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descrease model server logspam (#4166)
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pablonyx
GitHub
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@ -62,6 +62,60 @@ _OPENAI_MAX_INPUT_LEN = 2048
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# Cohere allows up to 96 embeddings in a single embedding calling
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_COHERE_MAX_INPUT_LEN = 96
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# Authentication error string constants
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_AUTH_ERROR_401 = "401"
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_AUTH_ERROR_UNAUTHORIZED = "unauthorized"
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_AUTH_ERROR_INVALID_API_KEY = "invalid api key"
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_AUTH_ERROR_PERMISSION = "permission"
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def is_authentication_error(error: Exception) -> bool:
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"""Check if an exception is related to authentication issues.
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Args:
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error: The exception to check
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Returns:
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bool: True if the error appears to be authentication-related
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"""
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error_str = str(error).lower()
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return (
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_AUTH_ERROR_401 in error_str
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or _AUTH_ERROR_UNAUTHORIZED in error_str
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or _AUTH_ERROR_INVALID_API_KEY in error_str
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or _AUTH_ERROR_PERMISSION in error_str
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)
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def format_embedding_error(
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error: Exception,
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service_name: str,
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model: str | None,
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provider: EmbeddingProvider,
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status_code: int | None = None,
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) -> str:
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"""
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Format a standardized error string for embedding errors.
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"""
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detail = f"Status {status_code}" if status_code else f"{type(error)}"
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return (
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f"{'HTTP error' if status_code else 'Exception'} embedding text with {service_name} - {detail}: "
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f"Model: {model} "
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f"Provider: {provider} "
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f"Exception: {error}"
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)
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# Custom exception for authentication errors
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class AuthenticationError(Exception):
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"""Raised when authentication fails with a provider."""
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def __init__(self, provider: str, message: str = "API key is invalid or expired"):
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self.provider = provider
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self.message = message
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super().__init__(f"{provider} authentication failed: {message}")
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class CloudEmbedding:
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def __init__(
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@ -92,31 +146,17 @@ class CloudEmbedding:
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)
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final_embeddings: list[Embedding] = []
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try:
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for text_batch in batch_list(texts, _OPENAI_MAX_INPUT_LEN):
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response = await client.embeddings.create(
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input=text_batch,
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model=model,
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dimensions=reduced_dimension or openai.NOT_GIVEN,
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)
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final_embeddings.extend(
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[embedding.embedding for embedding in response.data]
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)
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return final_embeddings
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except Exception as e:
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error_string = (
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f"Exception embedding text with OpenAI - {type(e)}: "
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f"Model: {model} "
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f"Provider: {self.provider} "
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f"Exception: {e}"
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for text_batch in batch_list(texts, _OPENAI_MAX_INPUT_LEN):
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response = await client.embeddings.create(
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input=text_batch,
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model=model,
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dimensions=reduced_dimension or openai.NOT_GIVEN,
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)
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logger.error(error_string)
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# only log text when it's not an authentication error.
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if not isinstance(e, openai.AuthenticationError):
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logger.debug(f"Exception texts: {texts}")
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raise RuntimeError(error_string)
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final_embeddings.extend(
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[embedding.embedding for embedding in response.data]
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)
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return final_embeddings
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async def _embed_cohere(
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self, texts: list[str], model: str | None, embedding_type: str
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@ -155,7 +195,6 @@ class CloudEmbedding:
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input_type=embedding_type,
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truncation=True,
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)
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return response.embeddings
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async def _embed_azure(
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@ -239,22 +278,51 @@ class CloudEmbedding:
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deployment_name: str | None = None,
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reduced_dimension: int | None = None,
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) -> list[Embedding]:
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if self.provider == EmbeddingProvider.OPENAI:
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return await self._embed_openai(texts, model_name, reduced_dimension)
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elif self.provider == EmbeddingProvider.AZURE:
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return await self._embed_azure(texts, f"azure/{deployment_name}")
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elif self.provider == EmbeddingProvider.LITELLM:
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return await self._embed_litellm_proxy(texts, model_name)
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try:
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if self.provider == EmbeddingProvider.OPENAI:
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return await self._embed_openai(texts, model_name, reduced_dimension)
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elif self.provider == EmbeddingProvider.AZURE:
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return await self._embed_azure(texts, f"azure/{deployment_name}")
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elif self.provider == EmbeddingProvider.LITELLM:
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return await self._embed_litellm_proxy(texts, model_name)
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embedding_type = EmbeddingModelTextType.get_type(self.provider, text_type)
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if self.provider == EmbeddingProvider.COHERE:
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return await self._embed_cohere(texts, model_name, embedding_type)
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elif self.provider == EmbeddingProvider.VOYAGE:
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return await self._embed_voyage(texts, model_name, embedding_type)
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elif self.provider == EmbeddingProvider.GOOGLE:
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return await self._embed_vertex(texts, model_name, embedding_type)
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else:
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raise ValueError(f"Unsupported provider: {self.provider}")
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embedding_type = EmbeddingModelTextType.get_type(self.provider, text_type)
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if self.provider == EmbeddingProvider.COHERE:
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return await self._embed_cohere(texts, model_name, embedding_type)
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elif self.provider == EmbeddingProvider.VOYAGE:
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return await self._embed_voyage(texts, model_name, embedding_type)
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elif self.provider == EmbeddingProvider.GOOGLE:
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return await self._embed_vertex(texts, model_name, embedding_type)
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else:
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raise ValueError(f"Unsupported provider: {self.provider}")
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except openai.AuthenticationError:
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raise AuthenticationError(provider="OpenAI")
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except httpx.HTTPStatusError as e:
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if e.response.status_code == 401:
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raise AuthenticationError(provider=str(self.provider))
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error_string = format_embedding_error(
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e,
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str(self.provider),
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model_name or deployment_name,
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self.provider,
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status_code=e.response.status_code,
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)
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logger.error(error_string)
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logger.debug(f"Exception texts: {texts}")
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raise RuntimeError(error_string)
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except Exception as e:
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if is_authentication_error(e):
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raise AuthenticationError(provider=str(self.provider))
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error_string = format_embedding_error(
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e, str(self.provider), model_name or deployment_name, self.provider
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)
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logger.error(error_string)
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logger.debug(f"Exception texts: {texts}")
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raise RuntimeError(error_string)
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@staticmethod
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def create(
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@ -569,6 +637,13 @@ async def process_embed_request(
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gpu_type=gpu_type,
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)
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return EmbedResponse(embeddings=embeddings)
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except AuthenticationError as e:
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# Handle authentication errors consistently
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logger.error(f"Authentication error: {e.provider}")
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raise HTTPException(
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status_code=401,
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detail=f"Authentication failed: {e.message}",
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)
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except RateLimitError as e:
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raise HTTPException(
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status_code=429,
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