fixed acompletion import bug

build/lib/litellm/__init__.py

@@ -3,13 +3,22 @@ failure_callback = []
 set_verbose=False
 telemetry=True
 max_tokens = 256 # OpenAI Defaults
+retry = True # control tenacity retries. 
 ####### PROXY PARAMS ################### configurable params if you use proxy models like Helicone
 api_base = None
 headers = None
 ####### COMPLETION MODELS ###################
 open_ai_chat_completion_models = [
+  "gpt-4",
+  "gpt-4-0613",
+  "gpt-4-32k",
+  "gpt-4-32k-0613",
+  #################
+  "gpt-3.5-turbo",
+  "gpt-3.5-turbo-16k",
+  "gpt-3.5-turbo-0613",
+  "gpt-3.5-turbo-16k-0613",
   'gpt-3.5-turbo', 
-  'gpt-4',
   'gpt-3.5-turbo-16k-0613',
   'gpt-3.5-turbo-16k'
 ]
@@ -32,7 +41,6 @@ model_list = open_ai_chat_completion_models + open_ai_text_completion_models + c
 open_ai_embedding_models = [
     'text-embedding-ada-002'
 ]
-
 from .timeout import timeout
 from .utils import client, logging, exception_type  # Import all the symbols from main.py
 from .main import *  # Import all the symbols from main.py

build/lib/litellm/main.py

@@ -2,11 +2,18 @@ import os, openai, cohere, replicate, sys
 from typing import Any
 from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
 import traceback
+from functools import partial
 import dotenv
 import traceback
 import litellm
 from litellm import client, logging, exception_type, timeout, success_callback, failure_callback
 import random
+import asyncio
+from tenacity import (
+    retry,
+    stop_after_attempt,
+    wait_random_exponential,
+)  # for exponential backoff
 ####### ENVIRONMENT VARIABLES ###################
 dotenv.load_dotenv() # Loading env variables using dotenv
 
@@ -24,6 +31,7 @@ def get_optional_params(
     frequency_penalty = 0,
     logit_bias = {},
     user = "",
+    deployment_id = None
 ):
   optional_params = {}
   if functions != []:
@@ -50,27 +58,39 @@ def get_optional_params(
       optional_params["logit_bias"] = logit_bias
   if user != "":
       optional_params["user"] = user
+  if deployment_id != None:
+      optional_params["deployment_id"] = user
   return optional_params
 
 ####### COMPLETION ENDPOINTS ################
 #############################################
+async def acompletion(*args, **kwargs):
+  loop = asyncio.get_event_loop()
+  
+  # Use a partial function to pass your keyword arguments
+  func = partial(completion, *args, **kwargs)
+
+  # Call the synchronous function using run_in_executor
+  return await loop.run_in_executor(None, func)
+
 @client
+@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(2), reraise=True, retry_error_callback=lambda retry_state: setattr(retry_state.outcome, 'retry_variable', litellm.retry)) # retry call, turn this off by setting `litellm.retry = False`
 @timeout(60) ## set timeouts, in case calls hang (e.g. Azure) - default is 60s, override with `force_timeout`
 def completion(
     model, messages, # required params
     # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create
     functions=[], function_call="", # optional params
     temperature=1, top_p=1, n=1, stream=False, stop=None, max_tokens=float('inf'),
-    presence_penalty=0, frequency_penalty=0, logit_bias={}, user="",
+    presence_penalty=0, frequency_penalty=0, logit_bias={}, user="", deployment_id=None,
     # Optional liteLLM function params
-    *, api_key=None, force_timeout=60, azure=False, logger_fn=None, verbose=False
+    *, return_async=False, api_key=None, force_timeout=60, azure=False, logger_fn=None, verbose=False
   ):
   try:
     # check if user passed in any of the OpenAI optional params
     optional_params = get_optional_params(
       functions=functions, function_call=function_call, 
       temperature=temperature, top_p=top_p, n=n, stream=stream, stop=stop, max_tokens=max_tokens,
-      presence_penalty=presence_penalty, frequency_penalty=frequency_penalty, logit_bias=logit_bias, user=user
+      presence_penalty=presence_penalty, frequency_penalty=frequency_penalty, logit_bias=logit_bias, user=user, deployment_id=deployment_id
     )
     if azure == True:
       # azure configs
@@ -247,7 +267,6 @@ def completion(
     ## Map to OpenAI Exception
     raise exception_type(model=model, original_exception=e)
 
-
 ### EMBEDDING ENDPOINTS ####################
 @client
 @timeout(60) ## set timeouts, in case calls hang (e.g. Azure) - default is 60s, override with `force_timeout`

build/lib/litellm/timeout.py

@@ -37,26 +37,29 @@ def timeout(
             thread = _LoopWrapper()
             thread.start()
             future = asyncio.run_coroutine_threadsafe(async_func(), thread.loop)
+            local_timeout_duration = timeout_duration
+            if "force_timeout" in kwargs:
+                local_timeout_duration = kwargs["force_timeout"]
             try:
-                local_timeout_duration = timeout_duration
-                if "force_timeout" in kwargs:
-                    local_timeout_duration = kwargs["force_timeout"]
                 result = future.result(timeout=local_timeout_duration)
             except futures.TimeoutError:
                 thread.stop_loop()
-                raise exception_to_raise()
+                raise exception_to_raise(f"A timeout error occurred. The function call took longer than {local_timeout_duration} second(s).")
             thread.stop_loop()
             return result
 
         @wraps(func)
         async def async_wrapper(*args, **kwargs):
+            local_timeout_duration = timeout_duration
+            if "force_timeout" in kwargs:
+                local_timeout_duration = kwargs["force_timeout"]
             try:
                 value = await asyncio.wait_for(
                     func(*args, **kwargs), timeout=timeout_duration
                 )
                 return value
             except asyncio.TimeoutError:
-                raise exception_to_raise()
+                raise exception_to_raise(f"A timeout error occurred. The function call took longer than {local_timeout_duration} second(s).")
 
         if iscoroutinefunction(func):
             return async_wrapper

litellm.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: litellm
-Version: 0.1.226
+Version: 0.1.2291
 Summary: Library to easily interface with LLM API providers
 Author: BerriAI
 License-File: LICENSE

litellm.egg-info/requires.txt

@@ -5,3 +5,4 @@ anthropic
 replicate
 python-dotenv
 openai[datalib]
+tenacity

litellm/__init__.py

@@ -41,7 +41,6 @@ model_list = open_ai_chat_completion_models + open_ai_text_completion_models + c
 open_ai_embedding_models = [
     'text-embedding-ada-002'
 ]
-
 from .timeout import timeout
 from .utils import client, logging, exception_type  # Import all the symbols from main.py
 from .main import *  # Import all the symbols from main.py

litellm/main.py

@@ -64,6 +64,15 @@ def get_optional_params(
 
 ####### COMPLETION ENDPOINTS ################
 #############################################
+async def acompletion(*args, **kwargs):
+  loop = asyncio.get_event_loop()
+  
+  # Use a partial function to pass your keyword arguments
+  func = partial(completion, *args, **kwargs)
+
+  # Call the synchronous function using run_in_executor
+  return await loop.run_in_executor(None, func)
+
 @client
 @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(2), reraise=True, retry_error_callback=lambda retry_state: setattr(retry_state.outcome, 'retry_variable', litellm.retry)) # retry call, turn this off by setting `litellm.retry = False`
 @timeout(60) ## set timeouts, in case calls hang (e.g. Azure) - default is 60s, override with `force_timeout`
@@ -258,16 +267,6 @@ def completion(
     ## Map to OpenAI Exception
     raise exception_type(model=model, original_exception=e)
 
-
-async def acompletion(*args, **kwargs):
-  loop = asyncio.get_event_loop()
-  
-  # Use a partial function to pass your keyword arguments
-  func = partial(completion, *args, **kwargs)
-
-  # Call the synchronous function using run_in_executor
-  return await loop.run_in_executor(None, func)
-
 ### EMBEDDING ENDPOINTS ####################
 @client
 @timeout(60) ## set timeouts, in case calls hang (e.g. Azure) - default is 60s, override with `force_timeout`

setup.py

@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(
     name='litellm',
-    version='0.1.228',
+    version='0.1.2291',
     description='Library to easily interface with LLM API providers',
     author='BerriAI',
     packages=[