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(refactor) - fix from enterprise.utils import ui_get_spend_by_tags (#7352)
* ui - refactor ui_get_spend_by_tags * fix typing
This commit is contained in:
Ishaan Jaff
GitHub
parent
ce41cd977c
commit
b3bd104f24
2 changed files with 115 additions and 231 deletions
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@ -1,214 +0,0 @@
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# Enterprise Proxy Util Endpoints
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from typing import Optional, List
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from litellm.proxy.proxy_server import PrismaClient, HTTPException
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from litellm.llms.custom_httpx.http_handler import HTTPHandler
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import collections
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import httpx
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from datetime import datetime
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async def get_spend_by_tags(
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prisma_client: PrismaClient, start_date=None, end_date=None
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):
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response = await prisma_client.db.query_raw(
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"""
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SELECT
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jsonb_array_elements_text(request_tags) AS individual_request_tag,
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COUNT(*) AS log_count,
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SUM(spend) AS total_spend
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FROM "LiteLLM_SpendLogs"
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GROUP BY individual_request_tag;
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"""
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)
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return response
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async def ui_get_spend_by_tags(
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start_date: str,
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end_date: str,
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prisma_client: Optional[PrismaClient] = None,
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tags_str: Optional[str] = None,
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):
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"""
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Should cover 2 cases:
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1. When user is getting spend for all_tags. "all_tags" in tags_list
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2. When user is getting spend for specific tags.
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"""
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# tags_str is a list of strings csv of tags
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# tags_str = tag1,tag2,tag3
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# convert to list if it's not None
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tags_list: Optional[List[str]] = None
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if tags_str is not None and len(tags_str) > 0:
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tags_list = tags_str.split(",")
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if prisma_client is None:
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raise HTTPException(status_code=500, detail={"error": "No db connected"})
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response = None
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if tags_list is None or (isinstance(tags_list, list) and "all-tags" in tags_list):
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# Get spend for all tags
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sql_query = """
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SELECT
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individual_request_tag,
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spend_date,
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log_count,
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total_spend
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FROM DailyTagSpend
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WHERE spend_date >= $1::date AND spend_date <= $2::date
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ORDER BY total_spend DESC;
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"""
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response = await prisma_client.db.query_raw(
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sql_query,
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start_date,
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end_date,
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)
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else:
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# filter by tags list
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sql_query = """
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SELECT
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individual_request_tag,
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SUM(log_count) AS log_count,
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SUM(total_spend) AS total_spend
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FROM DailyTagSpend
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WHERE spend_date >= $1::date AND spend_date <= $2::date
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AND individual_request_tag = ANY($3::text[])
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GROUP BY individual_request_tag
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ORDER BY total_spend DESC;
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"""
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response = await prisma_client.db.query_raw(
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sql_query,
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start_date,
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end_date,
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tags_list,
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)
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# print("tags - spend")
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# print(response)
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# Bar Chart 1 - Spend per tag - Top 10 tags by spend
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total_spend_per_tag: collections.defaultdict = collections.defaultdict(float)
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total_requests_per_tag: collections.defaultdict = collections.defaultdict(int)
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for row in response:
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tag_name = row["individual_request_tag"]
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tag_spend = row["total_spend"]
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total_spend_per_tag[tag_name] += tag_spend
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total_requests_per_tag[tag_name] += row["log_count"]
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sorted_tags = sorted(total_spend_per_tag.items(), key=lambda x: x[1], reverse=True)
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# convert to ui format
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ui_tags = []
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for tag in sorted_tags:
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current_spend = tag[1]
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if current_spend is not None and isinstance(current_spend, float):
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current_spend = round(current_spend, 4)
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ui_tags.append(
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{
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"name": tag[0],
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"spend": current_spend,
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"log_count": total_requests_per_tag[tag[0]],
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}
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)
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return {"spend_per_tag": ui_tags}
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def _forecast_daily_cost(data: list):
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from datetime import timedelta
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if len(data) == 0:
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return {
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"response": [],
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"predicted_spend": "Current Spend = $0, Predicted = $0",
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}
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first_entry = data[0]
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last_entry = data[-1]
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# get the date today
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today_date = datetime.today().date()
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today_day_month = today_date.month
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# Parse the date from the first entry
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first_entry_date = datetime.strptime(first_entry["date"], "%Y-%m-%d").date()
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last_entry_date = datetime.strptime(last_entry["date"], "%Y-%m-%d")
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print("last entry date", last_entry_date)
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# Calculate the last day of the month
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last_day_of_todays_month = datetime(
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today_date.year, today_date.month % 12 + 1, 1
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) - timedelta(days=1)
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print("last day of todays month", last_day_of_todays_month)
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# Calculate the remaining days in the month
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remaining_days = (last_day_of_todays_month - last_entry_date).days
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print("remaining days", remaining_days)
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current_spend_this_month = 0
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series = {}
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for entry in data:
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date = entry["date"]
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spend = entry["spend"]
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series[date] = spend
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# check if the date is in this month
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if datetime.strptime(date, "%Y-%m-%d").month == today_day_month:
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current_spend_this_month += spend
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if len(series) < 10:
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num_items_to_fill = 11 - len(series)
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# avg spend for all days in series
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avg_spend = sum(series.values()) / len(series)
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for i in range(num_items_to_fill):
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# go backwards from the first entry
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date = first_entry_date - timedelta(days=i)
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series[date.strftime("%Y-%m-%d")] = avg_spend
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series[date.strftime("%Y-%m-%d")] = avg_spend
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payload = {"series": series, "count": remaining_days}
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print("Prediction Data:", payload)
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headers = {
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"Content-Type": "application/json",
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}
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client = HTTPHandler()
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try:
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response = client.post(
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url="https://trend-api-production.up.railway.app/forecast",
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json=payload,
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headers=headers,
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)
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except httpx.HTTPStatusError as e:
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raise HTTPException(
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status_code=500,
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detail={"error": f"Error getting forecast: {e.response.text}"},
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)
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json_response = response.json()
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forecast_data = json_response["forecast"]
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# print("Forecast Data:", forecast_data)
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response_data = []
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total_predicted_spend = current_spend_this_month
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for date in forecast_data:
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spend = forecast_data[date]
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entry = {
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"date": date,
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"predicted_spend": spend,
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}
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total_predicted_spend += spend
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response_data.append(entry)
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# get month as a string, Jan, Feb, etc.
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today_month = today_date.strftime("%B")
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predicted_spend = (
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f"Predicted Spend for { today_month } 2024, ${total_predicted_spend}"
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)
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return {"response": response_data, "predicted_spend": predicted_spend}
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@ -1,10 +1,11 @@
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#### SPEND MANAGEMENT #####
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import collections
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import os
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from datetime import datetime, timedelta
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from typing import List, Optional
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from typing import TYPE_CHECKING, Any, List, Optional
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import fastapi
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from fastapi import APIRouter, Depends, HTTPException, Request, status
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from fastapi import APIRouter, Depends, HTTPException, status
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import litellm
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from litellm._logging import verbose_proxy_logger
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@ -16,6 +17,11 @@ from litellm.proxy.spend_tracking.spend_tracking_utils import (
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)
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from litellm.proxy.utils import handle_exception_on_proxy
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if TYPE_CHECKING:
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from litellm.proxy.proxy_server import PrismaClient
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else:
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PrismaClient = Any
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router = APIRouter()
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@ -1354,7 +1360,6 @@ async def global_view_spend_tags(
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"""
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import traceback
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from enterprise.utils import ui_get_spend_by_tags
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from litellm.proxy.proxy_server import prisma_client
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try:
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@ -2451,20 +2456,6 @@ async def global_spend_models(
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return response
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@router.post(
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"/global/predict/spend/logs",
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tags=["Budget & Spend Tracking"],
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dependencies=[Depends(user_api_key_auth)],
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include_in_schema=False,
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)
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async def global_predict_spend_logs(request: Request):
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from enterprise.utils import _forecast_daily_cost
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data = await request.json()
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data = data.get("data")
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return _forecast_daily_cost(data)
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@router.get("/provider/budgets", response_model=ProviderBudgetResponse)
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async def provider_budgets() -> ProviderBudgetResponse:
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"""
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@ -2554,3 +2545,110 @@ async def provider_budgets() -> ProviderBudgetResponse:
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"/provider/budgets: Exception occured - {}".format(str(e))
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)
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raise handle_exception_on_proxy(e)
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async def get_spend_by_tags(
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prisma_client: PrismaClient, start_date=None, end_date=None
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):
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response = await prisma_client.db.query_raw(
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"""
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SELECT
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jsonb_array_elements_text(request_tags) AS individual_request_tag,
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COUNT(*) AS log_count,
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SUM(spend) AS total_spend
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FROM "LiteLLM_SpendLogs"
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GROUP BY individual_request_tag;
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"""
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)
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return response
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async def ui_get_spend_by_tags(
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start_date: str,
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end_date: str,
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prisma_client: Optional[PrismaClient] = None,
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tags_str: Optional[str] = None,
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):
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"""
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Should cover 2 cases:
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1. When user is getting spend for all_tags. "all_tags" in tags_list
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2. When user is getting spend for specific tags.
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"""
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# tags_str is a list of strings csv of tags
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# tags_str = tag1,tag2,tag3
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# convert to list if it's not None
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tags_list: Optional[List[str]] = None
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if tags_str is not None and len(tags_str) > 0:
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tags_list = tags_str.split(",")
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if prisma_client is None:
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raise HTTPException(status_code=500, detail={"error": "No db connected"})
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response = None
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if tags_list is None or (isinstance(tags_list, list) and "all-tags" in tags_list):
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# Get spend for all tags
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sql_query = """
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SELECT
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individual_request_tag,
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spend_date,
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log_count,
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total_spend
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FROM DailyTagSpend
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WHERE spend_date >= $1::date AND spend_date <= $2::date
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ORDER BY total_spend DESC;
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"""
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response = await prisma_client.db.query_raw(
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sql_query,
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start_date,
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end_date,
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)
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else:
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# filter by tags list
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sql_query = """
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SELECT
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individual_request_tag,
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SUM(log_count) AS log_count,
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SUM(total_spend) AS total_spend
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FROM DailyTagSpend
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WHERE spend_date >= $1::date AND spend_date <= $2::date
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AND individual_request_tag = ANY($3::text[])
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GROUP BY individual_request_tag
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ORDER BY total_spend DESC;
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"""
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response = await prisma_client.db.query_raw(
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sql_query,
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start_date,
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end_date,
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tags_list,
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)
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# print("tags - spend")
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# print(response)
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# Bar Chart 1 - Spend per tag - Top 10 tags by spend
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total_spend_per_tag: collections.defaultdict = collections.defaultdict(float)
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total_requests_per_tag: collections.defaultdict = collections.defaultdict(int)
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for row in response:
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tag_name = row["individual_request_tag"]
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tag_spend = row["total_spend"]
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total_spend_per_tag[tag_name] += tag_spend
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total_requests_per_tag[tag_name] += row["log_count"]
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sorted_tags = sorted(total_spend_per_tag.items(), key=lambda x: x[1], reverse=True)
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# convert to ui format
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ui_tags = []
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for tag in sorted_tags:
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current_spend = tag[1]
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if current_spend is not None and isinstance(current_spend, float):
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current_spend = round(current_spend, 4)
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ui_tags.append(
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{
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"name": tag[0],
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"spend": current_spend,
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"log_count": total_requests_per_tag[tag[0]],
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}
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)
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return {"spend_per_tag": ui_tags}
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