mirror of
https://github.com/tiennm99/litellm.git
synced 2026-08-20 00:23:47 +00:00
Merge branch 'BerriAI:main' into update_helm_chart_deployment_2024-02-22
This commit is contained in:
Shaun Maher
@@ -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 $LITELLM_RUNTIME_IMAGE as runtime
|
||||
|
||||
|
||||
@@ -20,6 +20,9 @@ RUN pip install --upgrade pip && \
|
||||
# Copy the current directory contents into the container at /app
|
||||
COPY . .
|
||||
|
||||
# Build Admin UI
|
||||
RUN chmod +x build_admin_ui.sh && ./build_admin_ui.sh
|
||||
|
||||
# Build the package
|
||||
RUN rm -rf dist/* && python -m build
|
||||
|
||||
@@ -50,6 +53,9 @@ RUN pip install *.whl /wheels/* --no-index --find-links=/wheels/ && rm -f *.whl
|
||||
# install semantic-cache [Experimental]- we need this here and not in requirements.txt because redisvl pins to pydantic 1.0
|
||||
RUN pip install redisvl==0.0.7 --no-deps
|
||||
|
||||
# Build Admin UI
|
||||
RUN chmod +x build_admin_ui.sh && ./build_admin_ui.sh
|
||||
|
||||
# Generate prisma client
|
||||
RUN prisma generate
|
||||
RUN chmod +x entrypoint.sh
|
||||
|
||||
Executable
+62
@@ -0,0 +1,62 @@
|
||||
#!/bin/bash
|
||||
|
||||
# # try except this script
|
||||
# set -e
|
||||
|
||||
# print current dir
|
||||
echo
|
||||
pwd
|
||||
|
||||
|
||||
# only run this step for litellm enterprise, we run this if enterprise/enterprise_ui/_enterprise.json exists
|
||||
if [ ! -f "enterprise/enterprise_ui/enterprise_colors.json" ]; then
|
||||
echo "Admin UI - using default LiteLLM UI"
|
||||
exit 0
|
||||
fi
|
||||
|
||||
echo "Building Custom Admin UI..."
|
||||
|
||||
# Install dependencies
|
||||
# Check if we are on macOS
|
||||
if [[ "$(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
|
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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 ../..
|
||||
@@ -238,9 +238,11 @@ chat_completion = client.chat.completions.create(
|
||||
}
|
||||
],
|
||||
model="gpt-3.5-turbo",
|
||||
cache={
|
||||
"no-cache": True # will not return a cached response
|
||||
}
|
||||
extra_body = { # OpenAI python accepts extra args in extra_body
|
||||
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={
|
||||
"ttl": 600 # caches response for 10 minutes
|
||||
}
|
||||
extra_body = { # OpenAI python accepts extra args in extra_body
|
||||
cache: {
|
||||
"ttl": 600 # caches response for 10 minutes
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
@@ -288,13 +292,15 @@ chat_completion = client.chat.completions.create(
|
||||
}
|
||||
],
|
||||
model="gpt-3.5-turbo",
|
||||
cache={
|
||||
"s-maxage": 600 # only get responses cached within last 10 minutes
|
||||
}
|
||||
extra_body = { # OpenAI python accepts extra args in extra_body
|
||||
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:
|
||||
|
||||
@@ -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')} />
|
||||
|
||||
@@ -172,4 +177,37 @@ 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
|
||||
|
||||
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 90 KiB |
+56
-56
@@ -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",
|
||||
|
||||
@@ -0,0 +1,6 @@
|
||||
## Admin UI
|
||||
|
||||
Customize the Admin UI to your companies branding / logo
|
||||
|
||||
|
||||
## Docs to set up Custom Admin UI [here](https://docs.litellm.ai/docs/proxy/ui)
|
||||
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"brand": {
|
||||
"DEFAULT": "teal",
|
||||
"faint": "teal",
|
||||
"muted": "teal",
|
||||
"subtle": "teal",
|
||||
"emphasis": "teal",
|
||||
"inverted": "teal"
|
||||
}
|
||||
}
|
||||
|
||||
+3
-1
@@ -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()
|
||||
|
||||
@@ -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 kwargs["litellm_params"]["metadata"] is not None:
|
||||
if metadata:
|
||||
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(
|
||||
|
||||
+18
-15
@@ -171,22 +171,25 @@ def completion(
|
||||
if acompletion == True:
|
||||
|
||||
async def async_streaming():
|
||||
response = await _model.generate_content_async(
|
||||
contents=prompt,
|
||||
generation_config=genai.types.GenerationConfig(
|
||||
**inference_params
|
||||
),
|
||||
safety_settings=safety_settings,
|
||||
stream=True,
|
||||
)
|
||||
try:
|
||||
response = await _model.generate_content_async(
|
||||
contents=prompt,
|
||||
generation_config=genai.types.GenerationConfig(
|
||||
**inference_params
|
||||
),
|
||||
safety_settings=safety_settings,
|
||||
stream=True,
|
||||
)
|
||||
|
||||
response = litellm.CustomStreamWrapper(
|
||||
TextStreamer(response),
|
||||
model,
|
||||
custom_llm_provider="gemini",
|
||||
logging_obj=logging_obj,
|
||||
)
|
||||
return response
|
||||
response = litellm.CustomStreamWrapper(
|
||||
TextStreamer(response),
|
||||
model,
|
||||
custom_llm_provider="gemini",
|
||||
logging_obj=logging_obj,
|
||||
)
|
||||
return response
|
||||
except Exception as e:
|
||||
raise GeminiError(status_code=500, message=str(e))
|
||||
|
||||
return async_streaming()
|
||||
response = _model.generate_content(
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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",
|
||||
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
+1
-1
@@ -1 +1 @@
|
||||
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+1
-1
File diff suppressed because one or more lines are too long
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|
||||
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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>
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1:null
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|
||||
Binary file not shown.
|
After Width: | Height: | Size: 24 KiB |
@@ -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()
|
||||
request_query_params = request.query_params
|
||||
# get "code" from query params
|
||||
code = request_query_params.get("code")
|
||||
result = await generic_sso.verify_and_process(request)
|
||||
result = await generic_sso.verify_and_process(
|
||||
request, params={"include_client_id": generic_include_client_id}
|
||||
)
|
||||
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")
|
||||
generic_user_email_attribute_name = os.getenv(
|
||||
"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_id = getattr(result, "id", None)
|
||||
user_email = getattr(result, "email", 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(
|
||||
**{
|
||||
|
||||
+16
-4
@@ -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
|
||||
|
||||
|
||||
@@ -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}")
|
||||
|
||||
|
||||
@@ -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")
|
||||
|
||||
@@ -7,10 +7,9 @@ sys.path.insert(0, os.path.abspath("../.."))
|
||||
from litellm import completion
|
||||
import litellm
|
||||
|
||||
litellm.success_callback = ["promptlayer"]
|
||||
litellm.set_verbose = True
|
||||
import time
|
||||
import pytest
|
||||
|
||||
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():
|
||||
|
||||
@@ -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}")
|
||||
|
||||
|
||||
+77
-36
@@ -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}")
|
||||
if self.stream:
|
||||
print_verbose(
|
||||
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(
|
||||
logging_obj.async_success_handler(
|
||||
cached_result, start_time, end_time, 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,
|
||||
)
|
||||
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",
|
||||
)
|
||||
)
|
||||
threading.Thread(
|
||||
target=logging_obj.success_handler,
|
||||
args=(cached_result, start_time, end_time, cache_hit),
|
||||
).start()
|
||||
if kwargs.get("stream", False) == False:
|
||||
# LOG SUCCESS
|
||||
asyncio.create_task(
|
||||
logging_obj.async_success_handler(
|
||||
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:
|
||||
|
||||
@@ -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
|
||||
|
||||
+2
-2
@@ -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"
|
||||
]
|
||||
|
||||
Executable
+49
@@ -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/* "$destination_dir"
|
||||
|
||||
echo "Deployment completed."
|
||||
else
|
||||
echo "Build failed. Deployment aborted."
|
||||
fi
|
||||
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File diff suppressed because one or more lines are too long
+1
-1
@@ -1 +1 @@
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File diff suppressed because one or more lines are too long
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|
||||
0:["1VBZn00yyQmx3nbSpjbvI",[[["",{"children":["__PAGE__",{}]},"$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]],[[["$","link","0",{"rel":"stylesheet","href":"/ui/_next/static/css/c18941d97fb7245b.css","precedence":"next","crossOrigin":""}]],"$L6"]]]]
|
||||
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"]]]]
|
||||
6:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","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"}]]
|
||||
1:null
|
||||
|
||||
@@ -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>
|
||||
|
||||
@@ -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],
|
||||
muted: colors.indigo[200],
|
||||
subtle: colors.indigo[400],
|
||||
DEFAULT: colors.indigo[500],
|
||||
faint: userColors.brand.faint,
|
||||
muted: userColors.brand.muted,
|
||||
subtle: userColors.brand.subtle,
|
||||
DEFAULT: userColors.brand.DEFAULT,
|
||||
emphasis: colors.indigo[700],
|
||||
inverted: colors.white,
|
||||
},
|
||||
|
||||
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"brand": {
|
||||
"DEFAULT": "#6366f1",
|
||||
"faint": "#6c6fed",
|
||||
"muted": "#8688ef",
|
||||
"subtle": "#8e91eb",
|
||||
"emphasis": "#5558eb",
|
||||
"inverted": "indigo"
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user