Merge branch 'main' into litellm_common_auth_params

.gitignore

@@ -51,3 +51,4 @@ loadtest_kub.yaml
 litellm/proxy/_new_secret_config.yaml
 litellm/proxy/_new_secret_config.yaml
 litellm/proxy/_super_secret_config.yaml
+litellm/proxy/_super_secret_config.yaml

README.md

@@ -227,6 +227,7 @@ curl 'http://0.0.0.0:4000/key/generate' \
 | [perplexity-ai](https://docs.litellm.ai/docs/providers/perplexity)                  | ✅                                                      | ✅                                                                              | ✅                                                                                  | ✅                                                                                |
 | [Groq AI](https://docs.litellm.ai/docs/providers/groq)                              | ✅                                                      | ✅                                                                              | ✅                                                                                  | ✅                                                                                |
 | [anyscale](https://docs.litellm.ai/docs/providers/anyscale)                         | ✅                                                      | ✅                                                                              | ✅                                                                                  | ✅                                                                                |
+| [IBM - watsonx.ai](https://docs.litellm.ai/docs/providers/watsonx)                  | ✅                                                      | ✅                                                                              | ✅                                                                                  | ✅                                                                                | ✅ 
 | [voyage ai](https://docs.litellm.ai/docs/providers/voyage)                          |                                                         |                                                                                 |                                                                                     |                                                                                   | ✅                                                                            |
 | [xinference [Xorbits Inference]](https://docs.litellm.ai/docs/providers/xinference) |                                                         |                                                                                 |                                                                                     |                                                                                   | ✅                                                                            |
 

docs/my-website/docs/observability/langfuse_integration.md

@@ -167,6 +167,9 @@ messages = [
 chat(messages)
 ```
 
+## Redacting Messages, Response Content from Langfuse Logging 
+
+Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to langfuse, but request metadata will still be logged.
 
 ## Troubleshooting & Errors
 ### Data not getting logged to Langfuse ? 

docs/my-website/docs/observability/sentry.md

@@ -40,5 +40,9 @@ response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content
 print(response)
 ```
 
+## Redacting Messages, Response Content from Sentry Logging 
+
+Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to sentry, but request metadata will still be logged.
+
 [Let us know](https://github.com/BerriAI/litellm/issues/new?assignees=&labels=enhancement&projects=&template=feature_request.yml&title=%5BFeature%5D%3A+) if you need any additional options from Sentry. 
 

docs/my-website/docs/providers/watsonx.md

@@ -20,7 +20,7 @@ See [here](https://cloud.ibm.com/apidocs/watsonx-ai#api-authentication) for more
 
 ## Usage
 
-<a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/LiteLLM_IBM_Watsonx.ipynb">
+<a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_IBM_Watsonx.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>
 

docs/my-website/docs/proxy/alerting.md

@@ -1,13 +1,13 @@
-# Slack Alerting
+# 🚨 Alerting 
 
 Get alerts for:
-- hanging LLM api calls
-- failed LLM api calls
-- slow LLM api calls
-- budget Tracking per key/user:
+- Hanging LLM api calls
+- Failed LLM api calls
+- Slow LLM api calls
+- Budget Tracking per key/user:
     - When a User/Key crosses their Budget 
     - When a User/Key is 15% away from crossing their Budget
-- failed db read/writes
+- Failed db read/writes
 
 ## Quick Start
 

docs/my-website/docs/proxy/logging.md

@@ -569,6 +569,22 @@ curl -X POST 'http://0.0.0.0:4000/key/generate' \
 
 All requests made with these keys will log data to their team-specific logging.
 
+### Redacting Messages, Response Content from Langfuse Logging 
+
+Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to langfuse, but request metadata will still be logged.
+
+```yaml
+model_list:
+ - model_name: gpt-3.5-turbo
+    litellm_params:
+      model: gpt-3.5-turbo
+litellm_settings:
+  success_callback: ["langfuse"]
+  turn_off_message_logging: True
+```
+
+
+
 ## Logging Proxy Input/Output - DataDog
 We will use the `--config` to set `litellm.success_callback = ["datadog"]` this will log all successfull LLM calls to DataDog
 

docs/my-website/sidebars.js

@@ -43,6 +43,12 @@ const sidebars = {
         "proxy/user_keys",
         "proxy/enterprise",
         "proxy/virtual_keys",
+        "proxy/alerting",
+        {
+          type: "category",
+          label: "Logging",
+          items: ["proxy/logging", "proxy/streaming_logging"],
+        },
         "proxy/team_based_routing",
         "proxy/ui",
         "proxy/cost_tracking",
@@ -58,11 +64,6 @@ const sidebars = {
         "proxy/pii_masking",
         "proxy/prompt_injection",
         "proxy/caching",
-        {
-          type: "category",
-          label: "Logging, Alerting",
-          items: ["proxy/logging", "proxy/alerting", "proxy/streaming_logging"],
-        },
         "proxy/prometheus",
         "proxy/call_hooks",
         "proxy/rules",

litellm/__init__.py

@@ -45,6 +45,7 @@ _async_failure_callback: List[Callable] = (
 )  # internal variable - async custom callbacks are routed here.
 pre_call_rules: List[Callable] = []
 post_call_rules: List[Callable] = []
+turn_off_message_logging: Optional[bool] = False
 ## end of callbacks #############
 
 email: Optional[str] = (

litellm/integrations/langfuse.py

@@ -12,7 +12,9 @@ import litellm
 
 class LangFuseLogger:
     # Class variables or attributes
-    def __init__(self, langfuse_public_key=None, langfuse_secret=None):
+    def __init__(
+        self, langfuse_public_key=None, langfuse_secret=None, flush_interval=1
+    ):
         try:
             from langfuse import Langfuse
         except Exception as e:
@@ -31,7 +33,7 @@ class LangFuseLogger:
             host=self.langfuse_host,
             release=self.langfuse_release,
             debug=self.langfuse_debug,
-            flush_interval=1,  # flush interval in seconds
+            flush_interval=flush_interval,  # flush interval in seconds
         )
 
         # set the current langfuse project id in the environ

litellm/integrations/slack_alerting.py

@@ -12,6 +12,7 @@ from litellm.caching import DualCache
 import asyncio
 import aiohttp
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+import datetime
 
 
 class SlackAlerting:
@@ -47,6 +48,18 @@ class SlackAlerting:
         self.internal_usage_cache = DualCache()
         self.async_http_handler = AsyncHTTPHandler()
         self.alert_to_webhook_url = alert_to_webhook_url
+        self.langfuse_logger = None
+
+        try:
+            from litellm.integrations.langfuse import LangFuseLogger
+
+            self.langfuse_logger = LangFuseLogger(
+                os.getenv("LANGFUSE_PUBLIC_KEY"),
+                os.getenv("LANGFUSE_SECRET_KEY"),
+                flush_interval=1,
+            )
+        except:
+            pass
 
         pass
 
@@ -93,39 +106,68 @@ class SlackAlerting:
         request_info: str,
         request_data: Optional[dict] = None,
         kwargs: Optional[dict] = None,
+        type: Literal["hanging_request", "slow_response"] = "hanging_request",
+        start_time: Optional[datetime.datetime] = None,
+        end_time: Optional[datetime.datetime] = None,
     ):
         import uuid
 
         # For now: do nothing as we're debugging why this is not working as expected
+        if request_data is not None:
+            trace_id = request_data.get("metadata", {}).get(
+                "trace_id", None
+            )  # get langfuse trace id
+            if trace_id is None:
+                trace_id = "litellm-alert-trace-" + str(uuid.uuid4())
+                request_data["metadata"]["trace_id"] = trace_id
+        elif kwargs is not None:
+            _litellm_params = kwargs.get("litellm_params", {})
+            trace_id = _litellm_params.get("metadata", {}).get(
+                "trace_id", None
+            )  # get langfuse trace id
+            if trace_id is None:
+                trace_id = "litellm-alert-trace-" + str(uuid.uuid4())
+                _litellm_params["metadata"]["trace_id"] = trace_id
+
+        # Log hanging request as an error on langfuse
+        if type == "hanging_request":
+            if self.langfuse_logger is not None:
+                _logging_kwargs = copy.deepcopy(request_data)
+                if _logging_kwargs is None:
+                    _logging_kwargs = {}
+                _logging_kwargs["litellm_params"] = {}
+                request_data = request_data or {}
+                _logging_kwargs["litellm_params"]["metadata"] = request_data.get(
+                    "metadata", {}
+                )
+                # log to langfuse in a separate thread
+                import threading
+
+                threading.Thread(
+                    target=self.langfuse_logger.log_event,
+                    args=(
+                        _logging_kwargs,
+                        None,
+                        start_time,
+                        end_time,
+                        None,
+                        print,
+                        "ERROR",
+                        "Requests is hanging",
+                    ),
+                ).start()
+
+        _langfuse_host = os.environ.get("LANGFUSE_HOST", "https://cloud.langfuse.com")
+        _langfuse_project_id = os.environ.get("LANGFUSE_PROJECT_ID")
+
+        # langfuse urls look like: https://us.cloud.langfuse.com/project/************/traces/litellm-alert-trace-ididi9dk-09292-************
+
+        _langfuse_url = (
+            f"{_langfuse_host}/project/{_langfuse_project_id}/traces/{trace_id}"
+        )
+        request_info += f"\n🪢 Langfuse Trace: {_langfuse_url}"
         return request_info
 
-        # if request_data is not None:
-        #     trace_id = request_data.get("metadata", {}).get(
-        #         "trace_id", None
-        #     )  # get langfuse trace id
-        #     if trace_id is None:
-        #         trace_id = "litellm-alert-trace-" + str(uuid.uuid4())
-        #         request_data["metadata"]["trace_id"] = trace_id
-        # elif kwargs is not None:
-        #     _litellm_params = kwargs.get("litellm_params", {})
-        #     trace_id = _litellm_params.get("metadata", {}).get(
-        #         "trace_id", None
-        #     )  # get langfuse trace id
-        #     if trace_id is None:
-        #         trace_id = "litellm-alert-trace-" + str(uuid.uuid4())
-        #         _litellm_params["metadata"]["trace_id"] = trace_id
-
-        # _langfuse_host = os.environ.get("LANGFUSE_HOST", "https://cloud.langfuse.com")
-        # _langfuse_project_id = os.environ.get("LANGFUSE_PROJECT_ID")
-
-        # # langfuse urls look like: https://us.cloud.langfuse.com/project/************/traces/litellm-alert-trace-ididi9dk-09292-************
-
-        # _langfuse_url = (
-        #     f"{_langfuse_host}/project/{_langfuse_project_id}/traces/{trace_id}"
-        # )
-        # request_info += f"\n🪢 Langfuse Trace: {_langfuse_url}"
-        # return request_info
-
     def _response_taking_too_long_callback(
         self,
         kwargs,  # kwargs to completion
@@ -167,6 +209,14 @@ class SlackAlerting:
             _deployment_latencies = metadata["_latency_per_deployment"]
             if len(_deployment_latencies) == 0:
                 return None
+            try:
+                # try sorting deployments by latency
+                _deployment_latencies = sorted(
+                    _deployment_latencies.items(), key=lambda x: x[1]
+                )
+                _deployment_latencies = dict(_deployment_latencies)
+            except:
+                pass
             for api_base, latency in _deployment_latencies.items():
                 _message_to_send += f"\n{api_base}: {round(latency,2)}s"
             _message_to_send = "```" + _message_to_send + "```"
@@ -194,7 +244,7 @@ class SlackAlerting:
         if time_difference_float > self.alerting_threshold:
             if "langfuse" in litellm.success_callback:
                 request_info = self._add_langfuse_trace_id_to_alert(
-                    request_info=request_info, kwargs=kwargs
+                    request_info=request_info, kwargs=kwargs, type="slow_response"
                 )
             # add deployment latencies to alert
             if (
@@ -222,8 +272,8 @@ class SlackAlerting:
 
     async def response_taking_too_long(
         self,
-        start_time: Optional[float] = None,
-        end_time: Optional[float] = None,
+        start_time: Optional[datetime.datetime] = None,
+        end_time: Optional[datetime.datetime] = None,
         type: Literal["hanging_request", "slow_response"] = "hanging_request",
         request_data: Optional[dict] = None,
     ):
@@ -243,10 +293,6 @@ class SlackAlerting:
             except:
                 messages = ""
             request_info = f"\nRequest Model: `{model}`\nMessages: `{messages}`"
-            if "langfuse" in litellm.success_callback:
-                request_info = self._add_langfuse_trace_id_to_alert(
-                    request_info=request_info, request_data=request_data
-                )
         else:
             request_info = ""
 
@@ -288,6 +334,15 @@ class SlackAlerting:
                     f"`Requests are hanging - {self.alerting_threshold}s+ request time`"
                 )
 
+                if "langfuse" in litellm.success_callback:
+                    request_info = self._add_langfuse_trace_id_to_alert(
+                        request_info=request_info,
+                        request_data=request_data,
+                        type="hanging_request",
+                        start_time=start_time,
+                        end_time=end_time,
+                    )
+
                 # add deployment latencies to alert
                 _deployment_latency_map = self._get_deployment_latencies_to_alert(
                     metadata=request_data.get("metadata", {})

litellm/llms/openai.py

@@ -447,6 +447,7 @@ class OpenAIChatCompletion(BaseLLM):
                 )
             else:
                 openai_aclient = client
+
             ## LOGGING
             logging_obj.pre_call(
                 input=data["messages"],

litellm/llms/vertex_ai.py

@@ -1039,6 +1039,7 @@ async def async_streaming(
     instances=None,
     vertex_project=None,
     vertex_location=None,
+    safety_settings=None,
     **optional_params,
 ):
     """
@@ -1065,6 +1066,7 @@ async def async_streaming(
         response = await llm_model._generate_content_streaming_async(
             contents=content,
             generation_config=optional_params,
+            safety_settings=safety_settings,
             tools=tools,
         )
 

litellm/proxy/_experimental/out/index.html

@@ -1 +1 @@
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+<!DOCTYPE html><html id="__next_error__"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="preload" as="script" fetchPriority="low" href="/ui/_next/static/chunks/webpack-ccae12a25017afa5.js" crossorigin=""/><script src="/ui/_next/static/chunks/fd9d1056-dafd44dfa2da140c.js" async="" crossorigin=""></script><script src="/ui/_next/static/chunks/69-e49705773ae41779.js" async="" crossorigin=""></script><script src="/ui/_next/static/chunks/main-app-9b4fb13a7db53edf.js" async="" crossorigin=""></script><title>LiteLLM Dashboard</title><meta name="description" content="LiteLLM Proxy Admin UI"/><link rel="icon" href="/ui/favicon.ico" type="image/x-icon" sizes="16x16"/><meta name="next-size-adjust"/><script src="/ui/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js" crossorigin="" noModule=""></script></head><body><script src="/ui/_next/static/chunks/webpack-ccae12a25017afa5.js" crossorigin="" async=""></script><script>(self.__next_f=self.__next_f||[]).push([0]);self.__next_f.push([2,null])</script><script>self.__next_f.push([1,"1:HL[\"/ui/_next/static/media/c9a5bc6a7c948fb0-s.p.woff2\",\"font\",{\"crossOrigin\":\"\",\"type\":\"font/woff2\"}]\n2:HL[\"/ui/_next/static/css/5e699db73bf6f8c2.css\",\"style\",{\"crossOrigin\":\"\"}]\n0:\"$L3\"\n"])</script><script>self.__next_f.push([1,"4:I[47690,[],\"\"]\n6:I[77831,[],\"\"]\n7:I[27125,[\"447\",\"static/chunks/447-9f8d32190ff7d16d.js\",\"931\",\"static/chunks/app/page-508c39694bd40fe9.js\"],\"\"]\n8:I[5613,[],\"\"]\n9:I[31778,[],\"\"]\nb:I[48955,[],\"\"]\nc:[]\n"])</script><script>self.__next_f.push([1,"3:[[[\"$\",\"link\",\"0\",{\"rel\":\"stylesheet\",\"href\":\"/ui/_next/static/css/5e699db73bf6f8c2.css\",\"precedence\":\"next\",\"crossOrigin\":\"\"}]],[\"$\",\"$L4\",null,{\"buildId\":\"kbGdRQFfI6W3bEwfzmJDI\",\"assetPrefix\":\"/ui\",\"initialCanonicalUrl\":\"/\",\"initialTree\":[\"\",{\"children\":[\"__PAGE__\",{}]},\"$undefined\",\"$undefined\",true],\"initialSeedData\":[\"\",{\"children\":[\"__PAGE__\",{},[\"$L5\",[\"$\",\"$L6\",null,{\"propsForComponent\":{\"params\":{}},\"Component\":\"$7\",\"isStaticGeneration\":true}],null]]},[null,[\"$\",\"html\",null,{\"lang\":\"en\",\"children\":[\"$\",\"body\",null,{\"className\":\"__className_c23dc8\",\"children\":[\"$\",\"$L8\",null,{\"parallelRouterKey\":\"children\",\"segmentPath\":[\"children\"],\"loading\":\"$undefined\",\"loadingStyles\":\"$undefined\",\"loadingScripts\":\"$undefined\",\"hasLoading\":false,\"error\":\"$undefined\",\"errorStyles\":\"$undefined\",\"errorScripts\":\"$undefined\",\"template\":[\"$\",\"$L9\",null,{}],\"templateStyles\":\"$undefined\",\"templateScripts\":\"$undefined\",\"notFound\":[[\"$\",\"title\",null,{\"children\":\"404: This page could not be found.\"}],[\"$\",\"div\",null,{\"style\":{\"fontFamily\":\"system-ui,\\\"Segoe UI\\\",Roboto,Helvetica,Arial,sans-serif,\\\"Apple Color Emoji\\\",\\\"Segoe UI Emoji\\\"\",\"height\":\"100vh\",\"textAlign\":\"center\",\"display\":\"flex\",\"flexDirection\":\"column\",\"alignItems\":\"center\",\"justifyContent\":\"center\"},\"children\":[\"$\",\"div\",null,{\"children\":[[\"$\",\"style\",null,{\"dangerouslySetInnerHTML\":{\"__html\":\"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}\"}}],[\"$\",\"h1\",null,{\"className\":\"next-error-h1\",\"style\":{\"display\":\"inline-block\",\"margin\":\"0 20px 0 0\",\"padding\":\"0 23px 0 0\",\"fontSize\":24,\"fontWeight\":500,\"verticalAlign\":\"top\",\"lineHeight\":\"49px\"},\"children\":\"404\"}],[\"$\",\"div\",null,{\"style\":{\"display\":\"inline-block\"},\"children\":[\"$\",\"h2\",null,{\"style\":{\"fontSize\":14,\"fontWeight\":400,\"lineHeight\":\"49px\",\"margin\":0},\"children\":\"This page could not be found.\"}]}]]}]}]],\"notFoundStyles\":[],\"styles\":null}]}]}],null]],\"initialHead\":[false,\"$La\"],\"globalErrorComponent\":\"$b\",\"missingSlots\":\"$Wc\"}]]\n"])</script><script>self.__next_f.push([1,"a:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"LiteLLM Dashboard\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"LiteLLM Proxy Admin UI\"}],[\"$\",\"link\",\"4\",{\"rel\":\"icon\",\"href\":\"/ui/favicon.ico\",\"type\":\"image/x-icon\",\"sizes\":\"16x16\"}],[\"$\",\"meta\",\"5\",{\"name\":\"next-size-adjust\"}]]\n5:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>

litellm/proxy/_experimental/out/index.txt

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litellm/proxy/_super_secret_config.yaml

@@ -1,23 +1,23 @@
 model_list:
-- model_name: text-embedding-3-small
-  litellm_params: 
-    model: text-embedding-3-small
-- model_name: whisper
-  litellm_params:
-    model: azure/azure-whisper
-    api_version: 2024-02-15-preview
-    api_base: os.environ/AZURE_EUROPE_API_BASE
-    api_key: os.environ/AZURE_EUROPE_API_KEY
-  model_info:
-    mode: audio_transcription
 - litellm_params:
-    model: gpt-4
-  model_name: gpt-4
-- model_name: azure-mistral
-  litellm_params:
-    model: azure/mistral-large-latest
-    api_base: https://Mistral-large-nmefg-serverless.eastus2.inference.ai.azure.com
-    api_key: os.environ/AZURE_MISTRAL_API_KEY
-
-# litellm_settings:
-#   cache: True
+    api_base: http://0.0.0.0:8080
+    api_key: my-fake-key
+    model: openai/my-fake-model
+  model_name: fake-openai-endpoint
+- litellm_params:
+    api_base: http://0.0.0.0:8080
+    api_key: my-fake-key
+    model: openai/my-fake-model-2
+  model_name: fake-openai-endpoint
+- litellm_params:
+    api_base: http://0.0.0.0:8080
+    api_key: my-fake-key
+    model: openai/my-fake-model-3
+  model_name: fake-openai-endpoint
+- litellm_params:
+    api_base: http://0.0.0.0:8080
+    api_key: my-fake-key
+    model: openai/my-fake-model-4
+  model_name: fake-openai-endpoint
+router_settings:
+  num_retries: 0

litellm/proxy/auth/auth_checks.py

@@ -95,7 +95,15 @@ def common_checks(
                 f"'user' param not passed in. 'enforce_user_param'={general_settings['enforce_user_param']}"
             )
     # 7. [OPTIONAL] If 'litellm.max_budget' is set (>0), is proxy under budget
-    if litellm.max_budget > 0 and global_proxy_spend is not None:
+    if (
+        litellm.max_budget > 0
+        and global_proxy_spend is not None
+        # only run global budget checks for OpenAI routes
+        # Reason - the Admin UI should continue working if the proxy crosses it's global budget
+        and route in LiteLLMRoutes.openai_routes.value
+        and route != "/v1/models"
+        and route != "/models"
+    ):
         if global_proxy_spend > litellm.max_budget:
             raise Exception(
                 f"ExceededBudget: LiteLLM Proxy has exceeded its budget. Current spend: {global_proxy_spend}; Max Budget: {litellm.max_budget}"

litellm/router.py

@@ -50,7 +50,6 @@ class Router:
     model_names: List = []
     cache_responses: Optional[bool] = False
     default_cache_time_seconds: int = 1 * 60 * 60  # 1 hour
-    num_retries: int = 0
     tenacity = None
     leastbusy_logger: Optional[LeastBusyLoggingHandler] = None
     lowesttpm_logger: Optional[LowestTPMLoggingHandler] = None
@@ -70,9 +69,11 @@ class Router:
         ] = None,  # if you want to cache across model groups
         client_ttl: int = 3600,  # ttl for cached clients - will re-initialize after this time in seconds
         ## RELIABILITY ##
-        num_retries: int = 0,
+        num_retries: Optional[int] = None,
         timeout: Optional[float] = None,
-        default_litellm_params={},  # default params for Router.chat.completion.create
+        default_litellm_params: Optional[
+            dict
+        ] = None,  # default params for Router.chat.completion.create
         default_max_parallel_requests: Optional[int] = None,
         set_verbose: bool = False,
         debug_level: Literal["DEBUG", "INFO"] = "INFO",
@@ -158,6 +159,7 @@ class Router:
         router = Router(model_list=model_list, fallbacks=[{"azure-gpt-3.5-turbo": "openai-gpt-3.5-turbo"}])
         ```
         """
+
         if semaphore:
             self.semaphore = semaphore
         self.set_verbose = set_verbose
@@ -229,7 +231,14 @@ class Router:
         self.failed_calls = (
             InMemoryCache()
         )  # cache to track failed call per deployment, if num failed calls within 1 minute > allowed fails, then add it to cooldown
-        self.num_retries = num_retries or litellm.num_retries or 0
+
+        if num_retries is not None:
+            self.num_retries = num_retries
+        elif litellm.num_retries is not None:
+            self.num_retries = litellm.num_retries
+        else:
+            self.num_retries = openai.DEFAULT_MAX_RETRIES
+
         self.timeout = timeout or litellm.request_timeout
 
         self.retry_after = retry_after
@@ -255,6 +264,7 @@ class Router:
         )  # dict to store aliases for router, ex. {"gpt-4": "gpt-3.5-turbo"}, all requests with gpt-4 -> get routed to gpt-3.5-turbo group
 
         # make Router.chat.completions.create compatible for openai.chat.completions.create
+        default_litellm_params = default_litellm_params or {}
         self.chat = litellm.Chat(params=default_litellm_params, router_obj=self)
 
         # default litellm args
@@ -428,6 +438,7 @@ class Router:
             kwargs["messages"] = messages
             kwargs["original_function"] = self._acompletion
             kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
+
             timeout = kwargs.get("request_timeout", self.timeout)
             kwargs.setdefault("metadata", {}).update({"model_group": model})
 
@@ -469,6 +480,7 @@ class Router:
             )
             kwargs["model_info"] = deployment.get("model_info", {})
             data = deployment["litellm_params"].copy()
+
             model_name = data["model"]
             for k, v in self.default_litellm_params.items():
                 if (
@@ -1415,10 +1427,12 @@ class Router:
         context_window_fallbacks = kwargs.pop(
             "context_window_fallbacks", self.context_window_fallbacks
         )
-        verbose_router_logger.debug(
-            f"async function w/ retries: original_function - {original_function}"
-        )
+
         num_retries = kwargs.pop("num_retries")
+
+        verbose_router_logger.debug(
+            f"async function w/ retries: original_function - {original_function}, num_retries - {num_retries}"
+        )
         try:
             # if the function call is successful, no exception will be raised and we'll break out of the loop
             response = await original_function(*args, **kwargs)
@@ -1445,6 +1459,7 @@ class Router:
                 await asyncio.sleep(timeout)
             elif RouterErrors.user_defined_ratelimit_error.value in str(e):
                 raise e  # don't wait to retry if deployment hits user-defined rate-limit
+
             elif hasattr(original_exception, "status_code") and litellm._should_retry(
                 status_code=original_exception.status_code
             ):
@@ -1606,6 +1621,28 @@ class Router:
                 raise e
             raise original_exception
 
+    def _router_should_retry(
+        self, e: Exception, remaining_retries: int, num_retries: int
+    ):
+        """
+        Calculate back-off, then retry
+        """
+        if hasattr(e, "response") and hasattr(e.response, "headers"):
+            timeout = litellm._calculate_retry_after(
+                remaining_retries=remaining_retries,
+                max_retries=num_retries,
+                response_headers=e.response.headers,
+                min_timeout=self.retry_after,
+            )
+            time.sleep(timeout)
+        else:
+            timeout = litellm._calculate_retry_after(
+                remaining_retries=remaining_retries,
+                max_retries=num_retries,
+                min_timeout=self.retry_after,
+            )
+            time.sleep(timeout)
+
     def function_with_retries(self, *args, **kwargs):
         """
         Try calling the model 3 times. Shuffle between available deployments.
@@ -1625,9 +1662,6 @@ class Router:
             return response
         except Exception as e:
             original_exception = e
-            verbose_router_logger.debug(
-                f"num retries in function with retries: {num_retries}"
-            )
             ### CHECK IF RATE LIMIT / CONTEXT WINDOW ERROR
             if (
                 isinstance(original_exception, litellm.ContextWindowExceededError)
@@ -1641,6 +1675,11 @@ class Router:
             if num_retries > 0:
                 kwargs = self.log_retry(kwargs=kwargs, e=original_exception)
             ### RETRY
+            self._router_should_retry(
+                e=original_exception,
+                remaining_retries=num_retries,
+                num_retries=num_retries,
+            )
             for current_attempt in range(num_retries):
                 verbose_router_logger.debug(
                     f"retrying request. Current attempt - {current_attempt}; retries left: {num_retries}"
@@ -1654,34 +1693,11 @@ class Router:
                     ## LOGGING
                     kwargs = self.log_retry(kwargs=kwargs, e=e)
                     remaining_retries = num_retries - current_attempt
-                    if "No models available" in str(e):
-                        timeout = litellm._calculate_retry_after(
+                    self._router_should_retry(
+                        e=e,
                         remaining_retries=remaining_retries,
-                            max_retries=num_retries,
-                            min_timeout=self.retry_after,
+                        num_retries=num_retries,
                     )
-                        time.sleep(timeout)
-                    elif (
-                        hasattr(e, "status_code")
-                        and hasattr(e, "response")
-                        and litellm._should_retry(status_code=e.status_code)
-                    ):
-                        if hasattr(e.response, "headers"):
-                            timeout = litellm._calculate_retry_after(
-                                remaining_retries=remaining_retries,
-                                max_retries=num_retries,
-                                response_headers=e.response.headers,
-                                min_timeout=self.retry_after,
-                            )
-                        else:
-                            timeout = litellm._calculate_retry_after(
-                                remaining_retries=remaining_retries,
-                                max_retries=num_retries,
-                                min_timeout=self.retry_after,
-                            )
-                        time.sleep(timeout)
-                    else:
-                        raise e
             raise original_exception
 
     ### HELPER FUNCTIONS
@@ -1979,6 +1995,8 @@ class Router:
                 # check if it ends with a trailing slash
                 if api_base.endswith("/"):
                     api_base += "v1/"
+                elif api_base.endswith("/v1"):
+                    api_base += "/"
                 else:
                     api_base += "/v1/"
 
@@ -2004,7 +2022,9 @@ class Router:
                 stream_timeout = litellm.get_secret(stream_timeout_env_name)
                 litellm_params["stream_timeout"] = stream_timeout
 
-            max_retries = litellm_params.pop("max_retries", 2)
+            max_retries = litellm_params.pop(
+                "max_retries", 0
+            )  # router handles retry logic
             if isinstance(max_retries, str) and max_retries.startswith("os.environ/"):
                 max_retries_env_name = max_retries.replace("os.environ/", "")
                 max_retries = litellm.get_secret(max_retries_env_name)

litellm/tests/test_acooldowns_router.py

@@ -119,7 +119,9 @@ def test_multiple_deployments_parallel():
 
 
 # test_multiple_deployments_parallel()
-def test_cooldown_same_model_name():
+@pytest.mark.parametrize("sync_mode", [True, False])
+@pytest.mark.asyncio
+async def test_cooldown_same_model_name(sync_mode):
     # users could have the same model with different api_base
     # example
     # azure/chatgpt, api_base: 1234
@@ -161,6 +163,7 @@ def test_cooldown_same_model_name():
             num_retries=3,
         )  # type: ignore
 
+        if sync_mode:
             response = router.completion(
                 model="gpt-3.5-turbo",
                 messages=[{"role": "user", "content": "hello this request will pass"}],
@@ -176,6 +179,23 @@ def test_cooldown_same_model_name():
                 model_ids[0] != model_ids[1]
             )  # ensure both models have a uuid added, and they have different names
 
+            print("\ngot response\n", response)
+        else:
+            response = await router.acompletion(
+                model="gpt-3.5-turbo",
+                messages=[{"role": "user", "content": "hello this request will pass"}],
+            )
+            print(router.model_list)
+            model_ids = []
+            for model in router.model_list:
+                model_ids.append(model["model_info"]["id"])
+            print("\n litellm model ids ", model_ids)
+
+            # example litellm_model_names ['azure/chatgpt-v-2-ModelID-64321', 'azure/chatgpt-v-2-ModelID-63960']
+            assert (
+                model_ids[0] != model_ids[1]
+            )  # ensure both models have a uuid added, and they have different names
+
             print("\ngot response\n", response)
     except Exception as e:
         pytest.fail(f"Got unexpected exception on router! - {e}")

litellm/tests/test_alangfuse.py

@@ -161,40 +161,54 @@ async def make_async_calls():
     return total_time
 
 
-# def test_langfuse_logging_async_text_completion():
-#     try:
-#         pre_langfuse_setup()
-#         litellm.set_verbose = False
-#         litellm.success_callback = ["langfuse"]
+@pytest.mark.asyncio
+@pytest.mark.parametrize("stream", [False, True])
+async def test_langfuse_logging_without_request_response(stream):
+    try:
+        import uuid
 
-#         async def _test_langfuse():
-#             response = await litellm.atext_completion(
-#                 model="gpt-3.5-turbo-instruct",
-#                 prompt="this is a test",
-#                 max_tokens=5,
-#                 temperature=0.7,
-#                 timeout=5,
-#                 user="test_user",
-#                 stream=True
-#             )
-#             async for chunk in response:
-#                 print()
-#                 print(chunk)
-#             await asyncio.sleep(1)
-#             return response
+        _unique_trace_name = f"litellm-test-{str(uuid.uuid4())}"
+        litellm.set_verbose = True
+        litellm.turn_off_message_logging = True
+        litellm.success_callback = ["langfuse"]
+        response = await litellm.acompletion(
+            model="gpt-3.5-turbo",
+            mock_response="It's simple to use and easy to get started",
+            messages=[{"role": "user", "content": "Hi 👋 - i'm claude"}],
+            max_tokens=10,
+            temperature=0.2,
+            stream=stream,
+            metadata={"trace_id": _unique_trace_name},
+        )
+        print(response)
+        if stream:
+            async for chunk in response:
+                print(chunk)
 
-#         response = asyncio.run(_test_langfuse())
-#         print(f"response: {response}")
+        await asyncio.sleep(3)
 
-#         # # check langfuse.log to see if there was a failed response
-#         search_logs("langfuse.log")
-#     except litellm.Timeout as e:
-#         pass
-#     except Exception as e:
-#         pytest.fail(f"An exception occurred - {e}")
+        import langfuse
 
+        langfuse_client = langfuse.Langfuse(
+            public_key=os.environ["LANGFUSE_PUBLIC_KEY"],
+            secret_key=os.environ["LANGFUSE_SECRET_KEY"],
+        )
 
-# test_langfuse_logging_async_text_completion()
+        # get trace with _unique_trace_name
+        trace = langfuse_client.get_generations(trace_id=_unique_trace_name)
+
+        print("trace_from_langfuse", trace)
+
+        _trace_data = trace.data
+
+        assert _trace_data[0].input == {"messages": "redacted-by-litellm"}
+        assert _trace_data[0].output == {
+            "role": "assistant",
+            "content": "redacted-by-litellm",
+        }
+
+    except Exception as e:
+        pytest.fail(f"An exception occurred - {e}")
 
 
 @pytest.mark.skip(reason="beta test - checking langfuse output")

litellm/tests/test_completion.py

@@ -2730,7 +2730,6 @@ async def test_acompletion_watsonx():
             messages=messages,
             temperature=0.2,
             max_tokens=80,
-            space_id=os.getenv("WATSONX_SPACE_ID_TEST"),
         )
         # Add any assertions here to check the response
         print(response)

litellm/tests/test_embedding.py

@@ -483,6 +483,8 @@ def test_mistral_embeddings():
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
+
+@pytest.mark.skip(reason="local test")
 def test_watsonx_embeddings():
     try:
         litellm.set_verbose = True

litellm/tests/test_least_busy_routing.py

@@ -201,6 +201,7 @@ async def test_router_atext_completion_streaming():
 
 @pytest.mark.asyncio
 async def test_router_completion_streaming():
+    litellm.set_verbose = True
     messages = [
         {"role": "user", "content": "Hello, can you generate a 500 words poem?"}
     ]
@@ -219,9 +220,9 @@ async def test_router_completion_streaming():
         {
             "model_name": "azure-model",
             "litellm_params": {
-                "model": "azure/gpt-35-turbo",
-                "api_key": "os.environ/AZURE_EUROPE_API_KEY",
-                "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com",
+                "model": "azure/gpt-turbo",
+                "api_key": "os.environ/AZURE_FRANCE_API_KEY",
+                "api_base": "https://openai-france-1234.openai.azure.com",
                 "rpm": 6,
             },
             "model_info": {"id": 2},
@@ -229,9 +230,9 @@ async def test_router_completion_streaming():
         {
             "model_name": "azure-model",
             "litellm_params": {
-                "model": "azure/gpt-35-turbo",
-                "api_key": "os.environ/AZURE_CANADA_API_KEY",
-                "api_base": "https://my-endpoint-canada-berri992.openai.azure.com",
+                "model": "azure/gpt-turbo",
+                "api_key": "os.environ/AZURE_FRANCE_API_KEY",
+                "api_base": "https://openai-france-1234.openai.azure.com",
                 "rpm": 6,
             },
             "model_info": {"id": 3},
@@ -262,4 +263,4 @@ async def test_router_completion_streaming():
     ## check if calls equally distributed
     cache_dict = router.cache.get_cache(key=cache_key)
     for k, v in cache_dict.items():
-        assert v == 1
+        assert v == 1, f"Failed. K={k} called v={v} times, cache_dict={cache_dict}"

litellm/tests/test_router.py

@@ -1,7 +1,7 @@
 #### What this tests ####
 # This tests litellm router
 
-import sys, os, time
+import sys, os, time, openai
 import traceback, asyncio
 import pytest
 
@@ -19,6 +19,45 @@ import os, httpx
 load_dotenv()
 
 
+@pytest.mark.parametrize("num_retries", [None, 2])
+@pytest.mark.parametrize("max_retries", [None, 4])
+def test_router_num_retries_init(num_retries, max_retries):
+    """
+    - test when num_retries set v/s not
+    - test client value when max retries set v/s not
+    """
+    router = Router(
+        model_list=[
+            {
+                "model_name": "gpt-3.5-turbo",  # openai model name
+                "litellm_params": {  # params for litellm completion/embedding call
+                    "model": "azure/chatgpt-v-2",
+                    "api_key": "bad-key",
+                    "api_version": os.getenv("AZURE_API_VERSION"),
+                    "api_base": os.getenv("AZURE_API_BASE"),
+                    "max_retries": max_retries,
+                },
+                "model_info": {"id": 12345},
+            },
+        ],
+        num_retries=num_retries,
+    )
+
+    if num_retries is not None:
+        assert router.num_retries == num_retries
+    else:
+        assert router.num_retries == openai.DEFAULT_MAX_RETRIES
+
+    model_client = router._get_client(
+        {"model_info": {"id": 12345}}, client_type="async", kwargs={}
+    )
+
+    if max_retries is not None:
+        assert getattr(model_client, "max_retries") == max_retries
+    else:
+        assert getattr(model_client, "max_retries") == 0
+
+
 @pytest.mark.parametrize(
     "timeout", [10, 1.0, httpx.Timeout(timeout=300.0, connect=20.0)]
 )
@@ -99,6 +138,7 @@ def test_router_azure_ai_studio_init(mistral_api_base):
     print(f"uri_reference: {uri_reference}")
 
     assert "/v1/" in uri_reference
+    assert uri_reference.count("v1") == 1
 
 
 def test_exception_raising():

litellm/tests/test_router_fallbacks.py

@@ -258,6 +258,7 @@ def test_sync_fallbacks_embeddings():
         model_list=model_list,
         fallbacks=[{"bad-azure-embedding-model": ["good-azure-embedding-model"]}],
         set_verbose=False,
+        num_retries=0,
     )
     customHandler = MyCustomHandler()
     litellm.callbacks = [customHandler]
@@ -393,7 +394,7 @@ def test_dynamic_fallbacks_sync():
             },
         ]
 
-        router = Router(model_list=model_list, set_verbose=True)
+        router = Router(model_list=model_list, set_verbose=True, num_retries=0)
         kwargs = {}
         kwargs["model"] = "azure/gpt-3.5-turbo"
         kwargs["messages"] = [{"role": "user", "content": "Hey, how's it going?"}]
@@ -830,6 +831,7 @@ def test_usage_based_routing_fallbacks():
             routing_strategy="usage-based-routing",
             redis_host=os.environ["REDIS_HOST"],
             redis_port=os.environ["REDIS_PORT"],
+            num_retries=0,
         )
 
         messages = [

litellm/tests/test_router_init.py

@@ -203,7 +203,7 @@ def test_timeouts_router():
                 },
             },
         ]
-        router = Router(model_list=model_list)
+        router = Router(model_list=model_list, num_retries=0)
 
         print("PASSED !")
 
@@ -396,7 +396,9 @@ def test_router_init_gpt_4_vision_enhancements():
         pytest.fail(f"Error occurred: {e}")
 
 
-def test_openai_with_organization():
+@pytest.mark.parametrize("sync_mode", [True, False])
+@pytest.mark.asyncio
+async def test_openai_with_organization(sync_mode):
     try:
         print("Testing OpenAI with organization")
         model_list = [
@@ -418,6 +420,7 @@ def test_openai_with_organization():
         print(router.model_list)
         print(router.model_list[0])
 
+        if sync_mode:
             openai_client = router._get_client(
                 deployment=router.model_list[0],
                 kwargs={"input": ["hello"], "model": "openai-bad-org"},
@@ -433,7 +436,9 @@ def test_openai_with_organization():
                     model="openai-bad-org",
                     messages=[{"role": "user", "content": "this is a test"}],
                 )
-            pytest.fail("Request should have failed - This organization does not exist")
+                pytest.fail(
+                    "Request should have failed - This organization does not exist"
+                )
             except Exception as e:
                 print("Got exception: " + str(e))
                 assert "No such organization: org-ikDc4ex8NB" in str(e)
@@ -444,6 +449,36 @@ def test_openai_with_organization():
                 messages=[{"role": "user", "content": "this is a test"}],
                 max_tokens=5,
             )
+        else:
+            openai_client = router._get_client(
+                deployment=router.model_list[0],
+                kwargs={"input": ["hello"], "model": "openai-bad-org"},
+                client_type="async",
+            )
+            print(vars(openai_client))
+
+            assert openai_client.organization == "org-ikDc4ex8NB"
+
+            # bad org raises error
+
+            try:
+                response = await router.acompletion(
+                    model="openai-bad-org",
+                    messages=[{"role": "user", "content": "this is a test"}],
+                )
+                pytest.fail(
+                    "Request should have failed - This organization does not exist"
+                )
+            except Exception as e:
+                print("Got exception: " + str(e))
+                assert "No such organization: org-ikDc4ex8NB" in str(e)
+
+            # good org works
+            response = await router.acompletion(
+                model="openai-good-org",
+                messages=[{"role": "user", "content": "this is a test"}],
+                max_tokens=5,
+            )
 
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")

litellm/tests/test_router_timeout.py

@@ -57,6 +57,7 @@ def test_router_timeouts():
         redis_password=os.getenv("REDIS_PASSWORD"),
         redis_port=int(os.getenv("REDIS_PORT")),
         timeout=10,
+        num_retries=0,
     )
 
     print("***** TPM SETTINGS *****")
@@ -89,15 +90,15 @@ def test_router_timeouts():
 
 @pytest.mark.asyncio
 async def test_router_timeouts_bedrock():
-    import openai
+    import openai, uuid
 
     # Model list for OpenAI and Anthropic models
-    model_list = [
+    _model_list = [
         {
             "model_name": "bedrock",
             "litellm_params": {
                 "model": "bedrock/anthropic.claude-instant-v1",
-                "timeout": 0.001,
+                "timeout": 0.00001,
             },
             "tpm": 80000,
         },
@@ -105,17 +106,18 @@ async def test_router_timeouts_bedrock():
 
     # Configure router
     router = Router(
-        model_list=model_list,
+        model_list=_model_list,
         routing_strategy="usage-based-routing",
         debug_level="DEBUG",
         set_verbose=True,
+        num_retries=0,
     )
 
     litellm.set_verbose = True
     try:
         response = await router.acompletion(
             model="bedrock",
-            messages=[{"role": "user", "content": "hello, who are u"}],
+            messages=[{"role": "user", "content": f"hello, who are u {uuid.uuid4()}"}],
         )
         print(response)
         pytest.fail("Did not raise error `openai.APITimeoutError`")

litellm/tests/test_timeout.py

@@ -78,7 +78,8 @@ def test_hanging_request_azure():
                     "model_name": "openai-gpt",
                     "litellm_params": {"model": "gpt-3.5-turbo"},
                 },
-            ]
+            ],
+            num_retries=0,
         )
 
         encoded = litellm.utils.encode(model="gpt-3.5-turbo", text="blue")[0]
@@ -131,7 +132,8 @@ def test_hanging_request_openai():
                     "model_name": "openai-gpt",
                     "litellm_params": {"model": "gpt-3.5-turbo"},
                 },
-            ]
+            ],
+            num_retries=0,
         )
 
         encoded = litellm.utils.encode(model="gpt-3.5-turbo", text="blue")[0]
@@ -189,6 +191,7 @@ def test_timeout_streaming():
 # test_timeout_streaming()
 
 
+@pytest.mark.skip(reason="local test")
 def test_timeout_ollama():
     # this Will Raise a timeout
     import litellm

litellm/types/router.py

@@ -110,7 +110,7 @@ class LiteLLM_Params(BaseModel):
     stream_timeout: Optional[Union[float, str]] = (
         None  # timeout when making stream=True calls, if str, pass in as os.environ/
     )
-    max_retries: int = 2  # follows openai default of 2
+    max_retries: Optional[int] = None
     organization: Optional[str] = None  # for openai orgs
     ## VERTEX AI ##
     vertex_project: Optional[str] = None
@@ -148,9 +148,7 @@ class LiteLLM_Params(BaseModel):
         args.pop("self", None)
         args.pop("params", None)
         args.pop("__class__", None)
-        if max_retries is None:
-            max_retries = 2
-        elif isinstance(max_retries, str):
+        if max_retries is not None and isinstance(max_retries, str):
             max_retries = int(max_retries)  # cast to int
         super().__init__(max_retries=max_retries, **args, **params)
 

litellm/utils.py

@@ -1212,7 +1212,6 @@ class Logging:
                     print_verbose(
                         f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}"
                     )
-
             # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made
             callbacks = litellm.input_callback + self.dynamic_input_callbacks
             for callback in callbacks:
@@ -1229,29 +1228,17 @@ class Logging:
                             litellm_call_id=self.litellm_params["litellm_call_id"],
                             print_verbose=print_verbose,
                         )
-
-                    elif callback == "lite_debugger":
-                        print_verbose(
-                            f"reaches litedebugger for logging! - model_call_details {self.model_call_details}"
-                        )
-                        model = self.model_call_details["model"]
-                        messages = self.model_call_details["input"]
-                        print_verbose(f"liteDebuggerClient: {liteDebuggerClient}")
-                        liteDebuggerClient.input_log_event(
-                            model=model,
-                            messages=messages,
-                            end_user=self.model_call_details.get("user", "default"),
-                            litellm_call_id=self.litellm_params["litellm_call_id"],
-                            litellm_params=self.model_call_details["litellm_params"],
-                            optional_params=self.model_call_details["optional_params"],
-                            print_verbose=print_verbose,
-                            call_type=self.call_type,
-                        )
                     elif callback == "sentry" and add_breadcrumb:
-                        print_verbose("reaches sentry breadcrumbing")
+                        details_to_log = copy.deepcopy(self.model_call_details)
+                        if litellm.turn_off_message_logging:
+                            # make a copy of the _model_Call_details and log it
+                            details_to_log.pop("messages", None)
+                            details_to_log.pop("input", None)
+                            details_to_log.pop("prompt", None)
+
                         add_breadcrumb(
                             category="litellm.llm_call",
-                            message=f"Model Call Details pre-call: {self.model_call_details}",
+                            message=f"Model Call Details pre-call: {details_to_log}",
                             level="info",
                         )
                     elif isinstance(callback, CustomLogger):  # custom logger class
@@ -1315,7 +1302,7 @@ class Logging:
                     print_verbose(
                         f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}"
                     )
-
+            self.redact_message_input_output_from_logging(result=original_response)
             # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made
 
             callbacks = litellm.input_callback + self.dynamic_input_callbacks
@@ -1333,9 +1320,17 @@ class Logging:
                         )
                     elif callback == "sentry" and add_breadcrumb:
                         print_verbose("reaches sentry breadcrumbing")
+
+                        details_to_log = copy.deepcopy(self.model_call_details)
+                        if litellm.turn_off_message_logging:
+                            # make a copy of the _model_Call_details and log it
+                            details_to_log.pop("messages", None)
+                            details_to_log.pop("input", None)
+                            details_to_log.pop("prompt", None)
+
                         add_breadcrumb(
                             category="litellm.llm_call",
-                            message=f"Model Call Details post-call: {self.model_call_details}",
+                            message=f"Model Call Details post-call: {details_to_log}",
                             level="info",
                         )
                     elif isinstance(callback, CustomLogger):  # custom logger class
@@ -1527,6 +1522,8 @@ class Logging:
             else:
                 callbacks = litellm.success_callback
 
+            self.redact_message_input_output_from_logging(result=result)
+
             for callback in callbacks:
                 try:
                     litellm_params = self.model_call_details.get("litellm_params", {})
@@ -2071,6 +2068,9 @@ class Logging:
                     callbacks.append(callback)
         else:
             callbacks = litellm._async_success_callback
+
+        self.redact_message_input_output_from_logging(result=result)
+
         print_verbose(f"Async success callbacks: {callbacks}")
         for callback in callbacks:
             # check if callback can run for this request
@@ -2232,7 +2232,10 @@ class Logging:
                 start_time=start_time,
                 end_time=end_time,
             )
+
             result = None  # result sent to all loggers, init this to None incase it's not created
+
+            self.redact_message_input_output_from_logging(result=result)
             for callback in litellm.failure_callback:
                 try:
                     if callback == "lite_debugger":
@@ -2417,6 +2420,33 @@ class Logging:
                     f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}"
                 )
 
+    def redact_message_input_output_from_logging(self, result):
+        """
+        Removes messages, prompts, input, response from logging. This modifies the data in-place
+        only redacts when litellm.turn_off_message_logging == True
+        """
+        # check if user opted out of logging message/response to callbacks
+        if litellm.turn_off_message_logging == True:
+            # remove messages, prompts, input, response from logging
+            self.model_call_details["messages"] = "redacted-by-litellm"
+            self.model_call_details["prompt"] = ""
+            self.model_call_details["input"] = ""
+
+            # response cleaning
+            # ChatCompletion Responses
+            if self.stream and "complete_streaming_response" in self.model_call_details:
+                _streaming_response = self.model_call_details[
+                    "complete_streaming_response"
+                ]
+                for choice in _streaming_response.choices:
+                    choice.message.content = "redacted-by-litellm"
+            else:
+                if result is not None:
+                    if isinstance(result, litellm.ModelResponse):
+                        if hasattr(result, "choices"):
+                            for choice in result.choices:
+                                choice.message.content = "redacted-by-litellm"
+
 
 def exception_logging(
     additional_args={},
@@ -2598,9 +2628,15 @@ def function_setup(
             dynamic_success_callbacks = kwargs.pop("success_callback")
 
         if add_breadcrumb:
+            details_to_log = copy.deepcopy(kwargs)
+            if litellm.turn_off_message_logging:
+                # make a copy of the _model_Call_details and log it
+                details_to_log.pop("messages", None)
+                details_to_log.pop("input", None)
+                details_to_log.pop("prompt", None)
             add_breadcrumb(
                 category="litellm.llm_call",
-                message=f"Positional Args: {args}, Keyword Args: {kwargs}",
+                message=f"Positional Args: {args}, Keyword Args: {details_to_log}",
                 level="info",
             )
         if "logger_fn" in kwargs:

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.35.30"
+version = "1.35.31"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -80,7 +80,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.35.30"
+version = "1.35.31"
 version_files = [
     "pyproject.toml:^version"
 ]

ui/litellm-dashboard/out/index.html

@@ -1 +1 @@
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+<!DOCTYPE html><html id="__next_error__"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="preload" as="script" fetchPriority="low" href="/ui/_next/static/chunks/webpack-ccae12a25017afa5.js" crossorigin=""/><script src="/ui/_next/static/chunks/fd9d1056-dafd44dfa2da140c.js" async="" crossorigin=""></script><script src="/ui/_next/static/chunks/69-e49705773ae41779.js" async="" crossorigin=""></script><script src="/ui/_next/static/chunks/main-app-9b4fb13a7db53edf.js" async="" crossorigin=""></script><title>LiteLLM Dashboard</title><meta name="description" content="LiteLLM Proxy Admin UI"/><link rel="icon" href="/ui/favicon.ico" type="image/x-icon" sizes="16x16"/><meta name="next-size-adjust"/><script src="/ui/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js" crossorigin="" noModule=""></script></head><body><script src="/ui/_next/static/chunks/webpack-ccae12a25017afa5.js" crossorigin="" async=""></script><script>(self.__next_f=self.__next_f||[]).push([0]);self.__next_f.push([2,null])</script><script>self.__next_f.push([1,"1:HL[\"/ui/_next/static/media/c9a5bc6a7c948fb0-s.p.woff2\",\"font\",{\"crossOrigin\":\"\",\"type\":\"font/woff2\"}]\n2:HL[\"/ui/_next/static/css/5e699db73bf6f8c2.css\",\"style\",{\"crossOrigin\":\"\"}]\n0:\"$L3\"\n"])</script><script>self.__next_f.push([1,"4:I[47690,[],\"\"]\n6:I[77831,[],\"\"]\n7:I[27125,[\"447\",\"static/chunks/447-9f8d32190ff7d16d.js\",\"931\",\"static/chunks/app/page-508c39694bd40fe9.js\"],\"\"]\n8:I[5613,[],\"\"]\n9:I[31778,[],\"\"]\nb:I[48955,[],\"\"]\nc:[]\n"])</script><script>self.__next_f.push([1,"3:[[[\"$\",\"link\",\"0\",{\"rel\":\"stylesheet\",\"href\":\"/ui/_next/static/css/5e699db73bf6f8c2.css\",\"precedence\":\"next\",\"crossOrigin\":\"\"}]],[\"$\",\"$L4\",null,{\"buildId\":\"kbGdRQFfI6W3bEwfzmJDI\",\"assetPrefix\":\"/ui\",\"initialCanonicalUrl\":\"/\",\"initialTree\":[\"\",{\"children\":[\"__PAGE__\",{}]},\"$undefined\",\"$undefined\",true],\"initialSeedData\":[\"\",{\"children\":[\"__PAGE__\",{},[\"$L5\",[\"$\",\"$L6\",null,{\"propsForComponent\":{\"params\":{}},\"Component\":\"$7\",\"isStaticGeneration\":true}],null]]},[null,[\"$\",\"html\",null,{\"lang\":\"en\",\"children\":[\"$\",\"body\",null,{\"className\":\"__className_c23dc8\",\"children\":[\"$\",\"$L8\",null,{\"parallelRouterKey\":\"children\",\"segmentPath\":[\"children\"],\"loading\":\"$undefined\",\"loadingStyles\":\"$undefined\",\"loadingScripts\":\"$undefined\",\"hasLoading\":false,\"error\":\"$undefined\",\"errorStyles\":\"$undefined\",\"errorScripts\":\"$undefined\",\"template\":[\"$\",\"$L9\",null,{}],\"templateStyles\":\"$undefined\",\"templateScripts\":\"$undefined\",\"notFound\":[[\"$\",\"title\",null,{\"children\":\"404: This page could not be found.\"}],[\"$\",\"div\",null,{\"style\":{\"fontFamily\":\"system-ui,\\\"Segoe UI\\\",Roboto,Helvetica,Arial,sans-serif,\\\"Apple Color Emoji\\\",\\\"Segoe UI Emoji\\\"\",\"height\":\"100vh\",\"textAlign\":\"center\",\"display\":\"flex\",\"flexDirection\":\"column\",\"alignItems\":\"center\",\"justifyContent\":\"center\"},\"children\":[\"$\",\"div\",null,{\"children\":[[\"$\",\"style\",null,{\"dangerouslySetInnerHTML\":{\"__html\":\"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}\"}}],[\"$\",\"h1\",null,{\"className\":\"next-error-h1\",\"style\":{\"display\":\"inline-block\",\"margin\":\"0 20px 0 0\",\"padding\":\"0 23px 0 0\",\"fontSize\":24,\"fontWeight\":500,\"verticalAlign\":\"top\",\"lineHeight\":\"49px\"},\"children\":\"404\"}],[\"$\",\"div\",null,{\"style\":{\"display\":\"inline-block\"},\"children\":[\"$\",\"h2\",null,{\"style\":{\"fontSize\":14,\"fontWeight\":400,\"lineHeight\":\"49px\",\"margin\":0},\"children\":\"This page could not be found.\"}]}]]}]}]],\"notFoundStyles\":[],\"styles\":null}]}]}],null]],\"initialHead\":[false,\"$La\"],\"globalErrorComponent\":\"$b\",\"missingSlots\":\"$Wc\"}]]\n"])</script><script>self.__next_f.push([1,"a:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"LiteLLM Dashboard\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"LiteLLM Proxy Admin UI\"}],[\"$\",\"link\",\"4\",{\"rel\":\"icon\",\"href\":\"/ui/favicon.ico\",\"type\":\"image/x-icon\",\"sizes\":\"16x16\"}],[\"$\",\"meta\",\"5\",{\"name\":\"next-size-adjust\"}]]\n5:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>

ui/litellm-dashboard/out/index.txt

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ui/litellm-dashboard/src/components/create_key_button.tsx

@@ -39,6 +39,7 @@ const CreateKey: React.FC<CreateKeyProps> = ({
   const [apiKey, setApiKey] = useState(null);
   const [softBudget, setSoftBudget] = useState(null);
   const [userModels, setUserModels] = useState([]);
+  const [modelsToPick, setModelsToPick] = useState([]);
   const handleOk = () => {
     setIsModalVisible(false);
     form.resetFields();
@@ -95,6 +96,30 @@ const CreateKey: React.FC<CreateKeyProps> = ({
     message.success('API Key copied to clipboard');
 };
 
+  useEffect(() => {
+    let tempModelsToPick = [];
+
+    if (team) {
+      if (team.models.length > 0) {
+        if (team.models.includes("all-proxy-models")) {
+          // if the team has all-proxy-models show all available models
+          tempModelsToPick = userModels;
+        } else {
+          // show team models
+          tempModelsToPick = team.models;
+        }
+      } else {
+        // show all available models if the team has no models set
+        tempModelsToPick = userModels;
+      }
+    } else {
+      // no team set, show all available models
+      tempModelsToPick = userModels;
+    }
+
+    setModelsToPick(tempModelsToPick);
+  }, [team, userModels]);
+  
 
   return (
     <div>
@@ -161,30 +186,15 @@ const CreateKey: React.FC<CreateKeyProps> = ({
                     <Option key="all-team-models" value="all-team-models">
                       All Team Models
                     </Option>
-                    {team && team.models ? (
-                      team.models.includes("all-proxy-models") ? (
-                        userModels.map((model: string) => (
+                    {
+                      modelsToPick.map((model: string) => (
                           (
                             <Option key={model} value={model}>
                               {model}
                             </Option>
                           )
                         ))
-                      ) : (
-                        team.models.map((model: string) => (
-                          <Option key={model} value={model}>
-                            {model}
-                          </Option>
-                        ))
-                      )
-                    ) : (
-                      userModels.map((model: string) => (
-                        <Option key={model} value={model}>
-                          {model}
-                        </Option>
-                      ))
-                    )}
-
+                    }
                 </Select>
               </Form.Item>
               <Accordion className="mt-20 mb-8" >