fix linting issues

litellm/llms/vllm.py

@@ -3,7 +3,7 @@ import json
 from enum import Enum
 import requests
 import time
-from typing import Callable
+from typing import Callable, Any
 from litellm.utils import ModelResponse
 from .prompt_templates.factory import prompt_factory, custom_prompt
 llm = None
@@ -16,7 +16,7 @@ class VLLMError(Exception):
         )  # Call the base class constructor with the parameters it needs
 
 # check if vllm is installed
-def validate_environment(model: str, llm: any=None):
+def validate_environment(model: str, llm: Any =None):
     try: 
         from vllm import LLM, SamplingParams
         if llm is None:
@@ -37,6 +37,7 @@ def completion(
     litellm_params=None,
     logger_fn=None,
 ):
+    global llm
     try:
         llm, SamplingParams = validate_environment(model=model)
     except Exception as e:
@@ -62,7 +63,10 @@ def completion(
         additional_args={"complete_input_dict": sampling_params},
     )
 
-    outputs = llm.generate(prompt, sampling_params)
+    if llm:
+        outputs = llm.generate(prompt, sampling_params)
+    else:
+        raise VLLMError(status_code=0, message="Need to pass in a model name to initialize vllm")
 
     
     ## COMPLETION CALL
@@ -128,10 +132,11 @@ def batch_completions(
     try:
         llm, SamplingParams = validate_environment(model=model, llm=llm)
     except Exception as e:
-        if "data parallel group is already initialized" in e:
+        error_str = str(e)
+        if "data parallel group is already initialized" in error_str:
             pass 
         else:
-            raise VLLMError(status_code=0, message=str(e))
+            raise VLLMError(status_code=0, message=error_str)
     sampling_params = SamplingParams(**optional_params)
     prompts = [] 
     if model in custom_prompt_dict:
@@ -150,7 +155,10 @@ def batch_completions(
             prompt = prompt_factory(model=model, messages=message)
             prompts.append(prompt)
     
-    outputs = llm.generate(prompts, sampling_params)
+    if llm:
+        outputs = llm.generate(prompts, sampling_params)
+    else:
+        raise VLLMError(status_code=0, message="Need to pass in a model name to initialize vllm")
 
     final_outputs = []
     for output in outputs:

litellm/main.py

@@ -844,6 +844,13 @@ def batch_completion(
     frequency_penalty=0,
     logit_bias: dict = {},
     user: str = "",
+    # Optional liteLLM function params
+    *,
+    return_async=False,
+    api_key: Optional[str] = None,
+    api_version: Optional[str] = None,
+    api_base: Optional[str] = None,
+    force_timeout=600,
     # used by text-bison only
     top_k=40,
     custom_llm_provider=None,):