chore: initial public snapshot for github upload
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"""OpenAI Embeddings handler for Unified Guardrails."""
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from litellm.llms.openai.embeddings.guardrail_translation.handler import (
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OpenAIEmbeddingsHandler,
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)
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from litellm.types.utils import CallTypes
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guardrail_translation_mappings = {
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CallTypes.embedding: OpenAIEmbeddingsHandler,
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CallTypes.aembedding: OpenAIEmbeddingsHandler,
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}
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__all__ = ["guardrail_translation_mappings", "OpenAIEmbeddingsHandler"]
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"""
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OpenAI Embeddings Handler for Unified Guardrails
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This module provides guardrail translation support for OpenAI's embeddings endpoint.
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The handler processes the 'input' parameter for guardrails.
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"""
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from typing import TYPE_CHECKING, Any, List, Optional, Union
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from litellm._logging import verbose_proxy_logger
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from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation
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from litellm.types.utils import GenericGuardrailAPIInputs
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if TYPE_CHECKING:
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from litellm.integrations.custom_guardrail import CustomGuardrail
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from litellm.types.utils import EmbeddingResponse
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class OpenAIEmbeddingsHandler(BaseTranslation):
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"""
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Handler for processing OpenAI embeddings requests with guardrails.
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This class provides methods to:
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1. Process input text (pre-call hook)
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2. Process output response (post-call hook) - embeddings don't typically need output guardrails
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The handler specifically processes the 'input' parameter which can be:
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- A single string
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- A list of strings (for batch embeddings)
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- A list of integers (token IDs - not processed by guardrails)
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- A list of lists of integers (batch token IDs - not processed by guardrails)
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"""
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async def process_input_messages(
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self,
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data: dict,
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guardrail_to_apply: "CustomGuardrail",
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litellm_logging_obj: Optional[Any] = None,
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) -> Any:
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"""
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Process input text by applying guardrails to text content.
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Args:
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data: Request data dictionary containing 'input' parameter
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guardrail_to_apply: The guardrail instance to apply
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litellm_logging_obj: Optional logging object
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Returns:
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Modified data with guardrails applied to input
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"""
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input_data = data.get("input")
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if input_data is None:
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verbose_proxy_logger.debug(
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"OpenAI Embeddings: No input found in request data"
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)
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return data
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if isinstance(input_data, str):
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data = await self._process_string_input(
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data, input_data, guardrail_to_apply, litellm_logging_obj
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)
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elif isinstance(input_data, list):
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data = await self._process_list_input(
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data, input_data, guardrail_to_apply, litellm_logging_obj
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)
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else:
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verbose_proxy_logger.warning(
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"OpenAI Embeddings: Unexpected input type: %s. Expected string or list.",
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type(input_data),
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)
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return data
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async def _process_string_input(
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self,
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data: dict,
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input_data: str,
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guardrail_to_apply: "CustomGuardrail",
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litellm_logging_obj: Optional[Any],
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) -> dict:
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"""Process a single string input through the guardrail."""
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inputs = GenericGuardrailAPIInputs(texts=[input_data])
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if model := data.get("model"):
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inputs["model"] = model
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guardrailed_inputs = await guardrail_to_apply.apply_guardrail(
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inputs=inputs,
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request_data=data,
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input_type="request",
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logging_obj=litellm_logging_obj,
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)
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if guardrailed_texts := guardrailed_inputs.get("texts"):
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data["input"] = guardrailed_texts[0]
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verbose_proxy_logger.debug(
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"OpenAI Embeddings: Applied guardrail to string input. "
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"Original length: %d, New length: %d",
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len(input_data),
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len(data["input"]),
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)
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return data
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async def _process_list_input(
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self,
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data: dict,
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input_data: List[Union[str, int, List[int]]],
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guardrail_to_apply: "CustomGuardrail",
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litellm_logging_obj: Optional[Any],
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) -> dict:
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"""Process a list input through the guardrail (if it contains strings)."""
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if len(input_data) == 0:
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return data
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first_item = input_data[0]
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# Skip non-text inputs (token IDs)
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if isinstance(first_item, (int, list)):
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verbose_proxy_logger.debug(
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"OpenAI Embeddings: Input is token IDs, skipping guardrail processing"
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)
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return data
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if not isinstance(first_item, str):
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verbose_proxy_logger.warning(
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"OpenAI Embeddings: Unexpected input list item type: %s",
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type(first_item),
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)
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return data
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# List of strings - apply guardrail
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inputs = GenericGuardrailAPIInputs(texts=input_data) # type: ignore
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if model := data.get("model"):
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inputs["model"] = model
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guardrailed_inputs = await guardrail_to_apply.apply_guardrail(
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inputs=inputs,
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request_data=data,
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input_type="request",
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logging_obj=litellm_logging_obj,
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)
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if guardrailed_texts := guardrailed_inputs.get("texts"):
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data["input"] = guardrailed_texts
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verbose_proxy_logger.debug(
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"OpenAI Embeddings: Applied guardrail to %d inputs",
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len(guardrailed_texts),
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)
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return data
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async def process_output_response(
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self,
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response: "EmbeddingResponse",
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guardrail_to_apply: "CustomGuardrail",
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litellm_logging_obj: Optional[Any] = None,
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user_api_key_dict: Optional[Any] = None,
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) -> Any:
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"""
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Process output response - embeddings responses contain vectors, not text.
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For embeddings, the output is numerical vectors, so there's typically
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no text content to apply guardrails to. This method is a no-op but
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is included for interface consistency.
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Args:
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response: Embedding response object
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guardrail_to_apply: The guardrail instance to apply
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litellm_logging_obj: Optional logging object
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user_api_key_dict: User API key metadata
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Returns:
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Unmodified response (embeddings don't have text output to guard)
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"""
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verbose_proxy_logger.debug(
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"OpenAI Embeddings: Output response processing skipped - "
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"embeddings contain vectors, not text"
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)
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return response
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