Union -> |

weave/scorers/context_relevance_scorer.py

@@ -1,7 +1,9 @@
 import json
 import os
 from importlib.util import find_spec
-from typing import Any, Optional, Union
+from typing import Any, Optional
+
+from __future__ import annotations
 
 import numpy as np
 from pydantic import PrivateAttr
@@ -362,7 +364,7 @@ def score(
         self,
         output: str,
         query: str,
-        context: Union[str, list[str]],
+        context: str | list[str],
         verbose: bool = False,
     ) -> dict[str, Any]:
         """Score multiple documents and compute weighted average relevance."""

weave/scorers/llm_scorer.py

@@ -1,5 +1,7 @@
 from importlib.util import find_spec
-from typing import TYPE_CHECKING, Any, Optional, Union
+from typing import TYPE_CHECKING, Any, Optional
+
+from __future__ import annotations
 
 from pydantic import Field, field_validator
 
@@ -200,11 +202,11 @@ def tokenize_input(self, prompt: str) -> "Tensor":
             prompt, return_tensors="pt", truncation=False
         ).input_ids.to(self.device)
 
-    def predict_chunk(self, input_ids: "Tensor") -> list[Union[int, float]]:
+    def predict_chunk(self, input_ids: "Tensor") -> list[int | float]:
         raise NotImplementedError("Subclasses must implement predict_chunk method.")
 
     def aggregate_predictions(
-        self, all_predictions: list[list[Union[int, float]]]
+        self, all_predictions: list[list[int | float]]
     ) -> list[float]:
         """
         Aggregate predictions using the specified class attribute method.

weave/scorers/llm_utils.py

@@ -2,7 +2,8 @@
 
 import inspect
 import os
-from typing import TYPE_CHECKING, Any, Union
+from typing import TYPE_CHECKING, Any
+from __future__ import annotations
 
 OPENAI_DEFAULT_MODEL = "gpt-4o"
 OPENAI_DEFAULT_EMBEDDING_MODEL = "text-embedding-3-small"
@@ -27,16 +28,7 @@
     from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI
     from torch import device
 
-    _LLM_CLIENTS = Union[
-        OpenAI,
-        AsyncOpenAI,
-        AzureOpenAI,
-        AsyncAzureOpenAI,
-        Anthropic,
-        AsyncAnthropic,
-        Mistral,
-        GenerativeModel,
-    ]
+    _LLM_CLIENTS = OpenAI | AsyncOpenAI | AzureOpenAI | AsyncAzureOpenAI | Anthropic | AsyncAnthropic | Mistral | GenerativeModel
 else:
     _LLM_CLIENTS = object
 
@@ -94,7 +86,7 @@ def create(
 
 
 def embed(
-    client: _LLM_CLIENTS, model_id: str, texts: Union[str, list[str]], **kwargs: Any
+    client: _LLM_CLIENTS, model_id: str, texts: str | list[str], **kwargs: Any
 ) -> list[list[float]]:
     client_type = type(client).__name__.lower()
     if "openai" in client_type:

weave/scorers/moderation_scorer.py

@@ -1,6 +1,8 @@
 import os
 from typing import TYPE_CHECKING, Any, Optional
 
+from __future__ import annotations
+
 from pydantic import PrivateAttr, field_validator
 
 import weave

weave/scorers/robustness_scorer.py

@@ -1,7 +1,8 @@
 import math
 import random
 import string
-from typing import Any, Optional, Union
+from typing import Any, Optional
+from __future__ import annotations
 
 import numpy as np
 
@@ -79,15 +80,15 @@ def model_post_init(self, __context: Any) -> None:
     @weave.op
     def score(
         self,
-        output: list[Union[str, bool]],
-        ground_truths: Optional[list[Union[str, bool]]] = None,
+        output: list[str | bool],
+        ground_truths: Optional[list[str | bool]] = None,
     ) -> dict:
         """
         Computes the robustness score of the model's outputs.
 
         Args:
-            output (List[Union[str, bool]]): A list containing the original output followed by perturbed outputs.
-            ground_truths (Optional[List[Union[str, bool]]]): Optional list of ground truths corresponding to each output.
+            output (List[str | bool]): A list containing the original output followed by perturbed outputs.
+            ground_truths (Optional[List[str | bool]]): Optional list of ground truths corresponding to each output.
 
         Returns:
             dict: A dictionary containing the robustness metrics and scores.

weave/trace_server/requests.py

@@ -5,7 +5,9 @@
 import os
 import threading
 from time import time
-from typing import Any, Optional, Union
+from typing import Any, Optional
+
+from __future__ import annotations
 
 from requests import HTTPError as HTTPError
 from requests import PreparedRequest, Response, Session
@@ -34,7 +36,7 @@
 THEME_JSON = "ansi_dark"
 
 
-def decode_str(string: Union[str, bytes]) -> str:
+def decode_str(string: str | bytes) -> str:
     """Decode a bytes object to a string."""
     return string if isinstance(string, str) else string.decode("utf-8")
 
@@ -161,7 +163,7 @@ def get(url: str, params: Optional[dict[str, str]] = None, **kwargs: Any) -> Res
 
 def post(
     url: str,
-    data: Optional[Union[dict[str, Any], str]] = None,
+    data: Optional[dict[str, Any] | str] = None,
     json: Optional[dict[str, Any]] = None,
     **kwargs: Any,
 ) -> Response: