refactor(api): extract insights procedures
This commit is contained in:
@@ -0,0 +1,189 @@
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import { TRPCError } from "@trpc/server";
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import { beforeEach, describe, expect, it, vi } from "vitest";
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const aiCompletionCreate = vi.fn();
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vi.mock("../ai-client.js", async (importOriginal) => {
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const actual = await importOriginal<typeof import("../ai-client.js")>();
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return {
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...actual,
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createAiClient: vi.fn(() => ({
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chat: {
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completions: {
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create: aiCompletionCreate,
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},
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},
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})),
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isAiConfigured: vi.fn().mockReturnValue(true),
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loggedAiCall: vi.fn(async (_provider, _model, _promptLength, fn) => fn()),
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parseAiError: vi.fn((error: unknown) => error instanceof Error ? error.message : String(error)),
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};
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});
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import { createAiClient, isAiConfigured, loggedAiCall } from "../ai-client.js";
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import {
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countBusinessDays,
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generateProjectNarrative,
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getCachedNarrative,
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} from "../router/insights-procedure-support.js";
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function createContext(db: Record<string, unknown>) {
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return { db: db as never };
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}
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describe("insights procedure support", () => {
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beforeEach(() => {
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vi.clearAllMocks();
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aiCompletionCreate.mockResolvedValue({
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choices: [{ message: { content: "Project is stable but needs one more staffed role." } }],
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});
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});
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it("counts only weekdays between two dates", () => {
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expect(
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countBusinessDays(
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new Date("2026-03-27T00:00:00.000Z"),
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new Date("2026-03-31T00:00:00.000Z"),
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),
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).toBe(3);
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});
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it("generates and stores a project narrative", async () => {
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vi.useFakeTimers();
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vi.setSystemTime(new Date("2026-03-31T10:15:00.000Z"));
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const projectFindUnique = vi.fn().mockResolvedValue({
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id: "project_1",
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name: "Apollo",
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shortCode: "APO",
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status: "ACTIVE",
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startDate: new Date("2026-03-03T00:00:00.000Z"),
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endDate: new Date("2026-04-30T00:00:00.000Z"),
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budgetCents: 200_000_00,
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dynamicFields: { existingFlag: true },
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demandRequirements: [
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{
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id: "dem_1",
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role: "Developer",
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headcount: 2,
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hoursPerDay: 8,
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startDate: new Date("2026-03-10T00:00:00.000Z"),
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endDate: new Date("2026-04-10T00:00:00.000Z"),
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status: "OPEN",
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_count: { assignments: 1 },
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},
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],
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assignments: [
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{
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id: "asn_1",
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role: "Developer",
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hoursPerDay: 8,
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startDate: new Date("2026-03-10T00:00:00.000Z"),
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endDate: new Date("2026-04-15T00:00:00.000Z"),
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status: "ACTIVE",
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dailyCostCents: 50_000,
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resource: { displayName: "Carol Danvers" },
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},
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],
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});
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const projectUpdate = vi.fn().mockResolvedValue({ id: "project_1" });
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try {
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const result = await generateProjectNarrative(
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createContext({
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project: {
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findUnique: projectFindUnique,
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update: projectUpdate,
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},
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systemSettings: {
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findUnique: vi.fn().mockResolvedValue({
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id: "singleton",
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aiProvider: "openai",
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azureOpenAiDeployment: "gpt-4o-mini",
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aiMaxCompletionTokens: 220,
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aiTemperature: 0.4,
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}),
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},
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}),
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{ projectId: "project_1" },
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);
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expect(isAiConfigured).toHaveBeenCalledWith(
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expect.objectContaining({ azureOpenAiDeployment: "gpt-4o-mini" }),
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);
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expect(createAiClient).toHaveBeenCalledWith(
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expect.objectContaining({ azureOpenAiDeployment: "gpt-4o-mini" }),
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);
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expect(loggedAiCall).toHaveBeenCalledOnce();
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expect(aiCompletionCreate).toHaveBeenCalledWith(
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expect.objectContaining({
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model: "gpt-4o-mini",
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max_completion_tokens: 220,
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temperature: 0.4,
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messages: expect.arrayContaining([
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expect.objectContaining({ role: "system" }),
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expect.objectContaining({
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role: "user",
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content: expect.stringContaining("Staffing: 1/2 positions filled (50%)"),
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}),
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]),
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}),
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);
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expect(projectUpdate).toHaveBeenCalledWith({
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where: { id: "project_1" },
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data: {
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dynamicFields: {
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existingFlag: true,
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aiNarrative: "Project is stable but needs one more staffed role.",
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aiNarrativeGeneratedAt: "2026-03-31T10:15:00.000Z",
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},
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},
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});
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expect(result).toEqual({
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narrative: "Project is stable but needs one more staffed role.",
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generatedAt: "2026-03-31T10:15:00.000Z",
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});
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} finally {
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vi.useRealTimers();
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}
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});
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it("returns the cached narrative for a project", async () => {
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const result = await getCachedNarrative(
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createContext({
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project: {
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findUnique: vi.fn().mockResolvedValue({
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dynamicFields: {
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aiNarrative: "Cached summary",
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aiNarrativeGeneratedAt: "2026-03-30T08:00:00.000Z",
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},
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}),
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},
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}),
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{ projectId: "project_1" },
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);
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expect(result).toEqual({
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narrative: "Cached summary",
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generatedAt: "2026-03-30T08:00:00.000Z",
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});
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});
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it("fails when the project does not exist for the cached narrative lookup", async () => {
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await expect(
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getCachedNarrative(
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createContext({
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project: {
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findUnique: vi.fn().mockResolvedValue(null),
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},
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}),
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{ projectId: "missing" },
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),
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).rejects.toEqual(
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expect.objectContaining<Partial<TRPCError>>({
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code: "NOT_FOUND",
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message: "Project not found",
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}),
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);
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});
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});
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@@ -195,4 +195,29 @@ describe("insights router", () => {
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vi.useRealTimers();
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}
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});
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it("returns the cached narrative through the controller router", async () => {
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const projectFindUnique = vi.fn().mockResolvedValue({
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dynamicFields: {
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aiNarrative: "Cached summary",
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aiNarrativeGeneratedAt: "2026-03-30T08:00:00.000Z",
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},
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});
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const caller = createControllerCaller({
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project: {
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findUnique: projectFindUnique,
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},
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});
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const result = await caller.getCachedNarrative({ projectId: "project_1" });
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expect(projectFindUnique).toHaveBeenCalledWith({
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where: { id: "project_1" },
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select: { dynamicFields: true },
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});
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expect(result).toEqual({
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narrative: "Cached summary",
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generatedAt: "2026-03-30T08:00:00.000Z",
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});
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});
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});
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@@ -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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|
||||
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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@@ -1,215 +1,25 @@
|
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import { createAiClient, isAiConfigured, loggedAiCall, parseAiError } from "../ai-client.js";
|
||||
import { controllerProcedure, createTRPCRouter } from "../trpc.js";
|
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import { TRPCError } from "@trpc/server";
|
||||
import { z } from "zod";
|
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import { buildInsightSnapshot, type InsightsDbAccess } from "./insights-anomalies.js";
|
||||
|
||||
/**
|
||||
* Count business days between two dates (Mon–Fri).
|
||||
*/
|
||||
function countBusinessDays(start: Date, end: Date): number {
|
||||
let count = 0;
|
||||
const d = new Date(start);
|
||||
while (d <= end) {
|
||||
const dow = d.getDay();
|
||||
if (dow !== 0 && dow !== 6) count++;
|
||||
d.setDate(d.getDate() + 1);
|
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}
|
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return count;
|
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}
|
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|
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// ─── Router ──────────────────────────────────────────────────────────────────
|
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import {
|
||||
detectAnomalies,
|
||||
generateProjectNarrative,
|
||||
getAnomalyDetail,
|
||||
getCachedNarrative,
|
||||
getInsightsSummary,
|
||||
projectNarrativeInputSchema,
|
||||
} from "./insights-procedure-support.js";
|
||||
|
||||
export const insightsRouter = createTRPCRouter({
|
||||
getAnomalyDetail: controllerProcedure.query(async ({ ctx }) => {
|
||||
const snapshot = await buildInsightSnapshot(ctx.db as unknown as InsightsDbAccess);
|
||||
return {
|
||||
anomalies: snapshot.anomalies,
|
||||
count: snapshot.anomalies.length,
|
||||
};
|
||||
}),
|
||||
getAnomalyDetail: controllerProcedure.query(({ ctx }) => getAnomalyDetail(ctx)),
|
||||
|
||||
/**
|
||||
* Generate an AI-powered executive narrative for a project.
|
||||
* Caches the result in the project's dynamicFields.aiNarrative to avoid
|
||||
* calling the AI on every click.
|
||||
*/
|
||||
generateProjectNarrative: controllerProcedure
|
||||
.input(z.object({ projectId: z.string() }))
|
||||
.mutation(async ({ ctx, input }) => {
|
||||
const [project, settings] = await Promise.all([
|
||||
ctx.db.project.findUnique({
|
||||
where: { id: input.projectId },
|
||||
include: {
|
||||
demandRequirements: {
|
||||
select: {
|
||||
id: true,
|
||||
role: true,
|
||||
headcount: true,
|
||||
hoursPerDay: true,
|
||||
startDate: true,
|
||||
endDate: true,
|
||||
status: true,
|
||||
_count: { select: { assignments: true } },
|
||||
},
|
||||
},
|
||||
assignments: {
|
||||
select: {
|
||||
id: true,
|
||||
role: true,
|
||||
hoursPerDay: true,
|
||||
startDate: true,
|
||||
endDate: true,
|
||||
status: true,
|
||||
dailyCostCents: true,
|
||||
resource: { select: { displayName: true } },
|
||||
},
|
||||
},
|
||||
},
|
||||
}),
|
||||
ctx.db.systemSettings.findUnique({ where: { id: "singleton" } }),
|
||||
]);
|
||||
.input(projectNarrativeInputSchema)
|
||||
.mutation(({ ctx, input }) => generateProjectNarrative(ctx, input)),
|
||||
|
||||
if (!project) {
|
||||
throw new TRPCError({ code: "NOT_FOUND", message: "Project not found" });
|
||||
}
|
||||
detectAnomalies: controllerProcedure.query(({ ctx }) => detectAnomalies(ctx)),
|
||||
|
||||
if (!isAiConfigured(settings)) {
|
||||
throw new TRPCError({
|
||||
code: "PRECONDITION_FAILED",
|
||||
message: "AI is not configured. Please set credentials in Admin \u2192 Settings.",
|
||||
});
|
||||
}
|
||||
getInsightsSummary: controllerProcedure.query(({ ctx }) => getInsightsSummary(ctx)),
|
||||
|
||||
// Build context data for the prompt
|
||||
const now = new Date();
|
||||
const totalDays = countBusinessDays(project.startDate, project.endDate);
|
||||
const elapsedDays = countBusinessDays(project.startDate, now < project.endDate ? now : project.endDate);
|
||||
const progressPercent = totalDays > 0 ? Math.round((elapsedDays / totalDays) * 100) : 0;
|
||||
|
||||
const totalDemandHeadcount = project.demandRequirements.reduce((s, d) => s + d.headcount, 0);
|
||||
const filledDemandHeadcount = project.demandRequirements.reduce(
|
||||
(s, d) => s + Math.min(d._count.assignments, d.headcount),
|
||||
0,
|
||||
);
|
||||
const staffingPercent = totalDemandHeadcount > 0
|
||||
? Math.round((filledDemandHeadcount / totalDemandHeadcount) * 100)
|
||||
: 100;
|
||||
|
||||
// Estimated cost from assignments
|
||||
const totalCostCents = project.assignments.reduce((s, a) => {
|
||||
const days = countBusinessDays(a.startDate, a.endDate);
|
||||
return s + a.dailyCostCents * days;
|
||||
}, 0);
|
||||
|
||||
const budgetCents = project.budgetCents;
|
||||
const budgetUsedPercent = budgetCents > 0 ? Math.round((totalCostCents / budgetCents) * 100) : 0;
|
||||
|
||||
const overrunAssignments = project.assignments.filter(
|
||||
(a) => a.endDate > project.endDate,
|
||||
);
|
||||
|
||||
const dataContext = [
|
||||
`Project: ${project.name} (${project.shortCode})`,
|
||||
`Status: ${project.status}`,
|
||||
`Timeline: ${project.startDate.toISOString().slice(0, 10)} to ${project.endDate.toISOString().slice(0, 10)} (${progressPercent}% elapsed)`,
|
||||
`Budget: ${(budgetCents / 100).toLocaleString("en-US", { style: "currency", currency: "EUR" })} | Estimated cost: ${(totalCostCents / 100).toLocaleString("en-US", { style: "currency", currency: "EUR" })} (${budgetUsedPercent}% of budget)`,
|
||||
`Staffing: ${filledDemandHeadcount}/${totalDemandHeadcount} positions filled (${staffingPercent}%)`,
|
||||
`Active assignments: ${project.assignments.filter((a) => a.status === "ACTIVE" || a.status === "CONFIRMED").length}`,
|
||||
overrunAssignments.length > 0
|
||||
? `Timeline risk: ${overrunAssignments.length} assignment(s) extend beyond project end date`
|
||||
: "No timeline overruns detected",
|
||||
].join("\n");
|
||||
|
||||
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.
|
||||
|
||||
${dataContext}`;
|
||||
|
||||
const client = createAiClient(settings!);
|
||||
const model = settings!.azureOpenAiDeployment!;
|
||||
const maxTokens = settings!.aiMaxCompletionTokens ?? 300;
|
||||
const temperature = settings!.aiTemperature ?? 1;
|
||||
|
||||
const provider = settings!.aiProvider ?? "openai";
|
||||
let narrative = "";
|
||||
try {
|
||||
const completion = await loggedAiCall(provider, model, prompt.length, () =>
|
||||
client.chat.completions.create({
|
||||
messages: [
|
||||
{ role: "system", content: "You are a project management analyst providing brief executive summaries. Be factual and action-oriented." },
|
||||
{ role: "user", content: prompt },
|
||||
],
|
||||
max_completion_tokens: maxTokens,
|
||||
model,
|
||||
...(temperature !== 1 ? { temperature } : {}),
|
||||
}),
|
||||
);
|
||||
narrative = completion.choices[0]?.message?.content?.trim() ?? "";
|
||||
} catch (err) {
|
||||
throw new TRPCError({
|
||||
code: "INTERNAL_SERVER_ERROR",
|
||||
message: `AI call failed: ${parseAiError(err)}`,
|
||||
});
|
||||
}
|
||||
|
||||
if (!narrative) {
|
||||
throw new TRPCError({
|
||||
code: "INTERNAL_SERVER_ERROR",
|
||||
message: "AI returned an empty response.",
|
||||
});
|
||||
}
|
||||
|
||||
const generatedAt = new Date().toISOString();
|
||||
|
||||
// Cache in project dynamicFields
|
||||
const existingDynamic = (project.dynamicFields as Record<string, unknown>) ?? {};
|
||||
await ctx.db.project.update({
|
||||
where: { id: input.projectId },
|
||||
data: {
|
||||
dynamicFields: {
|
||||
...existingDynamic,
|
||||
aiNarrative: narrative,
|
||||
aiNarrativeGeneratedAt: generatedAt,
|
||||
},
|
||||
},
|
||||
});
|
||||
|
||||
return { narrative, generatedAt };
|
||||
}),
|
||||
|
||||
/**
|
||||
* Rule-based anomaly detection across all active projects.
|
||||
* No AI involved — pure data analysis.
|
||||
*/
|
||||
detectAnomalies: controllerProcedure.query(async ({ ctx }) => {
|
||||
const snapshot = await buildInsightSnapshot(ctx.db as unknown as InsightsDbAccess);
|
||||
return snapshot.anomalies;
|
||||
}),
|
||||
|
||||
/**
|
||||
* Dashboard-friendly summary: anomaly counts by category + total.
|
||||
*/
|
||||
getInsightsSummary: controllerProcedure.query(async ({ ctx }) => {
|
||||
const snapshot = await buildInsightSnapshot(ctx.db as unknown as InsightsDbAccess);
|
||||
return snapshot.summary;
|
||||
}),
|
||||
|
||||
/**
|
||||
* Retrieve a cached AI narrative for a project (if one was previously generated).
|
||||
*/
|
||||
getCachedNarrative: controllerProcedure
|
||||
.input(z.object({ projectId: z.string() }))
|
||||
.query(async ({ ctx, input }) => {
|
||||
const project = await ctx.db.project.findUnique({
|
||||
where: { id: input.projectId },
|
||||
select: { dynamicFields: true },
|
||||
});
|
||||
if (!project) {
|
||||
throw new TRPCError({ code: "NOT_FOUND", message: "Project not found" });
|
||||
}
|
||||
const df = project.dynamicFields as Record<string, unknown> | null;
|
||||
const narrative = (df?.aiNarrative as string) ?? null;
|
||||
const generatedAt = (df?.aiNarrativeGeneratedAt as string) ?? null;
|
||||
return { narrative, generatedAt };
|
||||
}),
|
||||
.input(projectNarrativeInputSchema)
|
||||
.query(({ ctx, input }) => getCachedNarrative(ctx, input)),
|
||||
});
|
||||
|
||||
Reference in New Issue
Block a user