for cost tracking.
+
+ Args:
+ func (Callable): An async function that returns (result, generator_name)
+
+ Returns:
+ Callable: The decorated function that returns only the result
+ """
@functools.wraps(func)
async def wrapper(*args, **kwargs):
- result, generator_name = await func(*args, **kwargs)
+ func_result = await func(*args, **kwargs)
+
+ if not isinstance(func_result, tuple) or len(func_result) != 2:
+ raise ValueError(f"Function {func.__name__} must return a tuple of (result, generator_name)")
+
+ result, generator_name = func_result
# WIP: deal with the case where the result is a list of dicts
if isinstance(result, dict):
if meta := result.get("meta", []):
+ if not isinstance(meta, list) or len(meta) == 0:
+ return result
langfuse_context.update_current_observation(
model=generator_name,
usage_details=meta[0].get("usage", {}),
)
return result
return wrapper📝 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 trace_cost(func): | |
| @functools.wraps(func) | |
| async def wrapper(*args, **kwargs): | |
| result, generator_name = await func(*args, **kwargs) | |
| # WIP: deal with the case where the result is a list of dicts | |
| if isinstance(result, dict): | |
| if meta := result.get("meta", []): | |
| langfuse_context.update_current_observation( | |
| model=generator_name, | |
| usage_details=meta[0].get("usage", {}), | |
| ) | |
| return result | |
| return wrapper | |
| def trace_cost(func): | |
| """ | |
| Decorator to trace cost and usage details for functions that return (result, generator_name). | |
| This decorator expects the wrapped function to return a tuple of (result, generator_name). | |
| It extracts usage metadata from the result and updates the current Langfuse observation | |
| context with the model name and usage details for cost tracking. | |
| Args: | |
| func (Callable): An async function that returns (result, generator_name) | |
| Returns: | |
| Callable: The decorated function that returns only the result | |
| """ | |
| @functools.wraps(func) | |
| async def wrapper(*args, **kwargs): | |
| func_result = await func(*args, **kwargs) | |
| if not isinstance(func_result, tuple) or len(func_result) != 2: | |
| raise ValueError(f"Function {func.__name__} must return a tuple of (result, generator_name)") | |
| result, generator_name = func_result | |
| # WIP: deal with the case where the result is a list of dicts | |
| if isinstance(result, dict): | |
| if meta := result.get("meta", []): | |
| if not isinstance(meta, list) or len(meta) == 0: | |
| return result | |
| langfuse_context.update_current_observation( | |
| model=generator_name, | |
| usage_details=meta[0].get("usage", {}), | |
| ) | |
| return result | |
| return wrapper |
🧰 Tools
🪛 Pylint (3.3.7)
[convention] 164-164: Missing function or method docstring
(C0116)
🤖 Prompt for AI Agents
In wren-ai-service/src/utils.py around lines 164 to 179, the trace_cost
decorator lacks a docstring and does not handle errors that might occur during
the decorated function execution. Add a clear docstring explaining the purpose
and behavior of the decorator. Wrap the call to the decorated function in a
try-except block to catch exceptions, log or handle them appropriately, and
optionally re-raise or return a default value to improve robustness.
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