test

weave/integrations/instructor/instructor_iterable_utils.py

@@ -4,6 +4,7 @@
 from pydantic import BaseModel
 
 import weave
+from weave.trace.autopatch import OpSettings
 from weave.trace.op_extensions.accumulator import add_accumulator
 
 
@@ -27,10 +28,10 @@ def should_accumulate_iterable(inputs: dict) -> bool:
     return False
 
 
-def instructor_wrapper_sync(name: str) -> Callable[[Callable], Callable]:
+def instructor_wrapper_sync(settings: OpSettings) -> Callable[[Callable], Callable]:
     def wrapper(fn: Callable) -> Callable:
-        op = weave.op(fn)
-        op.name = name  # type: ignore
+        op_kwargs = settings.model_dump()
+        op = weave.op(fn, **op_kwargs)
         return add_accumulator(
             op,  # type: ignore
             make_accumulator=lambda inputs: instructor_iterable_accumulator,
@@ -40,7 +41,7 @@ def wrapper(fn: Callable) -> Callable:
     return wrapper
 
 
-def instructor_wrapper_async(name: str) -> Callable[[Callable], Callable]:
+def instructor_wrapper_async(settings: OpSettings) -> Callable[[Callable], Callable]:
     def wrapper(fn: Callable) -> Callable:
         def _fn_wrapper(fn: Callable) -> Callable:
             @wraps(fn)
@@ -49,9 +50,8 @@ async def _async_wrapper(*args: Any, **kwargs: Any) -> Any:
 
             return _async_wrapper
 
-        "We need to do this so we can check if `stream` is used"
-        op = weave.op(_fn_wrapper(fn))
-        op.name = name  # type: ignore
+        op_kwargs = settings.model_dump()
+        op = weave.op(_fn_wrapper(fn), **op_kwargs)
         return add_accumulator(
             op,  # type: ignore
             make_accumulator=lambda inputs: instructor_iterable_accumulator,

weave/integrations/instructor/instructor_partial_utils.py

@@ -3,6 +3,7 @@
 from pydantic import BaseModel
 
 import weave
+from weave.trace.autopatch import OpSettings
 from weave.trace.op_extensions.accumulator import add_accumulator
 
 
@@ -14,10 +15,10 @@ def instructor_partial_accumulator(
     return acc
 
 
-def instructor_wrapper_partial(name: str) -> Callable[[Callable], Callable]:
+def instructor_wrapper_partial(settings: OpSettings) -> Callable[[Callable], Callable]:
     def wrapper(fn: Callable) -> Callable:
-        op = weave.op()(fn)
-        op.name = name  # type: ignore
+        op_kwargs = settings.model_dump()
+        op = weave.op(fn, **op_kwargs)
         return add_accumulator(
             op,  # type: ignore
             make_accumulator=lambda inputs: instructor_partial_accumulator,

weave/integrations/instructor/instructor_sdk.py

@@ -1,31 +1,65 @@
+from __future__ import annotations
+
 import importlib
 
-from weave.trace.patcher import MultiPatcher, SymbolPatcher
+from weave.trace.autopatch import IntegrationSettings
+from weave.trace.patcher import MultiPatcher, NoOpPatcher, SymbolPatcher
 
 from .instructor_iterable_utils import instructor_wrapper_async, instructor_wrapper_sync
 from .instructor_partial_utils import instructor_wrapper_partial
 
-instructor_patcher = MultiPatcher(
-    [
-        SymbolPatcher(
-            lambda: importlib.import_module("instructor.client"),
-            "Instructor.create",
-            instructor_wrapper_sync(name="Instructor.create"),
-        ),
-        SymbolPatcher(
-            lambda: importlib.import_module("instructor.client"),
-            "AsyncInstructor.create",
-            instructor_wrapper_async(name="AsyncInstructor.create"),
-        ),
-        SymbolPatcher(
-            lambda: importlib.import_module("instructor.client"),
-            "Instructor.create_partial",
-            instructor_wrapper_partial(name="Instructor.create_partial"),
-        ),
-        SymbolPatcher(
-            lambda: importlib.import_module("instructor.client"),
-            "AsyncInstructor.create_partial",
-            instructor_wrapper_partial(name="AsyncInstructor.create_partial"),
-        ),
-    ]
-)
+_instructor_patcher: MultiPatcher | None = None
+
+
+def get_instructor_patcher(
+    settings: IntegrationSettings | None = None,
+) -> MultiPatcher | NoOpPatcher:
+    if settings is None:
+        settings = IntegrationSettings()
+
+    if not settings.enabled:
+        return NoOpPatcher()
+
+    global _instructor_patcher
+    if _instructor_patcher is not None:
+        return _instructor_patcher
+
+    base = settings.op_settings
+
+    create_settings = base.model_copy(update={"name": base.name or "Instructor.create"})
+    async_create_settings = base.model_copy(
+        update={"name": base.name or "AsyncInstructor.create"}
+    )
+    create_partial_settings = base.model_copy(
+        update={"name": base.name or "Instructor.create_partial"}
+    )
+    async_create_partial_settings = base.model_copy(
+        update={"name": base.name or "AsyncInstructor.create_partial"}
+    )
+
+    _instructor_patcher = MultiPatcher(
+        [
+            SymbolPatcher(
+                lambda: importlib.import_module("instructor.client"),
+                "Instructor.create",
+                instructor_wrapper_sync(create_settings),
+            ),
+            SymbolPatcher(
+                lambda: importlib.import_module("instructor.client"),
+                "AsyncInstructor.create",
+                instructor_wrapper_async(async_create_settings),
+            ),
+            SymbolPatcher(
+                lambda: importlib.import_module("instructor.client"),
+                "Instructor.create_partial",
+                instructor_wrapper_partial(create_partial_settings),
+            ),
+            SymbolPatcher(
+                lambda: importlib.import_module("instructor.client"),
+                "AsyncInstructor.create_partial",
+                instructor_wrapper_partial(async_create_partial_settings),
+            ),
+        ]
+    )
+
+    return _instructor_patcher

weave/trace/autopatch.py

@@ -58,7 +58,7 @@ def autopatch(settings: Optional[AutopatchSettings] = None) -> None:
         get_google_genai_patcher,
     )
     from weave.integrations.groq.groq_sdk import get_groq_patcher
-    from weave.integrations.instructor.instructor_sdk import instructor_patcher
+    from weave.integrations.instructor.instructor_sdk import get_instructor_patcher
     from weave.integrations.langchain.langchain import langchain_patcher
     from weave.integrations.litellm.litellm import get_litellm_patcher
     from weave.integrations.llamaindex.llamaindex import llamaindex_patcher
@@ -75,7 +75,7 @@ def autopatch(settings: Optional[AutopatchSettings] = None) -> None:
     get_litellm_patcher(settings.litellm).attempt_patch()
     get_anthropic_patcher(settings.anthropic).attempt_patch()
     get_groq_patcher(settings.groq).attempt_patch()
-    instructor_patcher.attempt_patch()
+    get_instructor_patcher(settings.instructor).attempt_patch()
     get_dspy_patcher(settings.dspy).attempt_patch()
     get_cerebras_patcher(settings.cerebras).attempt_patch()
     get_cohere_patcher(settings.cohere).attempt_patch()
@@ -96,7 +96,7 @@ def reset_autopatch() -> None:
         get_google_genai_patcher,
     )
     from weave.integrations.groq.groq_sdk import get_groq_patcher
-    from weave.integrations.instructor.instructor_sdk import instructor_patcher
+    from weave.integrations.instructor.instructor_sdk import get_instructor_patcher
     from weave.integrations.langchain.langchain import langchain_patcher
     from weave.integrations.litellm.litellm import get_litellm_patcher
     from weave.integrations.llamaindex.llamaindex import llamaindex_patcher
@@ -110,7 +110,7 @@ def reset_autopatch() -> None:
     get_litellm_patcher().undo_patch()
     get_anthropic_patcher().undo_patch()
     get_groq_patcher().undo_patch()
-    instructor_patcher.undo_patch()
+    get_instructor_patcher().undo_patch()
     get_dspy_patcher().undo_patch()
     get_cerebras_patcher().undo_patch()
     get_cohere_patcher().undo_patch()