chore(weave): Move accumulator exts into op #3874
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WalkthroughThis pull request replaces calls to Changes
Sequence Diagram(s)sequenceDiagram
participant Caller as Client Code
participant OpDecorator as op Decorator
participant Operation as Operation Function
participant Accumulator as _add_accumulator
participant Iterator as IteratorWrapper
Caller->>OpDecorator: Call decorated function
OpDecorator->>Operation: Invoke original function logic
Operation->>Accumulator: Wrap operation with _add_accumulator
Accumulator->>Iterator: Create and manage iterator for accumulation
Iterator->>Caller: Yield accumulated results
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weave/trace/op.py (1)
939-951: Dynamic attribute delegation.
Using__getattr__to delegate to the wrapped iterator is a neat approach. Just ensure that any special attributes or dunder methods needed by advanced consumers of this wrapper are explicitly handled.Consider clarifying the docstring that only normal attributes are forwarded, so devs do not rely on unimplemented dunder methods from the underlying iterator.
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weave/integrations/anthropic/anthropic_sdk.py(4 hunks)weave/integrations/bedrock/bedrock_sdk.py(2 hunks)weave/integrations/cohere/cohere_sdk.py(3 hunks)weave/integrations/google_ai_studio/google_ai_studio_sdk.py(3 hunks)weave/integrations/groq/groq_sdk.py(2 hunks)weave/integrations/huggingface/huggingface_inference_client_sdk.py(3 hunks)weave/integrations/instructor/instructor_iterable_utils.py(3 hunks)weave/integrations/instructor/instructor_partial_utils.py(2 hunks)weave/integrations/langchain_nvidia_ai_endpoints/langchain_nv_ai_endpoints.py(2 hunks)weave/integrations/litellm/litellm.py(2 hunks)weave/integrations/mistral/v0/mistral.py(2 hunks)weave/integrations/mistral/v1/mistral.py(2 hunks)weave/integrations/openai/openai_sdk.py(3 hunks)weave/integrations/vertexai/vertexai_sdk.py(3 hunks)weave/trace/op.py(6 hunks)weave/trace/op_extensions/accumulator.py(0 hunks)
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- weave/trace/op_extensions/accumulator.py
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`**/*.py`: Focus on pythonic code patterns. Check for proper...
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weave/integrations/mistral/v0/mistral.pyweave/integrations/openai/openai_sdk.pyweave/integrations/instructor/instructor_partial_utils.pyweave/integrations/litellm/litellm.pyweave/integrations/langchain_nvidia_ai_endpoints/langchain_nv_ai_endpoints.pyweave/integrations/groq/groq_sdk.pyweave/integrations/mistral/v1/mistral.pyweave/integrations/huggingface/huggingface_inference_client_sdk.pyweave/integrations/bedrock/bedrock_sdk.pyweave/integrations/anthropic/anthropic_sdk.pyweave/integrations/vertexai/vertexai_sdk.pyweave/integrations/instructor/instructor_iterable_utils.pyweave/integrations/google_ai_studio/google_ai_studio_sdk.pyweave/integrations/cohere/cohere_sdk.pyweave/trace/op.py
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🔇 Additional comments (52)
weave/integrations/mistral/v0/mistral.py (2)
9-9: Import updated to use private function from centralized location.The import has been changed to use
_add_accumulatorfromweave.trace.opinstead ofadd_accumulatorfromweave.trace.op_extensions.accumulator. This change centralizes accumulator functionality in the core op module.
84-84: Function call updated to match import change.The function call has been updated to use
_add_accumulatorinstead ofadd_accumulator, maintaining consistent behavior while using the newly centralized implementation.weave/integrations/instructor/instructor_partial_utils.py (2)
7-7: Import updated to use private function from centralized location.The import has been changed to use
_add_accumulatorfromweave.trace.opinstead of importing from the extensions directory, aligning with the refactoring to consolidate accumulator functionality.
22-26: Function call updated while preserving behavior.The function call has been updated to use
_add_accumulatorwhile maintaining the same parameter structure and behavior. The change is consistent with the import update.weave/integrations/google_ai_studio/google_ai_studio_sdk.py (3)
10-10: Import updated to use private function from centralized location.The import has been changed to use
_add_accumulatorfromweave.trace.opinstead of importing from the extensions directory, consistent with the refactoring pattern across multiple files.
103-108: Function call updated in synchronous wrapper.The function call has been updated to use
_add_accumulatorinstead ofadd_accumulator, maintaining the same parameters and behavior for the accumulator in the synchronous wrapper.
128-133: Function call updated in asynchronous wrapper.The function call has been updated to use
_add_accumulatorinstead ofadd_accumulator, maintaining the same parameters and behavior for the accumulator in the asynchronous wrapper.weave/integrations/cohere/cohere_sdk.py (3)
10-10: Import updated to use private function from centralized location.The import has been changed to use
_add_accumulatorfromweave.trace.opinstead of importing from the extensions directory, consistent with the refactoring pattern across integration modules.
170-170: Function call updated in stream wrapper.The function call has been updated to use
_add_accumulatorinstead ofadd_accumulatorwhile maintaining the same parameter pattern and behavior.
179-179: Function call updated in v2 stream wrapper.The function call has been updated to use
_add_accumulatorinstead ofadd_accumulatorwhile preserving the same behavior for the v2 accumulator.weave/integrations/litellm/litellm.py (2)
9-9: Import update looks good.The import has been updated to use
_add_accumulatorfromweave.trace.opinstead ofadd_accumulatorfrom the extensions module, which aligns with the PR objective of moving accumulator functionality into the main op module.
94-99: Function call updated correctly.The function call to
add_accumulatorhas been properly replaced with_add_accumulatorwhile maintaining all the same parameters and functionality. This ensures backward compatibility while supporting the architectural change.weave/integrations/openai/openai_sdk.py (3)
10-10: Import update looks good.Import statement correctly updated to include
_add_accumulatorfrom the centralizedweave.trace.opmodule. This follows the PR's objective of consolidating accumulator functionality.
334-341: Function call updated correctly in sync wrapper.The call to
add_accumulatorhas been properly replaced with_add_accumulatorin the synchronous wrapper implementation while maintaining the same parameter structure and functionality.
370-377: Function call updated correctly in async wrapper.The call to
add_accumulatorhas been properly replaced with_add_accumulatorin the asynchronous wrapper implementation. This maintains consistency between sync and async implementations.weave/integrations/huggingface/huggingface_inference_client_sdk.py (3)
8-8: Import update looks good.The import has been correctly updated to use
_add_accumulatorfrom the centralizedweave.trace.opmodule instead of the previous extension module location.
82-87: Function call updated correctly in sync wrapper.The call to
add_accumulatorhas been properly replaced with_add_accumulatorin the synchronous wrapper implementation while maintaining the same parameter structure and functionality.
106-111: Function call updated correctly in async wrapper.The call to
add_accumulatorhas been properly replaced with_add_accumulatorin the asynchronous wrapper implementation, maintaining consistency with the synchronous version.weave/integrations/bedrock/bedrock_sdk.py (2)
4-4: Import update looks good.Import statement properly updated to include both
_add_accumulatorand_IteratorWrapperfrom the centralizedweave.trace.opmodule, which aligns with the PR's objective of consolidating these utilities in one place.
136-141: Function call updated correctly.The function call has been properly updated from
add_accumulatorto_add_accumulatorwhile maintaining all the same parameters, including the customiterator_wrapperimplementation. This ensures backward compatibility while supporting the architectural change.weave/integrations/langchain_nvidia_ai_endpoints/langchain_nv_ai_endpoints.py (2)
17-17: Import updated to use private API.The import has been updated to use
_add_accumulatorfromweave.trace.opinstead of using the publicadd_accumulatorfromweave.trace.op_extensions.accumulator. This change is part of the effort to move accumulator functionality into the main op module.
164-169: Function call updated to use private API.The function call has been updated to use
_add_accumulatorinstead ofadd_accumulator, maintaining the same parameters and functionality. This change is consistent with the import change on line 17.weave/integrations/mistral/v1/mistral.py (2)
9-9: Import updated to use private API.The import has been updated to use
_add_accumulatorfromweave.trace.opinstead of the previous public API. This change aligns with the PR objective of moving accumulator extensions into the op module.
91-91: Function call updated to use private API.The function call has been updated to use
_add_accumulatorinstead ofadd_accumulator, maintaining the same functionality with identical parameters. This modification is consistent with the import change on line 9.weave/integrations/vertexai/vertexai_sdk.py (3)
10-10: Import updated to use private API.The import statement has been updated to use
_add_accumulatorfromweave.trace.opinstead of importing it from the previous location. This change is part of the effort to consolidate accumulator functionality into the op module.
98-103: Function call updated in sync wrapper.The
vertexai_wrapper_syncfunction now uses_add_accumulatorinstead ofadd_accumulatorwhile maintaining the same parameters and functionality. This change is consistent with the import update on line 10.
123-128: Function call updated in async wrapper.The
vertexai_wrapper_asyncfunction now uses_add_accumulatorinstead ofadd_accumulatorwhile maintaining the same parameters and functionality. This change ensures consistent usage of the private API across both synchronous and asynchronous wrappers.weave/integrations/anthropic/anthropic_sdk.py (4)
11-11: Import updated to use private APIs.The import has been updated to use both
_add_accumulatorand_IteratorWrapperfromweave.trace.opmodule instead of their previous locations. This change aligns with the PR objective of moving accumulator extensions into the op module.
80-84: Function call updated in sync wrapper.The
create_wrapper_syncfunction now uses_add_accumulatorinstead ofadd_accumulatorwhile maintaining the same parameters and functionality. This change is consistent with the import update on line 11.
104-108: Function call updated in async wrapper.The
create_wrapper_asyncfunction now uses_add_accumulatorinstead ofadd_accumulatorwhile maintaining the same parameters and functionality. This ensures consistent usage of the private API across different wrapper types.
173-178: Function call updated in stream wrapper.The
create_stream_wrapperfunction now uses_add_accumulatorinstead ofadd_accumulatorwhile maintaining the same parameters, including theAnthropicIteratorWrapper. This change completes the transition to using the private API throughout the file.weave/integrations/instructor/instructor_iterable_utils.py (3)
8-8: Confirm stability of private function import.
Replacingadd_accumulatorwith_add_accumulatoris consistent with the broader refactor in this PR. Note, however, that_add_accumulatoris a private or semi-private function; ensure it is officially supported for future maintenance.We can verify official usage support by checking the project’s documentation or release notes. Would you like to search for references in the repository or open an issue to confirm its stability?
35-39: No functional concerns.
Switching to_add_accumulatorretains the existing parameters (make_accumulator,should_accumulate) without changing the core logic. This is a straightforward swap that appears correct.
55-59: No functional concerns (async wrapper).
The asynchronous wrapper also appropriately transitions to_add_accumulator. The arguments and functionality remain aligned with the synchronous counterpart, so no issues noted.weave/integrations/groq/groq_sdk.py (2)
96-100: Straightforward accumulator replacement.
The call to_add_accumulatorwith the same parameters (make_accumulator,should_accumulate) is consistent with the pattern elsewhere in this PR, ensuring the refactor remains uniform.
9-9:✅ Verification successful
Ensure consistency with the new import.
The import line now pulls_add_accumulatorfromweave.trace.op. Verify that all references to_add_accumulatorelsewhere in this file and across the codebase match the updated import path.
🏁 Script executed:
#!/bin/bash # Check all references to `_add_accumulator` in the codebase to confirm consistency. rg '_add_accumulator'Length of output: 3533
Reference Consistency Verified
The grep output confirms that all references to
_add_accumulatorconsistently use the updated import (from weave.trace.op import _add_accumulator) both inweave/integrations/groq/groq_sdk.pyand across other modules in the codebase. No further changes are necessary.weave/trace/op.py (16)
5-5: New import for process lifecycle management.
Importingatexitcan help manage resources at interpreter shutdown. Ensure it does not introduce side effects in multi-threaded or long-running environments if used outside typical shutdown flows.
11-12: Weak reference & additional collection imports.
Addingweakrefand importing these abstract collection types (e.g.,AsyncIterator) broadens the module’s capabilities for generic, iterator-like patterns. No issues spotted in principle.
20-20: TypeVar imports for generic typing.
Defining generic typesSandVpaves the way for strongly typed accumulators and iterators. Looks good for maintainability.Also applies to: 24-24
45-46: Introduction of type variables S and V.
These type variables help enforce correct usage of accumulators and iterators, improving type safety. No concerns.
617-621: New optional parameter 'accumulator' and updated docstring.
The decorator now supports anaccumulatorparameter for enhanced iterator handling. The docstring clarifies the feature adequately. Validate that existing code paths with no accumulator supplied remain unaffected.Do you want to run integration tests or generate them to confirm backward compatibility and ensure no breakage for code not passing an accumulator?
632-636: Iterator detection logic.
The lines now explicitly distinguish generator functions from async generators. This combined check setsis_iteratoreffectively. The approach is correct and aligns well with Python’s introspection capabilities.
702-706: Runtime flags for op wrappers.
Marking_is_async,_is_iterator,_is_generator, and_is_async_generatorclarifies runtime type checks. This is a clean approach to unify handling of different function types.
710-711: Refactor step for wrapper creation.
Assigning the wrapped op towrapped_opappears consistent with the prior logic, preserving the function’s identity and allowing subsequent modifications. No concerns.
713-723: Conditional accumulator assignment for iterators.
This code conditionally applies the accumulator ifis_iteratoris true and an accumulator is provided. This is a neat extension that does not disrupt non-iterator paths. Looks valid.
804-812: New definitions for yield/error/close callbacks.
Introducing_OnYieldType,_OnErrorType,_OnCloseTypesets up strong type hints for iterator lifecycle hooks. The new constants clarify logging messages.
815-935: _IteratorWrapper(Generic[V])
This large class manages iterator lifecycle—including error handling, closure, and context management. Theatexitusage ensures a fallback closure, but confirm that it won’t lead to unexpected calls in embedded or partial interpreter kicks. Overall, the design is robust for capturing iteration outputs.
952-971: Context management (sync).
These methods properly manage errors and final close calls on synchronous iteration. The usage of_call_on_error_onceand_call_on_close_onceis consistent with the error-lifecycle pattern.
972-990: Context management (async).
The same pattern extends to async context management, ensuring symmetrical handling for asynchronous iterators. Implementation is aligned with best practices.
992-1015: _Accumulator(Generic[S, V])
A container for maintaining shared state across yielded values. It cleanly separates accumulator logic from the iteration mechanics, promoting modular design.
1017-1035: _build_iterator_from_accumulator_for_op(...)
Wraps the raw iterator to pipe its items through the accumulator. Theon_yieldcallback is particularly helpful, catching each item without halting normal iteration if issues occur. Nicely done.
1037-1094: _add_accumulator(...)
Provides an internal mechanism for hooking an accumulator into any op that returns an iterator. This final bridging function unifies the logic elegantly, using the new_IteratorWrapperand_Accumulatorclasses.
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weave/trace/op.py (1)
826-826: Consider adding explicit type annotation for _on_close.Line 826 contains a type ignore comment. Consider explicitly typing the
_on_closemethod to avoid the need for this suppression.- def _call_on_close_once(self) -> None: - if not self._on_finished_called: - try: - self._on_close() # type: ignore + def _call_on_close_once(self) -> None: + if not self._on_finished_called: + try: + self._on_close()
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weave/trace/op.py
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- GitHub Check: Legacy (Query Service) Python unit tests (1)
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- GitHub Check: Trace nox tests (3, 13, pandas-test)
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- GitHub Check: Trace nox tests (3, 12, pandas-test)
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- GitHub Check: Trace nox tests (3, 10, pandas-test)
- GitHub Check: Trace nox tests (3, 10, scorers)
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- GitHub Check: Trace nox tests (3, 9, pandas-test)
- GitHub Check: Trace nox tests (3, 9, scorers)
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- GitHub Check: Trace nox tests (3, 9, notdiamond)
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- GitHub Check: Trace nox tests (3, 9, litellm)
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- GitHub Check: notify-wandb-core
- GitHub Check: Legacy (Query Service) Python unit tests (1)
- GitHub Check: Legacy (Query Service) Python unit tests (0)
- GitHub Check: Trace nox tests (3, 13, pandas-test)
- GitHub Check: Trace nox tests (3, 13, scorers)
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- GitHub Check: Trace nox tests (3, 12, pandas-test)
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- GitHub Check: Trace nox tests (3, 10, pandas-test)
- GitHub Check: Trace nox tests (3, 10, scorers)
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- GitHub Check: Trace nox tests (3, 9, pandas-test)
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- GitHub Check: Legacy (Query Service) Python unit tests (1)
- GitHub Check: Legacy (Query Service) Python unit tests (0)
- GitHub Check: Trace nox tests (3, 13, pandas-test)
- GitHub Check: Trace nox tests (3, 13, scorers)
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- GitHub Check: Trace nox tests (3, 12, pandas-test)
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- GitHub Check: Trace nox tests (3, 10, pandas-test)
- GitHub Check: Trace nox tests (3, 10, scorers)
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- GitHub Check: Trace nox tests (3, 9, pandas-test)
- GitHub Check: Trace nox tests (3, 9, scorers)
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- GitHub Check: Trace nox tests (3, 9, litellm)
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- GitHub Check: notify-wandb-core
- GitHub Check: Legacy (Query Service) Python unit tests (1)
- GitHub Check: Legacy (Query Service) Python unit tests (0)
- GitHub Check: Trace nox tests (3, 13, pandas-test)
- GitHub Check: Trace nox tests (3, 13, scorers)
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🔇 Additional comments (12)
weave/trace/op.py (12)
5-6: Well-organized new imports for iterator functionality.The added imports for
atexit,weakref, and the abstract iterator types fromcollections.abcprovide the necessary foundations for the new iterator and accumulator functionality.Also applies to: 11-12
45-46: Good type variable definitions.The addition of type variables
SandVenhances type safety for the generic implementations of accumulators and iterators. The names are appropriately chosen to represent State and Value.
49-61: Excellent backward compatibility implementation.The conditional implementation of
aiterandanextfunctions for Python versions below 3.10 ensures compatibility across Python versions for asynchronous iteration. The error handling in theanextfunction is thorough and matches Python's built-in behavior.
619-621: Updated docstring reflects new iterator functionality.The docstring now correctly mentions that the
opdecorator automatically detects iterator functions, which aligns with the new functionality being added.
631-636: Enhanced function type detection.The implementation now properly detects generator and async generator functions, which is essential for correctly handling iterator functionality. The combined
is_iteratorflag simplifies downstream logic.
701-704: Good function metadata for runtime type checking.Adding these flags to the wrapper provides valuable metadata for runtime type checking and potential specialization of behavior based on function type.
792-801: Clear type definitions for iterator callbacks.The type definitions are well-structured and the error messages provide clear context for debugging issues with iterators.
803-939: Comprehensive implementation of iterator wrapper.The
_IteratorWrapperclass effectively manages the lifecycle of iterators, handling both synchronous and asynchronous scenarios. The error handling is thorough, with appropriate logging. The use ofweakrefandatexitensures proper cleanup even when iterators are not explicitly closed.A few minor observations:
- The error handling between sync and async methods has some duplication, but this appears to be necessary given the different nature of these operations.
- The
__getattr__method implementation protects important attributes while delegating all others to the wrapped iterator.
940-978: Well-implemented context manager support.The context manager methods (
__enter__,__exit__,__aenter__,__aexit__) properly handle both synchronous and asynchronous context protocols, ensuring resources are cleaned up appropriately regardless of how the iterator is used.
980-1003: Clean implementation of accumulator pattern.The
_Accumulatorclass provides a simple yet effective implementation of the accumulator pattern. The handling ofStopIterationto extract final state is a nice touch that allows accumulators to signal completion with a final value.
1005-1022: Well-factored iterator builder function.The
_build_iterator_from_accumulator_for_opfunction cleanly separates the creation of iterator wrappers with accumulators, making the code more modular and reusable. The callback functions are clearly defined.
1025-1082:✅ Verification successful
Comprehensive implementation of internal accumulator function.
The
_add_accumulatorfunction is well-designed with a clear docstring indicating its internal use. The function provides flexibility through optional parameters for conditional accumulation and post-processing.Note that according to the AI summary, this function replaces a previously public
add_accumulatorfunction and is now used across multiple integration files. Since it's now marked as internal, ensure all usage sites understand this is an implementation detail that may change.
🏁 Script executed:
#!/bin/bash # Check if there are any remaining references to the deprecated public add_accumulator function rg "add_accumulator" --glob "!weave/trace/op.py" --no-ignoreLength of output: 6154
Internal Accumulator Implementation Verified
The
_add_accumulatorfunction (inweave/trace/op.py, lines 1025–1082) is implemented comprehensively for integration with streaming libraries. Its flexibility via optional parameters for conditional accumulation and post-processing, along with its detailed docstring, confirms its intended internal use. We verified that all integration files (e.g., in instructor, vertexai, mistral, huggingface, etc.) now correctly reference_add_accumulator, ensuring that the deprecated publicadd_accumulatoris no longer in use. Please make sure that all consumers of this API are aware that this implementation detail may change in the future.
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| @@ -8,7 +8,7 @@ | |||
| import weave | |||
| from weave.integrations.patcher import MultiPatcher, NoOpPatcher, SymbolPatcher | |||
| from weave.trace.autopatch import IntegrationSettings, OpSettings | |||
| from weave.trace.op_extensions.accumulator import _IteratorWrapper, add_accumulator | |||
| from weave.trace.op import _add_accumulator, _IteratorWrapper | |||
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If we are importing these into a whole bunch of non-test modules, shouldn't we mark them as (internally) public, i.e. removing the leading _?
If we are worried that it would expose them to users, maybe we should keep them in the original module?
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Good questions:
- I want to mark them with underscore because while they are technically public, they will historically have not been and they will go away soon once broader generator support lands.
- The code is being moved here to simplify refactors ahead of adding generator support
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Actionable comments posted: 0
🧹 Nitpick comments (3)
weave/trace/op.py (3)
613-624: Updated docstring reflects new iterator handling capabilities.The docstring appropriately explains that the decorator now automatically detects iterator functions. However, it could be more detailed about how to use these new capabilities.
Consider expanding the docstring to include examples of how to use the decorator with regular generators, async generators, and how to leverage accumulators:
- A decorator to weave op-ify a function or method. Works for both sync and async. - Automatically detects iterator functions and applies appropriate behavior. + A decorator to weave op-ify a function or method. Works for sync, async, generators, + and async generators. + + Automatically detects iterator functions and applies appropriate behavior: + + Examples: + ```python + # Regular function + @weave.op + def add(a, b): + return a + b + + # Generator function (automatically handles iterators) + @weave.op + def count_to(n): + for i in range(n): + yield i + + # Async generator function + @weave.op + async def stream_data(source): + async for item in source: + yield process(item) + ``` + + The tracing_sample_rate parameter controls the frequency of traced calls.
801-937: Comprehensive iterator wrapper implementation with proper lifecycle management.The
_IteratorWrapperclass effectively handles both synchronous and asynchronous iterators with appropriate error handling and resource cleanup viaatexit. The implementation includes handling for context managers as well, which is a nice addition.A few notes on potential improvements:
- The error handling in lines 858-870 and 897-909 has significant duplication.
- The class relies on
weakref.WeakMethodfor lifecycle management, which generally works but could be problematic if the reference cycle is complex.Consider extracting the duplicated error handling logic into a helper method to reduce code duplication:
+ def _handle_yield_error(self, e: Exception, msg: str) -> None: + if isinstance(e, (StopAsyncIteration, StopIteration)): + raise + if get_raise_on_captured_errors(): + raise + log_once(logger.error, msg.format(traceback.format_exc())) def __next__(self) -> Generator[None, None, V]: # ...existing code... try: self._on_yield(value) except Exception as e: - if isinstance(e, (StopAsyncIteration, StopIteration)): - raise - if get_raise_on_captured_errors(): - raise - log_once(logger.error, ON_YIELD_MSG.format(traceback.format_exc())) + self._handle_yield_error(e, ON_YIELD_MSG)
1023-1080: Comprehensive implementation of the accumulator addition function.The
_add_accumulatorfunction is well-documented and handles both conditional accumulation and post-processing of accumulated values. The docstring includes a clear example of how to use the function.A potential enhancement would be to add type annotations for the return type of
make_accumulatorto ensure that it matches the expected interface.Consider adding more specific type annotations for the
make_accumulatorfunction:-def _add_accumulator( - op: Op, - make_accumulator: Callable[[dict], Callable[[S, V], S]], +def _add_accumulator( + op: Op, + make_accumulator: Callable[[dict[str, Any]], Callable[[S | None, V], S]],
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📒 Files selected for processing (18)
tests/trace/test_op_return_forms.py(17 hunks)tests/trace/test_tracing_resilience.py(11 hunks)weave/integrations/anthropic/anthropic_sdk.py(4 hunks)weave/integrations/bedrock/bedrock_sdk.py(2 hunks)weave/integrations/cohere/cohere_sdk.py(3 hunks)weave/integrations/google_ai_studio/google_ai_studio_sdk.py(3 hunks)weave/integrations/groq/groq_sdk.py(2 hunks)weave/integrations/huggingface/huggingface_inference_client_sdk.py(3 hunks)weave/integrations/instructor/instructor_iterable_utils.py(3 hunks)weave/integrations/instructor/instructor_partial_utils.py(2 hunks)weave/integrations/langchain_nvidia_ai_endpoints/langchain_nv_ai_endpoints.py(2 hunks)weave/integrations/litellm/litellm.py(2 hunks)weave/integrations/mistral/v0/mistral.py(2 hunks)weave/integrations/mistral/v1/mistral.py(2 hunks)weave/integrations/openai/openai_sdk.py(3 hunks)weave/integrations/vertexai/vertexai_sdk.py(3 hunks)weave/trace/op.py(6 hunks)weave/trace/op_extensions/accumulator.py(0 hunks)
💤 Files with no reviewable changes (1)
- weave/trace/op_extensions/accumulator.py
🚧 Files skipped from review as they are similar to previous changes (16)
- weave/integrations/instructor/instructor_partial_utils.py
- weave/integrations/litellm/litellm.py
- weave/integrations/langchain_nvidia_ai_endpoints/langchain_nv_ai_endpoints.py
- weave/integrations/google_ai_studio/google_ai_studio_sdk.py
- weave/integrations/instructor/instructor_iterable_utils.py
- weave/integrations/mistral/v1/mistral.py
- tests/trace/test_tracing_resilience.py
- weave/integrations/cohere/cohere_sdk.py
- weave/integrations/groq/groq_sdk.py
- weave/integrations/vertexai/vertexai_sdk.py
- weave/integrations/huggingface/huggingface_inference_client_sdk.py
- weave/integrations/openai/openai_sdk.py
- weave/integrations/bedrock/bedrock_sdk.py
- weave/integrations/mistral/v0/mistral.py
- weave/integrations/anthropic/anthropic_sdk.py
- tests/trace/test_op_return_forms.py
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`**/*.py`: Focus on pythonic code patterns. Check for proper exception handling. Verify type hints usage where applicable. Look for potential performance improvements. Don't commen...
**/*.py: Focus on pythonic code patterns.
Check for proper exception handling.
Verify type hints usage where applicable.
Look for potential performance improvements.
Don't comment on formatting if black/isort is configured.
Check for proper dependency injection patterns.
Verify proper async handling if applicable.
weave/trace/op.py
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- GitHub Check: Trace nox tests (3, 11, instructor)
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🔇 Additional comments (9)
weave/trace/op.py (9)
5-11: Proper use of module imports for new iterator functionality.The additions of
atexitandweakrefimports are necessary for ensuring proper lifecycle management of iterators, particularly for cleanup operations when iterators are not explicitly closed.
12-24: Good type annotations for improved static analysis.The updated imports from
collections.abcand the addition ofGenericandTypeVarprovide stronger typing support for the new iterator handling functionality, which will help catch potential type errors at development time.
67-68: Well-defined type variables for generic programming.The addition of type variables
SandVenables proper generic type definitions for the accumulator and iterator wrapper classes, improving type safety throughout the codebase.
71-84: Good backward compatibility for Python versions below 3.10.The implementation of
aiterandanextfunctions provides compatibility with Python versions that don't have these built-in functions, ensuring the code works across different Python versions.
86-86: Logger defined at module level.Properly defining a logger at the module level follows best practices for Python logging.
702-704: Helpful function type flags for runtime checking.Adding these flags to the wrapped function allows for runtime type checking, which can be useful for determining the appropriate behavior based on the function type.
790-798: Well-defined callback types and error messages.The type definitions for callbacks and error messages are clear and descriptive, helping with both code readability and maintainability.
978-1001: Solid implementation of the accumulator pattern.The
_Accumulatorclass provides a clean way to accumulate values from iterators. The handling ofStopIterationfor early termination is a nice touch.
1003-1021: Well-designed iterator factory function.This function effectively sets up the necessary callbacks for the iterator wrapper, connecting the accumulator to the iterator lifecycle events.
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This PR just moves code from
op_extensionsintoop.pysince it is a core piece of the op.Summary by CodeRabbit