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https://github.com/BerriAI/litellm.git
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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
GitHub
parent
b155f4f4a6
commit
cdcc8ea9b7
14 changed files with 909 additions and 22 deletions
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@ -14,8 +14,9 @@ These are members of a Team on LiteLLM
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import asyncio
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import traceback
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import uuid
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from datetime import datetime, timedelta, timezone
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from typing import Any, List, Optional, Union, cast
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from datetime import date, datetime, timedelta, timezone
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from enum import Enum
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from typing import Any, Dict, List, Optional, TypedDict, Union, cast
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import fastapi
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from fastapi import APIRouter, Depends, Header, HTTPException, Request, status
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@ -1242,3 +1243,291 @@ async def ui_view_users(
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except Exception as e:
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verbose_proxy_logger.exception(f"Error searching users: {str(e)}")
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raise HTTPException(status_code=500, detail=f"Error searching users: {str(e)}")
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class GroupByDimension(str, Enum):
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DATE = "date"
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MODEL = "model"
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API_KEY = "api_key"
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TEAM = "team"
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ORGANIZATION = "organization"
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MODEL_GROUP = "model_group"
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PROVIDER = "custom_llm_provider"
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class SpendMetrics(BaseModel):
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spend: float = Field(default=0.0)
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prompt_tokens: int = Field(default=0)
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completion_tokens: int = Field(default=0)
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total_tokens: int = Field(default=0)
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api_requests: int = Field(default=0)
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class BreakdownMetrics(BaseModel):
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"""Breakdown of spend by different dimensions"""
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models: Dict[str, SpendMetrics] = Field(default_factory=dict) # model -> metrics
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providers: Dict[str, SpendMetrics] = Field(
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default_factory=dict
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) # provider -> metrics
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api_keys: Dict[str, SpendMetrics] = Field(
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default_factory=dict
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) # api_key -> metrics
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class DailySpendData(BaseModel):
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date: date
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metrics: SpendMetrics
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breakdown: BreakdownMetrics = Field(default_factory=BreakdownMetrics)
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class DailySpendMetadata(BaseModel):
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total_spend: float = Field(default=0.0)
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total_prompt_tokens: int = Field(default=0)
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total_completion_tokens: int = Field(default=0)
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total_api_requests: int = Field(default=0)
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page: int = Field(default=1)
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total_pages: int = Field(default=1)
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has_more: bool = Field(default=False)
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class SpendAnalyticsPaginatedResponse(BaseModel):
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results: List[DailySpendData]
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metadata: DailySpendMetadata = Field(default_factory=DailySpendMetadata)
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class LiteLLM_DailyUserSpend(BaseModel):
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id: str
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user_id: str
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date: str
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api_key: str
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model: str
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model_group: Optional[str] = None
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custom_llm_provider: Optional[str] = None
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prompt_tokens: int = 0
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completion_tokens: int = 0
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spend: float = 0.0
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api_requests: int = 0
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class GroupedData(TypedDict):
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metrics: SpendMetrics
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breakdown: BreakdownMetrics
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def update_metrics(
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group_metrics: SpendMetrics, record: LiteLLM_DailyUserSpend
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) -> SpendMetrics:
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group_metrics.spend += record.spend
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group_metrics.prompt_tokens += record.prompt_tokens
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group_metrics.completion_tokens += record.completion_tokens
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group_metrics.total_tokens += record.prompt_tokens + record.completion_tokens
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group_metrics.api_requests += record.api_requests
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return group_metrics
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def update_breakdown_metrics(
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breakdown: BreakdownMetrics, record: LiteLLM_DailyUserSpend
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) -> BreakdownMetrics:
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"""Updates breakdown metrics for a single record using the existing update_metrics function"""
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# Update model breakdown
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if record.model not in breakdown.models:
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breakdown.models[record.model] = SpendMetrics()
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breakdown.models[record.model] = update_metrics(
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breakdown.models[record.model], record
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)
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# Update provider breakdown
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provider = record.custom_llm_provider or "unknown"
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if provider not in breakdown.providers:
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breakdown.providers[provider] = SpendMetrics()
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breakdown.providers[provider] = update_metrics(
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breakdown.providers[provider], record
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)
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# Update api key breakdown
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if record.api_key not in breakdown.api_keys:
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breakdown.api_keys[record.api_key] = SpendMetrics()
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breakdown.api_keys[record.api_key] = update_metrics(
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breakdown.api_keys[record.api_key], record
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)
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return breakdown
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@router.get(
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"/user/daily/activity",
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tags=["Budget & Spend Tracking", "Internal User management"],
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dependencies=[Depends(user_api_key_auth)],
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response_model=SpendAnalyticsPaginatedResponse,
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)
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async def get_user_daily_activity(
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start_date: Optional[str] = fastapi.Query(
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default=None,
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description="Start date in YYYY-MM-DD format",
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),
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end_date: Optional[str] = fastapi.Query(
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default=None,
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description="End date in YYYY-MM-DD format",
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),
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group_by: List[GroupByDimension] = fastapi.Query(
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default=[GroupByDimension.DATE],
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description="Dimensions to group by. Can combine multiple (e.g. date,team)",
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),
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view_by: Literal["team", "organization", "user"] = fastapi.Query(
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default="user",
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description="View spend at team/org/user level",
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),
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team_id: Optional[str] = fastapi.Query(
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default=None,
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description="Filter by specific team",
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),
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org_id: Optional[str] = fastapi.Query(
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default=None,
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description="Filter by specific organization",
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),
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model: Optional[str] = fastapi.Query(
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default=None,
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description="Filter by specific model",
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),
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api_key: Optional[str] = fastapi.Query(
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default=None,
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description="Filter by specific API key",
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),
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page: int = fastapi.Query(
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default=1, description="Page number for pagination", ge=1
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),
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page_size: int = fastapi.Query(
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default=50, description="Items per page", ge=1, le=100
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),
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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) -> SpendAnalyticsPaginatedResponse:
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"""
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[BETA] This is a beta endpoint. It will change.
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Meant to optimize querying spend data for analytics for a user.
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Returns:
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(by date/team/org/user/model/api_key/model_group/provider)
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- spend
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- prompt_tokens
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- completion_tokens
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- total_tokens
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- api_requests
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- breakdown by team, organization, user, model, api_key, model_group, provider
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"""
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from litellm.proxy.proxy_server import prisma_client
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if prisma_client is None:
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raise HTTPException(
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status_code=500,
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detail={"error": CommonProxyErrors.db_not_connected_error.value},
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)
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if start_date is None or end_date is None:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail={"error": "Please provide start_date and end_date"},
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)
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try:
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# Build filter conditions
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where_conditions: Dict[str, Any] = {
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"date": {
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"gte": start_date,
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"lte": end_date,
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}
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}
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if team_id:
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where_conditions["team_id"] = team_id
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if org_id:
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where_conditions["organization_id"] = org_id
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if model:
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where_conditions["model"] = model
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if api_key:
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where_conditions["api_key"] = api_key
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# Get total count for pagination
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total_count = await prisma_client.db.litellm_dailyuserspend.count(
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where=where_conditions
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)
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# Fetch paginated results
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daily_spend_data = await prisma_client.db.litellm_dailyuserspend.find_many(
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where=where_conditions,
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order=[
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{"date": "desc"},
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],
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skip=(page - 1) * page_size,
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take=page_size,
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)
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# Process results
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results = []
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total_metrics = SpendMetrics()
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# Group data by date and other dimensions
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grouped_data: Dict[str, Dict[str, Any]] = {}
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for record in daily_spend_data:
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date_str = record.date
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if date_str not in grouped_data:
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grouped_data[date_str] = {
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"metrics": SpendMetrics(),
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"breakdown": BreakdownMetrics(),
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}
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# Update metrics
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grouped_data[date_str]["metrics"] = update_metrics(
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grouped_data[date_str]["metrics"], record
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)
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# Update breakdowns
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grouped_data[date_str]["breakdown"] = update_breakdown_metrics(
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grouped_data[date_str]["breakdown"], record
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)
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# Update total metrics
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total_metrics.spend += record.spend
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total_metrics.prompt_tokens += record.prompt_tokens
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total_metrics.completion_tokens += record.completion_tokens
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total_metrics.total_tokens += (
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record.prompt_tokens + record.completion_tokens
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)
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total_metrics.api_requests += 1
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# Convert grouped data to response format
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for date_str, data in grouped_data.items():
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results.append(
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DailySpendData(
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date=datetime.strptime(date_str, "%Y-%m-%d").date(),
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metrics=data["metrics"],
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breakdown=data["breakdown"],
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)
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)
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# Sort results by date
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results.sort(key=lambda x: x.date, reverse=True)
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return SpendAnalyticsPaginatedResponse(
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results=results,
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metadata=DailySpendMetadata(
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total_spend=total_metrics.spend,
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total_prompt_tokens=total_metrics.prompt_tokens,
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total_completion_tokens=total_metrics.completion_tokens,
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total_api_requests=total_metrics.api_requests,
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page=page,
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total_pages=-(-total_count // page_size), # Ceiling division
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has_more=(page * page_size) < total_count,
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),
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)
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except Exception as e:
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verbose_proxy_logger.exception(
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"/spend/daily/analytics: Exception occured - {}".format(str(e))
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)
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail={"error": f"Failed to fetch analytics: {str(e)}"},
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)
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