updates to proxy

litellm/proxy/proxy_cli.py

@@ -9,9 +9,10 @@ load_dotenv()
 @click.option('--debug', is_flag=True, help='To debug the input') 
 @click.option('--temperature', default=None, type=float, help='Set temperature for the model') 
 @click.option('--max_tokens', default=None, help='Set max tokens for the model') 
-def run_server(port, api_base, model, debug, temperature, max_tokens):
+@click.option('--telemetry', default=True, type=bool, help='Helps us know if people are using this feature. Turn this off by doing `--telemetry False`') 
+def run_server(port, api_base, model, debug, temperature, max_tokens, telemetry):
     from .proxy_server import app, initialize
-    initialize(model, api_base, debug, temperature, max_tokens)
+    initialize(model, api_base, debug, temperature, max_tokens, telemetry)
     try:
         import uvicorn
     except:

litellm/proxy/proxy_server.py

@@ -3,8 +3,20 @@ sys.path.insert(
     0, os.path.abspath("../..")
 )  # Adds the parent directory to the system path
 
+try:
+    import uvicorn
+    import fastapi
+except ImportError:
+    import subprocess
+    import sys
+
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "uvicorn", "fastapi"])
+print()
+print("\033[1;31mGive Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new\033[0m")
+print()
+print()
+
 import litellm
-print(litellm.__file__)
 from fastapi import FastAPI, Request
 from fastapi.responses import StreamingResponse
 import json
@@ -16,23 +28,31 @@ user_model = None
 user_debug = False
 user_max_tokens = None
 user_temperature = None
+user_telemetry = False
 
+#### HELPER FUNCTIONS ####
 def print_verbose(print_statement):
     global user_debug 
     print(f"user_debug: {user_debug}")
     if user_debug: 
          print(print_statement)
 
-def initialize(model, api_base, debug, temperature, max_tokens):
+def usage_telemetry(): # helps us know if people are using this feature. Set `litellm --telemetry False` to your cli call to turn this off
-    global user_model, user_api_base, user_debug, user_max_tokens, user_temperature
+    if user_telemetry: 
+        data = {
+            "feature": "local_proxy_server"
+        }
+        litellm.utils.litellm_telemetry(data=data)
+
+def initialize(model, api_base, debug, temperature, max_tokens, telemetry):
+    global user_model, user_api_base, user_debug, user_max_tokens, user_temperature, user_telemetry
     user_model = model
     user_api_base = api_base
     user_debug = debug
     user_max_tokens = max_tokens
     user_temperature = temperature
-
+    user_telemetry = telemetry
-    # if debug: 
+    usage_telemetry()
-    #     litellm.set_verbose = True
 
 # for streaming
 def data_generator(response):
@@ -42,6 +62,7 @@ def data_generator(response):
         print_verbose(f"returned chunk: {chunk}")
         yield f"data: {json.dumps(chunk)}\n\n"
 
+#### API ENDPOINTS ####
 @app.get("/models") # if project requires model list 
 def model_list(): 
     return dict(

litellm/utils.py

@@ -2776,10 +2776,16 @@ def litellm_telemetry(data):
         uuid_value = str(uuid.uuid4())
     try:
         # Prepare the data to send to litellm logging api
+        try: 
+            pkg_version = importlib.metadata.version("litellm")
+        except:
+            pkg_version = None
+        if "model" not in data:
+            data["model"] = None
         payload = {
             "uuid": uuid_value,
             "data": data,
-            "version:": importlib.metadata.version("litellm"),
+            "version:": pkg_version 
         }
         # Make the POST request to litellm logging api
         response = requests.post(