mirror of
https://github.com/BerriAI/litellm.git
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126 lines
3.9 KiB
Python
126 lines
3.9 KiB
Python
from asyncio import Future
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from collections.abc import Coroutine, Mapping, Sequence
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from typing import Never, final
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from litellm.llms.base_llm.ocr.transformation import OCRResponse
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from litellm.rust_bridge.ocr.entrypoints import LiteLLMOcrRequest
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class RustBridgeDeclined(Exception): ...
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class RustUpstreamError(Exception): ...
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def ocr(
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request: LiteLLMOcrRequest,
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args: tuple[object, ...],
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kwargs: dict[str, object],
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) -> OCRResponse: ...
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def aocr(
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request: LiteLLMOcrRequest,
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args: tuple[object, ...],
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kwargs: dict[str, object],
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) -> Coroutine[object, object, OCRResponse]: ...
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def transcription(
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model: str,
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audio: object,
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api_key: str | None = None,
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api_base: str | None = None,
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custom_llm_provider: str | None = None,
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extra_headers: Mapping[str, object] | None = None,
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optional_params: Mapping[str, object] | None = None,
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timeout_seconds: float | None = None,
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) -> dict[str, object]: ...
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def atranscription(
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model: str,
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audio: object,
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api_key: str | None = None,
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api_base: str | None = None,
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custom_llm_provider: str | None = None,
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extra_headers: Mapping[str, object] | None = None,
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optional_params: Mapping[str, object] | None = None,
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timeout_seconds: float | None = None,
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) -> Future[dict[str, object]]: ...
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def messages(
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model: str,
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body: Mapping[str, object],
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api_key: str | None = None,
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api_base: str | None = None,
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custom_llm_provider: str | None = None,
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extra_headers: Mapping[str, object] | None = None,
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timeout_seconds: float | None = None,
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) -> dict[str, object]: ...
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def amessages(
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model: str,
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body: Mapping[str, object],
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api_key: str | None = None,
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api_base: str | None = None,
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custom_llm_provider: str | None = None,
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extra_headers: Mapping[str, object] | None = None,
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timeout_seconds: float | None = None,
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) -> Future[dict[str, object]]: ...
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def chat_completions_decline(
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model: str,
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messages: Sequence[object],
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optional_params: Mapping[str, object] | None = None,
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custom_llm_provider: str | None = None,
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) -> str | None: ...
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def chat_completions(
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model: str,
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messages: Sequence[object],
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optional_params: Mapping[str, object] | None = None,
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api_key: str | None = None,
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api_base: str | None = None,
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custom_llm_provider: str | None = None,
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extra_headers: Mapping[str, object] | None = None,
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timeout_seconds: float | None = None,
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) -> dict[str, object]: ...
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def achat_completions(
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model: str,
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messages: Sequence[object],
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optional_params: Mapping[str, object] | None = None,
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api_key: str | None = None,
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api_base: str | None = None,
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custom_llm_provider: str | None = None,
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extra_headers: Mapping[str, object] | None = None,
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timeout_seconds: float | None = None,
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) -> Future[dict[str, object]]: ...
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@final
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class ResponsesWebSocketConnection:
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def __new__(cls, _uninstantiable: Never, /) -> Never: ...
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@classmethod
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def connect(
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cls,
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url: str,
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headers: Mapping[str, str] | None = None,
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timeout_seconds: float | None = None,
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) -> Future[ResponsesWebSocketConnection]: ...
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def send_text(self, text: str) -> Future[None]: ...
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def recv_text(self) -> Future[str | None]: ...
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def close(self) -> Future[None]: ...
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@final
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class TokenCounter:
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def __new__(cls, tokenizer_json: str) -> TokenCounter: ...
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@staticmethod
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def from_cl100k_ranks(rank_file: str) -> TokenCounter: ...
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@staticmethod
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def from_o200k_ranks(rank_file: str) -> TokenCounter: ...
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def acount_request(self, body: bytes) -> Future[dict[str, object]]: ...
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def gil_stats() -> dict[str, int]: ...
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__all__ = [
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"ResponsesWebSocketConnection",
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"RustBridgeDeclined",
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"RustUpstreamError",
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"TokenCounter",
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"achat_completions",
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"amessages",
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"aocr",
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"atranscription",
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"chat_completions",
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"chat_completions_decline",
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"gil_stats",
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"messages",
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"ocr",
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"transcription",
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]
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