fix(utils.py): support image gen logging to langfuse

litellm/integrations/langfuse.py

@@ -110,9 +110,16 @@ class LangFuseLogger:
             ):
                 input = prompt
                 output = response_obj["data"]
-            elif response_obj is not None:
+            elif response_obj is not None and isinstance(
+                response_obj, litellm.ModelResponse
+            ):
                 input = prompt
                 output = response_obj["choices"][0]["message"].json()
+            elif response_obj is not None and isinstance(
+                response_obj, litellm.ImageResponse
+            ):
+                input = prompt
+                output = response_obj["data"]
             print_verbose(f"OUTPUT IN LANGFUSE: {output}; original: {response_obj}")
             if self._is_langfuse_v2():
                 self._log_langfuse_v2(

litellm/llms/azure.py

@@ -741,7 +741,7 @@ class AzureChatCompletion(BaseLLM):
             response = azure_client.images.generate(**data, timeout=timeout)  # type: ignore
             ## LOGGING
             logging_obj.post_call(
-                input=input,
+                input=prompt,
                 api_key=api_key,
                 additional_args={"complete_input_dict": data},
                 original_response=response,

litellm/main.py

@@ -3197,6 +3197,7 @@ def image_generation(
                 "preset_cache_key": None,
                 "stream_response": {},
             },
+            custom_llm_provider=custom_llm_provider,
         )
 
         if custom_llm_provider == "azure":

litellm/tests/test_custom_callback_input.py

@@ -3,6 +3,7 @@
 import sys, os, time, inspect, asyncio, traceback
 from datetime import datetime
 import pytest
+from pydantic import BaseModel
 
 sys.path.insert(0, os.path.abspath("../.."))
 from typing import Optional, Literal, List, Union
@@ -94,7 +95,8 @@ class CompletionCustomHandler(
             assert isinstance(kwargs["api_key"], (str, type(None)))
             assert (
                 isinstance(
-                    kwargs["original_response"], (str, litellm.CustomStreamWrapper)
+                    kwargs["original_response"],
+                    (str, litellm.CustomStreamWrapper, BaseModel),
                 )
                 or inspect.iscoroutine(kwargs["original_response"])
                 or inspect.isasyncgen(kwargs["original_response"])
@@ -471,7 +473,7 @@ async def test_async_chat_azure_stream():
         pytest.fail(f"An exception occurred: {str(e)}")
 
 
-asyncio.run(test_async_chat_azure_stream())
+# asyncio.run(test_async_chat_azure_stream())
 
 
 ## Test Bedrock + sync
@@ -556,6 +558,7 @@ async def test_async_chat_bedrock_stream():
 
 # asyncio.run(test_async_chat_bedrock_stream())
 
+
 ## Test Sagemaker + Async
 @pytest.mark.asyncio
 async def test_async_chat_sagemaker_stream():
@@ -769,14 +772,18 @@ async def test_async_completion_azure_caching():
     unique_time = time.time()
     response1 = await litellm.acompletion(
         model="azure/chatgpt-v-2",
-        messages=[{"role": "user", "content": f"Hi 👋 - i'm async azure {unique_time}"}],
+        messages=[
+            {"role": "user", "content": f"Hi 👋 - i'm async azure {unique_time}"}
+        ],
         caching=True,
     )
     await asyncio.sleep(1)
     print(f"customHandler_caching.states pre-cache hit: {customHandler_caching.states}")
     response2 = await litellm.acompletion(
         model="azure/chatgpt-v-2",
-        messages=[{"role": "user", "content": f"Hi 👋 - i'm async azure {unique_time}"}],
+        messages=[
+            {"role": "user", "content": f"Hi 👋 - i'm async azure {unique_time}"}
+        ],
         caching=True,
     )
     await asyncio.sleep(1)  # success callbacks are done in parallel
@@ -825,21 +832,25 @@ def test_image_generation_openai():
     try:
         customHandler_success = CompletionCustomHandler()
         customHandler_failure = CompletionCustomHandler()
-        # litellm.callbacks = [customHandler_success]
+        litellm.callbacks = [customHandler_success]
 
-        # litellm.set_verbose = True
+        litellm.set_verbose = True
 
-        # response = litellm.image_generation(
+        response = litellm.image_generation(
-        #     prompt="A cute baby sea otter", model="dall-e-3"
+            prompt="A cute baby sea otter",
-        # )
+            model="azure/",
+            api_base=os.getenv("AZURE_API_BASE"),
+            api_key=os.getenv("AZURE_API_KEY"),
+            api_version="2023-06-01-preview",
+        )
 
-        # print(f"response: {response}")
+        print(f"response: {response}")
-        # assert len(response.data) > 0
+        assert len(response.data) > 0
 
-        # print(f"customHandler_success.errors: {customHandler_success.errors}")
+        print(f"customHandler_success.errors: {customHandler_success.errors}")
-        # print(f"customHandler_success.states: {customHandler_success.states}")
+        print(f"customHandler_success.states: {customHandler_success.states}")
-        # assert len(customHandler_success.errors) == 0
+        assert len(customHandler_success.errors) == 0
-        # assert len(customHandler_success.states) == 3  # pre, post, success
+        assert len(customHandler_success.states) == 3  # pre, post, success
         # test failure callback
         litellm.callbacks = [customHandler_failure]
         try:
@@ -862,7 +873,7 @@ def test_image_generation_openai():
         pytest.fail(f"An exception occurred - {str(e)}")
 
 
-test_image_generation_openai()
+# test_image_generation_openai()
 ## Test OpenAI + Async
 
 ## Test Azure + Sync

litellm/utils.py

@@ -44,9 +44,9 @@ except:
     filename = str(
         resources.files(litellm).joinpath("llms/tokenizers")  # for python 3.10
     )  # for python 3.10+
-os.environ[
+os.environ["TIKTOKEN_CACHE_DIR"] = (
-    "TIKTOKEN_CACHE_DIR"
+    filename  # use local copy of tiktoken b/c of - https://github.com/BerriAI/litellm/issues/1071
-] = filename  # use local copy of tiktoken b/c of - https://github.com/BerriAI/litellm/issues/1071
+)
 
 encoding = tiktoken.get_encoding("cl100k_base")
 import importlib.metadata
@@ -1110,6 +1110,9 @@ class Logging:
                                     completion_response=result,
                                     model=self.model,
                                     call_type=self.call_type,
+                                    custom_llm_provider=self.model_call_details.get(
+                                        "custom_llm_provider", None
+                                    ),  # set for img gen models
                                 )
                             )
                         else:
@@ -1789,14 +1792,14 @@ class Logging:
 
                         input = self.model_call_details["input"]
 
-                        type = (
+                        _type = (
                             "embed"
                             if self.call_type == CallTypes.embedding.value
                             else "llm"
                         )
 
                         llmonitorLogger.log_event(
-                            type=type,
+                            type=_type,
                             event="error",
                             user_id=self.model_call_details.get("user", "default"),
                             model=model,
@@ -3512,6 +3515,15 @@ def completion_cost(
         - If an error occurs during execution, the function returns 0.0 without blocking the user's execution path.
     """
     try:
+
+        if (
+            (call_type == "aimage_generation" or call_type == "image_generation")
+            and model is not None
+            and isinstance(model, str)
+            and len(model) == 0
+            and custom_llm_provider == "azure"
+        ):
+            model = "dall-e-2"  # for dall-e-2, azure expects an empty model name
         # Handle Inputs to completion_cost
         prompt_tokens = 0
         completion_tokens = 0
@@ -3565,12 +3577,15 @@ def completion_cost(
             or call_type == CallTypes.aimage_generation.value
         ):
             ### IMAGE GENERATION COST CALCULATION ###
+            # fix size to match naming convention
+            if "x" in size and "-x-" not in size:
+                size = size.replace("x", "-x-")
             image_gen_model_name = f"{size}/{model}"
             image_gen_model_name_with_quality = image_gen_model_name
             if quality is not None:
                 image_gen_model_name_with_quality = f"{quality}/{image_gen_model_name}"
             size = size.split("-x-")
-            height = int(size[0])
+            height = int(size[0])  # if it's 1024-x-1024 vs. 1024x1024
             width = int(size[1])
             verbose_logger.debug(f"image_gen_model_name: {image_gen_model_name}")
             verbose_logger.debug(
@@ -5968,73 +5983,6 @@ def convert_to_model_response_object(
         raise Exception(f"Invalid response object {e}")
 
 
-# NOTE: DEPRECATING this in favor of using success_handler() in Logging:
-def handle_success(args, kwargs, result, start_time, end_time):
-    global heliconeLogger, aispendLogger, supabaseClient, liteDebuggerClient, llmonitorLogger
-    try:
-        model = args[0] if len(args) > 0 else kwargs["model"]
-        input = (
-            args[1]
-            if len(args) > 1
-            else kwargs.get("messages", kwargs.get("input", None))
-        )
-        success_handler = additional_details.pop("success_handler", None)
-        failure_handler = additional_details.pop("failure_handler", None)
-        additional_details["Event_Name"] = additional_details.pop(
-            "successful_event_name", "litellm.succes_query"
-        )
-        for callback in litellm.success_callback:
-            try:
-                if callback == "posthog":
-                    ph_obj = {}
-                    for detail in additional_details:
-                        ph_obj[detail] = additional_details[detail]
-                    event_name = additional_details["Event_Name"]
-                    if "user_id" in additional_details:
-                        posthog.capture(
-                            additional_details["user_id"], event_name, ph_obj
-                        )
-                    else:  # PostHog calls require a unique id to identify a user - https://posthog.com/docs/libraries/python
-                        unique_id = str(uuid.uuid4())
-                        posthog.capture(unique_id, event_name, ph_obj)
-                    pass
-                elif callback == "slack":
-                    slack_msg = ""
-                    for detail in additional_details:
-                        slack_msg += f"{detail}: {additional_details[detail]}\n"
-                    slack_app.client.chat_postMessage(
-                        channel=alerts_channel, text=slack_msg
-                    )
-                elif callback == "aispend":
-                    print_verbose("reaches aispend for logging!")
-                    model = args[0] if len(args) > 0 else kwargs["model"]
-                    aispendLogger.log_event(
-                        model=model,
-                        response_obj=result,
-                        start_time=start_time,
-                        end_time=end_time,
-                        print_verbose=print_verbose,
-                    )
-            except Exception as e:
-                # LOGGING
-                exception_logging(logger_fn=user_logger_fn, exception=e)
-                print_verbose(
-                    f"[Non-Blocking] Success Callback Error - {traceback.format_exc()}"
-                )
-                pass
-
-        if success_handler and callable(success_handler):
-            success_handler(args, kwargs)
-        pass
-    except Exception as e:
-        # LOGGING
-        exception_logging(logger_fn=user_logger_fn, exception=e)
-        print_verbose(
-            f"[Non-Blocking] Success Callback Error - {traceback.format_exc()}"
-        )
-        pass
-
-
 def acreate(*args, **kwargs):  ## Thin client to handle the acreate langchain call
     return litellm.acompletion(*args, **kwargs)