forked from phoenix/litellm-mirror
2b9db05e08
* feat(proxy_cli.py): add new 'log_config' cli param Allows passing logging.conf to uvicorn on startup * docs(cli.md): add logging conf to uvicorn cli docs * fix(get_llm_provider_logic.py): fix default api base for litellm_proxy Fixes https://github.com/BerriAI/litellm/issues/6332 * feat(openai_like/embedding): Add support for jina ai embeddings Closes https://github.com/BerriAI/litellm/issues/6337 * docs(deploy.md): update entrypoint.sh filepath post-refactor Fixes outdated docs * feat(prometheus.py): emit time_to_first_token metric on prometheus Closes https://github.com/BerriAI/litellm/issues/6334 * fix(prometheus.py): only emit time to first token metric if stream is True enables more accurate ttft usage * test: handle vertex api instability * fix(get_llm_provider_logic.py): fix import * fix(openai.py): fix deepinfra default api base * fix(anthropic/transformation.py): remove anthropic beta header (#6361)
79 lines
2.3 KiB
Python
79 lines
2.3 KiB
Python
"""
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Transformation logic from OpenAI /v1/embeddings format to Jina AI's `/v1/embeddings` format.
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Why separate file? Make it easy to see how transformation works
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Docs - https://jina.ai/embeddings/
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"""
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import types
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from typing import List, Optional, Tuple
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from litellm import LlmProviders
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.utils import Embedding, EmbeddingResponse, Usage
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class JinaAIEmbeddingConfig:
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"""
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Reference: https://jina.ai/embeddings/
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"""
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def __init__(
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self,
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) -> None:
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locals_ = locals()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@classmethod
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def get_config(cls):
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return {
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k: v
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for k, v in cls.__dict__.items()
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if not k.startswith("__")
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and not isinstance(
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v,
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(
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types.FunctionType,
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types.BuiltinFunctionType,
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classmethod,
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staticmethod,
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),
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)
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and v is not None
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}
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def get_supported_openai_params(self) -> List[str]:
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return ["dimensions"]
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def map_openai_params(
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self, non_default_params: dict, optional_params: dict
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) -> dict:
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if "dimensions" in non_default_params:
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optional_params["dimensions"] = non_default_params["dimensions"]
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return optional_params
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def _get_openai_compatible_provider_info(
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self,
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api_base: Optional[str],
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api_key: Optional[str],
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) -> Tuple[str, Optional[str], Optional[str]]:
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"""
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Returns:
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Tuple[str, Optional[str], Optional[str]]:
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- custom_llm_provider: str
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- api_base: str
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- dynamic_api_key: str
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"""
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api_base = (
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api_base or get_secret_str("JINA_AI_API_BASE") or "https://api.jina.ai/v1"
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) # type: ignore
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dynamic_api_key = api_key or (
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get_secret_str("JINA_AI_API_KEY")
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or get_secret_str("JINA_AI_API_KEY")
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or get_secret_str("JINA_AI_API_KEY")
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or get_secret_str("JINA_AI_TOKEN")
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)
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return LlmProviders.OPENAI_LIKE.value, api_base, dynamic_api_key
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