mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-08 22:21:35 +00:00
fix: fix minor linting errors
This commit is contained in:
Krrish Dholakia
parent
305fff8ffb
commit
7ab737e4a8
4 changed files with 41 additions and 28 deletions
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@ -1,5 +1,5 @@
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"""
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Support for gpt model family
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Support for gpt model family
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"""
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from typing import List, Optional, Union
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@ -87,7 +87,7 @@ class OpenAITextCompletionConfig(BaseTextCompletionConfig, OpenAIGPTConfig):
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## RESPONSE OBJECT
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if response_object is None or model_response_object is None:
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raise ValueError("Error in response object format")
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choice_list = []
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choice_list: List[Choices] = []
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for idx, choice in enumerate(response_object["choices"]):
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message = Message(
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content=choice["text"],
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@ -100,7 +100,7 @@ class OpenAITextCompletionConfig(BaseTextCompletionConfig, OpenAIGPTConfig):
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logprobs=choice.get("logprobs", None),
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)
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choice_list.append(choice)
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model_response_object.choices = choice_list
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model_response_object.choices = choice_list # type: ignore
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if "usage" in response_object:
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setattr(model_response_object, "usage", response_object["usage"])
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@ -111,9 +111,9 @@ class OpenAITextCompletionConfig(BaseTextCompletionConfig, OpenAIGPTConfig):
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if "model" in response_object:
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model_response_object.model = response_object["model"]
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model_response_object._hidden_params[
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"original_response"
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] = response_object # track original response, if users make a litellm.text_completion() request, we can return the original response
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model_response_object._hidden_params["original_response"] = (
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response_object # track original response, if users make a litellm.text_completion() request, we can return the original response
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)
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return model_response_object
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except Exception as e:
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raise e
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@ -64,9 +64,9 @@ class VertexFineTuningAPI(VertexLLM):
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)
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if create_fine_tuning_job_data.validation_file:
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supervised_tuning_spec[
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"validation_dataset"
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] = create_fine_tuning_job_data.validation_file
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supervised_tuning_spec["validation_dataset"] = (
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create_fine_tuning_job_data.validation_file
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)
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_vertex_hyperparameters = (
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self._transform_openai_hyperparameters_to_vertex_hyperparameters(
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@ -140,7 +140,9 @@ class VertexFineTuningAPI(VertexLLM):
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fine_tuned_model=response.get("tunedModelDisplayName", ""),
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finished_at=None,
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hyperparameters=self._translate_vertex_response_hyperparameters(
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vertex_hyper_parameters=_supervisedTuningSpec.get("hyperParameters", {})
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vertex_hyper_parameters=_supervisedTuningSpec.get(
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"hyperParameters", FineTuneHyperparameters()
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)
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or {}
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),
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model=response.get("baseModel", "") or "",
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@ -343,9 +345,9 @@ class VertexFineTuningAPI(VertexLLM):
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elif "cachedContents" in request_route:
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_model = request_data.get("model")
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if _model is not None and "/publishers/google/models/" not in _model:
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request_data[
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"model"
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] = f"projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{_model}"
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request_data["model"] = (
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f"projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{_model}"
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)
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url = f"https://{vertex_location}-aiplatform.googleapis.com/v1beta1/projects/{vertex_project}/locations/{vertex_location}{request_route}"
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else:
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@ -70,7 +70,7 @@ class WatsonXChatHandler(OpenAILikeChatHandler):
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)
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return super().completion(
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model=watsonx_auth_payload.get("model_id", None),
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model=watsonx_auth_payload.get("model_id") or "",
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messages=messages,
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api_base=api_base,
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custom_llm_provider=custom_llm_provider,
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@ -17,7 +17,6 @@ import logging
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import threading
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import time
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import traceback
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from litellm._uuid import uuid
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from collections import defaultdict
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from functools import lru_cache
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from typing import (
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@ -45,6 +44,7 @@ import litellm.litellm_core_utils
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import litellm.litellm_core_utils.exception_mapping_utils
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from litellm import get_secret_str
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from litellm._logging import verbose_router_logger
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from litellm._uuid import uuid
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from litellm.caching.caching import (
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DualCache,
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InMemoryCache,
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@ -2005,11 +2005,17 @@ class Router:
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# Filter out prompt management specific parameters from data before merging
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prompt_management_params = {
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"bitbucket_config", "dotprompt_config", "prompt_id",
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"prompt_variables", "prompt_label", "prompt_version"
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"bitbucket_config",
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"dotprompt_config",
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"prompt_id",
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"prompt_variables",
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"prompt_label",
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"prompt_version",
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}
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filtered_data = {k: v for k, v in data.items() if k not in prompt_management_params}
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filtered_data = {
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k: v for k, v in data.items() if k not in prompt_management_params
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}
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kwargs = {**filtered_data, **kwargs, **optional_params}
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kwargs["model"] = model
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kwargs["messages"] = messages
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@ -4108,7 +4114,9 @@ class Router:
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"""
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model_group = kwargs.get("model")
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response = original_function(*args, **kwargs)
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if coroutine_checker.is_async_callable(response) or inspect.isawaitable(response):
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if coroutine_checker.is_async_callable(response) or inspect.isawaitable(
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response
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):
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response = await response
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## PROCESS RESPONSE HEADERS
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response = await self.set_response_headers(
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@ -4517,7 +4525,9 @@ class Router:
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_time_to_cooldown = self.cooldown_time
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if isinstance(_model_info, dict):
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deployment_id = _model_info.get("id", None)
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deployment_id: Optional[str] = _model_info.get("id")
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if deployment_id is None:
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return False
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increment_deployment_failures_for_current_minute(
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litellm_router_instance=self,
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deployment_id=deployment_id,
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@ -5134,12 +5144,12 @@ class Router:
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# Check if this is a prompt management model before validating as LLM provider
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litellm_model = deployment.litellm_params.model
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is_prompt_management_model = False
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if "/" in litellm_model:
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split_litellm_model = litellm_model.split("/")[0]
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if split_litellm_model in litellm._known_custom_logger_compatible_callbacks:
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is_prompt_management_model = True
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if is_prompt_management_model:
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# For prompt management models, skip LLM provider validation
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# The actual model will be resolved at runtime from the prompt file
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@ -5229,11 +5239,12 @@ class Router:
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# litellm_router_instance=self, model=deployment.to_json(exclude_none=True)
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# )
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self._initialize_deployment_for_pass_through(
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deployment=deployment,
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custom_llm_provider=custom_llm_provider,
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model=deployment.litellm_params.model,
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)
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if custom_llm_provider is not None:
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self._initialize_deployment_for_pass_through(
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deployment=deployment,
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custom_llm_provider=custom_llm_provider,
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model=deployment.litellm_params.model,
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)
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#########################################################
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# Check if this is an auto-router deployment
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