feat(proxy_server): adds create-proxy feature

litellm/proxy/proxy_server.py

@@ -1,9 +1,10 @@
-import sys, os, platform 
+import sys, os, platform, time, copy
 import threading
 import shutil, random, traceback
-sys.path.insert(
-    0, os.path.abspath("../..")
-)  # Adds the parent directory to the system path
+# sys.path.insert(
+#     0, os.path.abspath("../..")
+# )  # Adds the parent directory to the system path - for litellm local dev
+
 
 try:
     import uvicorn
@@ -76,12 +77,10 @@ user_max_tokens = None
 user_temperature = None
 user_telemetry = False
 user_config = None
-config_filename = "litellm.secrets.toml"
-pkg_config_filename = "template.secrets.toml"
-# Using appdirs to determine user-specific config path
-config_dir = appdirs.user_config_dir("litellm")
+config_filename = "secrets.toml"
+config_dir = os.getcwd()
 user_config_path = os.path.join(config_dir, config_filename)
-
+log_file = 'api_log.json'
 #### HELPER FUNCTIONS ####
 def print_verbose(print_statement):
     global user_debug 
@@ -98,15 +97,6 @@ def usage_telemetry(): # helps us know if people are using this feature. Set `li
 def load_config():
     try: 
         global user_config, user_api_base, user_max_tokens, user_temperature, user_model
-        if not os.path.exists(user_config_path):
-            # If user's config doesn't exist, copy the default config from the package
-            here = os.path.abspath(os.path.dirname(__file__))
-            parent_dir = os.path.dirname(here)
-            default_config_path = os.path.join(parent_dir, pkg_config_filename)
-            # Ensure the user-specific directory exists
-            os.makedirs(config_dir, exist_ok=True)
-            # Copying the file using shutil.copy
-            shutil.copy(default_config_path, user_config_path)
         # As the .env file is typically much simpler in structure, we use load_dotenv here directly
         with open(user_config_path, "rb") as f:
             user_config = tomllib.load(f)
@@ -133,11 +123,8 @@ def load_config():
 
         ## load model config - to set this run `litellm --config`
         model_config = None
-        if user_model == "local": 
-            model_config = user_config["local_model"]
-        elif user_model == "hosted":
-            model_config = user_config["hosted_model"]
-            litellm.max_budget = model_config.get("max_budget", None) # check if user set a budget for hosted model - e.g. gpt-4
+        if user_model in user_config["model"]: 
+            model_config = user_config["model"][user_model]
         
         print_verbose(f"user_config: {user_config}")
         print_verbose(f"model_config: {model_config}")
@@ -317,7 +304,55 @@ def track_cost_callback(
     except:
         pass
 
-litellm.success_callback = [track_cost_callback]
+def logger(
+    kwargs,                 # kwargs to completion
+    completion_response=None,    # response from completion
+    start_time=None, 
+    end_time=None    # start/end time
+):
+  log_event_type = kwargs['log_event_type']
+  print(f"REACHES LOGGER: {log_event_type}")
+  try: 
+    if log_event_type == 'pre_api_call':
+        inference_params = copy.deepcopy(kwargs)
+        timestamp = inference_params.pop('start_time')
+        dt_key = timestamp.strftime("%Y%m%d%H%M%S%f")[:23]
+        log_data = {
+            dt_key: {
+                'pre_api_call': inference_params
+            }
+        }
+        
+        try:
+            with open(log_file, 'r') as f:
+                existing_data = json.load(f)
+        except FileNotFoundError:
+            existing_data = {}
+            
+        existing_data.update(log_data)
+        
+        with open(log_file, 'w') as f:
+            json.dump(existing_data, f, indent=2)
+    elif log_event_type == 'post_api_call':
+        print(f"post api call kwargs: {kwargs}")
+        if "stream" not in kwargs["optional_params"] or kwargs["optional_params"]["stream"] is False or kwargs.get("complete_streaming_response", False):
+            inference_params = copy.deepcopy(kwargs)
+            timestamp = inference_params.pop('start_time')
+            dt_key = timestamp.strftime("%Y%m%d%H%M%S%f")[:23]
+            
+            with open(log_file, 'r') as f:
+                existing_data = json.load(f)
+            
+            existing_data[dt_key]['post_api_call'] = inference_params
+            
+            with open(log_file, 'w') as f:
+                json.dump(existing_data, f, indent=2)
+  except: 
+      traceback.print_exc()
+
+litellm.input_callback = [logger]
+litellm.success_callback = [logger]
+litellm.failure_callback = [logger]
 
 def litellm_completion(data, type): 
     try: