refactor(api): extract insights procedures
This commit is contained in:
@@ -0,0 +1,209 @@
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import { TRPCError } from "@trpc/server";
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import { z } from "zod";
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import { createAiClient, isAiConfigured, loggedAiCall, parseAiError } from "../ai-client.js";
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import type { TRPCContext } from "../trpc.js";
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import { buildInsightSnapshot, type InsightsDbAccess } from "./insights-anomalies.js";
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type InsightsProcedureContext = Pick<TRPCContext, "db">;
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export const projectNarrativeInputSchema = z.object({ projectId: z.string() });
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type ProjectNarrativeInput = z.infer<typeof projectNarrativeInputSchema>;
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/**
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* Count business days between two dates (Mon-Fri).
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*/
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export function countBusinessDays(start: Date, end: Date): number {
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let count = 0;
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const d = new Date(start);
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while (d <= end) {
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const dow = d.getDay();
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if (dow !== 0 && dow !== 6) count++;
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d.setDate(d.getDate() + 1);
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}
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return count;
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}
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export async function getAnomalyDetail(ctx: InsightsProcedureContext) {
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const snapshot = await buildInsightSnapshot(ctx.db as unknown as InsightsDbAccess);
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return {
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anomalies: snapshot.anomalies,
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count: snapshot.anomalies.length,
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};
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}
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export async function generateProjectNarrative(
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ctx: InsightsProcedureContext,
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input: ProjectNarrativeInput,
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) {
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const [project, settings] = await Promise.all([
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ctx.db.project.findUnique({
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where: { id: input.projectId },
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include: {
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demandRequirements: {
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select: {
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id: true,
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role: true,
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headcount: true,
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hoursPerDay: true,
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startDate: true,
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endDate: true,
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status: true,
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_count: { select: { assignments: true } },
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},
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},
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assignments: {
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select: {
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id: true,
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role: true,
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hoursPerDay: true,
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startDate: true,
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endDate: true,
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status: true,
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dailyCostCents: true,
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resource: { select: { displayName: true } },
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},
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},
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},
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}),
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ctx.db.systemSettings.findUnique({ where: { id: "singleton" } }),
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]);
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if (!project) {
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throw new TRPCError({ code: "NOT_FOUND", message: "Project not found" });
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}
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if (!isAiConfigured(settings)) {
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throw new TRPCError({
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code: "PRECONDITION_FAILED",
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message: "AI is not configured. Please set credentials in Admin → Settings.",
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});
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}
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const now = new Date();
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const totalDays = countBusinessDays(project.startDate, project.endDate);
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const elapsedDays = countBusinessDays(
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project.startDate,
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now < project.endDate ? now : project.endDate,
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);
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const progressPercent = totalDays > 0 ? Math.round((elapsedDays / totalDays) * 100) : 0;
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const totalDemandHeadcount = project.demandRequirements.reduce((sum, demand) => sum + demand.headcount, 0);
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const filledDemandHeadcount = project.demandRequirements.reduce(
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(sum, demand) => sum + Math.min(demand._count.assignments, demand.headcount),
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0,
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);
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const staffingPercent = totalDemandHeadcount > 0
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? Math.round((filledDemandHeadcount / totalDemandHeadcount) * 100)
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: 100;
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const totalCostCents = project.assignments.reduce((sum, assignment) => {
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const days = countBusinessDays(assignment.startDate, assignment.endDate);
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return sum + assignment.dailyCostCents * days;
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}, 0);
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const budgetCents = project.budgetCents;
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const budgetUsedPercent = budgetCents > 0 ? Math.round((totalCostCents / budgetCents) * 100) : 0;
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const overrunAssignments = project.assignments.filter(
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(assignment) => assignment.endDate > project.endDate,
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);
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const dataContext = [
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`Project: ${project.name} (${project.shortCode})`,
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`Status: ${project.status}`,
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`Timeline: ${project.startDate.toISOString().slice(0, 10)} to ${project.endDate.toISOString().slice(0, 10)} (${progressPercent}% elapsed)`,
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`Budget: ${(budgetCents / 100).toLocaleString("en-US", { style: "currency", currency: "EUR" })} | Estimated cost: ${(totalCostCents / 100).toLocaleString("en-US", { style: "currency", currency: "EUR" })} (${budgetUsedPercent}% of budget)`,
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`Staffing: ${filledDemandHeadcount}/${totalDemandHeadcount} positions filled (${staffingPercent}%)`,
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`Active assignments: ${project.assignments.filter((assignment) => assignment.status === "ACTIVE" || assignment.status === "CONFIRMED").length}`,
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overrunAssignments.length > 0
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? `Timeline risk: ${overrunAssignments.length} assignment(s) extend beyond project end date`
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: "No timeline overruns detected",
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].join("\n");
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const prompt = `Generate a concise executive summary for this project covering: budget status, staffing completeness, timeline risk, and key action items. Be specific with numbers. Keep it to 3-5 sentences.
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${dataContext}`;
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const client = createAiClient(settings);
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const model = settings.azureOpenAiDeployment;
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const maxTokens = settings.aiMaxCompletionTokens ?? 300;
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const temperature = settings.aiTemperature ?? 1;
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const provider = settings.aiProvider ?? "openai";
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let narrative = "";
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try {
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const completion = await loggedAiCall(provider, model, prompt.length, () =>
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client.chat.completions.create({
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messages: [
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{
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role: "system",
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content: "You are a project management analyst providing brief executive summaries. Be factual and action-oriented.",
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},
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{ role: "user", content: prompt },
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],
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max_completion_tokens: maxTokens,
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model,
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...(temperature !== 1 ? { temperature } : {}),
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}),
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);
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narrative = completion.choices[0]?.message?.content?.trim() ?? "";
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} catch (error) {
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throw new TRPCError({
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code: "INTERNAL_SERVER_ERROR",
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message: `AI call failed: ${parseAiError(error)}`,
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});
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}
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if (!narrative) {
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throw new TRPCError({
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code: "INTERNAL_SERVER_ERROR",
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message: "AI returned an empty response.",
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});
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}
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const generatedAt = new Date().toISOString();
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const existingDynamic = (project.dynamicFields as Record<string, unknown>) ?? {};
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await ctx.db.project.update({
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where: { id: input.projectId },
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data: {
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dynamicFields: {
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...existingDynamic,
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aiNarrative: narrative,
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aiNarrativeGeneratedAt: generatedAt,
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},
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},
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});
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return { narrative, generatedAt };
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}
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export async function detectAnomalies(ctx: InsightsProcedureContext) {
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const snapshot = await buildInsightSnapshot(ctx.db as unknown as InsightsDbAccess);
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return snapshot.anomalies;
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}
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export async function getInsightsSummary(ctx: InsightsProcedureContext) {
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const snapshot = await buildInsightSnapshot(ctx.db as unknown as InsightsDbAccess);
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return snapshot.summary;
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}
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export async function getCachedNarrative(
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ctx: InsightsProcedureContext,
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input: ProjectNarrativeInput,
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) {
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const project = await ctx.db.project.findUnique({
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where: { id: input.projectId },
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select: { dynamicFields: true },
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});
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if (!project) {
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throw new TRPCError({ code: "NOT_FOUND", message: "Project not found" });
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}
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const dynamicFields = project.dynamicFields as Record<string, unknown> | null;
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const narrative = (dynamicFields?.aiNarrative as string) ?? null;
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const generatedAt = (dynamicFields?.aiNarrativeGeneratedAt as string) ?? null;
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return { narrative, generatedAt };
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}
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@@ -1,215 +1,25 @@
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import { createAiClient, isAiConfigured, loggedAiCall, parseAiError } from "../ai-client.js";
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import { controllerProcedure, createTRPCRouter } from "../trpc.js";
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import { TRPCError } from "@trpc/server";
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import { z } from "zod";
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import { buildInsightSnapshot, type InsightsDbAccess } from "./insights-anomalies.js";
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/**
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* Count business days between two dates (Mon–Fri).
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*/
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function countBusinessDays(start: Date, end: Date): number {
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let count = 0;
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const d = new Date(start);
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while (d <= end) {
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const dow = d.getDay();
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if (dow !== 0 && dow !== 6) count++;
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d.setDate(d.getDate() + 1);
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}
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return count;
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}
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// ─── Router ──────────────────────────────────────────────────────────────────
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import {
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detectAnomalies,
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generateProjectNarrative,
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getAnomalyDetail,
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getCachedNarrative,
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getInsightsSummary,
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projectNarrativeInputSchema,
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} from "./insights-procedure-support.js";
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export const insightsRouter = createTRPCRouter({
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getAnomalyDetail: controllerProcedure.query(async ({ ctx }) => {
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const snapshot = await buildInsightSnapshot(ctx.db as unknown as InsightsDbAccess);
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return {
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anomalies: snapshot.anomalies,
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count: snapshot.anomalies.length,
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};
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}),
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getAnomalyDetail: controllerProcedure.query(({ ctx }) => getAnomalyDetail(ctx)),
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/**
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* Generate an AI-powered executive narrative for a project.
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* Caches the result in the project's dynamicFields.aiNarrative to avoid
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* calling the AI on every click.
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*/
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generateProjectNarrative: controllerProcedure
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.input(z.object({ projectId: z.string() }))
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.mutation(async ({ ctx, input }) => {
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const [project, settings] = await Promise.all([
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ctx.db.project.findUnique({
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where: { id: input.projectId },
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include: {
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demandRequirements: {
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select: {
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id: true,
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role: true,
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headcount: true,
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hoursPerDay: true,
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startDate: true,
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endDate: true,
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status: true,
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_count: { select: { assignments: true } },
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},
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},
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assignments: {
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select: {
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id: true,
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role: true,
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hoursPerDay: true,
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startDate: true,
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endDate: true,
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status: true,
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dailyCostCents: true,
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resource: { select: { displayName: true } },
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},
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},
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},
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}),
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ctx.db.systemSettings.findUnique({ where: { id: "singleton" } }),
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]);
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.input(projectNarrativeInputSchema)
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.mutation(({ ctx, input }) => generateProjectNarrative(ctx, input)),
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if (!project) {
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throw new TRPCError({ code: "NOT_FOUND", message: "Project not found" });
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}
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detectAnomalies: controllerProcedure.query(({ ctx }) => detectAnomalies(ctx)),
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if (!isAiConfigured(settings)) {
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throw new TRPCError({
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code: "PRECONDITION_FAILED",
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message: "AI is not configured. Please set credentials in Admin \u2192 Settings.",
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});
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}
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getInsightsSummary: controllerProcedure.query(({ ctx }) => getInsightsSummary(ctx)),
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// Build context data for the prompt
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const now = new Date();
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const totalDays = countBusinessDays(project.startDate, project.endDate);
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const elapsedDays = countBusinessDays(project.startDate, now < project.endDate ? now : project.endDate);
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const progressPercent = totalDays > 0 ? Math.round((elapsedDays / totalDays) * 100) : 0;
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const totalDemandHeadcount = project.demandRequirements.reduce((s, d) => s + d.headcount, 0);
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const filledDemandHeadcount = project.demandRequirements.reduce(
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(s, d) => s + Math.min(d._count.assignments, d.headcount),
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0,
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);
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const staffingPercent = totalDemandHeadcount > 0
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? Math.round((filledDemandHeadcount / totalDemandHeadcount) * 100)
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: 100;
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// Estimated cost from assignments
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const totalCostCents = project.assignments.reduce((s, a) => {
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const days = countBusinessDays(a.startDate, a.endDate);
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return s + a.dailyCostCents * days;
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}, 0);
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const budgetCents = project.budgetCents;
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const budgetUsedPercent = budgetCents > 0 ? Math.round((totalCostCents / budgetCents) * 100) : 0;
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const overrunAssignments = project.assignments.filter(
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(a) => a.endDate > project.endDate,
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);
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const dataContext = [
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`Project: ${project.name} (${project.shortCode})`,
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`Status: ${project.status}`,
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`Timeline: ${project.startDate.toISOString().slice(0, 10)} to ${project.endDate.toISOString().slice(0, 10)} (${progressPercent}% elapsed)`,
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`Budget: ${(budgetCents / 100).toLocaleString("en-US", { style: "currency", currency: "EUR" })} | Estimated cost: ${(totalCostCents / 100).toLocaleString("en-US", { style: "currency", currency: "EUR" })} (${budgetUsedPercent}% of budget)`,
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`Staffing: ${filledDemandHeadcount}/${totalDemandHeadcount} positions filled (${staffingPercent}%)`,
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`Active assignments: ${project.assignments.filter((a) => a.status === "ACTIVE" || a.status === "CONFIRMED").length}`,
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overrunAssignments.length > 0
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? `Timeline risk: ${overrunAssignments.length} assignment(s) extend beyond project end date`
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: "No timeline overruns detected",
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].join("\n");
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const prompt = `Generate a concise executive summary for this project covering: budget status, staffing completeness, timeline risk, and key action items. Be specific with numbers. Keep it to 3-5 sentences.
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${dataContext}`;
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const client = createAiClient(settings!);
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const model = settings!.azureOpenAiDeployment!;
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const maxTokens = settings!.aiMaxCompletionTokens ?? 300;
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const temperature = settings!.aiTemperature ?? 1;
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const provider = settings!.aiProvider ?? "openai";
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let narrative = "";
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try {
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const completion = await loggedAiCall(provider, model, prompt.length, () =>
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client.chat.completions.create({
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messages: [
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{ role: "system", content: "You are a project management analyst providing brief executive summaries. Be factual and action-oriented." },
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{ role: "user", content: prompt },
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],
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max_completion_tokens: maxTokens,
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model,
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...(temperature !== 1 ? { temperature } : {}),
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}),
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);
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narrative = completion.choices[0]?.message?.content?.trim() ?? "";
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} catch (err) {
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throw new TRPCError({
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code: "INTERNAL_SERVER_ERROR",
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message: `AI call failed: ${parseAiError(err)}`,
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});
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}
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if (!narrative) {
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throw new TRPCError({
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code: "INTERNAL_SERVER_ERROR",
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message: "AI returned an empty response.",
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});
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}
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const generatedAt = new Date().toISOString();
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// Cache in project dynamicFields
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const existingDynamic = (project.dynamicFields as Record<string, unknown>) ?? {};
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await ctx.db.project.update({
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where: { id: input.projectId },
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data: {
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dynamicFields: {
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...existingDynamic,
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aiNarrative: narrative,
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aiNarrativeGeneratedAt: generatedAt,
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},
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},
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});
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return { narrative, generatedAt };
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}),
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/**
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* Rule-based anomaly detection across all active projects.
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* No AI involved — pure data analysis.
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*/
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detectAnomalies: controllerProcedure.query(async ({ ctx }) => {
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const snapshot = await buildInsightSnapshot(ctx.db as unknown as InsightsDbAccess);
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return snapshot.anomalies;
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}),
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/**
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* Dashboard-friendly summary: anomaly counts by category + total.
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*/
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getInsightsSummary: controllerProcedure.query(async ({ ctx }) => {
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const snapshot = await buildInsightSnapshot(ctx.db as unknown as InsightsDbAccess);
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return snapshot.summary;
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}),
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/**
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* Retrieve a cached AI narrative for a project (if one was previously generated).
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*/
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getCachedNarrative: controllerProcedure
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.input(z.object({ projectId: z.string() }))
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.query(async ({ ctx, input }) => {
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const project = await ctx.db.project.findUnique({
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where: { id: input.projectId },
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select: { dynamicFields: true },
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});
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if (!project) {
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throw new TRPCError({ code: "NOT_FOUND", message: "Project not found" });
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}
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const df = project.dynamicFields as Record<string, unknown> | null;
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const narrative = (df?.aiNarrative as string) ?? null;
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const generatedAt = (df?.aiNarrativeGeneratedAt as string) ?? null;
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return { narrative, generatedAt };
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}),
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.input(projectNarrativeInputSchema)
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.query(({ ctx, input }) => getCachedNarrative(ctx, input)),
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});
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