adding telemetry to litellm

litellm/__init__.py

@@ -1,7 +1,7 @@
 success_callback = []
 failure_callback = []
 set_verbose=False
-
+telemetry=True
 ####### COMPLETION MODELS ###################
 open_ai_chat_completion_models = [
   'gpt-3.5-turbo', 

litellm/main.py

@@ -252,35 +252,40 @@ def completion(
 @client
 @timeout(60) ## set timeouts, in case calls hang (e.g. Azure) - default is 60s, override with `force_timeout`
 def embedding(model, input=[], azure=False, force_timeout=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")
-    ## 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 litellm.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)
-    args = locals()
-    raise ValueError(f"No valid embedding model args passed in - {args}")
-  
-  return response
-
+  try:
+    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")
+      ## 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 litellm.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)
+      args = locals()
+      raise ValueError(f"No valid embedding model args passed in - {args}")
+    
+    return response
+  except Exception as e:
+    # log the original exception
+    logging(model=model, input=input, azure=azure, logger_fn=logger_fn, exception=e)
+    ## Map to OpenAI Exception
+    raise exception_type(model=model, original_exception=e)
 ####### HELPER FUNCTIONS ################
 ## Set verbose to true -> ```litellm.set_verbose = True```    
 def print_verbose(print_statement):

litellm/tests/litellm_uuid.txt

@@ -0,0 +1 @@
+80888ede-4881-4876-ab3f-765d47282e66

litellm/utils.py

@@ -1,14 +1,7 @@
-import dotenv
-import json
-import traceback
-import threading
-import traceback
-import subprocess
-import uuid
-import litellm
-import os 
-import openai 
-import random
+import dotenv, json, traceback, threading
+import subprocess, os 
+import litellm, openai 
+import random, uuid, requests
 from openai.error import AuthenticationError, InvalidRequestError, RateLimitError, ServiceUnavailableError, OpenAIError
 ####### ENVIRONMENT VARIABLES ###################
 dotenv.load_dotenv() # Loading env variables using dotenv
@@ -34,11 +27,15 @@ def logging(model, input, azure=False, additional_args={}, logger_fn=None, excep
   try:
     model_call_details = {}
     model_call_details["model"] = model
-    model_call_details["input"] = input
     model_call_details["azure"] = azure
     # log exception details
     if exception:
       model_call_details["original_exception"] = exception
+
+    if litellm.telemetry:
+      safe_crash_reporting(model=model, exception=exception, azure=azure) # log usage-crash details. Do not log any user details. If you want to turn this off, set `litellm.telemetry=False`.
+
+    model_call_details["input"] = input
     # log additional call details -> api key, etc. 
     if azure == True or model in litellm.open_ai_chat_completion_models or model in litellm.open_ai_chat_completion_models or model in litellm.open_ai_embedding_models:
       model_call_details["api_type"] = openai.api_type
@@ -273,4 +270,48 @@ def exception_type(model, original_exception):
       raise original_exception # base case - return the original exception
     else:
       raise original_exception
-                        
+
+def safe_crash_reporting(model=None, exception=None, azure=None):
+    data = {
+      "model": model,
+      "exception": str(exception),
+      "azure": azure
+    }
+    print(f"data in crash reporting: {data}")
+    threading.Thread(target=litellm_telemetry, args=(data,), daemon=True).start()
+
+def litellm_telemetry(data):
+    print(f"data in in litellm telemetry: {data}")
+    # Load or generate the UUID
+    uuid_file = 'litellm_uuid.txt'
+    try:
+        # Try to open the file and load the UUID
+        with open(uuid_file, 'r') as file:
+            uuid_value = file.read()
+            if uuid_value:
+                uuid_value = uuid_value.strip()
+                print(f"Loaded UUID: {uuid_value}")
+            else:
+                raise FileNotFoundError
+    except FileNotFoundError:
+        # Generate a new UUID if the file doesn't exist or is empty
+        new_uuid = uuid.uuid4()
+        uuid_value = str(new_uuid)
+        with open(uuid_file, 'w') as file:
+            file.write(uuid_value)
+        print(f"Generated and stored UUID: {uuid_value}")
+
+    # Prepare the data to send to localhost:3000
+    payload = {
+        'uuid': uuid_value,
+        'data': data
+    }
+    print_verbose(f"payload: {payload}")
+    try:
+      # Make the POST request to localhost:3000
+      response = requests.post('https://litellm.berri.ai/logging', json=payload)
+      response.raise_for_status()  # Raise an exception for HTTP errors
+      print('Request successfully sent!')
+    except requests.exceptions.RequestException as e:
+        # Handle any errors in the request
+        print(f'Error: {e}')