fix(vertex_ai/gemini/transformation.py): handle 'http://' in gemini p… (#7660)

* fix(vertex_ai/gemini/transformation.py): handle 'http://' in gemini process url

* refactor(router.py): refactor '_prompt_management_factory' to use logging obj get_chat_completion logic

deduplicates code

* fix(litellm_logging.py): update 'get_chat_completion_prompt' to update logging object messages

* docs(prompt_management.md): update prompt management to be in beta

given feedback - this still needs to be revised (e.g. passing in user message, not ignoring)

* refactor(prompt_management_base.py): introduce base class for prompt management

allows consistent behaviour across prompt management integrations

* feat(prompt_management_base.py): support adding client message to template message + refactor langfuse prompt management to use prompt management base

* fix(litellm_logging.py): log prompt id + prompt variables to langfuse if set

allows tracking what prompt was used for what purpose

* feat(litellm_logging.py): log prompt management metadata in standard logging payload + use in langfuse

allows logging prompt id / prompt variables to langfuse

* test: fix test

* fix(router.py): cleanup unused imports

* fix: fix linting error

* fix: fix trace param typing

* fix: fix linting errors

* fix: fix code qa check

litellm/router.py

@@ -47,9 +47,6 @@ from litellm.caching.caching import DualCache, InMemoryCache, RedisCache
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.litellm_core_utils.core_helpers import _get_parent_otel_span_from_kwargs
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
-from litellm.litellm_core_utils.litellm_logging import (
-    _init_custom_logger_compatible_class,
-)
 from litellm.router_strategy.budget_limiter import RouterBudgetLimiting
 from litellm.router_strategy.least_busy import LeastBusyLoggingHandler
 from litellm.router_strategy.lowest_cost import LowestCostLoggingHandler
@@ -120,6 +117,8 @@ from litellm.utils import (
     CustomStreamWrapper,
     EmbeddingResponse,
     ModelResponse,
+    Rules,
+    function_setup,
     get_llm_provider,
     get_non_default_completion_params,
     get_secret,
@@ -1457,6 +1456,17 @@ class Router:
         messages: List[AllMessageValues],
         kwargs: Dict[str, Any],
     ):
+        litellm_logging_object = kwargs.get("litellm_logging_obj", None)
+        if litellm_logging_object is None:
+            litellm_logging_object, kwargs = function_setup(
+                **{
+                    "original_function": "acompletion",
+                    "rules_obj": Rules(),
+                    "start_time": get_utc_datetime(),
+                    **kwargs,
+                }
+            )
+        litellm_logging_object = cast(LiteLLMLogging, litellm_logging_object)
         prompt_management_deployment = self.get_available_deployment(
             model=model,
             messages=[{"role": "user", "content": "prompt"}],
@@ -1475,38 +1485,31 @@ class Router:
             "prompt_variables", None
         )
 
-        if litellm_model is None or "/" not in litellm_model:
+        if prompt_id is None or not isinstance(prompt_id, str):
             raise ValueError(
-                f"Model is not a custom logger compatible callback. Got={litellm_model}"
+                f"Prompt ID is not set or not a string. Got={prompt_id}, type={type(prompt_id)}"
+            )
+        if prompt_variables is not None and not isinstance(prompt_variables, dict):
+            raise ValueError(
+                f"Prompt variables is set but not a dictionary. Got={prompt_variables}, type={type(prompt_variables)}"
             )
 
-        custom_logger_compatible_callback = litellm_model.split("/", 1)[0]
-        split_litellm_model = litellm_model.split("/", 1)[1]
-
-        custom_logger = _init_custom_logger_compatible_class(
-            logging_integration=custom_logger_compatible_callback,
-            internal_usage_cache=None,
-            llm_router=None,
-        )
-
-        if custom_logger is None:
-            raise ValueError(
-                f"Custom logger is not initialized. Got={custom_logger_compatible_callback}"
-            )
         model, messages, optional_params = (
-            await custom_logger.async_get_chat_completion_prompt(
-                model=split_litellm_model,
+            litellm_logging_object.get_chat_completion_prompt(
+                model=litellm_model,
                 messages=messages,
                 non_default_params=get_non_default_completion_params(kwargs=kwargs),
                 prompt_id=prompt_id,
                 prompt_variables=prompt_variables,
-                dynamic_callback_params={},
             )
         )
 
         kwargs = {**kwargs, **optional_params}
         kwargs["model"] = model
         kwargs["messages"] = messages
+        kwargs["litellm_logging_obj"] = litellm_logging_object
+        kwargs["prompt_id"] = prompt_id
+        kwargs["prompt_variables"] = prompt_variables
 
         _model_list = self.get_model_list(model_name=model)
         if _model_list is None or len(_model_list) == 0:  # if direct call to model