litellm-mirror/litellm/llms/base.py
Krish Dholakia 516c2a6a70
Litellm remove circular imports (#7232)
* fix(utils.py): initial commit to remove circular imports - moves llmproviders to utils.py

* fix(router.py): fix 'litellm.EmbeddingResponse' import from router.py

'

* refactor: fix litellm.ModelResponse import on pass through endpoints

* refactor(litellm_logging.py): fix circular import for custom callbacks literal

* fix(factory.py): fix circular imports inside prompt factory

* fix(cost_calculator.py): fix circular import for 'litellm.Usage'

* fix(proxy_server.py): fix potential circular import with `litellm.Router'

* fix(proxy/utils.py): fix potential circular import in `litellm.Router`

* fix: remove circular imports in 'auth_checks' and 'guardrails/'

* fix(prompt_injection_detection.py): fix router impor t

* fix(vertex_passthrough_logging_handler.py): fix potential circular imports in vertex pass through

* fix(anthropic_pass_through_logging_handler.py): fix potential circular imports

* fix(slack_alerting.py-+-ollama_chat.py): fix modelresponse import

* fix(base.py): fix potential circular import

* fix(handler.py): fix potential circular ref in codestral + cohere handler's

* fix(azure.py): fix potential circular imports

* fix(gpt_transformation.py): fix modelresponse import

* fix(litellm_logging.py): add logging base class - simplify typing

makes it easy for other files to type check the logging obj without introducing circular imports

* fix(azure_ai/embed): fix potential circular import on handler.py

* fix(databricks/): fix potential circular imports in databricks/

* fix(vertex_ai/): fix potential circular imports on vertex ai embeddings

* fix(vertex_ai/image_gen): fix import

* fix(watsonx-+-bedrock): cleanup imports

* refactor(anthropic-pass-through-+-petals): cleanup imports

* refactor(huggingface/): cleanup imports

* fix(ollama-+-clarifai): cleanup circular imports

* fix(openai_like/): fix impor t

* fix(openai_like/): fix embedding handler

cleanup imports

* refactor(openai.py): cleanup imports

* fix(sagemaker/transformation.py): fix import

* ci(config.yml): add circular import test to ci/cd
2024-12-14 16:28:34 -08:00

91 lines
2.7 KiB
Python

## This is a template base class to be used for adding new LLM providers via API calls
from typing import Any, Optional, Union
import httpx
import requests
import litellm
from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
from litellm.types.utils import ModelResponse, TextCompletionResponse
class BaseLLM:
_client_session: Optional[httpx.Client] = None
def process_response(
self,
model: str,
response: Union[requests.Response, httpx.Response],
model_response: ModelResponse,
stream: bool,
logging_obj: Any,
optional_params: dict,
api_key: str,
data: Union[dict, str],
messages: list,
print_verbose,
encoding,
) -> Union[ModelResponse, CustomStreamWrapper]:
"""
Helper function to process the response across sync + async completion calls
"""
return model_response
def process_text_completion_response(
self,
model: str,
response: Union[requests.Response, httpx.Response],
model_response: TextCompletionResponse,
stream: bool,
logging_obj: Any,
optional_params: dict,
api_key: str,
data: Union[dict, str],
messages: list,
print_verbose,
encoding,
) -> Union[TextCompletionResponse, CustomStreamWrapper]:
"""
Helper function to process the response across sync + async completion calls
"""
return model_response
def create_client_session(self):
if litellm.client_session:
_client_session = litellm.client_session
else:
_client_session = httpx.Client()
return _client_session
def create_aclient_session(self):
if litellm.aclient_session:
_aclient_session = litellm.aclient_session
else:
_aclient_session = httpx.AsyncClient()
return _aclient_session
def __exit__(self):
if hasattr(self, "_client_session") and self._client_session is not None:
self._client_session.close()
async def __aexit__(self, exc_type, exc_val, exc_tb):
if hasattr(self, "_aclient_session"):
await self._aclient_session.aclose() # type: ignore
def validate_environment(
self, *args, **kwargs
) -> Optional[Any]: # set up the environment required to run the model
return None
def completion(
self, *args, **kwargs
) -> Any: # logic for parsing in - calling - parsing out model completion calls
return None
def embedding(
self, *args, **kwargs
) -> Any: # logic for parsing in - calling - parsing out model embedding calls
return None