chore: initial public snapshot for github upload

llm-gateway-competitors/litellm-wheel-src/litellm/llms/base_llm/guardrail_translation/base_translation.py

@@ -0,0 +1,107 @@
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+
+if TYPE_CHECKING:
+    from litellm.integrations.custom_guardrail import CustomGuardrail
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+    from litellm.proxy._types import UserAPIKeyAuth
+
+
+class BaseTranslation(ABC):
+    @staticmethod
+    def transform_user_api_key_dict_to_metadata(
+        user_api_key_dict: Optional[Any],
+    ) -> Dict[str, Any]:
+        """
+        Transform user_api_key_dict to a metadata dict with prefixed keys.
+
+        Converts keys like 'user_id' to 'user_api_key_user_id' to clearly indicate
+        the source of the metadata.
+
+        Args:
+            user_api_key_dict: UserAPIKeyAuth object or dict with user information
+
+        Returns:
+            Dict with keys prefixed with 'user_api_key_'
+        """
+        if user_api_key_dict is None:
+            return {}
+
+        # Convert to dict if it's a Pydantic object
+        user_dict = (
+            user_api_key_dict.model_dump()
+            if hasattr(user_api_key_dict, "model_dump")
+            else user_api_key_dict
+        )
+
+        if not isinstance(user_dict, dict):
+            return {}
+
+        # Transform keys to be prefixed with 'user_api_key_'
+        transformed = {}
+        for key, value in user_dict.items():
+            # Skip None values and internal fields
+            if value is None or key.startswith("_"):
+                continue
+
+            # If key already has the prefix, use as-is, otherwise add prefix
+            if key.startswith("user_api_key_"):
+                transformed[key] = value
+            else:
+                transformed[f"user_api_key_{key}"] = value
+
+        return transformed
+
+    @abstractmethod
+    async def process_input_messages(
+        self,
+        data: dict,
+        guardrail_to_apply: "CustomGuardrail",
+        litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None,
+    ) -> Any:
+        """
+        Process input messages with guardrails.
+
+        Note: user_api_key_dict metadata should be available in the data dict.
+        """
+        pass
+
+    @abstractmethod
+    async def process_output_response(
+        self,
+        response: Any,
+        guardrail_to_apply: "CustomGuardrail",
+        litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None,
+        user_api_key_dict: Optional["UserAPIKeyAuth"] = None,
+    ) -> Any:
+        """
+        Process output response with guardrails.
+
+        Args:
+            response: The response object from the LLM
+            guardrail_to_apply: The guardrail instance to apply
+            litellm_logging_obj: Optional logging object
+            user_api_key_dict: User API key metadata (passed separately since response doesn't contain it)
+        """
+        pass
+
+    async def process_output_streaming_response(
+        self,
+        responses_so_far: List[Any],
+        guardrail_to_apply: "CustomGuardrail",
+        litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None,
+        user_api_key_dict: Optional["UserAPIKeyAuth"] = None,
+    ) -> Any:
+        """
+        Process output streaming response with guardrails.
+
+        Optional to override in subclasses.
+        """
+        return responses_so_far
+
+    def extract_request_tool_names(self, data: dict) -> List[str]:
+        """
+        Extract tool names from the request body for allowlist/policy checks.
+        Override in tool-capable handlers; default returns [].
+        """
+        return []