add text_completion

docs/my-website/docs/tutorials/text_completion.md

@@ -12,7 +12,8 @@ response = openai.Completion.create(
 ## Using LiteLLM in the Text Completion format
 ### With gpt-3.5-turbo
 ```python
-response = openai.Completion.create(
+from litellm import text_completion
+response = text_completion(
     model="gpt-3.5-turbo",
     prompt='Write a tagline for a traditional bavarian tavern',
     temperature=0,
@@ -21,7 +22,7 @@ response = openai.Completion.create(
 
 ### With text-davinci-003
 ```python
-response = openai.Completion.create(
+response = text_completion(
     model="text-davinci-003",
     prompt='Write a tagline for a traditional bavarian tavern',
     temperature=0,
@@ -30,7 +31,7 @@ response = openai.Completion.create(
 
 ### With llama2
 ```python
-response = openai.Completion.create(
+response = text_completion(
     model="togethercomputer/llama-2-70b-chat",
     prompt='Write a tagline for a traditional bavarian tavern',
     temperature=0,

litellm/main.py

@@ -91,7 +91,6 @@ def completion(
     custom_llm_provider=None,
     custom_api_base=None,
     litellm_call_id=None,
-    prompt="", # allow completion to be used as textCompletion or as ChatCompletion
     # model specific optional params
     # used by text-bison only
     top_k=40,
@@ -102,8 +101,6 @@ def completion(
     try:
         if fallbacks != []:
             return completion_with_fallbacks(**args)
-        if messages == [] and prompt!="":
-            messages = [{"role": "user", "content": prompt}]
         if litellm.model_alias_map and model in litellm.model_alias_map:
             args["model_alias_map"] = litellm.model_alias_map
             model = litellm.model_alias_map[model] # update the model to the actual value if an alias has been passed in
@@ -867,6 +864,13 @@ def embedding(
             custom_llm_provider="azure" if azure == True else None,
         )
 
+###### Text Completion ################
+def text_completion(*args, **kwargs):
+    if 'prompt' in kwargs:
+        messages = [{'role': 'system', 'content': kwargs['prompt']}]
+        kwargs['messages'] = messages
+        kwargs.pop('prompt')
+        return completion(*args, **kwargs)
 
 ####### HELPER FUNCTIONS ################
 ## Set verbose to true -> ```litellm.set_verbose = True```

litellm/tests/test_completion.py

@@ -10,7 +10,7 @@ sys.path.insert(
 )  # Adds the parent directory to the system path
 import pytest
 import litellm
-from litellm import embedding, completion
+from litellm import embedding, completion, text_completion
 
 # from infisical import InfisicalClient
 
@@ -144,7 +144,7 @@ def test_completion_openai():
 
 def test_completion_openai_prompt():
     try:
-        response = completion(model="gpt-3.5-turbo", prompt="What's the weather in SF?")
+        response = text_completion(model="gpt-3.5-turbo", prompt="What's the weather in SF?")
         response_str = response["choices"][0]["message"]["content"]
         response_str_2 = response.choices[0].message.content
         print(response)