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* VoyageEmbeddingConfig * fix voyage logic to get params * add voyage embedding transformation * add get_provider_embedding_config * use BaseEmbeddingConfig * voyage clean up * use llm http handler for embedding transformations * test_voyage_ai_embedding_extra_params * add voyage async * test_voyage_ai_embedding_extra_params * add async for llm http handler * update BaseLLMEmbeddingTest * test_voyage_ai_embedding_extra_params * fix linting * fix get_provider_embedding_config * fix anthropic text test * update location of base/chat/transformation * fix import path * fix IBMWatsonXAIConfig
46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
"""
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This file contains common utils for anthropic calls.
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"""
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from typing import Optional, Union
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import httpx
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from litellm.llms.base_llm.chat.transformation import BaseLLMException
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class AnthropicError(BaseLLMException):
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def __init__(
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self,
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status_code: int,
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message,
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headers: Optional[httpx.Headers] = None,
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):
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super().__init__(status_code=status_code, message=message, headers=headers)
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def process_anthropic_headers(headers: Union[httpx.Headers, dict]) -> dict:
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openai_headers = {}
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if "anthropic-ratelimit-requests-limit" in headers:
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openai_headers["x-ratelimit-limit-requests"] = headers[
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"anthropic-ratelimit-requests-limit"
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]
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if "anthropic-ratelimit-requests-remaining" in headers:
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openai_headers["x-ratelimit-remaining-requests"] = headers[
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"anthropic-ratelimit-requests-remaining"
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]
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if "anthropic-ratelimit-tokens-limit" in headers:
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openai_headers["x-ratelimit-limit-tokens"] = headers[
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"anthropic-ratelimit-tokens-limit"
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]
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if "anthropic-ratelimit-tokens-remaining" in headers:
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openai_headers["x-ratelimit-remaining-tokens"] = headers[
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"anthropic-ratelimit-tokens-remaining"
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]
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llm_response_headers = {
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"{}-{}".format("llm_provider", k): v for k, v in headers.items()
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}
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additional_headers = {**llm_response_headers, **openai_headers}
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return additional_headers
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