autoformat

Author: CalebCourier

guardrails/llm_providers.py

@@ -278,8 +278,7 @@ def _invoke_llm(
             import torch
         except ImportError:
             raise PromptCallableException(
-                "The `torch` package is not installed. "
-                "Install with `pip install torch`"
+                "The `torch` package is not installed. Install with `pip install torch`"
             )
         prompt = messages_to_prompt_string(messages)
         tokenizer = kwargs.pop("tokenizer")
@@ -384,8 +383,7 @@ def _invoke_llm(
             import torch  # noqa: F401 # type: ignore
         except ImportError:
             raise PromptCallableException(
-                "The `torch` package is not installed. "
-                "Install with `pip install torch`"
+                "The `torch` package is not installed. Install with `pip install torch`"
             )
 
         content_key = kwargs.pop("content_key", "generated_text")

guardrails/utils/openai_utils/v1.py

@@ -15,7 +15,7 @@
 
 def get_static_openai_create_func():
     warnings.warn(
-        "This function is deprecated. " " and will be removed in 0.6.0",
+        "This function is deprecated.  and will be removed in 0.6.0",
         DeprecationWarning,
     )
     return openai.completions.create

guardrails/utils/prompt_utils.py

@@ -26,7 +26,7 @@ def prompt_content_for_string_schema(
     description = output_schema.get("description")
     if description:
         prompt_content += (
-            "Here's a description of what I want you to generate: " f"{description}"
+            f"Here's a description of what I want you to generate: {description}"
         )
     validators = validator_map.get(json_path, [])
     if len(validators):

guardrails/utils/structured_data_utils.py

@@ -29,10 +29,10 @@ def set_additional_properties_false_iteratively(schema):
                     current["properties"].keys()
                 )  # this has to be set
             if "maximum" in current:
-                logger.warn("Property maximum is not supported." " Dropping")
+                logger.warn("Property maximum is not supported. Dropping")
                 current.pop("maximum")  # the api does not like these set
             if "minimum" in current:
-                logger.warn("Property maximum is not supported." " Dropping")
+                logger.warn("Property maximum is not supported. Dropping")
                 current.pop("minimum")  # the api does not like these set
             if "default" in current:
                 logger.warn("Property default is not supported. Marking field Required")

guardrails/utils/validator_utils.py

@@ -37,8 +37,7 @@ def parse_rail_arguments(arg_tokens: List[str]) -> List[Any]:
                 t = literal_eval(t)
             except (ValueError, SyntaxError, NameError) as e:
                 raise ValueError(
-                    f"Python expression `{t}` is not valid, "
-                    f"and raised an error: {e}."
+                    f"Python expression `{t}` is not valid, and raised an error: {e}."
                 )
         validator_args.append(t)
     return validator_args

guardrails/validator_service/__init__.py

@@ -41,8 +41,7 @@ def should_run_sync():
     bool_values = ["true", "false"]
     if run_sync.lower() not in bool_values:
         warnings.warn(
-            f"GUARDRAILS_RUN_SYNC must be one of {bool_values}!"
-            f" Defaulting to 'false'."
+            f"GUARDRAILS_RUN_SYNC must be one of {bool_values}! Defaulting to 'false'."
         )
     return process_count == 1 or run_sync.lower() == "true"
 

tests/integration_tests/integrations/langchain/test_validator_runnable.py

@@ -51,7 +51,7 @@ def invoke(
             chain.invoke({"topic": topic})
 
         assert str(exc_info.value) == (
-            "The response from the LLM failed validation!" f" {expected_error}"
+            f"The response from the LLM failed validation! {expected_error}"
         )
 
     else:

tests/integration_tests/mock_llm_outputs.py

@@ -138,9 +138,7 @@ def _invoke_llm(
                 if hasattr(mock_llm_responses[key], "read"):
                     out_text = mock_llm_responses[key]
             else:
-                raise ValueError(
-                    "specify either prompt and instructions " "or messages"
-                )
+                raise ValueError("specify either prompt and instructions or messages")
             return LLMResponse(
                 output=out_text,
                 prompt_token_count=123,

tests/integration_tests/test_assets/validators/detect_pii.py

@@ -140,7 +140,7 @@ def validate(self, value: Any, metadata: Dict[str, Any]) -> ValidationResult:
             entities_to_filter = pii_entities
         else:
             raise ValueError(
-                f"`pii_entities` must be one of {pii_keys}" " or a list of strings."
+                f"`pii_entities` must be one of {pii_keys} or a list of strings."
             )
 
         # Analyze the text, and anonymize it if there is PII
@@ -174,7 +174,7 @@ def validate(self, value: Any, metadata: Dict[str, Any]) -> ValidationResult:
                 ErrorSpan(
                     start=diff_range[0],
                     end=diff_range[1],
-                    reason=f"PII detected in {value[diff_range[0]:diff_range[1]]}",
+                    reason=f"PII detected in {value[diff_range[0] : diff_range[1]]}",
                 )
             )
 

tests/unit_tests/cli/hub/test_uninstall.py

@@ -29,7 +29,7 @@ def test_remove_from_hub_inits(mocker):
     expected_calls = [
         call(
             "/site-packages/guardrails/hub/__init__.py",
-            "from guardrails_grhub_test_package import " "Validator, Helper",
+            "from guardrails_grhub_test_package import Validator, Helper",
         ),
     ]