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temp/typings/langchain_openai/__init__.pyi

63 lines
1.7 KiB
Python

"""Type stubs for langchain_openai package."""
from collections.abc import Sequence
from typing import Any
from langchain_core.embeddings import Embeddings as _Embeddings
from langchain_core.language_models import BaseLanguageModel
class ChatOpenAI(BaseLanguageModel):
"""OpenAI chat model wrapper."""
def __init__(
self,
*,
model: str = ...,
temperature: float = ...,
max_tokens: int | None = None,
timeout: float | None = None,
max_retries: int = ...,
api_key: Any = None,
base_url: str | None = None,
organization: str | None = None,
streaming: bool = ...,
n: int | None = None,
openai_api_base: str | None = None,
openai_api_key: Any = None,
**kwargs: Any,
) -> None: ...
class AzureChatOpenAI(BaseLanguageModel):
"""Azure OpenAI chat model wrapper."""
def __init__(
self,
*,
deployment_name: str = ...,
azure_endpoint: str | None = None,
api_version: str | None = None,
api_key: Any = None,
temperature: float = ...,
max_tokens: int | None = None,
timeout: float | None = None,
max_retries: int = ...,
streaming: bool = ...,
**kwargs: Any,
) -> None: ...
class OpenAIEmbeddings(_Embeddings):
"""OpenAI embeddings wrapper used for vector stores."""
def __init__(
self,
*,
model: str = ...,
dimensions: int | None = None,
api_key: Any = None,
**kwargs: Any,
) -> None: ...
def embed_documents(self, texts: Sequence[str]) -> list[list[float]]: ...
def embed_query(self, text: str) -> list[float]: ...
__all__ = ["AzureChatOpenAI", "ChatOpenAI", "OpenAIEmbeddings"]