test

Author: andrewtruong

README.md

@@ -6,7 +6,7 @@
 [![Github Checks](https://img.shields.io/github/check-runs/wandb/weave/master
 )](https://github.com/wandb/weave)
 
-Weave is a toolkit for developing Generative AI applications, built by [Weights & Biases](https://wandb.ai/)!
+Weave is a toolkit for developing Generative AI applications, built by [Weights & Biases](https://wandb.ai/).
 
 ---
 

docs/docs/guides/tracking/redact-pii.md

@@ -1,9 +1,5 @@
 # Redact PII from Traces
 
-:::important
-This feature is only available for Enterprise users, and is only accessible via the Python SDK.
-:::
-
 Some organizations process Personally Identifiable Information (PII) such as names, phone numbers, and email addresses in their Large Language Model (LLM) workflows. Storing this data in Weights & Biases (W&B) Weave poses compliance and security risks.
 
 The _Sensitive Data Protection_ feature allows you to automatically redact Personally Identifiable Information (PII) from a [trace](../tracking/index.md) before it is sent to Weave servers. This feature integrates [Microsoft Presidio](https://microsoft.github.io/presidio/) into the Weave Python SDK, which means that you can control redaction settings at the SDK level.

pyproject.toml

@@ -76,6 +76,7 @@ huggingface = ["huggingface-hub>=0.28.1"]
 instructor = [
   "instructor>=1.4.3,<1.7.0; python_version <= '3.9'",
   "instructor>=1.4.3; python_version > '3.9'",
+  "google-genai>=1.5.0"
 ]
 langchain = [
   "langchain-core>=0.2.1",

weave/trace/op.py

@@ -40,27 +40,6 @@
 from weave.trace.refs import ObjectRef
 from weave.trace.util import log_once
 
-logger = logging.getLogger(__name__)
-
-S = TypeVar("S")
-V = TypeVar("V")
-
-
-if sys.version_info < (3, 10):
-
-    def aiter(obj: AsyncIterator[V]) -> AsyncIterator[V]:
-        return obj.__aiter__()
-
-    async def anext(obj: AsyncIterator[V], default: Optional[V] = None) -> V:  # noqa: UP007
-        try:
-            return await obj.__anext__()
-        except StopAsyncIteration:
-            if default is not None:
-                return default
-            else:
-                raise
-
-
 if TYPE_CHECKING:
     from weave.trace.weave_client import Call, CallsIter
 
@@ -84,6 +63,29 @@ async def anext(obj: AsyncIterator[V], default: Optional[V] = None) -> V:  # noq
 except ImportError:
     CEREBRAS_NOT_GIVEN = None
 
+
+S = TypeVar("S")
+V = TypeVar("V")
+
+
+if sys.version_info < (3, 10):
+
+    def aiter(obj: AsyncIterator[V]) -> AsyncIterator[V]:
+        return obj.__aiter__()
+
+    async def anext(obj: AsyncIterator[V], default: Optional[V] = None) -> V:  # noqa: UP007,UP045
+        try:
+            return await obj.__anext__()
+        except StopAsyncIteration:
+            if default is not None:
+                return default
+            else:
+                raise
+
+
+logger = logging.getLogger(__name__)
+
+
 CALL_CREATE_MSG = "Error creating call:\n{}"
 ASYNC_CALL_CREATE_MSG = "Error creating async call:\n{}"
 ON_OUTPUT_MSG = "Error capturing call output:\n{}"
@@ -614,7 +616,6 @@ def op(
     postprocess_inputs: PostprocessInputsFunc | None = None,
     postprocess_output: PostprocessOutputFunc | None = None,
     tracing_sample_rate: float = 1.0,
-    accumulator: Callable | None = None,
 ) -> Callable[[Callable], Op] | Op:
     """
     A decorator to weave op-ify a function or method. Works for both sync and async.
@@ -632,9 +633,6 @@ def op_deco(func: Callable) -> Op:
         is_generator = inspect.isgeneratorfunction(func)
         is_async_generator = inspect.isasyncgenfunction(func)
 
-        # Detect if this is an iterator function
-        is_iterator = is_generator or is_async_generator
-
         # Create the appropriate wrapper based on function type
         def create_wrapper(func: Callable) -> Op:
             if is_async:
@@ -700,27 +698,13 @@ def wrapper(*args: Any, **kwargs: Any) -> Any:
 
             # Mark what type of function this is for runtime type checking
             wrapper._is_async = is_async  # type: ignore
-            wrapper._is_iterator = is_iterator  # type: ignore
             wrapper._is_generator = is_generator  # type: ignore
             wrapper._is_async_generator = is_async_generator  # type: ignore
 
             return cast(Op, wrapper)
 
         # Create the wrapper
-        wrapped_op = create_wrapper(func)
-
-        # Apply accumulator if this is an iterator and accumulator was provided
-        if is_iterator and accumulator is not None:
-            # Create an appropriate make_accumulator function
-            def make_accumulator(inputs: dict) -> Callable:
-                if accumulator is None:
-                    return lambda acc, value: value  # Default accumulator if None
-                return accumulator
-
-            # Apply the accumulator
-            _add_accumulator(wrapped_op, make_accumulator)
-
-        return wrapped_op
+        return create_wrapper(func)
 
     if func is None:
         return op_deco