Connect UI to "LiteLLM_DailyUserSpend" spend table - enables usage tab to work at 1m+ spend logs (#9603)

* feat(spend_management_endpoints.py): expose new endpoint for querying user's usage at 1m+ spend logs

Allows user to view their spend at 1m+ spend logs

* build(schema.prisma): add api_requests to dailyuserspend table

* build(migration.sql): add migration file for new column to daily user spend table

* build(prisma_client.py): add logic for copying over migration folder, if deploy/migrations present in expected location

enables easier testing of prisma migration flow

* build(ui/): initial commit successfully using the dailyuserspend table on the UI

* refactor(internal_user_endpoints.py): refactor `/user/daily/activity` to give breakdowns by provider/model/key

* feat: feature parity (cost page) with existing 'usage' page

* build(ui/): add activity tab to new_usage.tsx

gets to feature parity on 'All Up' page of 'usage.tsx'

* fix(proxy/utils.py): count number of api requests in daily user spend table

allows us to see activity by model on new usage tab

* style(new_usage.tsx): fix y-axis to be in ascending order of date

* fix: fix linting errors

* fix: fix ruff check errors
This commit is contained in:
Krish Dholakia 2025-03-27 23:29:15 -07:00 committed by GitHub
parent b155f4f4a6
commit cdcc8ea9b7
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
14 changed files with 909 additions and 22 deletions

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@ -1,4 +1,3 @@
import os
import subprocess
from pathlib import Path
from datetime import datetime

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@ -0,0 +1,3 @@
-- AlterTable
ALTER TABLE "LiteLLM_DailyUserSpend" ADD COLUMN "api_requests" INTEGER NOT NULL DEFAULT 0;

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@ -2718,3 +2718,4 @@ class DailyUserSpendTransaction(TypedDict):
prompt_tokens: int
completion_tokens: int
spend: float
api_requests: int

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@ -3,6 +3,7 @@ This file contains the PrismaWrapper class, which is used to wrap the Prisma cli
"""
import asyncio
import glob
import os
import random
import subprocess
@ -178,6 +179,69 @@ class PrismaManager:
verbose_proxy_logger.warning(f"Error creating baseline migration: {e}")
return False
@staticmethod
def _copy_spend_tracking_migrations(prisma_dir: str) -> bool:
import shutil
from pathlib import Path
"""
Check for and copy over spend tracking migrations if they exist in the deploy directory.
Returns True if migrations were found and copied, False otherwise.
"""
try:
# Get the current file's directory
current_dir = Path(__file__).parent
# Check for migrations in the deploy directory (../../deploy/migrations)
deploy_migrations_dir = (
current_dir.parent.parent.parent / "deploy" / "migrations"
)
# Local migrations directory
local_migrations_dir = Path(prisma_dir + "/migrations")
if deploy_migrations_dir.exists():
# Create local migrations directory if it doesn't exist
local_migrations_dir.mkdir(parents=True, exist_ok=True)
# Copy all migration files
# Copy entire migrations folder recursively
shutil.copytree(
deploy_migrations_dir, local_migrations_dir, dirs_exist_ok=True
)
return True
return False
except Exception:
return False
@staticmethod
def _get_migration_names(migrations_dir: str) -> list:
"""Get all migration directory names from the migrations folder"""
migration_paths = glob.glob(f"{migrations_dir}/*/migration.sql")
return [Path(p).parent.name for p in migration_paths]
@staticmethod
def _resolve_all_migrations(migrations_dir: str):
"""Mark all existing migrations as applied"""
migration_names = PrismaManager._get_migration_names(migrations_dir)
for migration_name in migration_names:
try:
verbose_proxy_logger.info(f"Resolving migration: {migration_name}")
subprocess.run(
["prisma", "migrate", "resolve", "--applied", migration_name],
timeout=60,
check=True,
capture_output=True,
text=True,
)
verbose_proxy_logger.debug(f"Resolved migration: {migration_name}")
except subprocess.CalledProcessError as e:
if "is already recorded as applied in the database." not in e.stderr:
verbose_proxy_logger.warning(
f"Failed to resolve migration {migration_name}: {e.stderr}"
)
@staticmethod
def setup_database(use_migrate: bool = False) -> bool:
"""
@ -194,8 +258,10 @@ class PrismaManager:
os.chdir(prisma_dir)
try:
if use_migrate:
PrismaManager._copy_spend_tracking_migrations(
prisma_dir
) # place a migration in the migrations directory
verbose_proxy_logger.info("Running prisma migrate deploy")
# First try to run migrate deploy directly
try:
subprocess.run(
["prisma", "migrate", "deploy"],
@ -205,25 +271,31 @@ class PrismaManager:
text=True,
)
verbose_proxy_logger.info("prisma migrate deploy completed")
# Resolve all migrations in the migrations directory
migrations_dir = os.path.join(prisma_dir, "migrations")
PrismaManager._resolve_all_migrations(migrations_dir)
return True
except subprocess.CalledProcessError as e:
# Check if this is the non-empty schema error
verbose_proxy_logger.warning(
f"prisma db error: {e.stderr}, e: {e.stdout}"
)
if (
"P3005" in e.stderr
and "database schema is not empty" in e.stderr
):
# Create baseline migration
verbose_proxy_logger.info("Creating baseline migration")
if PrismaManager._create_baseline_migration(schema_path):
# Try migrate deploy again after baseline
subprocess.run(
["prisma", "migrate", "deploy"],
timeout=60,
check=True,
verbose_proxy_logger.info(
"Resolving all migrations after baseline"
)
# Resolve all migrations after baseline
migrations_dir = os.path.join(prisma_dir, "migrations")
PrismaManager._resolve_all_migrations(migrations_dir)
return True
else:
# If it's a different error, raise it
raise e
else:
# Use prisma db push with increased timeout
subprocess.run(

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@ -14,8 +14,9 @@ These are members of a Team on LiteLLM
import asyncio
import traceback
import uuid
from datetime import datetime, timedelta, timezone
from typing import Any, List, Optional, Union, cast
from datetime import date, datetime, timedelta, timezone
from enum import Enum
from typing import Any, Dict, List, Optional, TypedDict, Union, cast
import fastapi
from fastapi import APIRouter, Depends, Header, HTTPException, Request, status
@ -1242,3 +1243,291 @@ async def ui_view_users(
except Exception as e:
verbose_proxy_logger.exception(f"Error searching users: {str(e)}")
raise HTTPException(status_code=500, detail=f"Error searching users: {str(e)}")
class GroupByDimension(str, Enum):
DATE = "date"
MODEL = "model"
API_KEY = "api_key"
TEAM = "team"
ORGANIZATION = "organization"
MODEL_GROUP = "model_group"
PROVIDER = "custom_llm_provider"
class SpendMetrics(BaseModel):
spend: float = Field(default=0.0)
prompt_tokens: int = Field(default=0)
completion_tokens: int = Field(default=0)
total_tokens: int = Field(default=0)
api_requests: int = Field(default=0)
class BreakdownMetrics(BaseModel):
"""Breakdown of spend by different dimensions"""
models: Dict[str, SpendMetrics] = Field(default_factory=dict) # model -> metrics
providers: Dict[str, SpendMetrics] = Field(
default_factory=dict
) # provider -> metrics
api_keys: Dict[str, SpendMetrics] = Field(
default_factory=dict
) # api_key -> metrics
class DailySpendData(BaseModel):
date: date
metrics: SpendMetrics
breakdown: BreakdownMetrics = Field(default_factory=BreakdownMetrics)
class DailySpendMetadata(BaseModel):
total_spend: float = Field(default=0.0)
total_prompt_tokens: int = Field(default=0)
total_completion_tokens: int = Field(default=0)
total_api_requests: int = Field(default=0)
page: int = Field(default=1)
total_pages: int = Field(default=1)
has_more: bool = Field(default=False)
class SpendAnalyticsPaginatedResponse(BaseModel):
results: List[DailySpendData]
metadata: DailySpendMetadata = Field(default_factory=DailySpendMetadata)
class LiteLLM_DailyUserSpend(BaseModel):
id: str
user_id: str
date: str
api_key: str
model: str
model_group: Optional[str] = None
custom_llm_provider: Optional[str] = None
prompt_tokens: int = 0
completion_tokens: int = 0
spend: float = 0.0
api_requests: int = 0
class GroupedData(TypedDict):
metrics: SpendMetrics
breakdown: BreakdownMetrics
def update_metrics(
group_metrics: SpendMetrics, record: LiteLLM_DailyUserSpend
) -> SpendMetrics:
group_metrics.spend += record.spend
group_metrics.prompt_tokens += record.prompt_tokens
group_metrics.completion_tokens += record.completion_tokens
group_metrics.total_tokens += record.prompt_tokens + record.completion_tokens
group_metrics.api_requests += record.api_requests
return group_metrics
def update_breakdown_metrics(
breakdown: BreakdownMetrics, record: LiteLLM_DailyUserSpend
) -> BreakdownMetrics:
"""Updates breakdown metrics for a single record using the existing update_metrics function"""
# Update model breakdown
if record.model not in breakdown.models:
breakdown.models[record.model] = SpendMetrics()
breakdown.models[record.model] = update_metrics(
breakdown.models[record.model], record
)
# Update provider breakdown
provider = record.custom_llm_provider or "unknown"
if provider not in breakdown.providers:
breakdown.providers[provider] = SpendMetrics()
breakdown.providers[provider] = update_metrics(
breakdown.providers[provider], record
)
# Update api key breakdown
if record.api_key not in breakdown.api_keys:
breakdown.api_keys[record.api_key] = SpendMetrics()
breakdown.api_keys[record.api_key] = update_metrics(
breakdown.api_keys[record.api_key], record
)
return breakdown
@router.get(
"/user/daily/activity",
tags=["Budget & Spend Tracking", "Internal User management"],
dependencies=[Depends(user_api_key_auth)],
response_model=SpendAnalyticsPaginatedResponse,
)
async def get_user_daily_activity(
start_date: Optional[str] = fastapi.Query(
default=None,
description="Start date in YYYY-MM-DD format",
),
end_date: Optional[str] = fastapi.Query(
default=None,
description="End date in YYYY-MM-DD format",
),
group_by: List[GroupByDimension] = fastapi.Query(
default=[GroupByDimension.DATE],
description="Dimensions to group by. Can combine multiple (e.g. date,team)",
),
view_by: Literal["team", "organization", "user"] = fastapi.Query(
default="user",
description="View spend at team/org/user level",
),
team_id: Optional[str] = fastapi.Query(
default=None,
description="Filter by specific team",
),
org_id: Optional[str] = fastapi.Query(
default=None,
description="Filter by specific organization",
),
model: Optional[str] = fastapi.Query(
default=None,
description="Filter by specific model",
),
api_key: Optional[str] = fastapi.Query(
default=None,
description="Filter by specific API key",
),
page: int = fastapi.Query(
default=1, description="Page number for pagination", ge=1
),
page_size: int = fastapi.Query(
default=50, description="Items per page", ge=1, le=100
),
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
) -> SpendAnalyticsPaginatedResponse:
"""
[BETA] This is a beta endpoint. It will change.
Meant to optimize querying spend data for analytics for a user.
Returns:
(by date/team/org/user/model/api_key/model_group/provider)
- spend
- prompt_tokens
- completion_tokens
- total_tokens
- api_requests
- breakdown by team, organization, user, model, api_key, model_group, provider
"""
from litellm.proxy.proxy_server import prisma_client
if prisma_client is None:
raise HTTPException(
status_code=500,
detail={"error": CommonProxyErrors.db_not_connected_error.value},
)
if start_date is None or end_date is None:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail={"error": "Please provide start_date and end_date"},
)
try:
# Build filter conditions
where_conditions: Dict[str, Any] = {
"date": {
"gte": start_date,
"lte": end_date,
}
}
if team_id:
where_conditions["team_id"] = team_id
if org_id:
where_conditions["organization_id"] = org_id
if model:
where_conditions["model"] = model
if api_key:
where_conditions["api_key"] = api_key
# Get total count for pagination
total_count = await prisma_client.db.litellm_dailyuserspend.count(
where=where_conditions
)
# Fetch paginated results
daily_spend_data = await prisma_client.db.litellm_dailyuserspend.find_many(
where=where_conditions,
order=[
{"date": "desc"},
],
skip=(page - 1) * page_size,
take=page_size,
)
# Process results
results = []
total_metrics = SpendMetrics()
# Group data by date and other dimensions
grouped_data: Dict[str, Dict[str, Any]] = {}
for record in daily_spend_data:
date_str = record.date
if date_str not in grouped_data:
grouped_data[date_str] = {
"metrics": SpendMetrics(),
"breakdown": BreakdownMetrics(),
}
# Update metrics
grouped_data[date_str]["metrics"] = update_metrics(
grouped_data[date_str]["metrics"], record
)
# Update breakdowns
grouped_data[date_str]["breakdown"] = update_breakdown_metrics(
grouped_data[date_str]["breakdown"], record
)
# Update total metrics
total_metrics.spend += record.spend
total_metrics.prompt_tokens += record.prompt_tokens
total_metrics.completion_tokens += record.completion_tokens
total_metrics.total_tokens += (
record.prompt_tokens + record.completion_tokens
)
total_metrics.api_requests += 1
# Convert grouped data to response format
for date_str, data in grouped_data.items():
results.append(
DailySpendData(
date=datetime.strptime(date_str, "%Y-%m-%d").date(),
metrics=data["metrics"],
breakdown=data["breakdown"],
)
)
# Sort results by date
results.sort(key=lambda x: x.date, reverse=True)
return SpendAnalyticsPaginatedResponse(
results=results,
metadata=DailySpendMetadata(
total_spend=total_metrics.spend,
total_prompt_tokens=total_metrics.prompt_tokens,
total_completion_tokens=total_metrics.completion_tokens,
total_api_requests=total_metrics.api_requests,
page=page,
total_pages=-(-total_count // page_size), # Ceiling division
has_more=(page * page_size) < total_count,
),
)
except Exception as e:
verbose_proxy_logger.exception(
"/spend/daily/analytics: Exception occured - {}".format(str(e))
)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail={"error": f"Failed to fetch analytics: {str(e)}"},
)

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@ -314,19 +314,19 @@ model LiteLLM_AuditLog {
updated_values Json? // value of the row after change
}
// Track daily user spend metrics per model and key
model LiteLLM_DailyUserSpend {
id String @id @default(uuid())
user_id String
date String
api_key String // Hashed API Token
model String // The specific model used
model_group String? // public model_name / model_group
custom_llm_provider String? // The LLM provider (e.g., "openai", "anthropic")
api_key String
model String
model_group String?
custom_llm_provider String?
prompt_tokens Int @default(0)
completion_tokens Int @default(0)
spend Float @default(0.0)
api_requests Int @default(0)
created_at DateTime @default(now())
updated_at DateTime @updatedAt

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@ -3,7 +3,7 @@ import collections
import os
from datetime import datetime, timedelta, timezone
from functools import lru_cache
from typing import TYPE_CHECKING, Any, List, Optional
from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional
import fastapi
from fastapi import APIRouter, Depends, HTTPException, status

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@ -1179,6 +1179,7 @@ class PrismaClient:
daily_transaction["spend"] += payload["spend"]
daily_transaction["prompt_tokens"] += payload["prompt_tokens"]
daily_transaction["completion_tokens"] += payload["completion_tokens"]
daily_transaction["api_requests"] += 1
else:
daily_transaction = DailyUserSpendTransaction(
user_id=payload["user"],
@ -1190,6 +1191,7 @@ class PrismaClient:
prompt_tokens=payload["prompt_tokens"],
completion_tokens=payload["completion_tokens"],
spend=payload["spend"],
api_requests=1,
)
self.daily_user_spend_transactions[daily_transaction_key] = (
@ -2598,6 +2600,7 @@ class ProxyUpdateSpend:
"completion_tokens"
],
"spend": transaction["spend"],
"api_requests": transaction["api_requests"],
},
"update": {
"prompt_tokens": {
@ -2609,6 +2612,9 @@ class ProxyUpdateSpend:
]
},
"spend": {"increment": transaction["spend"]},
"api_requests": {
"increment": transaction["api_requests"]
},
},
},
)

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@ -326,6 +326,7 @@ model LiteLLM_DailyUserSpend {
prompt_tokens Int @default(0)
completion_tokens Int @default(0)
spend Float @default(0.0)
api_requests Int @default(0)
created_at DateTime @default(now())
updated_at DateTime @updatedAt

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@ -20,6 +20,7 @@ import PassThroughSettings from "@/components/pass_through_settings";
import BudgetPanel from "@/components/budgets/budget_panel";
import SpendLogsTable from "@/components/view_logs";
import ModelHub from "@/components/model_hub";
import NewUsagePage from "@/components/new_usage";
import APIRef from "@/components/api_ref";
import ChatUI from "@/components/chat_ui";
import Sidebar from "@/components/leftnav";
@ -346,7 +347,14 @@ export default function CreateKeyPage() {
accessToken={accessToken}
allTeams={teams as Team[] ?? []}
/>
) : (
) : page == "new_usage" ? (
<NewUsagePage
userID={userID}
userRole={userRole}
accessToken={accessToken}
/>
) :
(
<Usage
userID={userID}
userRole={userRole}

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@ -68,6 +68,7 @@ const menuItems: MenuItem[] = [
{ key: "9", page: "caching", label: "Caching", icon: <DatabaseOutlined />, roles: all_admin_roles },
{ key: "10", page: "budgets", label: "Budgets", icon: <BankOutlined />, roles: all_admin_roles },
{ key: "11", page: "guardrails", label: "Guardrails", icon: <SafetyOutlined />, roles: all_admin_roles },
{ key: "12", page: "new_usage", label: "New Usage", icon: <BarChartOutlined />, roles: all_admin_roles },
]
},
{

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@ -1070,6 +1070,42 @@ export const organizationDeleteCall = async (
}
};
export const userDailyActivityCall = async (accessToken: String, startTime: Date, endTime: Date) => {
/**
* Get daily user activity on proxy
*/
try {
let url = proxyBaseUrl ? `${proxyBaseUrl}/user/daily/activity` : `/user/daily/activity`;
const queryParams = new URLSearchParams();
queryParams.append('start_date', startTime.toISOString());
queryParams.append('end_date', endTime.toISOString());
const queryString = queryParams.toString();
if (queryString) {
url += `?${queryString}`;
}
const response = await fetch(url, {
method: "GET",
headers: {
[globalLitellmHeaderName]: `Bearer ${accessToken}`,
"Content-Type": "application/json",
},
});
if (!response.ok) {
const errorData = await response.text();
handleError(errorData);
throw new Error("Network response was not ok");
}
const data = await response.json();
return data;
} catch (error) {
console.error("Failed to create key:", error);
throw error;
}
};
export const getTotalSpendCall = async (accessToken: String) => {
/**
* Get all models on proxy

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@ -0,0 +1,471 @@
/**
* New Usage Page
*
* Uses the new `/user/daily/activity` endpoint to get daily activity data for a user.
*
* Works at 1m+ spend logs, by querying an aggregate table instead.
*/
import React, { useState, useEffect } from "react";
import {
BarChart, Card, Title, Text,
Grid, Col, TabGroup, TabList, Tab,
TabPanel, TabPanels, DonutChart,
Table, TableHead, TableRow,
TableHeaderCell, TableBody, TableCell,
Subtitle
} from "@tremor/react";
import { AreaChart } from "@tremor/react";
import { userDailyActivityCall } from "./networking";
import ViewUserSpend from "./view_user_spend";
import TopKeyView from "./top_key_view";
interface NewUsagePageProps {
accessToken: string | null;
userRole: string | null;
userID: string | null;
}
interface SpendMetrics {
spend: number;
prompt_tokens: number;
completion_tokens: number;
total_tokens: number;
api_requests: number;
}
interface BreakdownMetrics {
models: { [key: string]: SpendMetrics };
providers: { [key: string]: SpendMetrics };
api_keys: { [key: string]: SpendMetrics };
}
interface DailyData {
date: string;
metrics: SpendMetrics;
breakdown: BreakdownMetrics;
}
const NewUsagePage: React.FC<NewUsagePageProps> = ({
accessToken,
userRole,
userID,
}) => {
const [userSpendData, setUserSpendData] = useState<{
results: DailyData[];
metadata: any;
}>({ results: [], metadata: {} });
// Derived states from userSpendData
const totalSpend = userSpendData.metadata?.total_spend || 0;
// Calculate top models from the breakdown data
const getTopModels = () => {
const modelSpend: { [key: string]: SpendMetrics } = {};
userSpendData.results.forEach(day => {
Object.entries(day.breakdown.models || {}).forEach(([model, metrics]) => {
if (!modelSpend[model]) {
modelSpend[model] = {
spend: 0,
prompt_tokens: 0,
completion_tokens: 0,
total_tokens: 0,
api_requests: 0
};
}
modelSpend[model].spend += metrics.spend;
modelSpend[model].prompt_tokens += metrics.prompt_tokens;
modelSpend[model].completion_tokens += metrics.completion_tokens;
modelSpend[model].total_tokens += metrics.total_tokens;
modelSpend[model].api_requests += metrics.api_requests;
});
});
return Object.entries(modelSpend)
.map(([model, metrics]) => ({
key: model,
spend: metrics.spend,
requests: metrics.api_requests,
tokens: metrics.total_tokens
}))
.sort((a, b) => b.spend - a.spend)
.slice(0, 5);
};
// Calculate provider spend from the breakdown data
const getProviderSpend = () => {
const providerSpend: { [key: string]: SpendMetrics } = {};
userSpendData.results.forEach(day => {
Object.entries(day.breakdown.providers || {}).forEach(([provider, metrics]) => {
if (!providerSpend[provider]) {
providerSpend[provider] = {
spend: 0,
prompt_tokens: 0,
completion_tokens: 0,
total_tokens: 0,
api_requests: 0
};
}
providerSpend[provider].spend += metrics.spend;
providerSpend[provider].prompt_tokens += metrics.prompt_tokens;
providerSpend[provider].completion_tokens += metrics.completion_tokens;
providerSpend[provider].total_tokens += metrics.total_tokens;
providerSpend[provider].api_requests += metrics.api_requests;
});
});
return Object.entries(providerSpend)
.map(([provider, metrics]) => ({
provider,
spend: metrics.spend,
requests: metrics.api_requests,
tokens: metrics.total_tokens
}));
};
// Calculate top API keys from the breakdown data
const getTopKeys = () => {
const keySpend: { [key: string]: SpendMetrics } = {};
userSpendData.results.forEach(day => {
Object.entries(day.breakdown.api_keys || {}).forEach(([key, metrics]) => {
if (!keySpend[key]) {
keySpend[key] = {
spend: 0,
prompt_tokens: 0,
completion_tokens: 0,
total_tokens: 0,
api_requests: 0
};
}
keySpend[key].spend += metrics.spend;
keySpend[key].prompt_tokens += metrics.prompt_tokens;
keySpend[key].completion_tokens += metrics.completion_tokens;
keySpend[key].total_tokens += metrics.total_tokens;
keySpend[key].api_requests += metrics.api_requests;
});
});
return Object.entries(keySpend)
.map(([api_key, metrics]) => ({
api_key,
key_alias: api_key.substring(0, 10), // Using truncated key as alias
spend: metrics.spend,
}))
.sort((a, b) => b.spend - a.spend)
.slice(0, 5);
};
const fetchUserSpendData = async () => {
if (!accessToken) return;
const startTime = new Date(Date.now() - 28 * 24 * 60 * 60 * 1000);
const endTime = new Date();
const data = await userDailyActivityCall(accessToken, startTime, endTime);
setUserSpendData(data);
};
useEffect(() => {
fetchUserSpendData();
}, [accessToken]);
return (
<div style={{ width: "100%" }} className="p-8">
<Text>Experimental Usage page, using new `/user/daily/activity` endpoint.</Text>
<TabGroup>
<TabList variant="solid" className="mt-1">
<Tab>Cost</Tab>
<Tab>Activity</Tab>
</TabList>
<TabPanels>
{/* Cost Panel */}
<TabPanel>
<Grid numItems={2} className="gap-2 h-[100vh] w-full">
{/* Total Spend Card */}
<Col numColSpan={2}>
<Text className="text-tremor-default text-tremor-content dark:text-dark-tremor-content mb-2 mt-2 text-lg">
Project Spend {new Date().toLocaleString('default', { month: 'long' })} 1 - {new Date(new Date().getFullYear(), new Date().getMonth() + 1, 0).getDate()}
</Text>
<ViewUserSpend
userID={userID}
userRole={userRole}
accessToken={accessToken}
userSpend={totalSpend}
selectedTeam={null}
userMaxBudget={null}
/>
</Col>
{/* Daily Spend Chart */}
<Col numColSpan={2}>
<Card>
<Title>Daily Spend</Title>
<BarChart
data={userSpendData.results}
index="date"
categories={["metrics.spend"]}
colors={["cyan"]}
valueFormatter={(value) => `$${value.toFixed(2)}`}
yAxisWidth={100}
showLegend={false}
customTooltip={({ payload, active }) => {
if (!active || !payload?.[0]) return null;
const data = payload[0].payload;
return (
<div className="bg-white p-4 shadow-lg rounded-lg border">
<p className="font-bold">{data.date}</p>
<p className="text-cyan-500">Spend: ${data.metrics.spend.toFixed(2)}</p>
<p className="text-gray-600">Requests: {data.metrics.api_requests}</p>
<p className="text-gray-600">Tokens: {data.metrics.total_tokens}</p>
</div>
);
}}
/>
</Card>
</Col>
{/* Top API Keys */}
<Col numColSpan={1}>
<Card className="h-full">
<Title>Top API Keys</Title>
<TopKeyView
topKeys={getTopKeys()}
accessToken={accessToken}
userID={userID}
userRole={userRole}
teams={null}
/>
</Card>
</Col>
{/* Top Models */}
<Col numColSpan={1}>
<Card className="h-full">
<Title>Top Models</Title>
<BarChart
className="mt-4 h-40"
data={getTopModels()}
index="key"
categories={["spend"]}
colors={["cyan"]}
valueFormatter={(value) => `$${value.toFixed(2)}`}
layout="vertical"
yAxisWidth={200}
showLegend={false}
customTooltip={({ payload, active }) => {
if (!active || !payload?.[0]) return null;
const data = payload[0].payload;
return (
<div className="bg-white p-4 shadow-lg rounded-lg border">
<p className="font-bold">{data.key}</p>
<p className="text-cyan-500">Spend: ${data.spend.toFixed(2)}</p>
<p className="text-gray-600">Requests: {data.requests.toLocaleString()}</p>
<p className="text-gray-600">Tokens: {data.tokens.toLocaleString()}</p>
</div>
);
}}
/>
</Card>
</Col>
{/* Spend by Provider */}
<Col numColSpan={2}>
<Card className="h-full">
<Title>Spend by Provider</Title>
<Grid numItems={2}>
<Col numColSpan={1}>
<DonutChart
className="mt-4 h-40"
data={getProviderSpend()}
index="provider"
category="spend"
valueFormatter={(value) => `$${value.toFixed(2)}`}
colors={["cyan"]}
/>
</Col>
<Col numColSpan={1}>
<Table>
<TableHead>
<TableRow>
<TableHeaderCell>Provider</TableHeaderCell>
<TableHeaderCell>Spend</TableHeaderCell>
<TableHeaderCell>Requests</TableHeaderCell>
<TableHeaderCell>Tokens</TableHeaderCell>
</TableRow>
</TableHead>
<TableBody>
{getProviderSpend().map((provider) => (
<TableRow key={provider.provider}>
<TableCell>{provider.provider}</TableCell>
<TableCell>
${provider.spend < 0.00001
? "less than 0.00"
: provider.spend.toFixed(2)}
</TableCell>
<TableCell>{provider.requests.toLocaleString()}</TableCell>
<TableCell>{provider.tokens.toLocaleString()}</TableCell>
</TableRow>
))}
</TableBody>
</Table>
</Col>
</Grid>
</Card>
</Col>
{/* Usage Metrics */}
<Col numColSpan={2}>
<Card>
<Title>Usage Metrics</Title>
<Grid numItems={3} className="gap-4 mt-4">
<Card>
<Title>Total Requests</Title>
<Text className="text-2xl font-bold mt-2">
{userSpendData.metadata?.total_api_requests?.toLocaleString() || 0}
</Text>
</Card>
<Card>
<Title>Total Tokens</Title>
<Text className="text-2xl font-bold mt-2">
{userSpendData.metadata?.total_tokens?.toLocaleString() || 0}
</Text>
</Card>
<Card>
<Title>Average Cost per Request</Title>
<Text className="text-2xl font-bold mt-2">
${((totalSpend || 0) / (userSpendData.metadata?.total_api_requests || 1)).toFixed(4)}
</Text>
</Card>
</Grid>
</Card>
</Col>
</Grid>
</TabPanel>
{/* Activity Panel */}
<TabPanel>
<Grid numItems={1} className="gap-2 h-[75vh] w-full">
<Card>
<Title>All Up</Title>
<Grid numItems={2}>
<Col>
<Subtitle style={{ fontSize: "15px", fontWeight: "normal", color: "#535452"}}>
API Requests {valueFormatterNumbers(userSpendData.metadata?.total_api_requests || 0)}
</Subtitle>
<AreaChart
className="h-40"
data={[...userSpendData.results].reverse()}
valueFormatter={valueFormatterNumbers}
index="date"
colors={['cyan']}
categories={['metrics.api_requests']}
/>
</Col>
<Col>
<Subtitle style={{ fontSize: "15px", fontWeight: "normal", color: "#535452"}}>
Tokens {valueFormatterNumbers(userSpendData.metadata?.total_tokens || 0)}
</Subtitle>
<BarChart
className="h-40"
data={[...userSpendData.results].reverse()}
valueFormatter={valueFormatterNumbers}
index="date"
colors={['cyan']}
categories={['metrics.total_tokens']}
/>
</Col>
</Grid>
</Card>
{/* Per Model Activity */}
{Object.entries(getModelActivityData(userSpendData)).map(([model, data], index) => (
<Card key={index}>
<Title>{model}</Title>
<Grid numItems={2}>
<Col>
<Subtitle style={{ fontSize: "15px", fontWeight: "normal", color: "#535452"}}>
API Requests {valueFormatterNumbers(data.total_requests)}
</Subtitle>
<AreaChart
className="h-40"
data={[...data.daily_data].reverse()}
index="date"
colors={['cyan']}
categories={['api_requests']}
valueFormatter={valueFormatterNumbers}
/>
</Col>
<Col>
<Subtitle style={{ fontSize: "15px", fontWeight: "normal", color: "#535452"}}>
Tokens {valueFormatterNumbers(data.total_tokens)}
</Subtitle>
<BarChart
className="h-40"
data={data.daily_data}
index="date"
colors={['cyan']}
categories={['total_tokens']}
valueFormatter={valueFormatterNumbers}
/>
</Col>
</Grid>
</Card>
))}
</Grid>
</TabPanel>
</TabPanels>
</TabGroup>
</div>
);
};
// Add this helper function to process model-specific activity data
const getModelActivityData = (userSpendData: {
results: DailyData[];
metadata: any;
}) => {
const modelData: {
[key: string]: {
total_requests: number;
total_tokens: number;
daily_data: Array<{
date: string;
api_requests: number;
total_tokens: number;
}>;
};
} = {};
userSpendData.results.forEach((day: DailyData) => {
Object.entries(day.breakdown.models || {}).forEach(([model, metrics]) => {
if (!modelData[model]) {
modelData[model] = {
total_requests: 0,
total_tokens: 0,
daily_data: []
};
}
modelData[model].total_requests += metrics.api_requests;
modelData[model].total_tokens += metrics.total_tokens;
modelData[model].daily_data.push({
date: day.date,
api_requests: metrics.api_requests,
total_tokens: metrics.total_tokens
});
});
});
return modelData;
};
// Add this helper function for number formatting
function valueFormatterNumbers(number: number) {
const formatter = new Intl.NumberFormat('en-US', {
maximumFractionDigits: 0,
notation: 'compact',
compactDisplay: 'short',
});
return formatter.format(number);
}
export default NewUsagePage;

View file

@ -1035,4 +1035,4 @@ const UsagePage: React.FC<UsagePageProps> = ({
);
};
export default UsagePage;
export default UsagePage;