From fa65f960e349a7ba617106fc69992c042c5405cf Mon Sep 17 00:00:00 2001
From: Krrish Dholakia <krrishdholakia@gmail.com>
Date: Mon, 31 Jul 2023 08:46:23 -0700
Subject: [PATCH] version 0.1.2

---
 .DS_Store                                | Bin 8196 -> 8196 bytes
 .env.example                             |   1 -
 build/lib/litellm/main.py                | 488 ++++++++++++++++++-----
 completion_test.py                       |   5 -
 dist/litellm-0.1.0-py3-none-any.whl      | Bin 2965 -> 0 bytes
 dist/litellm-0.1.0.tar.gz                | Bin 2495 -> 0 bytes
 dist/litellm-0.1.1-py3-none-any.whl      | Bin 2961 -> 0 bytes
 dist/litellm-0.1.1.tar.gz                | Bin 2488 -> 0 bytes
 dist/litellm-0.1.2-py3-none-any.whl      | Bin 0 -> 6039 bytes
 dist/litellm-0.1.2.tar.gz                | Bin 0 -> 6289 bytes
 litellm.egg-info/PKG-INFO                |  10 +-
 litellm/.DS_Store                        | Bin 0 -> 6148 bytes
 litellm/__pycache__/main.cpython-311.pyc | Bin 6147 -> 19936 bytes
 litellm/main.py                          | 487 +++++++++++++++++-----
 litellm/tests/test_bad_params.py         |  20 +
 litellm/tests/test_client.py             |  59 +++
 litellm/tests/test_logging.py            |  48 +++
 setup.py                                 |   4 +-
 18 files changed, 888 insertions(+), 234 deletions(-)
 delete mode 100644 dist/litellm-0.1.0-py3-none-any.whl
 delete mode 100644 dist/litellm-0.1.0.tar.gz
 delete mode 100644 dist/litellm-0.1.1-py3-none-any.whl
 delete mode 100644 dist/litellm-0.1.1.tar.gz
 create mode 100644 dist/litellm-0.1.2-py3-none-any.whl
 create mode 100644 dist/litellm-0.1.2.tar.gz
 create mode 100644 litellm/.DS_Store
 create mode 100644 litellm/tests/test_bad_params.py
 create mode 100644 litellm/tests/test_client.py
 create mode 100644 litellm/tests/test_logging.py

diff --git a/.DS_Store b/.DS_Store
index 51ab802dc6324429763fc183c8681238d2ca1e91..639e40acb9a4e4059e7fc62399755feea3a8a4a0 100644
GIT binary patch
delta 46
zcmZp1XmOa}&nUPtU^hRb;AS2H5f;vr;^ds9{QMlo$$TP*Hh+;3V4m2(vzcAuFFOEJ
CMGo2k

delta 33
pcmZp1XmOa}&nU1lU^hRbz-Ar+5thl;A{#be;4x?3%r5bl9RRW53T*%Y

diff --git a/.env.example b/.env.example
index 7889a204c..276feaba8 100644
--- a/.env.example
+++ b/.env.example
@@ -1,5 +1,4 @@
 OPENAI_API_KEY = ""
 COHERE_API_KEY = ""
-OPENROUTER_API_KEY = ""
 OR_SITE_URL = ""
 OR_APP_NAME = "LiteLLM Example app"
\ No newline at end of file
diff --git a/build/lib/litellm/main.py b/build/lib/litellm/main.py
index 053d796e1..d4fc60053 100644
--- a/build/lib/litellm/main.py
+++ b/build/lib/litellm/main.py
@@ -1,7 +1,17 @@
-import os, openai, cohere, dotenv
-
+import os, openai, cohere, replicate, sys
+from typing import Any
+from func_timeout import func_set_timeout, FunctionTimedOut
+from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
+import json
+import traceback
+import threading
+import dotenv
+import traceback
+import subprocess
+####### ENVIRONMENT VARIABLES ###################
 # Loading env variables using dotenv
 dotenv.load_dotenv()
+set_verbose = False
 
 ####### COMPLETION MODELS ###################
 open_ai_chat_completion_models = [
@@ -16,16 +26,9 @@ cohere_models = [
     'command-nightly',
 ]
 
-openrouter_models = [
-    'google/palm-2-codechat-bison',
-    'google/palm-2-chat-bison',
-    'openai/gpt-3.5-turbo',
-    'openai/gpt-3.5-turbo-16k',
-    'openai/gpt-4-32k',
-    'anthropic/claude-2',
-    'anthropic/claude-instant-v1',
-    'meta-llama/llama-2-13b-chat',
-    'meta-llama/llama-2-70b-chat'
+anthropic_models = [
+  "claude-2", 
+  "claude-instant-1"
 ]
 
 ####### EMBEDDING MODELS ###################
@@ -38,122 +41,389 @@ open_ai_embedding_models = [
 
 ####### COMPLETION ENDPOINTS ################
 #############################################
-def completion(model, messages, azure=False):
-  if azure == True:
-    # azure configs
-    openai.api_type = "azure"
-    openai.api_base = os.environ.get("AZURE_API_BASE")
-    openai.api_version = os.environ.get("AZURE_API_VERSION")
-    openai.api_key =   os.environ.get("AZURE_API_KEY")
-    response = openai.ChatCompletion.create(
-      engine=model,
-      messages = messages
-    )
-  elif "replicate" in model: 
-    prompt = " ".join([message["content"] for message in messages])
-    output = replicate.run(
-      model,
-      input={
-        "prompt": prompt,
-      })
-    print(f"output: {output}")
-    response = ""
-    for item in output: 
-      print(f"item: {item}")
-      response += item
-    new_response = {
-      "choices": [
-        {
-          "finish_reason": "stop",
-          "index": 0,
-          "message": {
-              "content": response,
-              "role": "assistant"
-          }
-        }
-      ]
-    }
-    print(f"new response: {new_response}")
-    response = new_response
-  elif model in cohere_models:
-    cohere_key = os.environ.get("COHERE_API_KEY")
-    co = cohere.Client(cohere_key)
-    prompt = " ".join([message["content"] for message in messages])
-    response = co.generate(  
-      model=model,
-      prompt = prompt
-    )
-    new_response = {
-        "choices": [
-            {
-                "finish_reason": "stop",
-                "index": 0,
-                "message": {
-                    "content": response[0],
-                    "role": "assistant"
-                }
-            }
-        ],
-    }
-
-    response = new_response
-
-  elif model in open_ai_chat_completion_models:
-    openai.api_type = "openai"
-    openai.api_base = "https://api.openai.com/v1"
-    openai.api_version = None
-    openai.api_key = os.environ.get("OPENAI_API_KEY")
-    response = openai.ChatCompletion.create(
-        model=model,
+@func_set_timeout(10, allowOverride=True) ## https://pypi.org/project/func-timeout/ - timeouts, in case calls hang (e.g. Azure)
+def completion(model, messages, max_tokens=None, forceTimeout=10, azure=False, logger_fn=None):
+  try:
+    if azure == True:
+      # azure configs
+      openai.api_type = "azure"
+      openai.api_base = os.environ.get("AZURE_API_BASE")
+      openai.api_version = os.environ.get("AZURE_API_VERSION")
+      openai.api_key = os.environ.get("AZURE_API_KEY")
+      ## LOGGING
+      logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
+      ## COMPLETION CALL
+      response = openai.ChatCompletion.create(
+        engine=model,
         messages = messages
-    )
-  elif model in open_ai_text_completion_models:
-    openai.api_type = "openai"
-    openai.api_base = "https://api.openai.com/v1"
-    openai.api_version = None
-    openai.api_key = os.environ.get("OPENAI_API_KEY")
-    prompt = " ".join([message["content"] for message in messages])
-    response = openai.Completion.create(
+      )
+    elif "replicate" in model: 
+      # replicate defaults to os.environ.get("REPLICATE_API_TOKEN")
+      # checking in case user set it to REPLICATE_API_KEY instead 
+      if not os.environ.get("REPLICATE_API_TOKEN") and  os.environ.get("REPLICATE_API_KEY"):
+        replicate_api_token = os.environ.get("REPLICATE_API_KEY")
+        os.environ["REPLICATE_API_TOKEN"] = replicate_api_token
+      prompt = " ".join([message["content"] for message in messages])
+      input = [{"prompt": prompt}]
+      if max_tokens:
+        input["max_length"] = max_tokens # for t5 models 
+        input["max_new_tokens"] = max_tokens # for llama2 models 
+      ## LOGGING
+      logging(model=model, input=input, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn)
+      ## COMPLETION CALL
+      output = replicate.run(
+        model,
+        input=input)
+      response = ""
+      for item in output: 
+        response += item
+      new_response = {
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "message": {
+                "content": response,
+                "role": "assistant"
+            }
+          }
+        ]
+      }
+      response = new_response
+    elif model in anthropic_models:
+      #anthropic defaults to os.environ.get("ANTHROPIC_API_KEY")
+      prompt = f"{HUMAN_PROMPT}" 
+      for message in messages:
+        if "role" in message:
+          if message["role"] == "user":
+            prompt += f"{HUMAN_PROMPT}{message['content']}"
+          else:
+            prompt += f"{AI_PROMPT}{message['content']}"
+        else:
+          prompt += f"{HUMAN_PROMPT}{message['content']}"
+      prompt += f"{AI_PROMPT}"
+      anthropic = Anthropic()
+      if max_tokens:
+        max_tokens_to_sample = max_tokens
+      else: 
+        max_tokens_to_sample = 300 # default in Anthropic docs https://docs.anthropic.com/claude/reference/client-libraries
+      ## LOGGING
+      logging(model=model, input=prompt, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn)
+      ## COMPLETION CALL
+      completion = anthropic.completions.create(
+          model=model,
+          prompt=prompt,
+          max_tokens_to_sample=max_tokens_to_sample
+      )
+      new_response = {
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "message": {
+                "content": completion.completion,
+                "role": "assistant"
+            }
+          }
+        ]
+      }
+      print(f"new response: {new_response}")
+      response = new_response
+    elif model in cohere_models:
+      cohere_key = os.environ.get("COHERE_API_KEY")
+      co = cohere.Client(cohere_key)
+      prompt = " ".join([message["content"] for message in messages])
+      ## LOGGING
+      logging(model=model, input=prompt, azure=azure, logger_fn=logger_fn)
+      ## COMPLETION CALL
+      response = co.generate(  
         model=model,
         prompt = prompt
-    )
-
-  elif model in openrouter_models:
-    openai.api_base = "https://openrouter.ai/api/v1"
-    openai.api_key = os.environ.get("OPENROUTER_API_KEY")
-
-    prompt = " ".join([message["content"] for message in messages])
-
-    response = openai.ChatCompletion.create(
-      model=model,
-      messages=messages,
-      headers={ 
-        "HTTP-Referer": os.environ.get("OR_SITE_URL"), # To identify your app
-        "X-Title": os.environ.get("OR_APP_NAME") 
-        },
-    )
-    reply = response.choices[0].message
-  return response
+      )
+      new_response = {
+          "choices": [
+              {
+                  "finish_reason": "stop",
+                  "index": 0,
+                  "message": {
+                      "content": response[0],
+                      "role": "assistant"
+                  }
+              }
+          ],
+      }
+      response = new_response
 
+    elif model in open_ai_chat_completion_models:
+      openai.api_type = "openai"
+      openai.api_base = "https://api.openai.com/v1"
+      openai.api_version = None
+      openai.api_key = os.environ.get("OPENAI_API_KEY")
+      ## LOGGING
+      logging(model=model, input=messages, azure=azure, logger_fn=logger_fn)
+      ## COMPLETION CALL
+      response = openai.ChatCompletion.create(
+          model=model,
+          messages = messages
+      )
+    elif model in open_ai_text_completion_models:
+      openai.api_type = "openai"
+      openai.api_base = "https://api.openai.com/v1"
+      openai.api_version = None
+      openai.api_key = os.environ.get("OPENAI_API_KEY")
+      prompt = " ".join([message["content"] for message in messages])
+      ## LOGGING
+      logging(model=model, input=prompt, azure=azure, logger_fn=logger_fn)
+      ## COMPLETION CALL
+      response = openai.Completion.create(
+          model=model,
+          prompt = prompt
+      )
+    else: 
+      logging(model=model, input=messages, azure=azure, logger_fn=logger_fn)
+    return response
+  except Exception as e:
+    logging(model=model, input=messages, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn)
+    raise e
 
 
 ### EMBEDDING ENDPOINTS ####################
-def embedding(model, input=[], azure=False):
+@func_set_timeout(60, allowOverride=True) ## https://pypi.org/project/func-timeout/
+def embedding(model, input=[], azure=False, forceTimeout=60, logger_fn=None):
+  response = None
   if azure == True:
     # azure configs
     openai.api_type = "azure"
     openai.api_base = os.environ.get("AZURE_API_BASE")
     openai.api_version = os.environ.get("AZURE_API_VERSION")
-    openai.api_key =   os.environ.get("AZURE_API_KEY")
+    openai.api_key = os.environ.get("AZURE_API_KEY")
+    ## LOGGING
+    logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
+    ## EMBEDDING CALL
     response = openai.Embedding.create(input=input, engine=model)
+    print_verbose(f"response_value: {str(response)[:50]}")
   elif model in open_ai_embedding_models:
     openai.api_type = "openai"
     openai.api_base = "https://api.openai.com/v1"
     openai.api_version = None
     openai.api_key = os.environ.get("OPENAI_API_KEY")
+    ## LOGGING
+    logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
+    ## EMBEDDING CALL
     response = openai.Embedding.create(input=input, model=model)
+    print_verbose(f"response_value: {str(response)[:50]}")
+  else: 
+    logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
+  
   return response
 
 
-#############################################
-#############################################
\ No newline at end of file
+### CLIENT CLASS #################### make it easy to push completion/embedding runs to different sources -> sentry/posthog/slack, etc.
+class litellm_client:
+  def __init__(self, success_callback=[], failure_callback=[], verbose=False):  # Constructor
+      set_verbose = verbose
+      self.success_callback = success_callback
+      self.failure_callback = failure_callback
+      self.logger_fn = None # if user passes in their own logging function
+      self.callback_list = list(set(self.success_callback + self.failure_callback))
+      self.set_callbacks()
+  
+  ## COMPLETION CALL 
+  def completion(self, model, messages, max_tokens=None, forceTimeout=10, azure=False, logger_fn=None, additional_details={}) -> Any:
+    try:
+      self.logger_fn = logger_fn
+      response = completion(model=model, messages=messages, max_tokens=max_tokens, forceTimeout=forceTimeout, azure=azure, logger_fn=self.handle_input)
+      my_thread = threading.Thread(target=self.handle_success, args=(model, messages, additional_details)) # don't interrupt execution of main thread
+      my_thread.start()
+      return response
+    except Exception as e: 
+      args = locals() # get all the param values
+      self.handle_failure(e, args)
+      raise e
+
+  ## EMBEDDING CALL 
+  def embedding(self, model, input=[], azure=False, logger_fn=None, forceTimeout=60, additional_details={}) -> Any:
+    try:
+      self.logger_fn = logger_fn
+      response = embedding(model, input, azure=azure, logger_fn=self.handle_input)
+      my_thread = threading.Thread(target=self.handle_success, args=(model, input, additional_details)) # don't interrupt execution of main thread
+      my_thread.start()
+      return response
+    except Exception as e:
+      args = locals() # get all the param values 
+      self.handle_failure(e, args)
+      raise e
+
+
+  def set_callbacks(self):  #instantiate any external packages
+    for callback in self.callback_list: # only install what's required
+      if callback == "sentry":
+        try:
+          import sentry_sdk
+        except ImportError:
+          print_verbose("Package 'sentry_sdk' is missing. Installing it...")
+          subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'sentry_sdk'])
+          import sentry_sdk
+        self.sentry_sdk = sentry_sdk
+        self.sentry_sdk.init(dsn=os.environ.get("SENTRY_API_URL"), traces_sample_rate=float(os.environ.get("SENTRY_API_TRACE_RATE")))
+        self.capture_exception = self.sentry_sdk.capture_exception
+        self.add_breadcrumb = self.sentry_sdk.add_breadcrumb
+      elif callback == "posthog":
+        try:
+          from posthog import Posthog
+        except:
+          print_verbose("Package 'posthog' is missing. Installing it...")
+          subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'posthog'])
+          from posthog import Posthog
+        self.posthog = Posthog(
+            project_api_key=os.environ.get("POSTHOG_API_KEY"),
+            host=os.environ.get("POSTHOG_API_URL"))
+      elif callback == "slack":
+        try:
+          from slack_bolt import App
+        except ImportError:
+          print_verbose("Package 'slack_bolt' is missing. Installing it...")
+          subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'slack_bolt'])
+          from slack_bolt import App
+        self.slack_app = App(
+          token=os.environ.get("SLACK_API_TOKEN"),
+          signing_secret=os.environ.get("SLACK_API_SECRET")
+        )
+        self.alerts_channel = os.environ["SLACK_API_CHANNEL"]
+
+  def handle_input(self, model_call_details={}):
+      if len(model_call_details.keys()) > 0:
+        model = model_call_details["model"] if "model" in model_call_details else None
+        if model:
+          for callback in self.callback_list:
+            if callback == "sentry": # add a sentry breadcrumb if user passed in sentry integration
+              self.add_breadcrumb(
+                category=f'{model}',
+                message='Trying request model {} input {}'.format(model, json.dumps(model_call_details)),
+                level='info',
+              )
+          if self.logger_fn and callable(self.logger_fn):
+            self.logger_fn(model_call_details)
+      pass
+
+  def handle_success(self, model, messages, additional_details):
+    success_handler = additional_details.pop("success_handler", None)
+    failure_handler = additional_details.pop("failure_handler", None)
+    additional_details["litellm_model"] = str(model)
+    additional_details["litellm_messages"] = str(messages)
+    for callback in self.success_callback:
+      try:
+        if callback == "posthog":
+          ph_obj = {}
+          for detail in additional_details:
+            ph_obj[detail] = additional_details[detail]
+          event_name = additional_details["successful_event"] if "successful_event" in additional_details else "litellm.succes_query"
+          if "user_id" in additional_details:
+            self.posthog.capture(additional_details["user_id"], event_name, ph_obj)
+          else: 
+            self.posthog.capture(event_name, ph_obj)
+          pass
+        elif callback == "slack":
+          slack_msg = "" 
+          if len(additional_details.keys()) > 0:
+            for detail in additional_details: 
+              slack_msg += f"{detail}: {additional_details[detail]}\n"
+          slack_msg += f"Successful call"
+          self.slack_app.client.chat_postMessage(channel=self.alerts_channel, text=slack_msg)
+      except:
+        pass
+    
+    if success_handler and callable(success_handler):
+      call_details = {
+        "model": model,
+        "messages": messages,
+        "additional_details": additional_details
+      }
+      success_handler(call_details)
+    pass
+
+  def handle_failure(self, exception, args):
+    args.pop("self")
+    additional_details = args.pop("additional_details", {})
+
+    success_handler = additional_details.pop("success_handler", None)
+    failure_handler = additional_details.pop("failure_handler", None)
+
+    for callback in self.failure_callback:
+      try:
+        if callback == "slack":
+          slack_msg = "" 
+          for param in args: 
+            slack_msg += f"{param}: {args[param]}\n"
+          if len(additional_details.keys()) > 0:
+            for detail in additional_details: 
+              slack_msg += f"{detail}: {additional_details[detail]}\n"
+          slack_msg += f"Traceback: {traceback.format_exc()}"
+          self.slack_app.client.chat_postMessage(channel=self.alerts_channel, text=slack_msg)
+        elif callback == "sentry":
+          self.capture_exception(exception)
+        elif callback == "posthog":
+          if len(additional_details.keys()) > 0:
+            ph_obj = {}
+            for param in args: 
+              ph_obj[param] += args[param]
+            for detail in additional_details:
+              ph_obj[detail] = additional_details[detail]
+            event_name = additional_details["failed_event"] if "failed_event" in additional_details else "litellm.failed_query"
+            if "user_id" in additional_details:
+              self.posthog.capture(additional_details["user_id"], event_name, ph_obj)
+            else: 
+              self.posthog.capture(event_name, ph_obj)
+          else: 
+            pass
+      except:
+        print(f"got an error calling {callback} - {traceback.format_exc()}")
+    
+    if failure_handler and callable(failure_handler):
+      call_details = {
+        "exception": exception,
+        "additional_details": additional_details
+      }
+      failure_handler(call_details)
+    pass
+####### HELPER FUNCTIONS ################
+
+#Logging function -> log the exact model details + what's being sent | Non-Blocking
+def logging(model, input, azure=False, additional_args={}, logger_fn=None):
+  try:
+    model_call_details = {}
+    model_call_details["model"] = model
+    model_call_details["input"] = input
+    model_call_details["azure"] = azure
+    model_call_details["additional_args"] = additional_args
+    if logger_fn and callable(logger_fn):
+      try:
+        # log additional call details -> api key, etc. 
+        if azure == True or model in open_ai_chat_completion_models or model in open_ai_chat_completion_models or model in open_ai_embedding_models:
+          model_call_details["api_type"] = openai.api_type
+          model_call_details["api_base"] = openai.api_base
+          model_call_details["api_version"] = openai.api_version
+          model_call_details["api_key"] = openai.api_key
+        elif "replicate" in model:
+          model_call_details["api_key"] = os.environ.get("REPLICATE_API_TOKEN")
+        elif model in anthropic_models:
+          model_call_details["api_key"] = os.environ.get("ANTHROPIC_API_KEY")
+        elif model in cohere_models:
+          model_call_details["api_key"] = os.environ.get("COHERE_API_KEY")
+        
+        logger_fn(model_call_details) # Expectation: any logger function passed in by the user should accept a dict object
+      except:
+        print_verbose(f"Basic model call details: {model_call_details}")
+        print_verbose(f"[Non-Blocking] Exception occurred while logging {traceback.format_exc()}")
+        pass
+    else:
+      print_verbose(f"Basic model call details: {model_call_details}")
+      pass
+  except:
+    pass
+
+## Set verbose to true -> ```litellm.verbose = True```    
+def print_verbose(print_statement):
+  if set_verbose:
+    print(f"LiteLLM: {print_statement}")
+    print("Get help - https://discord.com/invite/wuPM9dRgDw")
\ No newline at end of file
diff --git a/completion_test.py b/completion_test.py
index a657e42a4..c9e74932c 100644
--- a/completion_test.py
+++ b/completion_test.py
@@ -26,9 +26,4 @@ print(response)
 # cohere call
 response = completion("command-nightly", messages)
 print("\nCohere call")
-print(response)
-
-# openrouter call
-response = completion("google/palm-2-codechat-bison", messages)
-print("\OpenRouter call")
 print(response)
\ No newline at end of file
diff --git a/dist/litellm-0.1.0-py3-none-any.whl b/dist/litellm-0.1.0-py3-none-any.whl
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diff --git a/litellm.egg-info/PKG-INFO b/litellm.egg-info/PKG-INFO
index cf19261b2..e8f0962bc 100644
--- a/litellm.egg-info/PKG-INFO
+++ b/litellm.egg-info/PKG-INFO
@@ -1,12 +1,6 @@
 Metadata-Version: 2.1
 Name: litellm
-Version: 0.1.1
+Version: 0.1.2
 Summary: Library to easily interface with LLM API providers
-Home-page: UNKNOWN
-Author: Ishaan Jaffer
-License: UNKNOWN
-Platform: UNKNOWN
+Author: BerriAI
 License-File: LICENSE
-
-UNKNOWN
-
diff --git a/litellm/.DS_Store b/litellm/.DS_Store
new file mode 100644
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diff --git a/litellm/main.py b/litellm/main.py
index 14de0b02e..d4fc60053 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -1,7 +1,17 @@
-import os, openai, cohere, dotenv
-
+import os, openai, cohere, replicate, sys
+from typing import Any
+from func_timeout import func_set_timeout, FunctionTimedOut
+from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
+import json
+import traceback
+import threading
+import dotenv
+import traceback
+import subprocess
+####### ENVIRONMENT VARIABLES ###################
 # Loading env variables using dotenv
 dotenv.load_dotenv()
+set_verbose = False
 
 ####### COMPLETION MODELS ###################
 open_ai_chat_completion_models = [
@@ -16,16 +26,9 @@ cohere_models = [
     'command-nightly',
 ]
 
-openrouter_models = [
-    'google/palm-2-codechat-bison',
-    'google/palm-2-chat-bison',
-    'openai/gpt-3.5-turbo',
-    'openai/gpt-3.5-turbo-16k',
-    'openai/gpt-4-32k',
-    'anthropic/claude-2',
-    'anthropic/claude-instant-v1',
-    'meta-llama/llama-2-13b-chat',
-    'meta-llama/llama-2-70b-chat'
+anthropic_models = [
+  "claude-2", 
+  "claude-instant-1"
 ]
 
 ####### EMBEDDING MODELS ###################
@@ -38,123 +41,389 @@ open_ai_embedding_models = [
 
 ####### COMPLETION ENDPOINTS ################
 #############################################
-def completion(model, messages, azure=False):
-  if azure == True:
-    # azure configs
-    openai.api_type = "azure"
-    openai.api_base = os.environ.get("AZURE_API_BASE")
-    openai.api_version = os.environ.get("AZURE_API_VERSION")
-    openai.api_key =   os.environ.get("AZURE_API_KEY")
-    response = openai.ChatCompletion.create(
-      engine=model,
-      messages = messages
-    )
-  elif "replicate" in model: 
-    prompt = " ".join([message["content"] for message in messages])
-    output = replicate.run(
-      model,
-      input={
-        "prompt": prompt,
-      })
-    print(f"output: {output}")
-    response = ""
-    for item in output: 
-      print(f"item: {item}")
-      response += item
-    new_response = {
-      "choices": [
-        {
-          "finish_reason": "stop",
-          "index": 0,
-          "message": {
-              "content": response,
-              "role": "assistant"
-          }
-        }
-      ]
-    }
-    print(f"new response: {new_response}")
-    response = new_response
-  elif model in cohere_models:
-    cohere_key = os.environ.get("COHERE_API_KEY")
-    co = cohere.Client(cohere_key)
-    prompt = " ".join([message["content"] for message in messages])
-    response = co.generate(  
-      model=model,
-      prompt = prompt
-    )
-    new_response = {
-        "choices": [
-            {
-                "finish_reason": "stop",
-                "index": 0,
-                "message": {
-                    "content": response[0],
-                    "role": "assistant"
-                }
-            }
-        ],
-    }
-
-    response = new_response
-
-  elif model in open_ai_chat_completion_models:
-    openai.api_type = "openai"
-    openai.api_base = "https://api.openai.com/v1"
-    openai.api_version = None
-    openai.api_key = os.environ.get("OPENAI_API_KEY")
-    response = openai.ChatCompletion.create(
-        model=model,
+@func_set_timeout(10, allowOverride=True) ## https://pypi.org/project/func-timeout/ - timeouts, in case calls hang (e.g. Azure)
+def completion(model, messages, max_tokens=None, forceTimeout=10, azure=False, logger_fn=None):
+  try:
+    if azure == True:
+      # azure configs
+      openai.api_type = "azure"
+      openai.api_base = os.environ.get("AZURE_API_BASE")
+      openai.api_version = os.environ.get("AZURE_API_VERSION")
+      openai.api_key = os.environ.get("AZURE_API_KEY")
+      ## LOGGING
+      logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
+      ## COMPLETION CALL
+      response = openai.ChatCompletion.create(
+        engine=model,
         messages = messages
-    )
-  elif model in open_ai_text_completion_models:
-    openai.api_type = "openai"
-    openai.api_base = "https://api.openai.com/v1"
-    openai.api_version = None
-    openai.api_key = os.environ.get("OPENAI_API_KEY")
-    prompt = " ".join([message["content"] for message in messages])
-    response = openai.Completion.create(
+      )
+    elif "replicate" in model: 
+      # replicate defaults to os.environ.get("REPLICATE_API_TOKEN")
+      # checking in case user set it to REPLICATE_API_KEY instead 
+      if not os.environ.get("REPLICATE_API_TOKEN") and  os.environ.get("REPLICATE_API_KEY"):
+        replicate_api_token = os.environ.get("REPLICATE_API_KEY")
+        os.environ["REPLICATE_API_TOKEN"] = replicate_api_token
+      prompt = " ".join([message["content"] for message in messages])
+      input = [{"prompt": prompt}]
+      if max_tokens:
+        input["max_length"] = max_tokens # for t5 models 
+        input["max_new_tokens"] = max_tokens # for llama2 models 
+      ## LOGGING
+      logging(model=model, input=input, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn)
+      ## COMPLETION CALL
+      output = replicate.run(
+        model,
+        input=input)
+      response = ""
+      for item in output: 
+        response += item
+      new_response = {
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "message": {
+                "content": response,
+                "role": "assistant"
+            }
+          }
+        ]
+      }
+      response = new_response
+    elif model in anthropic_models:
+      #anthropic defaults to os.environ.get("ANTHROPIC_API_KEY")
+      prompt = f"{HUMAN_PROMPT}" 
+      for message in messages:
+        if "role" in message:
+          if message["role"] == "user":
+            prompt += f"{HUMAN_PROMPT}{message['content']}"
+          else:
+            prompt += f"{AI_PROMPT}{message['content']}"
+        else:
+          prompt += f"{HUMAN_PROMPT}{message['content']}"
+      prompt += f"{AI_PROMPT}"
+      anthropic = Anthropic()
+      if max_tokens:
+        max_tokens_to_sample = max_tokens
+      else: 
+        max_tokens_to_sample = 300 # default in Anthropic docs https://docs.anthropic.com/claude/reference/client-libraries
+      ## LOGGING
+      logging(model=model, input=prompt, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn)
+      ## COMPLETION CALL
+      completion = anthropic.completions.create(
+          model=model,
+          prompt=prompt,
+          max_tokens_to_sample=max_tokens_to_sample
+      )
+      new_response = {
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "message": {
+                "content": completion.completion,
+                "role": "assistant"
+            }
+          }
+        ]
+      }
+      print(f"new response: {new_response}")
+      response = new_response
+    elif model in cohere_models:
+      cohere_key = os.environ.get("COHERE_API_KEY")
+      co = cohere.Client(cohere_key)
+      prompt = " ".join([message["content"] for message in messages])
+      ## LOGGING
+      logging(model=model, input=prompt, azure=azure, logger_fn=logger_fn)
+      ## COMPLETION CALL
+      response = co.generate(  
         model=model,
         prompt = prompt
-    )
-
-  elif model in openrouter_models:
-    openai.api_base = "https://openrouter.ai/api/v1"
-    openai.api_key = os.environ.get("OPENROUTER_API_KEY")
-
-    prompt = " ".join([message["content"] for message in messages])
-
-    response = openai.ChatCompletion.create(
-      model=model,
-      messages=messages,
-      headers={ 
-        "HTTP-Referer": os.environ.get("OR_SITE_URL"), # To identify your app
-        "X-Title": os.environ.get("OR_APP_NAME") 
-        },
-    )
-    reply = response.choices[0].message
-  return response
+      )
+      new_response = {
+          "choices": [
+              {
+                  "finish_reason": "stop",
+                  "index": 0,
+                  "message": {
+                      "content": response[0],
+                      "role": "assistant"
+                  }
+              }
+          ],
+      }
+      response = new_response
 
+    elif model in open_ai_chat_completion_models:
+      openai.api_type = "openai"
+      openai.api_base = "https://api.openai.com/v1"
+      openai.api_version = None
+      openai.api_key = os.environ.get("OPENAI_API_KEY")
+      ## LOGGING
+      logging(model=model, input=messages, azure=azure, logger_fn=logger_fn)
+      ## COMPLETION CALL
+      response = openai.ChatCompletion.create(
+          model=model,
+          messages = messages
+      )
+    elif model in open_ai_text_completion_models:
+      openai.api_type = "openai"
+      openai.api_base = "https://api.openai.com/v1"
+      openai.api_version = None
+      openai.api_key = os.environ.get("OPENAI_API_KEY")
+      prompt = " ".join([message["content"] for message in messages])
+      ## LOGGING
+      logging(model=model, input=prompt, azure=azure, logger_fn=logger_fn)
+      ## COMPLETION CALL
+      response = openai.Completion.create(
+          model=model,
+          prompt = prompt
+      )
+    else: 
+      logging(model=model, input=messages, azure=azure, logger_fn=logger_fn)
+    return response
+  except Exception as e:
+    logging(model=model, input=messages, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn)
+    raise e
 
 
 ### EMBEDDING ENDPOINTS ####################
-def embedding(model, input=[], azure=False):
+@func_set_timeout(60, allowOverride=True) ## https://pypi.org/project/func-timeout/
+def embedding(model, input=[], azure=False, forceTimeout=60, logger_fn=None):
+  response = None
   if azure == True:
     # azure configs
     openai.api_type = "azure"
     openai.api_base = os.environ.get("AZURE_API_BASE")
     openai.api_version = os.environ.get("AZURE_API_VERSION")
-    openai.api_key =   os.environ.get("AZURE_API_KEY")
+    openai.api_key = os.environ.get("AZURE_API_KEY")
+    ## LOGGING
+    logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
+    ## EMBEDDING CALL
     response = openai.Embedding.create(input=input, engine=model)
+    print_verbose(f"response_value: {str(response)[:50]}")
   elif model in open_ai_embedding_models:
     openai.api_type = "openai"
     openai.api_base = "https://api.openai.com/v1"
     openai.api_version = None
     openai.api_key = os.environ.get("OPENAI_API_KEY")
+    ## LOGGING
+    logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
+    ## EMBEDDING CALL
     response = openai.Embedding.create(input=input, model=model)
+    print_verbose(f"response_value: {str(response)[:50]}")
+  else: 
+    logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
+  
   return response
 
 
-#############################################
-#############################################
+### CLIENT CLASS #################### make it easy to push completion/embedding runs to different sources -> sentry/posthog/slack, etc.
+class litellm_client:
+  def __init__(self, success_callback=[], failure_callback=[], verbose=False):  # Constructor
+      set_verbose = verbose
+      self.success_callback = success_callback
+      self.failure_callback = failure_callback
+      self.logger_fn = None # if user passes in their own logging function
+      self.callback_list = list(set(self.success_callback + self.failure_callback))
+      self.set_callbacks()
+  
+  ## COMPLETION CALL 
+  def completion(self, model, messages, max_tokens=None, forceTimeout=10, azure=False, logger_fn=None, additional_details={}) -> Any:
+    try:
+      self.logger_fn = logger_fn
+      response = completion(model=model, messages=messages, max_tokens=max_tokens, forceTimeout=forceTimeout, azure=azure, logger_fn=self.handle_input)
+      my_thread = threading.Thread(target=self.handle_success, args=(model, messages, additional_details)) # don't interrupt execution of main thread
+      my_thread.start()
+      return response
+    except Exception as e: 
+      args = locals() # get all the param values
+      self.handle_failure(e, args)
+      raise e
 
+  ## EMBEDDING CALL 
+  def embedding(self, model, input=[], azure=False, logger_fn=None, forceTimeout=60, additional_details={}) -> Any:
+    try:
+      self.logger_fn = logger_fn
+      response = embedding(model, input, azure=azure, logger_fn=self.handle_input)
+      my_thread = threading.Thread(target=self.handle_success, args=(model, input, additional_details)) # don't interrupt execution of main thread
+      my_thread.start()
+      return response
+    except Exception as e:
+      args = locals() # get all the param values 
+      self.handle_failure(e, args)
+      raise e
+
+
+  def set_callbacks(self):  #instantiate any external packages
+    for callback in self.callback_list: # only install what's required
+      if callback == "sentry":
+        try:
+          import sentry_sdk
+        except ImportError:
+          print_verbose("Package 'sentry_sdk' is missing. Installing it...")
+          subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'sentry_sdk'])
+          import sentry_sdk
+        self.sentry_sdk = sentry_sdk
+        self.sentry_sdk.init(dsn=os.environ.get("SENTRY_API_URL"), traces_sample_rate=float(os.environ.get("SENTRY_API_TRACE_RATE")))
+        self.capture_exception = self.sentry_sdk.capture_exception
+        self.add_breadcrumb = self.sentry_sdk.add_breadcrumb
+      elif callback == "posthog":
+        try:
+          from posthog import Posthog
+        except:
+          print_verbose("Package 'posthog' is missing. Installing it...")
+          subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'posthog'])
+          from posthog import Posthog
+        self.posthog = Posthog(
+            project_api_key=os.environ.get("POSTHOG_API_KEY"),
+            host=os.environ.get("POSTHOG_API_URL"))
+      elif callback == "slack":
+        try:
+          from slack_bolt import App
+        except ImportError:
+          print_verbose("Package 'slack_bolt' is missing. Installing it...")
+          subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'slack_bolt'])
+          from slack_bolt import App
+        self.slack_app = App(
+          token=os.environ.get("SLACK_API_TOKEN"),
+          signing_secret=os.environ.get("SLACK_API_SECRET")
+        )
+        self.alerts_channel = os.environ["SLACK_API_CHANNEL"]
+
+  def handle_input(self, model_call_details={}):
+      if len(model_call_details.keys()) > 0:
+        model = model_call_details["model"] if "model" in model_call_details else None
+        if model:
+          for callback in self.callback_list:
+            if callback == "sentry": # add a sentry breadcrumb if user passed in sentry integration
+              self.add_breadcrumb(
+                category=f'{model}',
+                message='Trying request model {} input {}'.format(model, json.dumps(model_call_details)),
+                level='info',
+              )
+          if self.logger_fn and callable(self.logger_fn):
+            self.logger_fn(model_call_details)
+      pass
+
+  def handle_success(self, model, messages, additional_details):
+    success_handler = additional_details.pop("success_handler", None)
+    failure_handler = additional_details.pop("failure_handler", None)
+    additional_details["litellm_model"] = str(model)
+    additional_details["litellm_messages"] = str(messages)
+    for callback in self.success_callback:
+      try:
+        if callback == "posthog":
+          ph_obj = {}
+          for detail in additional_details:
+            ph_obj[detail] = additional_details[detail]
+          event_name = additional_details["successful_event"] if "successful_event" in additional_details else "litellm.succes_query"
+          if "user_id" in additional_details:
+            self.posthog.capture(additional_details["user_id"], event_name, ph_obj)
+          else: 
+            self.posthog.capture(event_name, ph_obj)
+          pass
+        elif callback == "slack":
+          slack_msg = "" 
+          if len(additional_details.keys()) > 0:
+            for detail in additional_details: 
+              slack_msg += f"{detail}: {additional_details[detail]}\n"
+          slack_msg += f"Successful call"
+          self.slack_app.client.chat_postMessage(channel=self.alerts_channel, text=slack_msg)
+      except:
+        pass
+    
+    if success_handler and callable(success_handler):
+      call_details = {
+        "model": model,
+        "messages": messages,
+        "additional_details": additional_details
+      }
+      success_handler(call_details)
+    pass
+
+  def handle_failure(self, exception, args):
+    args.pop("self")
+    additional_details = args.pop("additional_details", {})
+
+    success_handler = additional_details.pop("success_handler", None)
+    failure_handler = additional_details.pop("failure_handler", None)
+
+    for callback in self.failure_callback:
+      try:
+        if callback == "slack":
+          slack_msg = "" 
+          for param in args: 
+            slack_msg += f"{param}: {args[param]}\n"
+          if len(additional_details.keys()) > 0:
+            for detail in additional_details: 
+              slack_msg += f"{detail}: {additional_details[detail]}\n"
+          slack_msg += f"Traceback: {traceback.format_exc()}"
+          self.slack_app.client.chat_postMessage(channel=self.alerts_channel, text=slack_msg)
+        elif callback == "sentry":
+          self.capture_exception(exception)
+        elif callback == "posthog":
+          if len(additional_details.keys()) > 0:
+            ph_obj = {}
+            for param in args: 
+              ph_obj[param] += args[param]
+            for detail in additional_details:
+              ph_obj[detail] = additional_details[detail]
+            event_name = additional_details["failed_event"] if "failed_event" in additional_details else "litellm.failed_query"
+            if "user_id" in additional_details:
+              self.posthog.capture(additional_details["user_id"], event_name, ph_obj)
+            else: 
+              self.posthog.capture(event_name, ph_obj)
+          else: 
+            pass
+      except:
+        print(f"got an error calling {callback} - {traceback.format_exc()}")
+    
+    if failure_handler and callable(failure_handler):
+      call_details = {
+        "exception": exception,
+        "additional_details": additional_details
+      }
+      failure_handler(call_details)
+    pass
+####### HELPER FUNCTIONS ################
+
+#Logging function -> log the exact model details + what's being sent | Non-Blocking
+def logging(model, input, azure=False, additional_args={}, logger_fn=None):
+  try:
+    model_call_details = {}
+    model_call_details["model"] = model
+    model_call_details["input"] = input
+    model_call_details["azure"] = azure
+    model_call_details["additional_args"] = additional_args
+    if logger_fn and callable(logger_fn):
+      try:
+        # log additional call details -> api key, etc. 
+        if azure == True or model in open_ai_chat_completion_models or model in open_ai_chat_completion_models or model in open_ai_embedding_models:
+          model_call_details["api_type"] = openai.api_type
+          model_call_details["api_base"] = openai.api_base
+          model_call_details["api_version"] = openai.api_version
+          model_call_details["api_key"] = openai.api_key
+        elif "replicate" in model:
+          model_call_details["api_key"] = os.environ.get("REPLICATE_API_TOKEN")
+        elif model in anthropic_models:
+          model_call_details["api_key"] = os.environ.get("ANTHROPIC_API_KEY")
+        elif model in cohere_models:
+          model_call_details["api_key"] = os.environ.get("COHERE_API_KEY")
+        
+        logger_fn(model_call_details) # Expectation: any logger function passed in by the user should accept a dict object
+      except:
+        print_verbose(f"Basic model call details: {model_call_details}")
+        print_verbose(f"[Non-Blocking] Exception occurred while logging {traceback.format_exc()}")
+        pass
+    else:
+      print_verbose(f"Basic model call details: {model_call_details}")
+      pass
+  except:
+    pass
+
+## Set verbose to true -> ```litellm.verbose = True```    
+def print_verbose(print_statement):
+  if set_verbose:
+    print(f"LiteLLM: {print_statement}")
+    print("Get help - https://discord.com/invite/wuPM9dRgDw")
\ No newline at end of file
diff --git a/litellm/tests/test_bad_params.py b/litellm/tests/test_bad_params.py
new file mode 100644
index 000000000..2b2e4bbcf
--- /dev/null
+++ b/litellm/tests/test_bad_params.py
@@ -0,0 +1,20 @@
+import sys, os
+import traceback
+sys.path.append('..')  # Adds the parent directory to the system path
+import main
+from main import litellm_client
+client = litellm_client(success_callback=["posthog"], failure_callback=["slack", "sentry", "posthog"], verbose=True)
+completion = client.completion
+embedding = client.embedding
+
+main.set_verbose = True
+
+user_message = "Hello, how are you?"
+messages = [{ "content": user_message,"role": "user"}]
+model_val = None
+# test on empty
+try:
+    response = completion(model=model_val, messages=messages)
+except:
+    print(f"error occurred: {traceback.format_exc()}") 
+    pass
diff --git a/litellm/tests/test_client.py b/litellm/tests/test_client.py
new file mode 100644
index 000000000..06850ea18
--- /dev/null
+++ b/litellm/tests/test_client.py
@@ -0,0 +1,59 @@
+import sys, os
+import traceback
+sys.path.append('..')  # Adds the parent directory to the system path
+import main
+from main import litellm_client
+client = litellm_client(success_callback=["posthog"], failure_callback=["slack", "sentry", "posthog"], verbose=True)
+completion = client.completion
+embedding = client.embedding
+
+main.set_verbose = True
+
+def logger_fn(model_call_object: dict):
+    print(f"model call details: {model_call_object}")
+
+user_message = "Hello, how are you?"
+messages = [{ "content": user_message,"role": "user"}]
+
+# test on openai completion call 
+try:
+    response = completion(model="gpt-3.5-turbo", messages=messages, logger_fn=logger_fn)
+except:
+    print(f"error occurred: {traceback.format_exc()}") 
+    pass
+
+
+# test on openai completion call 
+try:
+    response = completion(model="gpt-3.5-turbo", messages=messages, logger_fn=logger_fn)
+except:
+    print(f"error occurred: {traceback.format_exc()}") 
+    pass
+
+# test on non-openai completion call 
+try:
+    response = completion(model="claude-instant-1", messages=messages, logger_fn=logger_fn)
+except:
+    print(f"error occurred: {traceback.format_exc()}") 
+    pass
+
+# test on openai embedding call 
+try: 
+    response = embedding(model='text-embedding-ada-002', input=[user_message], logger_fn=logger_fn)
+    print(f"response: {str(response)[:50]}")
+except:
+    traceback.print_exc()
+
+# test on bad azure openai embedding call -> missing azure flag and this isn't an embedding model
+try: 
+    response = embedding(model='chatgpt-test', input=[user_message], logger_fn=logger_fn)
+    print(f"response: {str(response)[:50]}")
+except:
+    traceback.print_exc()
+
+# test on good azure openai embedding call 
+try: 
+    response = embedding(model='azure-embedding-model', input=[user_message], azure=True, logger_fn=logger_fn)
+    print(f"response: {str(response)[:50]}")
+except:
+    traceback.print_exc()
diff --git a/litellm/tests/test_logging.py b/litellm/tests/test_logging.py
new file mode 100644
index 000000000..95a75de47
--- /dev/null
+++ b/litellm/tests/test_logging.py
@@ -0,0 +1,48 @@
+import sys, os
+import traceback
+sys.path.append('..')  # Adds the parent directory to the system path
+import main
+from main import completion, embedding
+
+main.verbose = True ## Replace to: ```litellm.verbose = True``` when using pypi package
+
+def logger_fn(model_call_object: dict):
+    print(f"model call details: {model_call_object}")
+
+user_message = "Hello, how are you?"
+messages = [{ "content": user_message,"role": "user"}]
+
+# test on openai completion call 
+try:
+    response = completion(model="gpt-3.5-turbo", messages=messages)
+except:
+    print(f"error occurred: {traceback.format_exc()}") 
+    pass
+
+# test on non-openai completion call 
+try:
+    response = completion(model="claude-instant-1", messages=messages, logger_fn=logger_fn)
+except:
+    print(f"error occurred: {traceback.format_exc()}") 
+    pass
+
+# test on openai embedding call 
+try: 
+    response = embedding(model='text-embedding-ada-002', input=[user_message], logger_fn=logger_fn)
+    print(f"response: {str(response)[:50]}")
+except:
+    traceback.print_exc()
+
+# test on bad azure openai embedding call -> missing azure flag and this isn't an embedding model
+try: 
+    response = embedding(model='chatgpt-test', input=[user_message], logger_fn=logger_fn)
+    print(f"response: {str(response)[:50]}")
+except:
+    traceback.print_exc()
+
+# test on good azure openai embedding call 
+try: 
+    response = embedding(model='azure-embedding-model', input=[user_message], azure=True, logger_fn=logger_fn)
+    print(f"response: {str(response)[:50]}")
+except:
+    traceback.print_exc()
diff --git a/setup.py b/setup.py
index 2288a1f82..a143fbe5b 100644
--- a/setup.py
+++ b/setup.py
@@ -2,9 +2,9 @@ from setuptools import setup, find_packages
 
 setup(
     name='litellm',
-    version='0.1.202',
+    version='0.1.2',
     description='Library to easily interface with LLM API providers',
-    author='Ishaan Jaffer',
+    author='BerriAI',
     packages=[
         'litellm'
     ],