Merge branch 'BerriAI:main' into update_helm_chart_deployment_2024-02-22

Dockerfile

@@ -20,6 +20,9 @@ RUN pip install --upgrade pip && \
 # Copy the current directory contents into the container at /app
 COPY . .
 
+# Build Admin UI
+RUN chmod +x build_admin_ui.sh && ./build_admin_ui.sh
+
 # Build the package
 RUN rm -rf dist/* && python -m build
 
@@ -35,6 +38,9 @@ RUN pip wheel --no-cache-dir --wheel-dir=/wheels/ -r requirements.txt
 # install semantic-cache [Experimental]- we need this here and not in requirements.txt because redisvl pins to pydantic 1.0 
 RUN pip install redisvl==0.0.7 --no-deps
 
+# Build Admin UI
+RUN chmod +x build_admin_ui.sh && ./build_admin_ui.sh
+
 # Runtime stage
 FROM $LITELLM_RUNTIME_IMAGE as runtime
 

Dockerfile.database

@@ -20,6 +20,9 @@ RUN pip install --upgrade pip && \
 # Copy the current directory contents into the container at /app
 COPY . .
 
+# Build Admin UI
+RUN chmod +x build_admin_ui.sh && ./build_admin_ui.sh
+
 # Build the package
 RUN rm -rf dist/* && python -m build
 
@@ -50,6 +53,9 @@ RUN pip install *.whl /wheels/* --no-index --find-links=/wheels/ && rm -f *.whl
 # install semantic-cache [Experimental]- we need this here and not in requirements.txt because redisvl pins to pydantic 1.0 
 RUN pip install redisvl==0.0.7 --no-deps
 
+# Build Admin UI
+RUN chmod +x build_admin_ui.sh && ./build_admin_ui.sh
+
 # Generate prisma client
 RUN prisma generate
 RUN chmod +x entrypoint.sh

build_admin_ui.sh

@@ -0,0 +1,62 @@
+#!/bin/bash
+
+# # try except this script
+# set -e
+
+# print current dir 
+echo
+pwd
+
+
+# only run this step for litellm enterprise, we run this if enterprise/enterprise_ui/_enterprise.json exists
+if [ ! -f "enterprise/enterprise_ui/enterprise_colors.json" ]; then
+    echo "Admin UI - using default LiteLLM UI"
+    exit 0
+fi
+
+echo "Building Custom Admin UI..."
+
+# Install dependencies
+# Check if we are on macOS
+if [[ "$(uname)" == "Darwin" ]]; then
+    # Install dependencies using Homebrew
+    if ! command -v brew &> /dev/null; then
+        echo "Error: Homebrew not found. Please install Homebrew and try again."
+        exit 1
+    fi
+    brew update
+    brew install curl
+else
+    # Assume Linux, try using apt-get
+    if command -v apt-get &> /dev/null; then
+        apt-get update
+        apt-get install -y curl
+    elif command -v apk &> /dev/null; then
+        # Try using apk if apt-get is not available
+        apk update
+        apk add curl
+    else
+        echo "Error: Unsupported package manager. Cannot install dependencies."
+        exit 1
+    fi
+fi
+curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.38.0/install.sh | bash
+source ~/.nvm/nvm.sh
+nvm install v18.17.0
+nvm use v18.17.0
+npm install -g npm
+
+# copy _enterprise.json from this directory to /ui/litellm-dashboard, and rename it to ui_colors.json
+cp enterprise/enterprise_ui/enterprise_colors.json ui/litellm-dashboard/ui_colors.json
+
+# cd in to /ui/litellm-dashboard
+cd ui/litellm-dashboard
+
+# ensure have access to build_ui.sh
+chmod +x ./build_ui.sh
+
+# run ./build_ui.sh
+./build_ui.sh
+
+# return to root directory
+cd ../..

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

@@ -238,9 +238,11 @@ chat_completion = client.chat.completions.create(
         }
     ],
     model="gpt-3.5-turbo",
-    cache={
+    extra_body = {        # OpenAI python accepts extra args in extra_body
-			"no-cache": True # will not return a cached response 
+        cache: {
-		}
+          "no-cache": True # will not return a cached response 
+      }
+    }
 )
 ```
 
@@ -264,9 +266,11 @@ chat_completion = client.chat.completions.create(
         }
     ],
     model="gpt-3.5-turbo",
-    cache={
+    extra_body = {        # OpenAI python accepts extra args in extra_body
-			"ttl": 600 # caches response for 10 minutes 
+        cache: {
-		}
+          "ttl": 600 # caches response for 10 minutes 
+      }
+    }
 )
 ```
 
@@ -288,13 +292,15 @@ chat_completion = client.chat.completions.create(
         }
     ],
     model="gpt-3.5-turbo",
-    cache={
+    extra_body = {        # OpenAI python accepts extra args in extra_body
-			"s-maxage": 600 # only get responses cached within last 10 minutes 
+        cache: {
-		}
+          "s-maxage": 600 # only get responses cached within last 10 minutes 
+      }
+    }
 )
 ```
 
-## Supported `cache_params`
+## Supported `cache_params` on proxy config.yaml
 
 ```yaml
 cache_params:

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

@@ -53,10 +53,11 @@ UI_PASSWORD=langchain
 
 On accessing the LiteLLM UI, you will be prompted to enter your username, password
 
+## ✨ Enterprise Features
 
-## Setup SSO/Auth for UI
+### Setup SSO/Auth for UI
 
-### Step 1: Set upperbounds for keys
+#### Step 1: Set upperbounds for keys
 Control the upperbound that users can use for `max_budget`, `budget_duration` or any `key/generate` param per key. 
 
 ```yaml
@@ -71,7 +72,7 @@ litellm_settings:
 - Send a `/key/generate` request with `max_budget=200`
 - Key will be created with `max_budget=100` since 100 is the upper bound
 
-### Step 2: Setup Oauth Client
+#### Step 2: Setup Oauth Client
 <Tabs>
 <TabItem value="google" label="Google SSO">
 
@@ -132,8 +133,12 @@ The following can be used to customize attribute names when interacting with the
 ```shell
 GENERIC_USER_ID_ATTRIBUTE = "given_name"
 GENERIC_USER_EMAIL_ATTRIBUTE = "family_name"
+GENERIC_USER_DISPLAY_NAME_ATTRIBUTE = "display_name"
+GENERIC_USER_FIRST_NAME_ATTRIBUTE = "first_name"
+GENERIC_USER_LAST_NAME_ATTRIBUTE = "last_name"
 GENERIC_USER_ROLE_ATTRIBUTE = "given_role"
-
+GENERIC_CLIENT_STATE = "some-state" # if the provider needs a state parameter
+GENERIC_INCLUDE_CLIENT_ID = "false" # some providers enforce that the client_id is not in the body
 GENERIC_SCOPE = "openid profile email" # default scope openid is sometimes not enough to retrieve basic user info like first_name and last_name located in profile scope
 ```
 
@@ -147,24 +152,24 @@ GENERIC_SCOPE = "openid profile email" # default scope openid is sometimes not e
 
 </Tabs>
 
-### Step 3. Test flow
+#### Step 3. Test flow
 <Image img={require('../../img/litellm_ui_3.gif')} />
 
-## Set Admin view w/ SSO 
+### Set Admin view w/ SSO 
 
 You just need to set Proxy Admin ID
 
-### Step 1: Copy your ID from the UI 
+#### Step 1: Copy your ID from the UI 
 
 <Image img={require('../../img/litellm_ui_copy_id.png')} />
 
-### Step 2: Set it in your .env as the PROXY_ADMIN_ID 
+#### Step 2: Set it in your .env as the PROXY_ADMIN_ID 
 
 ```env
 export PROXY_ADMIN_ID="116544810872468347480"
 ```
 
-### Step 3: See all proxy keys
+#### Step 3: See all proxy keys
 
 <Image img={require('../../img/litellm_ui_admin.png')} />
 
@@ -173,3 +178,36 @@ export PROXY_ADMIN_ID="116544810872468347480"
 If you don't see all your keys this could be due to a cached token. So just re-login and it should work.
 
 :::
+
+### Custom Branding Admin UI
+
+Use your companies custom branding on the LiteLLM Admin UI
+We allow you to 
+- Customize the UI Logo
+- Customize the UI color scheme
+<Image img={require('../../img/litellm_custom_ai.png')} />
+
+#### Usage
+- Navigate to [/enterprise/enterprise_ui](https://github.com/BerriAI/litellm/blob/main/enterprise/enterprise_ui/_enterprise_colors.json)
+- Inside the `enterprise_ui` directory, rename `_enterprise_colors.json` to `enterprise_colors.json`
+- Set your companies custom color scheme in `enterprise_colors.json`
+Example contents of `enterprise_colors.json` 
+Set your colors to any of the following colors: https://www.tremor.so/docs/layout/color-palette#default-colors
+```json
+{
+    "brand": {
+      "DEFAULT": "teal",
+      "faint": "teal",
+      "muted": "teal",
+      "subtle": "teal",
+      "emphasis": "teal",
+      "inverted": "teal"
+    }
+}
+
+```
+
+- Set the path to your custom png/jpg logo as `UI_LOGO_PATH` in your .env
+- Deploy LiteLLM Proxy Server
+
+

docs/my-website/sidebars.js

@@ -18,6 +18,62 @@ const sidebars = {
   // But you can create a sidebar manually
   tutorialSidebar: [
     { type: "doc", id: "index" }, // NEW
+    {
+      type: "category",
+      label: "💥 OpenAI Proxy Server",
+      link: {
+        type: 'generated-index',
+        title: '💥 OpenAI Proxy Server',
+        description: `Proxy Server to call 100+ LLMs in a unified interface & track spend, set budgets per virtual key/user`,
+        slug: '/simple_proxy',
+      },
+      items: [
+        "proxy/quick_start", 
+        "proxy/configs",
+        {
+          type: 'link',
+          label: '📖 All Endpoints',
+          href: 'https://litellm-api.up.railway.app/',
+        },
+        "proxy/enterprise",
+        "proxy/user_keys",
+        "proxy/virtual_keys",
+        "proxy/users",
+        "proxy/ui",
+        "proxy/model_management",
+        "proxy/health",
+        "proxy/debugging",
+        "proxy/pii_masking",
+        {
+          "type": "category",
+          "label": "🔥 Load Balancing",
+          "items": [
+            "proxy/load_balancing", 
+            "proxy/reliability",
+          ]
+        },
+        "proxy/caching",
+        {
+          "type": "category",
+          "label": "Logging, Alerting",
+          "items": [
+            "proxy/logging", 
+            "proxy/alerting",
+            "proxy/streaming_logging",
+          ]
+        },
+        {
+          "type": "category",
+          "label": "Content Moderation",
+          "items": [
+            "proxy/call_hooks",
+            "proxy/rules",
+          ]
+        },
+        "proxy/deploy", 
+        "proxy/cli", 
+      ]
+    },
     {
       type: "category",
       label: "Completion()",
@@ -92,62 +148,6 @@ const sidebars = {
         "providers/petals",
       ]
     },
-    {
-      type: "category",
-      label: "💥 OpenAI Proxy Server",
-      link: {
-        type: 'generated-index',
-        title: '💥 OpenAI Proxy Server',
-        description: `Proxy Server to call 100+ LLMs in a unified interface & track spend, set budgets per virtual key/user`,
-        slug: '/simple_proxy',
-      },
-      items: [
-        "proxy/quick_start", 
-        "proxy/configs",
-        {
-          type: 'link',
-          label: '📖 All Endpoints',
-          href: 'https://litellm-api.up.railway.app/',
-        },
-        "proxy/enterprise",
-        "proxy/user_keys",
-        "proxy/virtual_keys",
-        "proxy/users",
-        "proxy/ui",
-        "proxy/model_management",
-        "proxy/health",
-        "proxy/debugging",
-        "proxy/pii_masking",
-        {
-          "type": "category",
-          "label": "🔥 Load Balancing",
-          "items": [
-            "proxy/load_balancing", 
-            "proxy/reliability",
-          ]
-        },
-        "proxy/caching",
-        {
-          "type": "category",
-          "label": "Logging, Alerting",
-          "items": [
-            "proxy/logging", 
-            "proxy/alerting",
-            "proxy/streaming_logging",
-          ]
-        },
-        {
-          "type": "category",
-          "label": "Content Moderation",
-          "items": [
-            "proxy/call_hooks",
-            "proxy/rules",
-          ]
-        },
-        "proxy/deploy", 
-        "proxy/cli", 
-      ]
-    },
     "proxy/custom_pricing",
     "routing",
     "rules",

enterprise/enterprise_ui/README.md

@@ -0,0 +1,6 @@
+## Admin UI
+
+Customize the Admin UI to your companies branding / logo
+![Group 204](https://github.com/BerriAI/litellm/assets/29436595/3b7dbfc2-6fcd-42af-996d-f734fb8f461b)
+
+## Docs to set up Custom Admin UI [here](https://docs.litellm.ai/docs/proxy/ui)

enterprise/enterprise_ui/_enterprise_colors.json

@@ -0,0 +1,11 @@
+{
+    "brand": {
+      "DEFAULT": "teal",
+      "faint": "teal",
+      "muted": "teal",
+      "subtle": "teal",
+      "emphasis": "teal",
+      "inverted": "teal"
+    }
+  }
+  

litellm/caching.py

@@ -124,7 +124,9 @@ class RedisCache(BaseCache):
             self.redis_client.set(name=key, value=str(value), ex=ttl)
         except Exception as e:
             # NON blocking - notify users Redis is throwing an exception
-            print_verbose("LiteLLM Caching: set() - Got exception from REDIS : ", e)
+            print_verbose(
+                f"LiteLLM Caching: set() - Got exception from REDIS : {str(e)}"
+            )
 
     async def async_set_cache(self, key, value, **kwargs):
         _redis_client = self.init_async_client()

litellm/integrations/prompt_layer.py

@@ -2,12 +2,11 @@
 #    On success, logs events to Promptlayer
 import dotenv, os
 import requests
-import requests
+from pydantic import BaseModel
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 
-
 class PromptLayerLogger:
     # Class variables or attributes
     def __init__(self):
@@ -25,16 +24,30 @@ class PromptLayerLogger:
             for optional_param in kwargs["optional_params"]:
                 new_kwargs[optional_param] = kwargs["optional_params"][optional_param]
 
+            # Extract PromptLayer tags from metadata, if such exists
+            tags = []
+            metadata = {}
+            if "metadata" in kwargs["litellm_params"]:
+                if "pl_tags" in kwargs["litellm_params"]["metadata"]:
+                    tags = kwargs["litellm_params"]["metadata"]["pl_tags"]
+
+                # Remove "pl_tags" from metadata
+                metadata = {k:v for k, v in kwargs["litellm_params"]["metadata"].items() if k != "pl_tags"}
+
             print_verbose(
                 f"Prompt Layer Logging - Enters logging function for model kwargs: {new_kwargs}\n, response: {response_obj}"
             )
 
+            # python-openai >= 1.0.0 returns Pydantic objects instead of jsons
+            if isinstance(response_obj, BaseModel):
+                response_obj = response_obj.model_dump()
+
             request_response = requests.post(
                 "https://api.promptlayer.com/rest/track-request",
                 json={
                     "function_name": "openai.ChatCompletion.create",
                     "kwargs": new_kwargs,
-                    "tags": ["hello", "world"],
+                    "tags": tags,
                     "request_response": dict(response_obj),
                     "request_start_time": int(start_time.timestamp()),
                     "request_end_time": int(end_time.timestamp()),
@@ -45,22 +58,23 @@ class PromptLayerLogger:
                     # "prompt_version":1,
                 },
             )
+
+            response_json = request_response.json()
+            if not request_response.json().get("success", False):
+                raise Exception("Promptlayer did not successfully log the response!")
+
             print_verbose(
                 f"Prompt Layer Logging: success - final response object: {request_response.text}"
             )
-            response_json = request_response.json()
-            if "success" not in request_response.json():
-                raise Exception("Promptlayer did not successfully log the response!")
 
             if "request_id" in response_json:
-                print(kwargs["litellm_params"]["metadata"])
+                if metadata:
-                if kwargs["litellm_params"]["metadata"] is not None:
                     response = requests.post(
                         "https://api.promptlayer.com/rest/track-metadata",
                         json={
                             "request_id": response_json["request_id"],
                             "api_key": self.key,
-                            "metadata": kwargs["litellm_params"]["metadata"],
+                            "metadata": metadata,
                         },
                     )
                     print_verbose(

litellm/llms/gemini.py

@@ -171,22 +171,25 @@ def completion(
             if acompletion == True:
 
                 async def async_streaming():
-                    response = await _model.generate_content_async(
+                    try:
-                        contents=prompt,
+                        response = await _model.generate_content_async(
-                        generation_config=genai.types.GenerationConfig(
+                            contents=prompt,
-                            **inference_params
+                            generation_config=genai.types.GenerationConfig(
-                        ),
+                                **inference_params
-                        safety_settings=safety_settings,
+                            ),
-                        stream=True,
+                            safety_settings=safety_settings,
-                    )
+                            stream=True,
+                        )
 
-                    response = litellm.CustomStreamWrapper(
+                        response = litellm.CustomStreamWrapper(
-                        TextStreamer(response),
+                            TextStreamer(response),
-                        model,
+                            model,
-                        custom_llm_provider="gemini",
+                            custom_llm_provider="gemini",
-                        logging_obj=logging_obj,
+                            logging_obj=logging_obj,
-                    )
+                        )
-                    return response
+                        return response
+                    except Exception as e:
+                        raise GeminiError(status_code=500, message=str(e))
 
                 return async_streaming()
             response = _model.generate_content(

litellm/main.py

@@ -12,6 +12,7 @@ from typing import Any, Literal, Union
 from functools import partial
 import dotenv, traceback, random, asyncio, time, contextvars
 from copy import deepcopy
+
 import httpx
 import litellm
 from ._logging import verbose_logger

litellm/model_prices_and_context_window_backup.json

@@ -936,7 +936,14 @@
         "mode": "chat"
     },
     "openrouter/mistralai/mistral-7b-instruct": {
-        "max_tokens": 4096,
+        "max_tokens": 8192,
+        "input_cost_per_token": 0.00000013,
+        "output_cost_per_token": 0.00000013,
+        "litellm_provider": "openrouter",
+        "mode": "chat"
+    },
+    "openrouter/mistralai/mistral-7b-instruct:free": {
+        "max_tokens": 8192,
         "input_cost_per_token": 0.0,
         "output_cost_per_token": 0.0,
         "litellm_provider": "openrouter",

litellm/proxy/_experimental/out/_next/static/chunks/webpack-ccaef0ba4c6e46ab.js

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+0:["qiuqcCJAvN0BfxnYr9J0N",[[["",{"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]],[[["$","link","0",{"rel":"stylesheet","href":"/ui/_next/static/css/4c08d108f5f39cf2.css","precedence":"next","crossOrigin":""}]],"$L6"]]]]
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 1:null

litellm/proxy/proxy_server.py

@@ -5097,7 +5097,15 @@ async def google_login(request: Request):
             scope=generic_scope,
         )
         with generic_sso:
-            return await generic_sso.get_login_redirect()
+            # TODO: state should be a random string and added to the user session with cookie
+            # or a cryptographicly signed state that we can verify stateless
+            # For simplification we are using a static state, this is not perfect but some
+            # SSO providers do not allow stateless verification
+            redirect_params = {}
+            state = os.getenv("GENERIC_CLIENT_STATE", None)
+            if state:
+                redirect_params["state"] = state
+            return await generic_sso.get_login_redirect(**redirect_params)  # type: ignore
     elif ui_username is not None:
         # No Google, Microsoft SSO
         # Use UI Credentials set in .env
@@ -5192,6 +5200,20 @@ async def login(request: Request):
         )
 
 
+@app.get("/get_image", include_in_schema=False)
+def get_image():
+    """Get logo to show on admin UI"""
+    from fastapi.responses import FileResponse
+
+    # get current_dir
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    default_logo = os.path.join(current_dir, "logo.jpg")
+
+    logo_path = os.getenv("UI_LOGO_PATH", default_logo)
+    verbose_proxy_logger.debug(f"Reading logo from {logo_path}")
+    return FileResponse(path=logo_path)
+
+
 @app.get("/sso/callback", tags=["experimental"])
 async def auth_callback(request: Request):
     """Verify login"""
@@ -5251,7 +5273,7 @@ async def auth_callback(request: Request):
         result = await microsoft_sso.verify_and_process(request)
     elif generic_client_id is not None:
         # make generic sso provider
-        from fastapi_sso.sso.generic import create_provider, DiscoveryDocument
+        from fastapi_sso.sso.generic import create_provider, DiscoveryDocument, OpenID
 
         generic_client_secret = os.getenv("GENERIC_CLIENT_SECRET", None)
         generic_scope = os.getenv("GENERIC_SCOPE", "openid email profile").split(" ")
@@ -5260,6 +5282,9 @@ async def auth_callback(request: Request):
         )
         generic_token_endpoint = os.getenv("GENERIC_TOKEN_ENDPOINT", None)
         generic_userinfo_endpoint = os.getenv("GENERIC_USERINFO_ENDPOINT", None)
+        generic_include_client_id = (
+            os.getenv("GENERIC_INCLUDE_CLIENT_ID", "false").lower() == "true"
+        )
         if generic_client_secret is None:
             raise ProxyException(
                 message="GENERIC_CLIENT_SECRET not set. Set it in .env file",
@@ -5294,12 +5319,50 @@ async def auth_callback(request: Request):
         verbose_proxy_logger.debug(
             f"GENERIC_REDIRECT_URI: {redirect_url}\nGENERIC_CLIENT_ID: {generic_client_id}\n"
         )
+
+        generic_user_id_attribute_name = os.getenv(
+            "GENERIC_USER_ID_ATTRIBUTE", "preferred_username"
+        )
+        generic_user_display_name_attribute_name = os.getenv(
+            "GENERIC_USER_DISPLAY_NAME_ATTRIBUTE", "sub"
+        )
+        generic_user_email_attribute_name = os.getenv(
+            "GENERIC_USER_EMAIL_ATTRIBUTE", "email"
+        )
+        generic_user_role_attribute_name = os.getenv(
+            "GENERIC_USER_ROLE_ATTRIBUTE", "role"
+        )
+        generic_user_first_name_attribute_name = os.getenv(
+            "GENERIC_USER_FIRST_NAME_ATTRIBUTE", "first_name"
+        )
+        generic_user_last_name_attribute_name = os.getenv(
+            "GENERIC_USER_LAST_NAME_ATTRIBUTE", "last_name"
+        )
+
+        verbose_proxy_logger.debug(
+            f" generic_user_id_attribute_name: {generic_user_id_attribute_name}\n generic_user_email_attribute_name: {generic_user_email_attribute_name}\n generic_user_role_attribute_name: {generic_user_role_attribute_name}"
+        )
+
         discovery = DiscoveryDocument(
             authorization_endpoint=generic_authorization_endpoint,
             token_endpoint=generic_token_endpoint,
             userinfo_endpoint=generic_userinfo_endpoint,
         )
-        SSOProvider = create_provider(name="oidc", discovery_document=discovery)
+
+        def response_convertor(response, client):
+            return OpenID(
+                id=response.get(generic_user_id_attribute_name),
+                display_name=response.get(generic_user_display_name_attribute_name),
+                email=response.get(generic_user_email_attribute_name),
+                first_name=response.get(generic_user_first_name_attribute_name),
+                last_name=response.get(generic_user_last_name_attribute_name),
+            )
+
+        SSOProvider = create_provider(
+            name="oidc",
+            discovery_document=discovery,
+            response_convertor=response_convertor,
+        )
         generic_sso = SSOProvider(
             client_id=generic_client_id,
             client_secret=generic_client_secret,
@@ -5308,43 +5371,36 @@ async def auth_callback(request: Request):
             scope=generic_scope,
         )
         verbose_proxy_logger.debug(f"calling generic_sso.verify_and_process")
-        request_body = await request.body()
+        result = await generic_sso.verify_and_process(
-        request_query_params = request.query_params
+            request, params={"include_client_id": generic_include_client_id}
-        # get "code" from query params
+        )
-        code = request_query_params.get("code")
-        result = await generic_sso.verify_and_process(request)
         verbose_proxy_logger.debug(f"generic result: {result}")
+
     # User is Authe'd in - generate key for the UI to access Proxy
     user_email = getattr(result, "email", None)
     user_id = getattr(result, "id", None)
 
     # generic client id
     if generic_client_id is not None:
-        generic_user_id_attribute_name = os.getenv("GENERIC_USER_ID_ATTRIBUTE", "email")
+        user_id = getattr(result, "id", None)
-        generic_user_email_attribute_name = os.getenv(
+        user_email = getattr(result, "email", None)
-            "GENERIC_USER_EMAIL_ATTRIBUTE", "email"
-        )
-        generic_user_role_attribute_name = os.getenv(
-            "GENERIC_USER_ROLE_ATTRIBUTE", "role"
-        )
-
-        verbose_proxy_logger.debug(
-            f" generic_user_id_attribute_name: {generic_user_id_attribute_name}\n generic_user_email_attribute_name: {generic_user_email_attribute_name}\n generic_user_role_attribute_name: {generic_user_role_attribute_name}"
-        )
-
-        user_id = getattr(result, generic_user_id_attribute_name, None)
-        user_email = getattr(result, generic_user_email_attribute_name, None)
         user_role = getattr(result, generic_user_role_attribute_name, None)
 
     if user_id is None:
         user_id = getattr(result, "first_name", "") + getattr(result, "last_name", "")
-    # get user_info from litellm DB
+
     user_info = None
-    if prisma_client is not None:
-        user_info = await prisma_client.get_data(user_id=user_id, table_name="user")
     user_id_models: List = []
-    if user_info is not None:
+
-        user_id_models = getattr(user_info, "models", [])
+    # User might not be already created on first generation of key
+    # But if it is, we want its models preferences
+    try:
+        if prisma_client is not None:
+            user_info = await prisma_client.get_data(user_id=user_id, table_name="user")
+            if user_info is not None:
+                user_id_models = getattr(user_info, "models", [])
+    except Exception as e:
+        pass
 
     response = await generate_key_helper_fn(
         **{

litellm/router.py

@@ -142,11 +142,13 @@ class Router:
             Router: An instance of the litellm.Router class.
         """
         self.set_verbose = set_verbose
-        if self.set_verbose:
+        self.debug_level = debug_level
+        if self.set_verbose == True:
             if debug_level == "INFO":
                 verbose_router_logger.setLevel(logging.INFO)
             elif debug_level == "DEBUG":
                 verbose_router_logger.setLevel(logging.DEBUG)
+
         self.deployment_names: List = (
             []
         )  # names of models under litellm_params. ex. azure/chatgpt-v-2
@@ -273,6 +275,16 @@ class Router:
             f"Intialized router with Routing strategy: {self.routing_strategy}\n"
         )
 
+    def print_deployment(self, deployment: dict):
+        """
+        returns a copy of the deployment with the api key masked
+        """
+        _deployment_copy = copy.deepcopy(deployment)
+        litellm_params: dict = _deployment_copy["litellm_params"]
+        if "api_key" in litellm_params:
+            litellm_params["api_key"] = litellm_params["api_key"][:2] + "*" * 10
+        return _deployment_copy
+
     ### COMPLETION, EMBEDDING, IMG GENERATION FUNCTIONS
 
     def completion(
@@ -2060,7 +2072,7 @@ class Router:
                 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: {deployment or deployment[0]} for model: {model}"
+                    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 #################
@@ -2077,7 +2089,7 @@ class Router:
                 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: {deployment or deployment[0]} for model: {model}"
+                    f"get_available_deployment for model: {model}, Selected deployment: {self.print_deployment(deployment) or deployment[0]} for model: {model}"
                 )
                 return deployment or deployment[0]
 
@@ -2108,7 +2120,7 @@ class Router:
             )
             raise ValueError("No models available.")
         verbose_router_logger.info(
-            f"get_available_deployment for model: {model}, Selected deployment: {deployment} for model: {model}"
+            f"get_available_deployment for model: {model}, Selected deployment: {self.print_deployment(deployment)} for model: {model}"
         )
         return deployment
 

litellm/tests/test_completion.py

@@ -1883,7 +1883,6 @@ def test_mistral_anyscale_stream():
 #         print(response)
 #     except Exception as e:
 #         pytest.fail(f"Error occurred: {e}")
-# test_baseten_falcon_7bcompletion()
 
 # def test_baseten_falcon_7bcompletion_withbase():
 #     model_name = "qvv0xeq"
@@ -1986,6 +1985,8 @@ def test_completion_gemini():
         response = completion(model=model_name, messages=messages)
         # Add any assertions here to check the response
         print(response)
+    except litellm.APIError as e:
+        pass
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
@@ -2015,6 +2016,8 @@ def test_completion_palm():
         response = completion(model=model_name, messages=messages)
         # Add any assertions here to check the response
         print(response)
+    except litellm.APIError as e:
+        pass
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
@@ -2037,6 +2040,8 @@ def test_completion_palm_stream():
         # Add any assertions here to check the response
         for chunk in response:
             print(chunk)
+    except litellm.APIError as e:
+        pass
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 

litellm/tests/test_custom_callback_input.py

@@ -2,7 +2,7 @@
 ## This test asserts the type of data passed into each method of the custom callback handler
 import sys, os, time, inspect, asyncio, traceback
 from datetime import datetime
-import pytest
+import pytest, uuid
 from pydantic import BaseModel
 
 sys.path.insert(0, os.path.abspath("../.."))
@@ -795,6 +795,53 @@ async def test_async_completion_azure_caching():
     assert len(customHandler_caching.states) == 4  # pre, post, success, success
 
 
+@pytest.mark.asyncio
+async def test_async_completion_azure_caching_streaming():
+    import copy
+
+    litellm.set_verbose = True
+    customHandler_caching = CompletionCustomHandler()
+    litellm.cache = Cache(
+        type="redis",
+        host=os.environ["REDIS_HOST"],
+        port=os.environ["REDIS_PORT"],
+        password=os.environ["REDIS_PASSWORD"],
+    )
+    litellm.callbacks = [customHandler_caching]
+    unique_time = uuid.uuid4()
+    response1 = await litellm.acompletion(
+        model="azure/chatgpt-v-2",
+        messages=[
+            {"role": "user", "content": f"Hi 👋 - i'm async azure {unique_time}"}
+        ],
+        caching=True,
+        stream=True,
+    )
+    async for chunk in response1:
+        print(f"chunk in response1: {chunk}")
+    await asyncio.sleep(1)
+    initial_customhandler_caching_states = len(customHandler_caching.states)
+    print(f"customHandler_caching.states pre-cache hit: {customHandler_caching.states}")
+    response2 = await litellm.acompletion(
+        model="azure/chatgpt-v-2",
+        messages=[
+            {"role": "user", "content": f"Hi 👋 - i'm async azure {unique_time}"}
+        ],
+        caching=True,
+        stream=True,
+    )
+    async for chunk in response2:
+        print(f"chunk in response2: {chunk}")
+    await asyncio.sleep(1)  # success callbacks are done in parallel
+    print(
+        f"customHandler_caching.states post-cache hit: {customHandler_caching.states}"
+    )
+    assert len(customHandler_caching.errors) == 0
+    assert (
+        len(customHandler_caching.states) > initial_customhandler_caching_states
+    )  # pre, post, streaming .., success, success
+
+
 @pytest.mark.asyncio
 async def test_async_embedding_azure_caching():
     print("Testing custom callback input - Azure Caching")

litellm/tests/test_promptlayer_integration.py

@@ -7,10 +7,9 @@ sys.path.insert(0, os.path.abspath("../.."))
 from litellm import completion
 import litellm
 
-litellm.success_callback = ["promptlayer"]
+import pytest
-litellm.set_verbose = True
-import time
 
+import time
 
 # def test_promptlayer_logging():
 #     try:
@@ -44,6 +43,8 @@ def test_promptlayer_logging_with_metadata():
         # Redirect stdout
         old_stdout = sys.stdout
         sys.stdout = new_stdout = io.StringIO()
+        litellm.set_verbose = True
+        litellm.success_callback = ["promptlayer"]
 
         response = completion(
             model="gpt-3.5-turbo",
@@ -58,14 +59,41 @@ def test_promptlayer_logging_with_metadata():
         sys.stdout = old_stdout
         output = new_stdout.getvalue().strip()
         print(output)
-        if "LiteLLM: Prompt Layer Logging: success" not in output:
+
-            raise Exception("Required log message not found!")
+        assert "Prompt Layer Logging: success" in output
 
     except Exception as e:
-        print(e)
+        pytest.fail(f"Error occurred: {e}")
 
 
-test_promptlayer_logging_with_metadata()
+def test_promptlayer_logging_with_metadata_tags():
+    try:
+        # Redirect stdout
+        litellm.set_verbose = True
+
+        litellm.success_callback = ["promptlayer"]
+        old_stdout = sys.stdout
+        sys.stdout = new_stdout = io.StringIO()
+
+        response = completion(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "Hi 👋 - i'm ai21"}],
+            temperature=0.2,
+            max_tokens=20,
+            metadata={"model": "ai21", "pl_tags": ["env:dev"]},
+            mock_response="this is a mock response",
+        )
+
+        # Restore stdout
+        time.sleep(1)
+        sys.stdout = old_stdout
+        output = new_stdout.getvalue().strip()
+        print(output)
+
+        assert "Prompt Layer Logging: success" in output
+
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
 
 
 # def test_chat_openai():

litellm/tests/test_streaming.py

@@ -392,6 +392,8 @@ def test_completion_palm_stream():
         if complete_response.strip() == "":
             raise Exception("Empty response received")
         print(f"completion_response: {complete_response}")
+    except litellm.APIError as e:
+        pass
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
@@ -425,6 +427,8 @@ def test_completion_gemini_stream():
         if complete_response.strip() == "":
             raise Exception("Empty response received")
         print(f"completion_response: {complete_response}")
+    except litellm.APIError as e:
+        pass
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
@@ -461,6 +465,8 @@ async def test_acompletion_gemini_stream():
         print(f"completion_response: {complete_response}")
         if complete_response.strip() == "":
             raise Exception("Empty response received")
+    except litellm.APIError as e:
+        pass
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 

litellm/utils.py

@@ -1411,7 +1411,7 @@ class Logging:
                                 print_verbose(
                                     f"success_callback: reaches cache for logging, there is no complete_streaming_response. Kwargs={kwargs}\n\n"
                                 )
-                                return
+                                pass
                             else:
                                 print_verbose(
                                     "success_callback: reaches cache for logging, there is a complete_streaming_response. Adding to cache"
@@ -1616,7 +1616,7 @@ class Logging:
                             print_verbose(
                                 f"async success_callback: reaches cache for logging, there is no complete_streaming_response. Kwargs={kwargs}\n\n"
                             )
-                            return
+                            pass
                         else:
                             print_verbose(
                                 "async success_callback: reaches cache for logging, there is a complete_streaming_response. Adding to cache"
@@ -1625,8 +1625,10 @@ class Logging:
                             # only add to cache once we have a complete streaming response
                             litellm.cache.add_cache(result, **kwargs)
                 if isinstance(callback, CustomLogger):  # custom logger class
-                    print_verbose(f"Async success callbacks: {callback}")
+                    print_verbose(
-                    if self.stream:
+                        f"Async success callbacks: {callback}; self.stream: {self.stream}; complete_streaming_response: {self.model_call_details.get('complete_streaming_response', None)}"
+                    )
+                    if self.stream == True:
                         if "complete_streaming_response" in self.model_call_details:
                             await callback.async_log_success_event(
                                 kwargs=self.model_call_details,
@@ -2328,6 +2330,13 @@ def client(original_function):
                                     model_response_object=ModelResponse(),
                                     stream=kwargs.get("stream", False),
                                 )
+                                if kwargs.get("stream", False) == True:
+                                    cached_result = CustomStreamWrapper(
+                                        completion_stream=cached_result,
+                                        model=model,
+                                        custom_llm_provider="cached_response",
+                                        logging_obj=logging_obj,
+                                    )
                             elif call_type == CallTypes.embedding.value and isinstance(
                                 cached_result, dict
                             ):
@@ -2624,28 +2633,6 @@ def client(original_function):
                         cached_result, list
                     ):
                         print_verbose(f"Cache Hit!")
-                        call_type = original_function.__name__
-                        if call_type == CallTypes.acompletion.value and isinstance(
-                            cached_result, dict
-                        ):
-                            if kwargs.get("stream", False) == True:
-                                cached_result = convert_to_streaming_response_async(
-                                    response_object=cached_result,
-                                )
-                            else:
-                                cached_result = convert_to_model_response_object(
-                                    response_object=cached_result,
-                                    model_response_object=ModelResponse(),
-                                )
-                        elif call_type == CallTypes.aembedding.value and isinstance(
-                            cached_result, dict
-                        ):
-                            cached_result = convert_to_model_response_object(
-                                response_object=cached_result,
-                                model_response_object=EmbeddingResponse(),
-                                response_type="embedding",
-                            )
-                        # LOG SUCCESS
                         cache_hit = True
                         end_time = datetime.datetime.now()
                         (
@@ -2685,15 +2672,44 @@ def client(original_function):
                             additional_args=None,
                             stream=kwargs.get("stream", False),
                         )
-                        asyncio.create_task(
+                        call_type = original_function.__name__
-                            logging_obj.async_success_handler(
+                        if call_type == CallTypes.acompletion.value and isinstance(
-                                cached_result, start_time, end_time, cache_hit
+                            cached_result, dict
+                        ):
+                            if kwargs.get("stream", False) == True:
+                                cached_result = convert_to_streaming_response_async(
+                                    response_object=cached_result,
+                                )
+                                cached_result = CustomStreamWrapper(
+                                    completion_stream=cached_result,
+                                    model=model,
+                                    custom_llm_provider="cached_response",
+                                    logging_obj=logging_obj,
+                                )
+                            else:
+                                cached_result = convert_to_model_response_object(
+                                    response_object=cached_result,
+                                    model_response_object=ModelResponse(),
+                                )
+                        elif call_type == CallTypes.aembedding.value and isinstance(
+                            cached_result, dict
+                        ):
+                            cached_result = convert_to_model_response_object(
+                                response_object=cached_result,
+                                model_response_object=EmbeddingResponse(),
+                                response_type="embedding",
                             )
-                        )
+                        if kwargs.get("stream", False) == False:
-                        threading.Thread(
+                            # LOG SUCCESS
-                            target=logging_obj.success_handler,
+                            asyncio.create_task(
-                            args=(cached_result, start_time, end_time, cache_hit),
+                                logging_obj.async_success_handler(
-                        ).start()
+                                    cached_result, start_time, end_time, cache_hit
+                                )
+                            )
+                            threading.Thread(
+                                target=logging_obj.success_handler,
+                                args=(cached_result, start_time, end_time, cache_hit),
+                            ).start()
                         return cached_result
                     elif (
                         call_type == CallTypes.aembedding.value
@@ -4296,7 +4312,9 @@ def get_optional_params(
                     parameters=tool["function"].get("parameters", {}),
                 )
                 gtool_func_declarations.append(gtool_func_declaration)
-            optional_params["tools"] = [generative_models.Tool(function_declarations=gtool_func_declarations)]
+            optional_params["tools"] = [
+                generative_models.Tool(function_declarations=gtool_func_declarations)
+            ]
     elif custom_llm_provider == "sagemaker":
         ## check if unsupported param passed in
         supported_params = ["stream", "temperature", "max_tokens", "top_p", "stop", "n"]
@@ -6795,7 +6813,7 @@ def exception_type(
                             llm_provider="vertex_ai",
                             request=original_exception.request,
                         )
-            elif custom_llm_provider == "palm":
+            elif custom_llm_provider == "palm" or custom_llm_provider == "gemini":
                 if "503 Getting metadata" in error_str:
                     # auth errors look like this
                     # 503 Getting metadata from plugin failed with error: Reauthentication is needed. Please run `gcloud auth application-default login` to reauthenticate.
@@ -6814,6 +6832,15 @@ def exception_type(
                         llm_provider="palm",
                         response=original_exception.response,
                     )
+                if "500 An internal error has occurred." in error_str:
+                    exception_mapping_worked = True
+                    raise APIError(
+                        status_code=original_exception.status_code,
+                        message=f"PalmException - {original_exception.message}",
+                        llm_provider="palm",
+                        model=model,
+                        request=original_exception.request,
+                    )
                 if hasattr(original_exception, "status_code"):
                     if original_exception.status_code == 400:
                         exception_mapping_worked = True
@@ -8524,6 +8551,19 @@ class CustomStreamWrapper:
                     ]
             elif self.custom_llm_provider == "text-completion-openai":
                 response_obj = self.handle_openai_text_completion_chunk(chunk)
+                completion_obj["content"] = response_obj["text"]
+                print_verbose(f"completion obj content: {completion_obj['content']}")
+                if response_obj["is_finished"]:
+                    model_response.choices[0].finish_reason = response_obj[
+                        "finish_reason"
+                    ]
+            elif self.custom_llm_provider == "cached_response":
+                response_obj = {
+                    "text": chunk.choices[0].delta.content,
+                    "is_finished": True,
+                    "finish_reason": chunk.choices[0].finish_reason,
+                }
+
                 completion_obj["content"] = response_obj["text"]
                 print_verbose(f"completion obj content: {completion_obj['content']}")
                 if response_obj["is_finished"]:
@@ -8732,6 +8772,7 @@ class CustomStreamWrapper:
                 or self.custom_llm_provider == "vertex_ai"
                 or self.custom_llm_provider == "sagemaker"
                 or self.custom_llm_provider == "gemini"
+                or self.custom_llm_provider == "cached_response"
                 or self.custom_llm_provider in litellm.openai_compatible_endpoints
             ):
                 async for chunk in self.completion_stream:

proxy_server_config.yaml

@@ -15,18 +15,6 @@ model_list:
     litellm_params:
       model: sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4
       input_cost_per_second: 0.000420  
-  - model_name: gpt-4
-    litellm_params:
-      model: azure/gpt-turbo
-      api_key: os.environ/AZURE_FRANCE_API_KEY
-      api_base: https://openai-france-1234.openai.azure.com/
-      rpm: 100
-  - model_name: gpt-4
-    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: 10
   - model_name: text-embedding-ada-002
     litellm_params: 
       model: azure/azure-embedding-model

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.26.7"
+version = "1.26.8"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -74,7 +74,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.26.7"
+version = "1.26.8"
 version_files = [
     "pyproject.toml:^version"
 ]

ui/litellm-dashboard/build_ui.sh

@@ -0,0 +1,49 @@
+#!/bin/bash
+
+# Check if nvm is not installed
+if ! command -v nvm &> /dev/null; then
+  # Install nvm
+  curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.38.0/install.sh | bash
+
+  # Source nvm script in the current session
+  export NVM_DIR="$HOME/.nvm"
+  [ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh"
+fi
+
+# Use nvm to set the required Node.js version
+nvm use v18.17.0
+
+# Check if nvm use was successful
+if [ $? -ne 0 ]; then
+  echo "Error: Failed to switch to Node.js v18.17.0. Deployment aborted."
+  exit 1
+fi
+
+# print contents of ui_colors.json
+echo "Contents of ui_colors.json:"
+cat ui_colors.json
+
+# Run npm build
+npm run build
+
+# Check if the build was successful
+if [ $? -eq 0 ]; then
+  echo "Build successful. Copying files..."
+
+  # echo current dir
+  echo
+  pwd
+
+  # Specify the destination directory
+  destination_dir="../../litellm/proxy/_experimental/out"
+
+  # Remove existing files in the destination directory
+  rm -rf "$destination_dir"/*
+
+  # Copy the contents of the output directory to the specified destination
+  cp -r ./out/* "$destination_dir"
+
+  echo "Deployment completed."
+else
+  echo "Build failed. Deployment aborted."
+fi

ui/litellm-dashboard/out/_next/static/chunks/webpack-ccaef0ba4c6e46ab.js

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ui/litellm-dashboard/out/index.html

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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-ccaef0ba4c6e46ab.js" crossorigin=""/><script src="/ui/_next/static/chunks/fd9d1056-a85b2c176012d8e5.js" async="" crossorigin=""></script><script src="/ui/_next/static/chunks/69-e1b183dda365ec86.js" async="" crossorigin=""></script><script src="/ui/_next/static/chunks/main-app-9b4fb13a7db53edf.js" async="" crossorigin=""></script><title>🚅 LiteLLM</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-ccaef0ba4c6e46ab.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/4c08d108f5f39cf2.css\",\"style\",{\"crossOrigin\":\"\"}]\n0:\"$L3\"\n"])</script><script>self.__next_f.push([1,"4:I[47690,[],\"\"]\n6:I[77831,[],\"\"]\n7:I[38695,[\"321\",\"static/chunks/321-87f0fad233104594.js\",\"931\",\"static/chunks/app/page-0ac2617bc86867d3.js\"],\"\"]\n8:I[5613,[],\"\"]\n9:I[31778,[],\"\"]\nb:I[48955,[],\"\"]\nc:[]\n"])</script><script>self.__next_f.push([1,"3:[[[\"$\",\"link\",\"0\",{\"rel\":\"stylesheet\",\"href\":\"/ui/_next/static/css/4c08d108f5f39cf2.css\",\"precedence\":\"next\",\"crossOrigin\":\"\"}]],[\"$\",\"$L4\",null,{\"buildId\":\"qiuqcCJAvN0BfxnYr9J0N\",\"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\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"LiteLLM Proxy Admin UI\"}],[\"$\",\"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/navbar.tsx

@@ -25,13 +25,18 @@ const Navbar: React.FC<NavbarProps> = ({ userID, userRole, userEmail }) => {
   console.log("User ID:", userID);
   console.log("userEmail:", userEmail);
 
+  // const userColors = require('./ui_colors.json') || {};
+  const isLocal = process.env.NODE_ENV === "development";
+  const imageUrl = isLocal ? "http://localhost:4000/get_image" : "/get_image";
+
+
   return (
     <nav className="left-0 right-0 top-0 flex justify-between items-center h-12 mb-4">
       <div className="text-left mx-4 my-2 absolute top-0 left-0">
         <div className="flex flex-col items-center">
           <Link href="/">
             <button className="text-gray-800 text-2xl px-4 py-1 rounded text-center">
-              🚅 LiteLLM
+              <img src={imageUrl} width={200} height={200} alt="LiteLLM Brand" className="mr-2" />
             </button>
           </Link>
         </div>

ui/litellm-dashboard/tailwind.config.js

@@ -1,6 +1,7 @@
 /** @type {import('tailwindcss').Config} */ 
 
 const colors = require("tailwindcss/colors");
+const userColors = require('./ui_colors.json') || {};
 module.exports = {
   content: [
     "./src/**/*.{js,ts,jsx,tsx}",
@@ -15,10 +16,10 @@ module.exports = {
         // light mode
         tremor: {
           brand: {
-            faint: colors.indigo[50],
+            faint: userColors.brand.faint,
-            muted: colors.indigo[200],
+            muted: userColors.brand.muted,
-            subtle: colors.indigo[400],
+            subtle: userColors.brand.subtle,
-            DEFAULT: colors.indigo[500],
+            DEFAULT: userColors.brand.DEFAULT,
             emphasis: colors.indigo[700],
             inverted: colors.white,
           },

ui/litellm-dashboard/ui_colors.json

@@ -0,0 +1,11 @@
+{
+    "brand": {
+      "DEFAULT": "#6366f1",
+      "faint": "#6c6fed",
+      "muted": "#8688ef",
+      "subtle": "#8e91eb",
+      "emphasis": "#5558eb",
+      "inverted": "indigo"
+    }
+  }
+