add token trimming to adapt to prompt flag in config

litellm/tests/test_config.py

@@ -88,4 +88,4 @@ def test_config_context_adapt_to_prompt():
         print(f"Exception: {e}")
         pytest.fail(f"An exception occurred: {e}")
 
-# test_config_context_adapt_to_prompt() 
+test_config_context_adapt_to_prompt() 

litellm/utils.py

@@ -2802,7 +2802,13 @@ def completion_with_config(config: Union[dict, str], **kwargs):
     fallback_models = config.get("default_fallback_models", None)
     available_models = config.get("available_models", None)
     adapt_to_prompt_size = config.get("adapt_to_prompt_size", False)
-    start_time = time.time()
+    trim_messages_flag = config.get("trim_messages", False)
+    prompt_larger_than_model = False
+    max_model = model
+    try: 
+        max_tokens = litellm.get_max_tokens(model)["max_tokens"]
+    except:
+        max_tokens = 2048 # assume curr model's max window is 2048 tokens 
     if adapt_to_prompt_size:
         ## Pick model based on token window 
         prompt_tokens = litellm.token_counter(model="gpt-3.5-turbo", text="".join(message["content"] for message in messages))
@@ -2811,14 +2817,22 @@ def completion_with_config(config: Union[dict, str], **kwargs):
         except:
             curr_max_tokens = 2048
         if curr_max_tokens < prompt_tokens:
+            prompt_larger_than_model = True
             for available_model in available_models:
                 try:
                     curr_max_tokens = litellm.get_max_tokens(available_model)["max_tokens"]
+                    if curr_max_tokens > max_tokens:
+                        max_tokens = curr_max_tokens
+                        max_model = available_model
                     if curr_max_tokens > prompt_tokens:
                         model = available_model
+                        prompt_larger_than_model = False
                 except:
                     continue
-    end_time = time.time() 
+        if prompt_larger_than_model:
+            messages = trim_messages(messages=messages, model=max_model)
+            kwargs["messages"] = messages
+            
     kwargs["model"] = model
     try: 
         if model in models_with_config: 
@@ -3052,8 +3066,7 @@ def shorten_message_to_fit_limit(
 # Credits for this code go to Killian Lucas
 def trim_messages(
     messages,
-    model = None,
+    model: Optional[str] = None,
-    system_message = None, # str of user system message
     trim_ratio: float = 0.75,
     return_response_tokens: bool = False,
     max_tokens = None
@@ -3086,6 +3099,11 @@ def trim_messages(
                 # do nothing, just return messages
                 return 
         
+        system_message = "" 
+        for message in messages:
+            if message["role"] == "system":
+                system_message += message["content"]
+
         current_tokens = token_counter(model=model, messages=messages)
 
         # Do nothing if current tokens under messages

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "0.1.731"
+version = "0.1.732"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT License"