Merge pull request #371 from promptmetheus/simplify-mock-logic

Simplify mock logic

litellm/main.py

@@ -72,6 +72,32 @@ async def acompletion(*args, **kwargs):
     else:
         return response
 
+## Use this in your testing pipeline, if you need to mock an LLM response
+def mock_completion(model: str, messages: List, stream: bool = False, mock_response: str = "This is a mock request", **kwargs):
+    try:
+        model_response = ModelResponse()
+        if stream: # return a generator object, iterate through the text in chunks of 3 char / chunk
+            for i in range(0, len(mock_response), 3):
+                completion_obj = {"role": "assistant", "content": mock_response[i: i+3]}
+                yield {
+                "choices": 
+                    [
+                        {
+                            "delta": completion_obj,
+                            "finish_reason": None
+                        },
+                    ]
+                }
+        else:
+            ## RESPONSE OBJECT
+            completion_response = "This is a mock request"
+            model_response["choices"][0]["message"]["content"] = completion_response
+            model_response["created"] = time.time()
+            model_response["model"] = "MockResponse"
+            return model_response
+    except:
+        raise Exception("Mock completion response failed")
+
 @client
 @timeout(  # type: ignore
     600
@@ -96,6 +122,7 @@ def completion(
     # Optional liteLLM function params
     *,
     return_async=False,
+    mock_response: Optional[str] = None,
     api_key: Optional[str] = None,
     api_version: Optional[str] = None,
     api_base: Optional[str] = None,
@@ -118,6 +145,10 @@ def completion(
     caching = False,
     cache_params = {}, # optional to specify metadata for caching
 ) -> ModelResponse:
+    # If `mock_response` is set, execute the `mock_completion` method instead.
+    if mock_response:
+        return mock_completion(model, messages, stream=stream, mock_response=mock_response)
+    
     args = locals()
     try:
         logging = litellm_logging_obj
@@ -978,31 +1009,6 @@ def batch_completion(
         results = [future.result() for future in completions]
     return results
 
-## Use this in your testing pipeline, if you need to mock an LLM response
-def mock_completion(model: str, messages: List, stream: bool = False, mock_response: str = "This is a mock request", **kwargs):
-    try:
-        model_response = ModelResponse()
-        if stream: # return a generator object, iterate through the text in chunks of 3 char / chunk
-            for i in range(0, len(mock_response), 3):
-                completion_obj = {"role": "assistant", "content": mock_response[i: i+3]}
-                yield {
-                "choices": 
-                    [
-                        {
-                            "delta": completion_obj,
-                            "finish_reason": None
-                        },
-                    ]
-                }
-        else:
-            ## RESPONSE OBJECT
-            completion_response = "This is a mock request"
-            model_response["choices"][0]["message"]["content"] = completion_response
-            model_response["created"] = time.time()
-            model_response["model"] = "MockResponse"
-            return model_response
-    except:
-        raise Exception("Mock completion response failed")
 ### EMBEDDING ENDPOINTS ####################
 @client
 @timeout(  # type: ignore