Merge branch 'BerriAI:main' into feature/watsonx-integration

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

@@ -57,7 +57,7 @@ os.environ["LANGSMITH_API_KEY"] = ""
 os.environ['OPENAI_API_KEY']=""
 
 # set langfuse as a callback, litellm will send the data to langfuse
-litellm.success_callback = ["langfuse"] 
+litellm.success_callback = ["langsmith"] 
  
 response = litellm.completion(
     model="gpt-3.5-turbo",

litellm/integrations/prometheus.py

@@ -25,27 +25,27 @@ class PrometheusLogger:
             self.litellm_llm_api_failed_requests_metric = Counter(
                 name="litellm_llm_api_failed_requests_metric",
                 documentation="Total number of failed LLM API calls via litellm",
-                labelnames=["end_user", "hashed_api_key", "model", "team"],
+                labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
             )
 
             self.litellm_requests_metric = Counter(
                 name="litellm_requests_metric",
                 documentation="Total number of LLM calls to litellm",
-                labelnames=["end_user", "hashed_api_key", "model", "team"],
+                labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
             )
 
             # Counter for spend
             self.litellm_spend_metric = Counter(
                 "litellm_spend_metric",
                 "Total spend on LLM requests",
-                labelnames=["end_user", "hashed_api_key", "model", "team"],
+                labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
             )
 
             # Counter for total_output_tokens
             self.litellm_tokens_metric = Counter(
                 "litellm_total_tokens",
                 "Total number of input + output tokens from LLM requests",
-                labelnames=["end_user", "hashed_api_key", "model", "team"],
+                labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
             )
         except Exception as e:
             print_verbose(f"Got exception on init prometheus client {str(e)}")
@@ -71,6 +71,9 @@ class PrometheusLogger:
             litellm_params = kwargs.get("litellm_params", {}) or {}
             proxy_server_request = litellm_params.get("proxy_server_request") or {}
             end_user_id = proxy_server_request.get("body", {}).get("user", None)
+            user_id = proxy_server_request.get("metadata", {}).get(
+                "user_api_key_user_id", None
+            )
             user_api_key = litellm_params.get("metadata", {}).get("user_api_key", None)
             user_api_team = litellm_params.get("metadata", {}).get(
                 "user_api_key_team_id", None
@@ -94,19 +97,19 @@ class PrometheusLogger:
                 user_api_key = hash_token(user_api_key)
 
             self.litellm_requests_metric.labels(
-                end_user_id, user_api_key, model, user_api_team
+                end_user_id, user_api_key, model, user_api_team, user_id
             ).inc()
             self.litellm_spend_metric.labels(
-                end_user_id, user_api_key, model, user_api_team
+                end_user_id, user_api_key, model, user_api_team, user_id
             ).inc(response_cost)
             self.litellm_tokens_metric.labels(
-                end_user_id, user_api_key, model, user_api_team
+                end_user_id, user_api_key, model, user_api_team, user_id
             ).inc(tokens_used)
 
             ### FAILURE INCREMENT ###
             if "exception" in kwargs:
                 self.litellm_llm_api_failed_requests_metric.labels(
-                    end_user_id, user_api_key, model, user_api_team
+                    end_user_id, user_api_key, model, user_api_team, user_id
                 ).inc()
         except Exception as e:
             traceback.print_exc()

litellm/llms/vertex_ai.py

@@ -22,6 +22,35 @@ class VertexAIError(Exception):
         )  # Call the base class constructor with the parameters it needs
 
 
+class ExtendedGenerationConfig(dict):
+    """Extended parameters for the generation."""
+
+    def __init__(
+        self,
+        *,
+        temperature: Optional[float] = None,
+        top_p: Optional[float] = None,
+        top_k: Optional[int] = None,
+        candidate_count: Optional[int] = None,
+        max_output_tokens: Optional[int] = None,
+        stop_sequences: Optional[List[str]] = None,
+        response_mime_type: Optional[str] = None,
+        frequency_penalty: Optional[float] = None,
+        presence_penalty: Optional[float] = None,
+    ):
+        super().__init__(
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            candidate_count=candidate_count,
+            max_output_tokens=max_output_tokens,
+            stop_sequences=stop_sequences,
+            response_mime_type=response_mime_type,
+            frequency_penalty=frequency_penalty,
+            presence_penalty=presence_penalty,
+        )
+
+
 class VertexAIConfig:
     """
     Reference: https://cloud.google.com/vertex-ai/docs/generative-ai/chat/test-chat-prompts
@@ -43,6 +72,10 @@ class VertexAIConfig:
 
     - `stop_sequences` (List[str]): The set of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response.
 
+    - `frequency_penalty` (float): This parameter is used to penalize the model from repeating the same output. The default value is 0.0.
+
+    - `presence_penalty` (float): This parameter is used to penalize the model from generating the same output as the input. The default value is 0.0.
+
     Note: Please make sure to modify the default parameters as required for your use case.
     """
 
@@ -53,6 +86,8 @@ class VertexAIConfig:
     response_mime_type: Optional[str] = None
     candidate_count: Optional[int] = None
     stop_sequences: Optional[list] = None
+    frequency_penalty: Optional[float] = None
+    presence_penalty: Optional[float] = None
 
     def __init__(
         self,
@@ -63,6 +98,8 @@ class VertexAIConfig:
         response_mime_type: Optional[str] = None,
         candidate_count: Optional[int] = None,
         stop_sequences: Optional[list] = None,
+        frequency_penalty: Optional[float] = None,
+        presence_penalty: Optional[float] = None,
     ) -> None:
         locals_ = locals()
         for key, value in locals_.items():
@@ -119,6 +156,10 @@ class VertexAIConfig:
                 optional_params["max_output_tokens"] = value
             if param == "response_format" and value["type"] == "json_object":
                 optional_params["response_mime_type"] = "application/json"
+            if param == "frequency_penalty":
+                optional_params["frequency_penalty"] = value
+            if param == "presence_penalty":
+                optional_params["presence_penalty"] = value
             if param == "tools" and isinstance(value, list):
                 from vertexai.preview import generative_models
 
@@ -363,42 +404,6 @@ def completion(
         from google.cloud.aiplatform_v1beta1.types import content as gapic_content_types  # type: ignore
         import google.auth  # type: ignore
 
-        class ExtendedGenerationConfig(GenerationConfig):
-            """Extended parameters for the generation."""
-
-            def __init__(
-                self,
-                *,
-                temperature: Optional[float] = None,
-                top_p: Optional[float] = None,
-                top_k: Optional[int] = None,
-                candidate_count: Optional[int] = None,
-                max_output_tokens: Optional[int] = None,
-                stop_sequences: Optional[List[str]] = None,
-                response_mime_type: Optional[str] = None,
-            ):
-                args_spec = inspect.getfullargspec(gapic_content_types.GenerationConfig)
-
-                if "response_mime_type" in args_spec.args:
-                    self._raw_generation_config = gapic_content_types.GenerationConfig(
-                        temperature=temperature,
-                        top_p=top_p,
-                        top_k=top_k,
-                        candidate_count=candidate_count,
-                        max_output_tokens=max_output_tokens,
-                        stop_sequences=stop_sequences,
-                        response_mime_type=response_mime_type,
-                    )
-                else:
-                    self._raw_generation_config = gapic_content_types.GenerationConfig(
-                        temperature=temperature,
-                        top_p=top_p,
-                        top_k=top_k,
-                        candidate_count=candidate_count,
-                        max_output_tokens=max_output_tokens,
-                        stop_sequences=stop_sequences,
-                    )
-
         ## Load credentials with the correct quota project ref: https://github.com/googleapis/python-aiplatform/issues/2557#issuecomment-1709284744
         print_verbose(
             f"VERTEX AI: vertex_project={vertex_project}; vertex_location={vertex_location}"
@@ -550,12 +555,12 @@ def completion(
 
                 model_response = llm_model.generate_content(
                     contents=content,
-                    generation_config=ExtendedGenerationConfig(**optional_params),
+                    generation_config=optional_params,
                     safety_settings=safety_settings,
                     stream=True,
                     tools=tools,
                 )
-                optional_params["stream"] = True
+
                 return model_response
 
             request_str += f"response = llm_model.generate_content({content})\n"
@@ -572,7 +577,7 @@ def completion(
             ## LLM Call
             response = llm_model.generate_content(
                 contents=content,
-                generation_config=ExtendedGenerationConfig(**optional_params),
+                generation_config=optional_params,
                 safety_settings=safety_settings,
                 tools=tools,
             )
@@ -627,7 +632,7 @@ def completion(
                     },
                 )
                 model_response = chat.send_message_streaming(prompt, **optional_params)
-                optional_params["stream"] = True
+
                 return model_response
 
             request_str += f"chat.send_message({prompt}, **{optional_params}).text\n"
@@ -659,7 +664,7 @@ def completion(
                     },
                 )
                 model_response = llm_model.predict_streaming(prompt, **optional_params)
-                optional_params["stream"] = True
+
                 return model_response
 
             request_str += f"llm_model.predict({prompt}, **{optional_params}).text\n"
@@ -811,45 +816,6 @@ async def async_completion(
     Add support for acompletion calls for gemini-pro
     """
     try:
-        from vertexai.preview.generative_models import GenerationConfig
-        from google.cloud.aiplatform_v1beta1.types import content as gapic_content_types  # type: ignore
-
-        class ExtendedGenerationConfig(GenerationConfig):
-            """Extended parameters for the generation."""
-
-            def __init__(
-                self,
-                *,
-                temperature: Optional[float] = None,
-                top_p: Optional[float] = None,
-                top_k: Optional[int] = None,
-                candidate_count: Optional[int] = None,
-                max_output_tokens: Optional[int] = None,
-                stop_sequences: Optional[List[str]] = None,
-                response_mime_type: Optional[str] = None,
-            ):
-                args_spec = inspect.getfullargspec(gapic_content_types.GenerationConfig)
-
-                if "response_mime_type" in args_spec.args:
-                    self._raw_generation_config = gapic_content_types.GenerationConfig(
-                        temperature=temperature,
-                        top_p=top_p,
-                        top_k=top_k,
-                        candidate_count=candidate_count,
-                        max_output_tokens=max_output_tokens,
-                        stop_sequences=stop_sequences,
-                        response_mime_type=response_mime_type,
-                    )
-                else:
-                    self._raw_generation_config = gapic_content_types.GenerationConfig(
-                        temperature=temperature,
-                        top_p=top_p,
-                        top_k=top_k,
-                        candidate_count=candidate_count,
-                        max_output_tokens=max_output_tokens,
-                        stop_sequences=stop_sequences,
-                    )
-
         if mode == "vision":
             print_verbose("\nMaking VertexAI Gemini Pro Vision Call")
             print_verbose(f"\nProcessing input messages = {messages}")
@@ -872,7 +838,7 @@ async def async_completion(
             ## LLM Call
             response = await llm_model._generate_content_async(
                 contents=content,
-                generation_config=ExtendedGenerationConfig(**optional_params),
+                generation_config=optional_params,
                 tools=tools,
             )
 
@@ -1056,45 +1022,6 @@ async def async_streaming(
     """
     Add support for async streaming calls for gemini-pro
     """
-    from vertexai.preview.generative_models import GenerationConfig
-    from google.cloud.aiplatform_v1beta1.types import content as gapic_content_types  # type: ignore
-
-    class ExtendedGenerationConfig(GenerationConfig):
-        """Extended parameters for the generation."""
-
-        def __init__(
-            self,
-            *,
-            temperature: Optional[float] = None,
-            top_p: Optional[float] = None,
-            top_k: Optional[int] = None,
-            candidate_count: Optional[int] = None,
-            max_output_tokens: Optional[int] = None,
-            stop_sequences: Optional[List[str]] = None,
-            response_mime_type: Optional[str] = None,
-        ):
-            args_spec = inspect.getfullargspec(gapic_content_types.GenerationConfig)
-
-            if "response_mime_type" in args_spec.args:
-                self._raw_generation_config = gapic_content_types.GenerationConfig(
-                    temperature=temperature,
-                    top_p=top_p,
-                    top_k=top_k,
-                    candidate_count=candidate_count,
-                    max_output_tokens=max_output_tokens,
-                    stop_sequences=stop_sequences,
-                    response_mime_type=response_mime_type,
-                )
-            else:
-                self._raw_generation_config = gapic_content_types.GenerationConfig(
-                    temperature=temperature,
-                    top_p=top_p,
-                    top_k=top_k,
-                    candidate_count=candidate_count,
-                    max_output_tokens=max_output_tokens,
-                    stop_sequences=stop_sequences,
-                )
-
     if mode == "vision":
         stream = optional_params.pop("stream")
         tools = optional_params.pop("tools", None)
@@ -1115,11 +1042,10 @@ async def async_streaming(
 
         response = await llm_model._generate_content_streaming_async(
             contents=content,
-            generation_config=ExtendedGenerationConfig(**optional_params),
+            generation_config=optional_params,
             tools=tools,
         )
-        optional_params["stream"] = True
+
-        optional_params["tools"] = tools
     elif mode == "chat":
         chat = llm_model.start_chat()
         optional_params.pop(
@@ -1138,7 +1064,7 @@ async def async_streaming(
             },
         )
         response = chat.send_message_streaming_async(prompt, **optional_params)
-        optional_params["stream"] = True
+
     elif mode == "text":
         optional_params.pop(
             "stream", None

litellm/llms/vertex_ai_anthropic.py

@@ -236,19 +236,17 @@ def completion(
         if client is None:
             if vertex_credentials is not None and isinstance(vertex_credentials, str):
                 import google.oauth2.service_account
+
+                json_obj = json.loads(vertex_credentials)
+
                 creds = (
                     google.oauth2.service_account.Credentials.from_service_account_info(
-                        json.loads(vertex_credentials),
+                        json_obj,
                         scopes=["https://www.googleapis.com/auth/cloud-platform"],
                     )
                 )
                 ### CHECK IF ACCESS
                 access_token = refresh_auth(credentials=creds)
-            else:
-                import google.auth
-                creds, _ = google.auth.default()
-                ### CHECK IF ACCESS
-                access_token = refresh_auth(credentials=creds)
 
             vertex_ai_client = AnthropicVertex(
                 project_id=vertex_project,

litellm/main.py

@@ -12,7 +12,6 @@ from typing import Any, Literal, Union, BinaryIO
 from functools import partial
 import dotenv, traceback, random, asyncio, time, contextvars
 from copy import deepcopy
-
 import httpx
 import litellm
 from ._logging import verbose_logger
@@ -1685,13 +1684,14 @@ def completion(
                 or optional_params.pop("vertex_ai_credentials", None)
                 or get_secret("VERTEXAI_CREDENTIALS")
             )
+            new_params = deepcopy(optional_params)
             if "claude-3" in model:
                 model_response = vertex_ai_anthropic.completion(
                     model=model,
                     messages=messages,
                     model_response=model_response,
                     print_verbose=print_verbose,
-                    optional_params=optional_params,
+                    optional_params=new_params,
                     litellm_params=litellm_params,
                     logger_fn=logger_fn,
                     encoding=encoding,
@@ -1707,7 +1707,7 @@ def completion(
                     messages=messages,
                     model_response=model_response,
                     print_verbose=print_verbose,
-                    optional_params=optional_params,
+                    optional_params=new_params,
                     litellm_params=litellm_params,
                     logger_fn=logger_fn,
                     encoding=encoding,

litellm/model_prices_and_context_window_backup.json

@@ -1535,6 +1535,13 @@
         "litellm_provider": "openrouter",
         "mode": "chat"
     },
+    "openrouter/meta-llama/llama-3-70b-instruct": {
+        "max_tokens": 8192,
+        "input_cost_per_token": 0.0000008,
+        "output_cost_per_token": 0.0000008,
+        "litellm_provider": "openrouter",
+        "mode": "chat"
+    },
     "j2-ultra": {
         "max_tokens": 8192,
         "max_input_tokens": 8192,

litellm/proxy/_experimental/out/_next/static/chunks/webpack-df98554e08b2d9e3.js

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+0:["hJl7wGLdUQXe4Q17Ixjho",[[["",{"children":["__PAGE__",{}]},"$undefined","$undefined",true],["",{"children":["__PAGE__",{},["$L1",["$","$L2",null,{"propsForComponent":{"params":{}},"Component":"$3","isStaticGeneration":true}],null]]},[null,["$","html",null,{"lang":"en","children":["$","body",null,{"className":"__className_c23dc8","children":["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children"],"loading":"$undefined","loadingStyles":"$undefined","loadingScripts":"$undefined","hasLoading":false,"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$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]],[[["$","link","0",{"rel":"stylesheet","href":"/ui/_next/static/css/11285608926963e0.css","precedence":"next","crossOrigin":""}]],"$L6"]]]]
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 1:null

litellm/proxy/_new_secret_config.yaml

@@ -29,7 +29,7 @@ model_list:
     # api_base: https://exampleopenaiendpoint-production.up.railway.app/
 
 router_settings:
-  routing_strategy: usage-based-routing-v2 
+  # routing_strategy: usage-based-routing-v2 
   # redis_url: "os.environ/REDIS_URL"
   redis_host: os.environ/REDIS_HOST
   redis_port: os.environ/REDIS_PORT

litellm/proxy/proxy_server.py

@@ -3471,6 +3471,7 @@ async def completion(
         fastapi_response.headers["x-litellm-model-id"] = model_id
         return response
     except Exception as e:
+        data["litellm_status"] = "fail"  # used for alerting
         verbose_proxy_logger.debug("EXCEPTION RAISED IN PROXY MAIN.PY")
         verbose_proxy_logger.debug(
             "\033[1;31mAn error occurred: %s\n\n Debug this by setting `--debug`, e.g. `litellm --model gpt-3.5-turbo --debug`",
@@ -3720,6 +3721,7 @@ async def chat_completion(
 
         return response
     except Exception as e:
+        data["litellm_status"] = "fail"  # used for alerting
         traceback.print_exc()
         await proxy_logging_obj.post_call_failure_hook(
             user_api_key_dict=user_api_key_dict, original_exception=e
@@ -3914,6 +3916,7 @@ async def embeddings(
 
         return response
     except Exception as e:
+        data["litellm_status"] = "fail"  # used for alerting
         await proxy_logging_obj.post_call_failure_hook(
             user_api_key_dict=user_api_key_dict, original_exception=e
         )
@@ -4069,6 +4072,7 @@ async def image_generation(
 
         return response
     except Exception as e:
+        data["litellm_status"] = "fail"  # used for alerting
         await proxy_logging_obj.post_call_failure_hook(
             user_api_key_dict=user_api_key_dict, original_exception=e
         )
@@ -4247,6 +4251,7 @@ async def audio_transcriptions(
         data["litellm_status"] = "success"  # used for alerting
         return response
     except Exception as e:
+        data["litellm_status"] = "fail"  # used for alerting
         await proxy_logging_obj.post_call_failure_hook(
             user_api_key_dict=user_api_key_dict, original_exception=e
         )
@@ -4408,6 +4413,7 @@ async def moderations(
 
         return response
     except Exception as e:
+        data["litellm_status"] = "fail"  # used for alerting
         await proxy_logging_obj.post_call_failure_hook(
             user_api_key_dict=user_api_key_dict, original_exception=e
         )
@@ -5552,10 +5558,12 @@ async def global_spend_per_tea():
             # get the team_id for this entry
             # get the spend for this entry
             spend = row["total_spend"]
+            spend = round(spend, 2)
             current_date_entries = spend_by_date[row_date]
             current_date_entries[team_alias] = spend
         else:
             spend = row["total_spend"]
+            spend = round(spend, 2)
             spend_by_date[row_date] = {team_alias: spend}
 
         if team_alias in total_spend_per_team:
@@ -5909,11 +5917,18 @@ async def user_info(
                 user_id=user_api_key_dict.user_id
             )
             # *NEW* get all teams in user 'teams' field
-            teams_2 = await prisma_client.get_data(
+            if getattr(caller_user_info, "user_role", None) == "proxy_admin":
-                team_id_list=caller_user_info.teams,
+                teams_2 = await prisma_client.get_data(
-                table_name="team",
+                    table_name="team",
-                query_type="find_all",
+                    query_type="find_all",
-            )
+                    team_id_list=None,
+                )
+            else:
+                teams_2 = await prisma_client.get_data(
+                    team_id_list=caller_user_info.teams,
+                    table_name="team",
+                    query_type="find_all",
+                )
 
             if teams_2 is not None and isinstance(teams_2, list):
                 for team in teams_2:
@@ -7873,7 +7888,7 @@ async def login(request: Request):
         )
         if os.getenv("DATABASE_URL") is not None:
             response = await generate_key_helper_fn(
-                **{"user_role": "proxy_admin", "duration": "1hr", "key_max_budget": 5, "models": [], "aliases": {}, "config": {}, "spend": 0, "user_id": key_user_id, "team_id": "litellm-dashboard"}  # type: ignore
+                **{"user_role": "proxy_admin", "duration": "2hr", "key_max_budget": 5, "models": [], "aliases": {}, "config": {}, "spend": 0, "user_id": key_user_id, "team_id": "litellm-dashboard"}  # type: ignore
             )
         else:
             raise ProxyException(
@@ -8125,7 +8140,7 @@ async def auth_callback(request: Request):
     # User might not be already created on first generation of key
     # But if it is, we want their models preferences
     default_ui_key_values = {
-        "duration": "1hr",
+        "duration": "2hr",
         "key_max_budget": 0.01,
         "aliases": {},
         "config": {},
@@ -8137,6 +8152,7 @@ async def auth_callback(request: Request):
         "user_id": user_id,
         "user_email": user_email,
     }
+    _user_id_from_sso = user_id
     try:
         user_role = None
         if prisma_client is not None:
@@ -8160,7 +8176,7 @@ async def auth_callback(request: Request):
             if user_info is not None:
                 user_defined_values = {
                     "models": getattr(user_info, "models", user_id_models),
-                    "user_id": getattr(user_info, "user_id", user_id),
+                    "user_id": user_id,
                     "user_email": getattr(user_info, "user_id", user_email),
                     "user_role": getattr(user_info, "user_role", None),
                 }
@@ -8191,6 +8207,10 @@ async def auth_callback(request: Request):
     )
     key = response["token"]  # type: ignore
     user_id = response["user_id"]  # type: ignore
+
+    # This should always be true
+    # User_id on SSO == user_id in the LiteLLM_VerificationToken Table
+    assert user_id == _user_id_from_sso
     litellm_dashboard_ui = "/ui/"
     user_role = user_role or "app_owner"
     if (

litellm/proxy/utils.py

@@ -271,11 +271,43 @@ class ProxyLogging:
         request_info = f"\nRequest Model: `{model}`\nAPI Base: `{api_base}`\nMessages: `{messages}`"
         slow_message = f"`Responses are slow - {round(time_difference_float,2)}s response time > Alerting threshold: {self.alerting_threshold}s`"
         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
+                )
             await self.alerting_handler(
                 message=slow_message + request_info,
                 level="Low",
             )
 
+    def _add_langfuse_trace_id_to_alert(
+        self,
+        request_info: str,
+        request_data: Optional[dict] = None,
+        kwargs: Optional[dict] = None,
+    ):
+        import uuid
+
+        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")
+        request_info += f"\n🪢 Langfuse Trace: {_langfuse_host}/trace/{trace_id}"
+        return request_info
+
     async def response_taking_too_long(
         self,
         start_time: Optional[float] = None,
@@ -289,22 +321,18 @@ class ProxyLogging:
             if messages is None:
                 # if messages does not exist fallback to "input"
                 messages = request_data.get("input", None)
-            trace_id = request_data.get("metadata", {}).get(
+
-                "trace_id", None
+            # try casting messages to str and get the first 100 characters, else mark as None
-            )  # get langfuse trace id
+            try:
-            if trace_id is not None:
                 messages = str(messages)
                 messages = messages[:100]
-                messages = f"{messages}\nLangfuse Trace Id: {trace_id}"
+            except:
-            else:
+                messages = ""
-                # try casting messages to str and get the first 100 characters, else mark as None
-                try:
-                    messages = str(messages)
-                    messages = messages[:100]
-                except:
-                    messages = None
-
             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 = ""
 
@@ -318,6 +346,7 @@ class ProxyLogging:
             if (
                 request_data is not None
                 and request_data.get("litellm_status", "") != "success"
+                and request_data.get("litellm_status", "") != "fail"
             ):
                 if request_data.get("deployment", None) is not None and isinstance(
                     request_data["deployment"], dict
@@ -493,14 +522,19 @@ class ProxyLogging:
             level: str - Low|Medium|High - if calls might fail (Medium) or are failing (High); Currently, no alerts would be 'Low'.
             message: str - what is the alert about
         """
+        if self.alerting is None:
+            return
+
         from datetime import datetime
 
         # Get the current timestamp
         current_time = datetime.now().strftime("%H:%M:%S")
-        _proxy_base_url = os.getenv("PROXY_BASE_URL", "None")
+        _proxy_base_url = os.getenv("PROXY_BASE_URL", None)
-        formatted_message = f"Level: `{level}`\nTimestamp: `{current_time}`\n\nMessage: {message} \n\nProxy URL: `{_proxy_base_url}`"
+        formatted_message = (
-        if self.alerting is None:
+            f"Level: `{level}`\nTimestamp: `{current_time}`\n\nMessage: {message}"
-            return
+        )
+        if _proxy_base_url is not None:
+            formatted_message += f"\n\nProxy URL: `{_proxy_base_url}`"
 
         for client in self.alerting:
             if client == "slack":
@@ -1157,6 +1191,8 @@ class PrismaClient:
                     response = await self.db.litellm_teamtable.find_many(
                         where={"team_id": {"in": team_id_list}}
                     )
+                elif query_type == "find_all" and team_id_list is None:
+                    response = await self.db.litellm_teamtable.find_many(take=20)
                 return response
             elif table_name == "user_notification":
                 if query_type == "find_unique":

litellm/router.py

@@ -2872,7 +2872,27 @@ class Router:
                     f"get_available_deployment for model: {model}, Selected deployment: {self.print_deployment(deployment) or deployment[0]} for model: {model}"
                 )
                 return deployment or deployment[0]
+            ############## Check if we can do a RPM/TPM based weighted pick #################
+            tpm = healthy_deployments[0].get("litellm_params").get("tpm", None)
+            if tpm is not None:
+                # use weight-random pick if rpms provided
+                tpms = [m["litellm_params"].get("tpm", 0) for m in healthy_deployments]
+                verbose_router_logger.debug(f"\ntpms {tpms}")
+                total_tpm = sum(tpms)
+                weights = [tpm / total_tpm for tpm in tpms]
+                verbose_router_logger.debug(f"\n weights {weights}")
+                # Perform weighted random pick
+                selected_index = random.choices(range(len(tpms)), weights=weights)[0]
+                verbose_router_logger.debug(f"\n selected index, {selected_index}")
+                deployment = healthy_deployments[selected_index]
+                verbose_router_logger.info(
+                    f"get_available_deployment for model: {model}, Selected deployment: {self.print_deployment(deployment) or deployment[0]} for model: {model}"
+                )
+                return deployment or deployment[0]
 
+            ############## No RPM/TPM passed, we do a random pick #################
+            item = random.choice(healthy_deployments)
+            return item or item[0]
         if deployment is None:
             verbose_router_logger.info(
                 f"get_available_deployment for model: {model}, No deployment available"

litellm/tests/test_amazing_vertex_completion.py

@@ -90,7 +90,7 @@ def load_vertex_ai_credentials():
 
     # Create a temporary file
     with tempfile.NamedTemporaryFile(mode="w+", delete=False) as temp_file:
-        # Write the updated content to the temporary file
+        # Write the updated content to the temporary files
         json.dump(service_account_key_data, temp_file, indent=2)
 
     # Export the temporary file as GOOGLE_APPLICATION_CREDENTIALS

litellm/tests/test_router_get_deployments.py

@@ -512,3 +512,76 @@ async def test_wildcard_openai_routing():
 
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
+
+
+"""
+Test async router get deployment (Simpl-shuffle)
+"""
+
+rpm_list = [[None, None], [6, 1440]]
+tpm_list = [[None, None], [6, 1440]]
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "rpm_list, tpm_list",
+    [(rpm, tpm) for rpm in rpm_list for tpm in tpm_list],
+)
+async def test_weighted_selection_router_async(rpm_list, tpm_list):
+    # this tests if load balancing works based on the provided rpms in the router
+    # it's a fast test, only tests get_available_deployment
+    # users can pass rpms as a litellm_param
+    try:
+        litellm.set_verbose = False
+        model_list = [
+            {
+                "model_name": "gpt-3.5-turbo",
+                "litellm_params": {
+                    "model": "gpt-3.5-turbo-0613",
+                    "api_key": os.getenv("OPENAI_API_KEY"),
+                    "rpm": rpm_list[0],
+                    "tpm": tpm_list[0],
+                },
+            },
+            {
+                "model_name": "gpt-3.5-turbo",
+                "litellm_params": {
+                    "model": "azure/chatgpt-v-2",
+                    "api_key": os.getenv("AZURE_API_KEY"),
+                    "api_base": os.getenv("AZURE_API_BASE"),
+                    "api_version": os.getenv("AZURE_API_VERSION"),
+                    "rpm": rpm_list[1],
+                    "tpm": tpm_list[1],
+                },
+            },
+        ]
+        router = Router(
+            model_list=model_list,
+        )
+        selection_counts = defaultdict(int)
+
+        # call get_available_deployment 1k times, it should pick azure/chatgpt-v-2 about 90% of the time
+        for _ in range(1000):
+            selected_model = await router.async_get_available_deployment(
+                "gpt-3.5-turbo"
+            )
+            selected_model_id = selected_model["litellm_params"]["model"]
+            selected_model_name = selected_model_id
+            selection_counts[selected_model_name] += 1
+        print(selection_counts)
+
+        total_requests = sum(selection_counts.values())
+
+        if rpm_list[0] is not None or tpm_list[0] is not None:
+            # Assert that 'azure/chatgpt-v-2' has about 90% of the total requests
+            assert (
+                selection_counts["azure/chatgpt-v-2"] / total_requests > 0.89
+            ), f"Assertion failed: 'azure/chatgpt-v-2' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}"
+        else:
+            # Assert both are used
+            assert selection_counts["azure/chatgpt-v-2"] > 0
+            assert selection_counts["gpt-3.5-turbo-0613"] > 0
+        router.reset()
+    except Exception as e:
+        traceback.print_exc()
+        pytest.fail(f"Error occurred: {e}")

litellm/tests/test_simple_shuffle.py

@@ -0,0 +1,53 @@
+# What is this?
+## unit tests for 'simple-shuffle'
+
+import sys, os, asyncio, time, random
+from datetime import datetime
+import traceback
+from dotenv import load_dotenv
+
+load_dotenv()
+import os
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+import pytest
+from litellm import Router
+
+"""
+Test random shuffle 
+- async 
+- sync 
+"""
+
+
+async def test_simple_shuffle():
+    model_list = [
+        {
+            "model_name": "azure-model",
+            "litellm_params": {
+                "model": "azure/gpt-turbo",
+                "api_key": "os.environ/AZURE_FRANCE_API_KEY",
+                "api_base": "https://openai-france-1234.openai.azure.com",
+                "rpm": 1440,
+            },
+            "model_info": {"id": 1},
+        },
+        {
+            "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",
+                "rpm": 6,
+            },
+            "model_info": {"id": 2},
+        },
+    ]
+    router = Router(
+        model_list=model_list,
+        routing_strategy="usage-based-routing-v2",
+        set_verbose=False,
+        num_retries=3,
+    )  # type: ignore

litellm/utils.py

@@ -5274,7 +5274,7 @@ def get_optional_params(
         if tool_choice is not None:
             optional_params["tool_choice"] = tool_choice
         if response_format is not None:
-            optional_params["response_format"] = tool_choice
+            optional_params["response_format"] = response_format
 
     elif custom_llm_provider == "openrouter":
         supported_params = get_supported_openai_params(
@@ -5750,6 +5750,7 @@ def get_supported_openai_params(model: str, custom_llm_provider: str):
             "frequency_penalty",
             "logit_bias",
             "user",
+            "response_format",
         ]
     elif custom_llm_provider == "perplexity":
         return [

model_prices_and_context_window.json

@@ -1535,6 +1535,13 @@
         "litellm_provider": "openrouter",
         "mode": "chat"
     },
+    "openrouter/meta-llama/llama-3-70b-instruct": {
+        "max_tokens": 8192,
+        "input_cost_per_token": 0.0000008,
+        "output_cost_per_token": 0.0000008,
+        "litellm_provider": "openrouter",
+        "mode": "chat"
+    },
     "j2-ultra": {
         "max_tokens": 8192,
         "max_input_tokens": 8192,

proxy_server_config.yaml

@@ -96,9 +96,9 @@ litellm_settings:
 
 router_settings:
   routing_strategy: usage-based-routing-v2 
-  # redis_host: os.environ/REDIS_HOST
+  redis_host: os.environ/REDIS_HOST
-  # redis_password: os.environ/REDIS_PASSWORD
+  redis_password: os.environ/REDIS_PASSWORD
-  # redis_port: os.environ/REDIS_PORT
+  redis_port: os.environ/REDIS_PORT
   enable_pre_call_checks: true
 
 general_settings: 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.35.18"
+version = "1.35.20"
 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 = "1.35.18"
+version = "1.35.20"
 version_files = [
     "pyproject.toml:^version"
 ]

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

@@ -116,7 +116,6 @@ const CreateKey: React.FC<CreateKeyProps> = ({
           wrapperCol={{ span: 16 }}
           labelAlign="left"
         >
-          {userRole === "App Owner" || userRole === "Admin" ? (
             <>
               <Form.Item 
                 label="Key Name" 
@@ -124,7 +123,7 @@ const CreateKey: React.FC<CreateKeyProps> = ({
                 rules={[{ required: true, message: 'Please input a key name' }]}
                 help="required"
               >
-                <Input />
+                <TextInput placeholder="" />
               </Form.Item>
               <Form.Item
                 label="Team ID"
@@ -188,147 +187,6 @@ const CreateKey: React.FC<CreateKeyProps> = ({
 
                 </Select>
               </Form.Item>
-              <Form.Item 
-                className="mt-8"
-                label="Max Budget (USD)" 
-                name="max_budget" 
-                help={`Budget cannot exceed team max budget: $${team?.max_budget !== null && team?.max_budget !== undefined ? team?.max_budget : 'unlimited'}`}
-                rules={[
-                  {
-                      validator: async (_, value) => {
-                          if (value && team && team.max_budget !== null && value > team.max_budget) {
-                              throw new Error(`Budget cannot exceed team max budget: $${team.max_budget}`);
-                          }
-                      },
-                  },
-              ]}
-              >
-                <InputNumber step={0.01} precision={2} width={200} />
-              </Form.Item>
-              <Form.Item 
-              className="mt-8"
-              label="Reset Budget" 
-              name="budget_duration"
-              help={`Team Reset Budget: ${team?.budget_duration !== null && team?.budget_duration !== undefined ? team?.budget_duration : 'None'}`}
-              >
-                <Select defaultValue={null} placeholder="n/a">
-                  <Select.Option value="24h">daily</Select.Option>
-                  <Select.Option value="30d">monthly</Select.Option>
-                </Select>
-              </Form.Item>
-              <Form.Item 
-                className="mt-8"
-                label="Tokens per minute Limit (TPM)" 
-                name="tpm_limit" 
-                help={`TPM cannot exceed team TPM limit: ${team?.tpm_limit !== null && team?.tpm_limit !== undefined ? team?.tpm_limit : 'unlimited'}`}
-                rules={[
-                  {
-                      validator: async (_, value) => {
-                          if (value && team && team.tpm_limit !== null && value > team.tpm_limit) {
-                              throw new Error(`TPM limit cannot exceed team TPM limit: ${team.tpm_limit}`);
-                          }
-                      },
-                  },
-              ]}
-                >
-                <InputNumber step={1} width={400} />
-              </Form.Item>
-              <Form.Item
-                className="mt-8"
-                label="Requests per minute Limit (RPM)"
-                name="rpm_limit"
-                help={`RPM cannot exceed team RPM limit: ${team?.rpm_limit !== null && team?.rpm_limit !== undefined ? team?.rpm_limit : 'unlimited'}`}
-                rules={[
-                  {
-                      validator: async (_, value) => {
-                          if (value && team && team.rpm_limit !== null && value > team.rpm_limit) {
-                              throw new Error(`RPM limit cannot exceed team RPM limit: ${team.rpm_limit}`);
-                          }
-                      },
-                  },
-              ]}
-              >
-                <InputNumber step={1} width={400} />
-              </Form.Item>
-              <Form.Item label="Expire Key (eg: 30s, 30h, 30d)" name="duration" className="mt-8">
-                <Input />
-              </Form.Item>
-              <Form.Item label="Metadata" name="metadata">
-                <Input.TextArea rows={4} placeholder="Enter metadata as JSON" />
-              </Form.Item>
-            </>
-          ) : (
-            <>
-              <Form.Item 
-                label="Key Name" 
-                name="key_alias"
-                rules={[{ required: true, message: 'Please input a key name' }]}
-                help="required"
-              >
-                <Input />
-              </Form.Item>
-              <Form.Item
-                label="Team ID"
-                name="team_id"
-                hidden={true}
-                initialValue={team ? team["team_id"] : null}
-                valuePropName="team_id"
-                className="mt-8"
-              >
-                <Input value={team ? team["team_alias"] : ""} disabled />
-              </Form.Item>
-
-              <Form.Item 
-                label="Models" 
-                name="models"
-                className="mb-12"
-                rules={[{ required: true, message: 'Please select a model' }]}
-                help="required"
-              >
-                <Select
-                  mode="multiple"
-                  placeholder="Select models"
-                  style={{ width: "100%" }}
-                  onChange={(values) => {
-                    const isAllTeamModelsSelected = values.includes("all-team-models");
-              
-                    if (isAllTeamModelsSelected) {
-                      const newValues = ["all-team-models"];
-                      form.setFieldsValue({ models: newValues });
-                    }
-                  }}
-
-                >
-                    <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) => (
-                          (
-                            <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" >
                 <AccordionHeader>
                   <b>Optional Settings</b>
@@ -397,7 +255,7 @@ const CreateKey: React.FC<CreateKeyProps> = ({
                 <InputNumber step={1} width={400} />
               </Form.Item>
               <Form.Item label="Expire Key (eg: 30s, 30h, 30d)" name="duration" className="mt-8">
-                <Input />
+                <TextInput placeholder="" />
               </Form.Item>
               <Form.Item label="Metadata" name="metadata">
                 <Input.TextArea rows={4} placeholder="Enter metadata as JSON" />
@@ -405,9 +263,8 @@ const CreateKey: React.FC<CreateKeyProps> = ({
 
                 </AccordionBody>
               </Accordion>
-
             </>
-          )}
+          
           <div style={{ textAlign: "right", marginTop: "10px" }}>
             <Button2 htmlType="submit">Create Key</Button2>
           </div>

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

@@ -1,6 +1,6 @@
 import React, { useState, useEffect } from "react";
 import { Button, Modal, Form, Input, message, Select, InputNumber } from "antd";
-import { Button as Button2, Text } from "@tremor/react";
+import { Button as Button2, Text, TextInput } from "@tremor/react";
 import { userCreateCall, modelAvailableCall } from "./networking";
 const { Option } = Select;
 
@@ -94,7 +94,7 @@ const Createuser: React.FC<CreateuserProps> = ({ userID, accessToken, teams }) =
           labelAlign="left"
         >
           <Form.Item label="User Email" name="user_email">
-            <Input placeholder="Enter User Email" />
+            <TextInput placeholder="" />
           </Form.Item>
           <Form.Item label="Team ID" name="team_id">
           <Select

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

@@ -46,8 +46,8 @@ const Sidebar: React.FC<SidebarProps> = ({
     );
   }
   return (
-    <Layout style={{ minHeight: "100vh", maxWidth: "130px" }}>
+    <Layout style={{ minHeight: "100vh", maxWidth: "145px" }}>
-      <Sider width={130}>
+      <Sider width={145}>
         <Menu
           mode="inline"
           defaultSelectedKeys={defaultSelectedKey ? defaultSelectedKey : ["1"]}
@@ -64,11 +64,7 @@ const Sidebar: React.FC<SidebarProps> = ({
             </Text>
           </Menu.Item>
 
-              <Menu.Item key="11" onClick={() => setPage("api_ref")}>
+              
-              <Text>
-                API Reference
-                </Text>
-              </Menu.Item>
 
           {
             userRole == "Admin" ? (
@@ -79,15 +75,6 @@ const Sidebar: React.FC<SidebarProps> = ({
               </Menu.Item>
             ) : null
           }
-
-          {userRole == "Admin" ? (
-            <Menu.Item key="6" onClick={() => setPage("teams")}>
-              <Text>
-              Teams
-              </Text>
-            </Menu.Item>
-          ) : null}
-
           {
             userRole == "Admin" ? (
               <Menu.Item key="4" onClick={() => setPage("usage")}>
@@ -99,6 +86,16 @@ const Sidebar: React.FC<SidebarProps> = ({
               ) : null
           }
 
+          {userRole == "Admin" ? (
+            <Menu.Item key="6" onClick={() => setPage("teams")}>
+              <Text>
+              Teams
+              </Text>
+            </Menu.Item>
+          ) : null}
+
+
+          
             {userRole == "Admin" ? (
             <Menu.Item key="5" onClick={() => setPage("users")}>
               <Text>
@@ -111,7 +108,7 @@ const Sidebar: React.FC<SidebarProps> = ({
             userRole == "Admin" ? (
               <Menu.Item key="8" onClick={() => setPage("settings")}>
                 <Text>
-                  Integrations
+                  Logging & Alerts
                 </Text>
                 </Menu.Item>
               ) : null
@@ -134,6 +131,11 @@ const Sidebar: React.FC<SidebarProps> = ({
               </Text>
             </Menu.Item>
           ) : null}
+          <Menu.Item key="11" onClick={() => setPage("api_ref")}>
+              <Text>
+                API Reference
+                </Text>
+              </Menu.Item>
         </Menu>
       </Sider>
     </Layout>

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

@@ -55,11 +55,11 @@ const Navbar: React.FC<NavbarProps> = ({
       <div className="text-left my-2 absolute top-0 left-0">
         <div className="flex flex-col items-center">
           <Link href="/">
-            <button className="text-gray-800 text-2xl py-1 rounded text-center">
+            <button className="text-gray-800 rounded text-center">
               <img
                 src={imageUrl}
-                width={200}
+                width={160}
-                height={200}
+                height={160}
                 alt="LiteLLM Brand"
                 className="mr-2"
               />

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

@@ -20,6 +20,7 @@ import {
   TableHead,
   TableHeaderCell,
   TableRow,
+  TextInput,
   Card,
   Icon,
   Button,
@@ -480,7 +481,7 @@ const handleEditSubmit = async (formValues: Record<string, any>) => {
                   name="team_alias"
                   rules={[{ required: true, message: 'Please input a team name' }]}
                 >
-                  <Input />
+                  <TextInput placeholder="" />
                 </Form.Item>
                 <Form.Item label="Models" name="models">
                   <Select2

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

@@ -295,6 +295,7 @@ const UsagePage: React.FC<UsagePageProps> = ({
             userRole={userRole}
             accessToken={accessToken}
             userSpend={null}
+            selectedTeam={null}
           />
       <TabGroup>
         <TabList className="mt-2">
@@ -391,6 +392,7 @@ const UsagePage: React.FC<UsagePageProps> = ({
                   index="date"
                   categories={uniqueTeamIds}
                   yAxisWidth={80}
+                  colors={["blue", "green", "yellow", "red", "purple"]}
                   
                   stack={true}
                 />

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

@@ -109,6 +109,10 @@ const ViewUserSpend: React.FC<ViewUserSpendProps> = ({ userID, userRole, accessT
     if (modelsToDisplay && modelsToDisplay.includes("all-proxy-models")) {
         console.log("user models:", userModels);
         modelsToDisplay = userModels;
+    } else if (modelsToDisplay && modelsToDisplay.includes("all-team-models")) {
+        modelsToDisplay = selectedTeam.models;
+    } else if (modelsToDisplay && modelsToDisplay.length === 0) {
+      modelsToDisplay = userModels;
     }
 
 
@@ -129,7 +133,7 @@ const ViewUserSpend: React.FC<ViewUserSpendProps> = ({ userID, userRole, accessT
         </div>
         <div className="ml-auto">
           <Accordion>
-            <AccordionHeader>Models</AccordionHeader>
+            <AccordionHeader><Text>Team Models</Text></AccordionHeader>
             <AccordionBody className="absolute right-0 z-10 bg-white p-2 shadow-lg max-w-xs">
               <List>
                 {modelsToDisplay.map((model: string) => (

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

@@ -21,7 +21,8 @@ import {
   SelectItem,
   Dialog, 
   DialogPanel,
-  Icon
+  Icon,
+  TextInput,
 } from "@tremor/react";
 import { userInfoCall, adminTopEndUsersCall } from "./networking";
 import { Badge, BadgeDelta, Button } from "@tremor/react";