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https://github.com/fosrl/pangolin.git
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basic ai analytics created
This commit is contained in:
Owen
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import { db, aiUsageRecords } from "@server/db";
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import { registry } from "@server/openApi";
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import { NextFunction } from "express";
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import { Request, Response } from "express";
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import { and, count, desc, eq, sql } from "drizzle-orm";
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import { OpenAPITags } from "@server/openApi";
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import { z } from "zod";
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import createHttpError from "http-errors";
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import HttpCode from "@server/types/HttpCode";
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import { fromError } from "zod-validation-error";
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import response from "@server/lib/response";
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import logger from "@server/logger";
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import {
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aiUsageAnalyticsFiltersQuery,
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aiUsageAnalyticsParams,
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aiUsageAnalyticsCombined,
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buildAiUsageWhere,
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resolveRoleUserIds,
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dayBucketExpr,
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pickTopNKeys,
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bucketTopNPerDay,
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DISTINCT_LIMIT,
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type AiUsageAnalyticsQuery
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} from "./aiUsageAnalyticsShared";
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type Q = AiUsageAnalyticsQuery;
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async function query(data: Q) {
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const roleUserIds = await resolveRoleUserIds(data.orgId, data.roleId);
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const baseConditions = buildAiUsageWhere(data, roleUserIds);
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const [totalsRow] = await db
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.select({
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requests: count(),
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promptTokens: sql<number>`COALESCE(SUM(${aiUsageRecords.promptTokens}), 0)`,
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cacheReadTokens: sql<number>`COALESCE(SUM(${aiUsageRecords.cacheReadTokens}), 0)`,
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cacheWriteTokens: sql<number>`COALESCE(SUM(${aiUsageRecords.cacheWriteTokens}), 0)`,
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completionTokens: sql<number>`COALESCE(SUM(${aiUsageRecords.completionTokens}), 0)`,
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reasoningTokens: sql<number>`COALESCE(SUM(${aiUsageRecords.reasoningTokens}), 0)`,
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totalTokens: sql<number>`COALESCE(SUM(${aiUsageRecords.totalTokens}), 0)`,
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costUsd: sql<number>`COALESCE(SUM(${aiUsageRecords.costUsd}), 0)`,
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estimatedRequests: sql<number>`SUM(CASE WHEN ${aiUsageRecords.estimated} THEN 1 ELSE 0 END)`
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})
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.from(aiUsageRecords)
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.where(baseConditions);
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const dayExpr = dayBucketExpr();
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const requestsPerDay = await db
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.select({
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day: dayExpr.as("day"),
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requests: count()
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})
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.from(aiUsageRecords)
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.where(baseConditions)
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.groupBy(dayExpr)
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.orderBy(dayExpr);
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const tokensPerDay = await db
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.select({
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day: dayExpr.as("day"),
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promptTokens: sql<number>`COALESCE(SUM(${aiUsageRecords.promptTokens}), 0)`,
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cacheReadTokens: sql<number>`COALESCE(SUM(${aiUsageRecords.cacheReadTokens}), 0)`,
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cacheWriteTokens: sql<number>`COALESCE(SUM(${aiUsageRecords.cacheWriteTokens}), 0)`,
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completionTokens: sql<number>`COALESCE(SUM(${aiUsageRecords.completionTokens}), 0)`,
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reasoningTokens: sql<number>`COALESCE(SUM(${aiUsageRecords.reasoningTokens}), 0)`
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})
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.from(aiUsageRecords)
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.where(baseConditions)
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.groupBy(dayExpr)
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.orderBy(dayExpr);
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const costPerDay = await db
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.select({
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day: dayExpr.as("day"),
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cost: sql<number>`COALESCE(SUM(${aiUsageRecords.costUsd}), 0)`
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})
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.from(aiUsageRecords)
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.where(baseConditions)
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.groupBy(dayExpr)
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.orderBy(dayExpr);
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const modelByDay = await db
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.select({
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day: dayExpr.as("day"),
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model: aiUsageRecords.requestedModel,
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cost: sql<number>`COALESCE(SUM(${aiUsageRecords.costUsd}), 0)`,
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tokens: sql<number>`COALESCE(SUM(${aiUsageRecords.totalTokens}), 0)`
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})
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.from(aiUsageRecords)
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.where(baseConditions)
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.groupBy(dayExpr, aiUsageRecords.requestedModel)
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.orderBy(dayExpr);
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const modelCostTotals = new Map<string, number>();
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const modelTokenTotals = new Map<string, number>();
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for (const row of modelByDay) {
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modelCostTotals.set(
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row.model,
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(modelCostTotals.get(row.model) ?? 0) + row.cost
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);
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modelTokenTotals.set(
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row.model,
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(modelTokenTotals.get(row.model) ?? 0) + row.tokens
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);
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}
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const topModelsByCost = pickTopNKeys(modelCostTotals);
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const topModelsByTokens = pickTopNKeys(modelTokenTotals);
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const modelCostPerDay = bucketTopNPerDay(
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modelByDay.map((r) => ({ day: r.day, key: r.model, value: r.cost })),
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topModelsByCost
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);
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const modelTokensPerDay = bucketTopNPerDay(
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modelByDay.map((r) => ({ day: r.day, key: r.model, value: r.tokens })),
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topModelsByTokens
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);
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const topModelsRaw = await db
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.select({
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model: aiUsageRecords.requestedModel,
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requests: count(),
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totalTokens: sql<number>`COALESCE(SUM(${aiUsageRecords.totalTokens}), 0)`,
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costUsd: sql<number>`COALESCE(SUM(${aiUsageRecords.costUsd}), 0)`
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})
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.from(aiUsageRecords)
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.where(baseConditions)
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.groupBy(aiUsageRecords.requestedModel)
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.orderBy(desc(sql`COALESCE(SUM(${aiUsageRecords.costUsd}), 0)`))
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.limit(DISTINCT_LIMIT + 1);
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if (topModelsRaw.length > DISTINCT_LIMIT) {
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throw createHttpError(
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HttpCode.BAD_REQUEST,
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"Too many distinct models. Please narrow your query."
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);
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}
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return {
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totalRequests: totalsRow.requests,
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totalTokens: totalsRow.totalTokens,
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totalCost: totalsRow.costUsd,
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estimatedPercent:
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totalsRow.requests > 0
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? (totalsRow.estimatedRequests / totalsRow.requests) * 100
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: 0,
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tokenBreakdown: {
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promptTokens: totalsRow.promptTokens,
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cacheReadTokens: totalsRow.cacheReadTokens,
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cacheWriteTokens: totalsRow.cacheWriteTokens,
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completionTokens: totalsRow.completionTokens,
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reasoningTokens: totalsRow.reasoningTokens
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},
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requestsPerDay,
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tokensPerDay,
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costPerDay,
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modelCostPerDay,
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modelTokensPerDay,
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topModels: topModelsRaw
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};
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}
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registry.registerPath({
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method: "get",
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path: "/org/{orgId}/logs/ai/usage/overview",
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description: "Query the AI usage analytics overview for an organization",
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tags: [OpenAPITags.Logs],
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request: {
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query: aiUsageAnalyticsFiltersQuery,
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params: aiUsageAnalyticsParams
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},
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responses: {
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200: {
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description: "Successful response",
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content: {
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"application/json": {
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schema: z.object({
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data: z.record(z.string(), z.any()).nullable(),
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success: z.boolean(),
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error: z.boolean(),
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message: z.string(),
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status: z.number()
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})
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}
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}
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}
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}
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});
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export type QueryAiUsageOverviewResponse = Awaited<ReturnType<typeof query>>;
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export async function queryAiUsageOverview(
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req: Request,
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res: Response,
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next: NextFunction
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): Promise<any> {
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try {
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const parsedQuery = aiUsageAnalyticsFiltersQuery.safeParse(req.query);
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if (!parsedQuery.success) {
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return next(
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createHttpError(HttpCode.BAD_REQUEST, fromError(parsedQuery.error))
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);
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}
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const parsedParams = aiUsageAnalyticsParams.safeParse(req.params);
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if (!parsedParams.success) {
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return next(
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createHttpError(HttpCode.BAD_REQUEST, fromError(parsedParams.error))
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);
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}
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const data = await query({ ...parsedQuery.data, ...parsedParams.data });
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return response<QueryAiUsageOverviewResponse>(res, {
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data,
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success: true,
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error: false,
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message: "AI usage overview retrieved successfully",
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status: HttpCode.OK
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});
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} catch (error) {
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logger.error(error);
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return next(
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createHttpError(HttpCode.INTERNAL_SERVER_ERROR, "An error occurred")
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);
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}
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}
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