diff --git a/ui/litellm-dashboard/package-lock.json b/ui/litellm-dashboard/package-lock.json
index 3787f451ad3..fc2aa1599d3 100644
--- a/ui/litellm-dashboard/package-lock.json
+++ b/ui/litellm-dashboard/package-lock.json
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- "devOptional": true,
+ "dev": true,
"license": "Apache-2.0",
"dependencies": {
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@@ -9700,7 +9747,7 @@
"version": "1.58.1",
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- "devOptional": true,
+ "dev": true,
"license": "Apache-2.0",
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+ "dev": true,
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@@ -9751,6 +9799,7 @@
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+ "dev": true,
"license": "MIT",
"dependencies": {
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@@ -9768,6 +9817,7 @@
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+ "dev": true,
"funding": [
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"type": "opencollective",
@@ -9793,6 +9843,7 @@
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+ "dev": true,
"funding": [
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@@ -9835,6 +9886,7 @@
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+ "dev": true,
"funding": [
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"type": "opencollective",
@@ -9860,6 +9912,7 @@
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+ "dev": true,
"license": "MIT",
"dependencies": {
"cssesc": "^3.0.0",
@@ -9873,6 +9926,7 @@
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+ "dev": true,
"license": "MIT"
},
"node_modules/prelude-ls": {
@@ -9986,6 +10040,7 @@
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+ "dev": true,
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@@ -10774,6 +10829,7 @@
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+ "dev": true,
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@@ -10783,6 +10839,7 @@
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+ "dev": true,
"license": "MIT",
"dependencies": {
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@@ -10795,6 +10852,7 @@
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+ "dev": true,
"license": "MIT",
"engines": {
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@@ -11059,6 +11117,7 @@
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+ "dev": true,
"license": "MIT",
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@@ -11099,6 +11158,7 @@
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"resolved": "https://registry.npmjs.org/reusify/-/reusify-1.1.0.tgz",
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+ "dev": true,
"license": "MIT",
"engines": {
"iojs": ">=1.0.0",
@@ -11154,6 +11214,7 @@
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"resolved": "https://registry.npmjs.org/run-parallel/-/run-parallel-1.2.0.tgz",
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+ "dev": true,
"funding": [
{
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@@ -11794,6 +11855,7 @@
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"resolved": "https://registry.npmjs.org/sucrase/-/sucrase-3.35.1.tgz",
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+ "dev": true,
"license": "MIT",
"dependencies": {
"@jridgewell/gen-mapping": "^0.3.2",
@@ -11829,6 +11891,7 @@
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+ "dev": true,
"license": "MIT",
"engines": {
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@@ -11864,6 +11927,7 @@
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+ "dev": true,
"license": "MIT",
"dependencies": {
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@@ -11901,6 +11965,7 @@
"version": "3.3.3",
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+ "dev": true,
"license": "MIT",
"dependencies": {
"@nodelib/fs.stat": "^2.0.2",
@@ -11917,6 +11982,7 @@
"version": "5.1.2",
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+ "dev": true,
"license": "ISC",
"dependencies": {
"is-glob": "^4.0.1"
@@ -11944,6 +12010,7 @@
"version": "3.3.1",
"resolved": "https://registry.npmjs.org/thenify/-/thenify-3.3.1.tgz",
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+ "dev": true,
"license": "MIT",
"dependencies": {
"any-promise": "^1.0.0"
@@ -11953,6 +12020,7 @@
"version": "1.6.0",
"resolved": "https://registry.npmjs.org/thenify-all/-/thenify-all-1.6.0.tgz",
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+ "dev": true,
"license": "MIT",
"dependencies": {
"thenify": ">= 3.1.0 < 4"
@@ -11994,6 +12062,7 @@
"version": "0.2.15",
"resolved": "https://registry.npmjs.org/tinyglobby/-/tinyglobby-0.2.15.tgz",
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+ "dev": true,
"license": "MIT",
"dependencies": {
"fdir": "^6.5.0",
@@ -12060,6 +12129,7 @@
"version": "5.0.1",
"resolved": "https://registry.npmjs.org/to-regex-range/-/to-regex-range-5.0.1.tgz",
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+ "dev": true,
"license": "MIT",
"dependencies": {
"is-number": "^7.0.0"
@@ -12147,6 +12217,7 @@
"version": "0.1.13",
"resolved": "https://registry.npmjs.org/ts-interface-checker/-/ts-interface-checker-0.1.13.tgz",
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+ "dev": true,
"license": "Apache-2.0"
},
"node_modules/tsconfig-paths": {
@@ -12263,7 +12334,7 @@
"version": "5.3.3",
"resolved": "https://registry.npmjs.org/typescript/-/typescript-5.3.3.tgz",
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- "devOptional": true,
+ "dev": true,
"license": "Apache-2.0",
"bin": {
"tsc": "bin/tsc",
@@ -12465,6 +12536,7 @@
"version": "1.0.2",
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+ "dev": true,
"license": "MIT"
},
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@@ -12918,7 +12990,7 @@
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- "devOptional": true,
+ "dev": true,
"license": "MIT",
"engines": {
"node": ">=10.0.0"
@@ -12975,17 +13047,6 @@
"url": "https://github.com/sponsors/sindresorhus"
}
},
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- "license": "MIT",
- "optional": true,
- "peer": true,
- "funding": {
- "url": "https://github.com/sponsors/colinhacks"
- }
- },
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diff --git a/ui/litellm-dashboard/src/components/CostTrackingSettings/pricing_calculator/index.test.tsx b/ui/litellm-dashboard/src/components/CostTrackingSettings/pricing_calculator/index.test.tsx
new file mode 100644
index 00000000000..b5cf2d8b346
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/CostTrackingSettings/pricing_calculator/index.test.tsx
@@ -0,0 +1,131 @@
+import React from "react";
+import { describe, it, expect, vi, beforeEach } from "vitest";
+import { screen, within } from "@testing-library/react";
+import userEvent from "@testing-library/user-event";
+import { renderWithProviders } from "../../../../tests/test-utils";
+import PricingCalculator from "./index";
+import type { ModelEntry } from "./types";
+import type { MultiModelResult } from "./types";
+
+vi.mock("./use_multi_cost_estimate", () => ({
+ useMultiCostEstimate: vi.fn(() => ({
+ debouncedFetchForEntry: vi.fn(),
+ removeEntry: vi.fn(),
+ getMultiModelResult: vi.fn((entries: ModelEntry[]): MultiModelResult => ({
+ entries: entries.map((e) => ({ entry: e, result: null, loading: false, error: null })),
+ totals: {
+ cost_per_request: 0,
+ daily_cost: null,
+ monthly_cost: null,
+ margin_per_request: 0,
+ daily_margin: null,
+ monthly_margin: null,
+ },
+ })),
+ })),
+}));
+
+vi.mock("./multi_export_utils", () => ({
+ exportMultiToPDF: vi.fn(),
+ exportMultiToCSV: vi.fn(),
+}));
+
+vi.mock("@/utils/dataUtils", () => ({
+ formatNumberWithCommas: vi.fn((v: number, d: number = 0) =>
+ Number.isFinite(v) ? v.toFixed(d) : "-"
+ ),
+}));
+
+const DEFAULT_PROPS = {
+ accessToken: "test-token",
+ models: ["gpt-4", "gpt-3.5-turbo", "claude-3-sonnet"],
+};
+
+describe("PricingCalculator", () => {
+ beforeEach(() => {
+ vi.clearAllMocks();
+ });
+
+ it("should render the calculator with an initial model row", () => {
+ renderWithProviders();
+ expect(screen.getByRole("table")).toBeInTheDocument();
+ });
+
+ it("should render the time period toggle with Per Day and Per Month options", () => {
+ renderWithProviders();
+ expect(screen.getByText("Per Day")).toBeInTheDocument();
+ expect(screen.getByText("Per Month")).toBeInTheDocument();
+ });
+
+ it("should render an Add Another Model button", () => {
+ renderWithProviders();
+ expect(screen.getByRole("button", { name: /add another model/i })).toBeInTheDocument();
+ });
+
+ it("should show the Requests/Month column header by default", () => {
+ renderWithProviders();
+ expect(screen.getByText("Requests/Month")).toBeInTheDocument();
+ });
+
+ it("should add a new row when Add Another Model is clicked", async () => {
+ const user = userEvent.setup();
+ renderWithProviders();
+
+ const table = screen.getByRole("table");
+ const initialRows = within(table).getAllByRole("row");
+
+ await user.click(screen.getByRole("button", { name: /add another model/i }));
+
+ const updatedRows = within(table).getAllByRole("row");
+ // One new data row added (header row + data rows)
+ expect(updatedRows.length).toBeGreaterThan(initialRows.length);
+ });
+
+ it("should have the delete button disabled when there is only one row", () => {
+ renderWithProviders();
+ const allButtons = screen.getAllByRole("button");
+ const disabledButtons = allButtons.filter((btn) => btn.hasAttribute("disabled"));
+ expect(disabledButtons.length).toBeGreaterThan(0);
+ });
+
+ it("should have no disabled buttons after adding a second row", async () => {
+ const user = userEvent.setup();
+ renderWithProviders();
+
+ await user.click(screen.getByRole("button", { name: /add another model/i }));
+
+ // With two rows, no delete buttons should be disabled
+ const allButtons = screen.getAllByRole("button");
+ const disabledButtons = allButtons.filter((btn) => btn.hasAttribute("disabled"));
+ expect(disabledButtons.length).toBe(0);
+ });
+
+ describe("time period toggle", () => {
+ it("should switch the column header to Requests/Day when Per Day is selected", async () => {
+ const user = userEvent.setup();
+ renderWithProviders();
+
+ await user.click(screen.getByText("Per Day"));
+
+ expect(screen.getByText("Requests/Day")).toBeInTheDocument();
+ });
+
+ it("should switch the column header back to Requests/Month when Per Month is selected", async () => {
+ const user = userEvent.setup();
+ renderWithProviders();
+
+ await user.click(screen.getByText("Per Day"));
+ expect(screen.getByText("Requests/Day")).toBeInTheDocument();
+
+ await user.click(screen.getByText("Per Month"));
+ expect(screen.getByText("Requests/Month")).toBeInTheDocument();
+ });
+ });
+
+ it("should render column headers for Model, Input Tokens, and Output Tokens", () => {
+ renderWithProviders();
+ expect(screen.getByText("Model")).toBeInTheDocument();
+ expect(screen.getByText("Input Tokens")).toBeInTheDocument();
+ expect(screen.getByText("Output Tokens")).toBeInTheDocument();
+ });
+});
diff --git a/ui/litellm-dashboard/src/components/CostTrackingSettings/pricing_calculator/multi_cost_results.test.tsx b/ui/litellm-dashboard/src/components/CostTrackingSettings/pricing_calculator/multi_cost_results.test.tsx
new file mode 100644
index 00000000000..6f6522f395e
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/CostTrackingSettings/pricing_calculator/multi_cost_results.test.tsx
@@ -0,0 +1,305 @@
+import React from "react";
+import { describe, it, expect, vi, beforeEach } from "vitest";
+import { screen, within } from "@testing-library/react";
+import userEvent from "@testing-library/user-event";
+import { renderWithProviders } from "../../../../tests/test-utils";
+import MultiCostResults from "./multi_cost_results";
+import type { MultiModelResult } from "./types";
+import type { CostEstimateResponse } from "../types";
+
+vi.mock("./multi_export_utils", () => ({
+ exportMultiToPDF: vi.fn(),
+ exportMultiToCSV: vi.fn(),
+}));
+
+vi.mock("@/utils/dataUtils", () => ({
+ formatNumberWithCommas: vi.fn((v: number, d: number = 0) =>
+ Number.isFinite(v) ? v.toFixed(d) : "-"
+ ),
+}));
+
+function makeCostResponse(overrides: Partial = {}): CostEstimateResponse {
+ return {
+ model: "gpt-4",
+ input_tokens: 1000,
+ output_tokens: 500,
+ num_requests_per_day: 100,
+ num_requests_per_month: null,
+ cost_per_request: 0.05,
+ input_cost_per_request: 0.03,
+ output_cost_per_request: 0.02,
+ margin_cost_per_request: 0,
+ daily_cost: 5.0,
+ daily_input_cost: 3.0,
+ daily_output_cost: 2.0,
+ daily_margin_cost: 0,
+ monthly_cost: null,
+ monthly_input_cost: null,
+ monthly_output_cost: null,
+ monthly_margin_cost: null,
+ input_cost_per_token: null,
+ output_cost_per_token: null,
+ provider: "openai",
+ ...overrides,
+ };
+}
+
+function makeMultiResult(overrides: Partial = {}): MultiModelResult {
+ return {
+ entries: [
+ {
+ entry: { id: "e1", model: "gpt-4", input_tokens: 1000, output_tokens: 500 },
+ result: makeCostResponse(),
+ loading: false,
+ error: null,
+ },
+ ],
+ totals: {
+ cost_per_request: 0.05,
+ daily_cost: 5.0,
+ monthly_cost: null,
+ margin_per_request: 0,
+ daily_margin: null,
+ monthly_margin: null,
+ },
+ ...overrides,
+ };
+}
+
+function emptyMultiResult(): MultiModelResult {
+ return {
+ entries: [
+ {
+ entry: { id: "e1", model: "", input_tokens: 1000, output_tokens: 500 },
+ result: null,
+ loading: false,
+ error: null,
+ },
+ ],
+ totals: {
+ cost_per_request: 0,
+ daily_cost: null,
+ monthly_cost: null,
+ margin_per_request: 0,
+ daily_margin: null,
+ monthly_margin: null,
+ },
+ };
+}
+
+describe("MultiCostResults", () => {
+ beforeEach(() => {
+ vi.clearAllMocks();
+ });
+
+ describe("when no model has been selected", () => {
+ it("should show a prompt to select models", () => {
+ renderWithProviders(
+
+ );
+ expect(screen.getByText(/select models above to see cost estimates/i)).toBeInTheDocument();
+ });
+ });
+
+ describe("when results are loading and no data has arrived yet", () => {
+ it("should show a calculating costs spinner", () => {
+ const multiResult: MultiModelResult = {
+ entries: [
+ {
+ entry: { id: "e1", model: "gpt-4", input_tokens: 1000, output_tokens: 500 },
+ result: null,
+ loading: true,
+ error: null,
+ },
+ ],
+ totals: {
+ cost_per_request: 0,
+ daily_cost: null,
+ monthly_cost: null,
+ margin_per_request: 0,
+ daily_margin: null,
+ monthly_margin: null,
+ },
+ };
+
+ renderWithProviders();
+ expect(screen.getByText(/calculating costs/i)).toBeInTheDocument();
+ });
+ });
+
+ describe("when there are errors but no valid results", () => {
+ it("should display the error message with the model name", () => {
+ const multiResult: MultiModelResult = {
+ entries: [
+ {
+ entry: { id: "e1", model: "bad-model", input_tokens: 0, output_tokens: 0 },
+ result: null,
+ loading: false,
+ error: "Pricing not found",
+ },
+ ],
+ totals: {
+ cost_per_request: 0,
+ daily_cost: null,
+ monthly_cost: null,
+ margin_per_request: 0,
+ daily_margin: null,
+ monthly_margin: null,
+ },
+ };
+
+ renderWithProviders();
+ expect(screen.getByText(/bad-model/i)).toBeInTheDocument();
+ expect(screen.getByText(/Pricing not found/i)).toBeInTheDocument();
+ });
+ });
+
+ describe("when valid results are available", () => {
+ it("should show the Cost Estimates heading", () => {
+ renderWithProviders(
+
+ );
+ expect(screen.getByText("Cost Estimates")).toBeInTheDocument();
+ });
+
+ it("should display the Total Per Request statistic", () => {
+ renderWithProviders(
+
+ );
+ expect(screen.getByText("Total Per Request")).toBeInTheDocument();
+ });
+
+ it("should display Total Daily statistic when timePeriod is day", () => {
+ renderWithProviders(
+
+ );
+ expect(screen.getByText("Total Daily")).toBeInTheDocument();
+ });
+
+ it("should display Total Monthly statistic when timePeriod is month", () => {
+ renderWithProviders(
+
+ );
+ expect(screen.getByText("Total Monthly")).toBeInTheDocument();
+ });
+
+ it("should show the model name in the summary table", () => {
+ renderWithProviders(
+
+ );
+ expect(screen.getByText("gpt-4")).toBeInTheDocument();
+ });
+
+ it("should show the provider tag next to the model name", () => {
+ renderWithProviders(
+
+ );
+ expect(screen.getByText("openai")).toBeInTheDocument();
+ });
+
+ it("should show the Export button when results are available", () => {
+ renderWithProviders(
+
+ );
+ expect(screen.getByRole("button", { name: /export/i })).toBeInTheDocument();
+ });
+
+ it("should expand the model breakdown row when the expand button is clicked", async () => {
+ const user = userEvent.setup();
+ renderWithProviders(
+
+ );
+
+ // The expand column renders a button (RightOutlined icon) for rows without errors
+ const expandButtons = screen.getAllByRole("button");
+ // Find the small expand button (not the Export button)
+ const expandButton = expandButtons.find(
+ (btn) => !btn.textContent?.toLowerCase().includes("export")
+ );
+ expect(expandButton).toBeDefined();
+
+ await user.click(expandButton!);
+
+ // After expanding, the SingleModelBreakdown should be visible
+ expect(screen.getByText("Total/Request")).toBeInTheDocument();
+ });
+
+ it("should show the collapse icon after expanding a row", async () => {
+ const user = userEvent.setup();
+ renderWithProviders(
+
+ );
+
+ const getExpandButton = () => {
+ const allButtons = screen.getAllByRole("button");
+ return allButtons.find((btn) => !btn.textContent?.toLowerCase().includes("export"));
+ };
+
+ // Before expand: button has the "down" aria-label (RightOutlined renders as down in ant icons)
+ // Just verify clicking works and the breakdown content appears
+ await user.click(getExpandButton()!);
+ expect(screen.getByText("Total/Request")).toBeInTheDocument();
+
+ // After a second click, the row collapses — content may be hidden or removed
+ await user.click(getExpandButton()!);
+ // The expanded content should no longer be visible
+ expect(screen.queryByText("Total/Request")).not.toBeVisible();
+ });
+ });
+
+ describe("margin section", () => {
+ it("should show margin fee details when margin per request is greater than zero", () => {
+ const multiResult = makeMultiResult({
+ entries: [
+ {
+ entry: { id: "e1", model: "gpt-4", input_tokens: 1000, output_tokens: 500 },
+ result: makeCostResponse({ margin_cost_per_request: 0.01, daily_margin_cost: 1.0 }),
+ loading: false,
+ error: null,
+ },
+ ],
+ totals: {
+ cost_per_request: 0.06,
+ daily_cost: 6.0,
+ monthly_cost: null,
+ margin_per_request: 0.01,
+ daily_margin: 1.0,
+ monthly_margin: null,
+ },
+ });
+
+ renderWithProviders();
+ expect(screen.getByText("Margin Fee/Request")).toBeInTheDocument();
+ });
+
+ it("should not show margin fee details when margin per request is zero", () => {
+ renderWithProviders(
+
+ );
+ expect(screen.queryByText("Margin Fee/Request")).not.toBeInTheDocument();
+ });
+ });
+
+ describe("when a model has zero cost", () => {
+ it("should show a warning about missing pricing data", () => {
+ const multiResult = makeMultiResult({
+ entries: [
+ {
+ entry: { id: "e1", model: "custom-model", input_tokens: 1000, output_tokens: 500 },
+ result: makeCostResponse({ model: "custom-model", cost_per_request: 0 }),
+ loading: false,
+ error: null,
+ },
+ ],
+ });
+
+ renderWithProviders();
+ expect(screen.getByText(/no pricing data found/i)).toBeInTheDocument();
+ });
+ });
+});
diff --git a/ui/litellm-dashboard/src/components/CostTrackingSettings/pricing_calculator/multi_export_dropdown.test.tsx b/ui/litellm-dashboard/src/components/CostTrackingSettings/pricing_calculator/multi_export_dropdown.test.tsx
new file mode 100644
index 00000000000..be1a89cf77f
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/CostTrackingSettings/pricing_calculator/multi_export_dropdown.test.tsx
@@ -0,0 +1,146 @@
+import React from "react";
+import { describe, it, expect, vi, beforeEach, afterEach } from "vitest";
+import { screen, fireEvent } from "@testing-library/react";
+import userEvent from "@testing-library/user-event";
+import { renderWithProviders } from "../../../../tests/test-utils";
+import MultiExportDropdown from "./multi_export_dropdown";
+import type { MultiModelResult } from "./types";
+
+vi.mock("./multi_export_utils", () => ({
+ exportMultiToPDF: vi.fn(),
+ exportMultiToCSV: vi.fn(),
+}));
+
+import { exportMultiToPDF, exportMultiToCSV } from "./multi_export_utils";
+
+function makeMultiResult(hasResult: boolean): MultiModelResult {
+ return {
+ entries: [
+ {
+ entry: { id: "e1", model: "gpt-4", input_tokens: 1000, output_tokens: 500 },
+ result: hasResult
+ ? {
+ model: "gpt-4",
+ input_tokens: 1000,
+ output_tokens: 500,
+ num_requests_per_day: null,
+ num_requests_per_month: null,
+ cost_per_request: 0.05,
+ input_cost_per_request: 0.03,
+ output_cost_per_request: 0.02,
+ margin_cost_per_request: 0,
+ daily_cost: null,
+ daily_input_cost: null,
+ daily_output_cost: null,
+ daily_margin_cost: null,
+ monthly_cost: null,
+ monthly_input_cost: null,
+ monthly_output_cost: null,
+ monthly_margin_cost: null,
+ input_cost_per_token: null,
+ output_cost_per_token: null,
+ provider: "openai",
+ }
+ : null,
+ loading: false,
+ error: null,
+ },
+ ],
+ totals: {
+ cost_per_request: hasResult ? 0.05 : 0,
+ daily_cost: null,
+ monthly_cost: null,
+ margin_per_request: 0,
+ daily_margin: null,
+ monthly_margin: null,
+ },
+ };
+}
+
+describe("MultiExportDropdown", () => {
+ beforeEach(() => {
+ vi.clearAllMocks();
+ });
+
+ it("should not render anything when no entries have results", () => {
+ const { container } = renderWithProviders(
+
+ );
+ expect(container.firstChild).toBeNull();
+ });
+
+ it("should render the Export button when at least one entry has a result", () => {
+ renderWithProviders();
+ expect(screen.getByRole("button", { name: /^export$/i })).toBeInTheDocument();
+ });
+
+ it("should show the export menu when the Export button is clicked", async () => {
+ const user = userEvent.setup();
+ renderWithProviders();
+
+ await user.click(screen.getByRole("button", { name: /^export$/i }));
+
+ expect(screen.getByText("Export as PDF")).toBeInTheDocument();
+ expect(screen.getByText("Export as CSV")).toBeInTheDocument();
+ });
+
+ it("should hide the export menu when the Export button is clicked again", async () => {
+ const user = userEvent.setup();
+ renderWithProviders();
+
+ await user.click(screen.getByRole("button", { name: /^export$/i }));
+ expect(screen.getByText("Export as PDF")).toBeInTheDocument();
+
+ await user.click(screen.getByRole("button", { name: /^export$/i }));
+ expect(screen.queryByText("Export as PDF")).not.toBeInTheDocument();
+ });
+
+ it("should call exportMultiToPDF and close the menu when Export as PDF is clicked", async () => {
+ const user = userEvent.setup();
+ renderWithProviders();
+
+ await user.click(screen.getByRole("button", { name: /^export$/i }));
+ await user.click(screen.getByText("Export as PDF"));
+
+ expect(exportMultiToPDF).toHaveBeenCalledTimes(1);
+ expect(screen.queryByText("Export as PDF")).not.toBeInTheDocument();
+ });
+
+ it("should call exportMultiToCSV and close the menu when Export as CSV is clicked", async () => {
+ const user = userEvent.setup();
+ renderWithProviders();
+
+ await user.click(screen.getByRole("button", { name: /^export$/i }));
+ await user.click(screen.getByText("Export as CSV"));
+
+ expect(exportMultiToCSV).toHaveBeenCalledTimes(1);
+ expect(screen.queryByText("Export as CSV")).not.toBeInTheDocument();
+ });
+
+ it("should pass the multiResult to the export functions", async () => {
+ const user = userEvent.setup();
+ const multiResult = makeMultiResult(true);
+ renderWithProviders();
+
+ await user.click(screen.getByRole("button", { name: /^export$/i }));
+ await user.click(screen.getByText("Export as PDF"));
+
+ expect(exportMultiToPDF).toHaveBeenCalledWith(multiResult);
+ });
+
+ it("should close the menu when clicking outside", async () => {
+ const user = userEvent.setup();
+ renderWithProviders(
+
+ );
+
+ await user.click(screen.getByRole("button", { name: /^export$/i }));
+ expect(screen.getByText("Export as PDF")).toBeInTheDocument();
+
+ fireEvent.mouseDown(screen.getByTestId("outside"));
+ expect(screen.queryByText("Export as PDF")).not.toBeInTheDocument();
+ });
+});
diff --git a/ui/litellm-dashboard/src/components/CostTrackingSettings/pricing_calculator/multi_export_utils.test.ts b/ui/litellm-dashboard/src/components/CostTrackingSettings/pricing_calculator/multi_export_utils.test.ts
new file mode 100644
index 00000000000..40d6879d1af
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/CostTrackingSettings/pricing_calculator/multi_export_utils.test.ts
@@ -0,0 +1,274 @@
+import { describe, it, expect, vi, beforeEach, afterEach } from "vitest";
+import { exportMultiToPDF, exportMultiToCSV } from "./multi_export_utils";
+import type { MultiModelResult } from "./types";
+import type { CostEstimateResponse } from "../types";
+
+vi.mock("@/utils/dataUtils", () => ({
+ formatNumberWithCommas: vi.fn((v: number, d: number = 0) =>
+ Number.isFinite(v) ? v.toFixed(d) : "-"
+ ),
+}));
+
+function makeCostResponse(overrides: Partial = {}): CostEstimateResponse {
+ return {
+ model: "gpt-4",
+ input_tokens: 1000,
+ output_tokens: 500,
+ num_requests_per_day: 100,
+ num_requests_per_month: 3000,
+ cost_per_request: 0.05,
+ input_cost_per_request: 0.03,
+ output_cost_per_request: 0.02,
+ margin_cost_per_request: 0,
+ daily_cost: 5.0,
+ daily_input_cost: 3.0,
+ daily_output_cost: 2.0,
+ daily_margin_cost: 0,
+ monthly_cost: 150.0,
+ monthly_input_cost: 90.0,
+ monthly_output_cost: 60.0,
+ monthly_margin_cost: 0,
+ input_cost_per_token: 0.00003,
+ output_cost_per_token: 0.00004,
+ provider: "openai",
+ ...overrides,
+ };
+}
+
+function makeMultiResult(overrides: Partial = {}): MultiModelResult {
+ return {
+ entries: [
+ {
+ entry: { id: "entry-1", model: "gpt-4", input_tokens: 1000, output_tokens: 500 },
+ result: makeCostResponse(),
+ loading: false,
+ error: null,
+ },
+ ],
+ totals: {
+ cost_per_request: 0.05,
+ daily_cost: 5.0,
+ monthly_cost: 150.0,
+ margin_per_request: 0,
+ daily_margin: null,
+ monthly_margin: null,
+ },
+ ...overrides,
+ };
+}
+
+describe("exportMultiToPDF", () => {
+ let mockPrintWindow: {
+ document: { write: ReturnType; close: ReturnType };
+ print: ReturnType;
+ onload: (() => void) | null;
+ };
+
+ beforeEach(() => {
+ mockPrintWindow = {
+ document: { write: vi.fn(), close: vi.fn() },
+ print: vi.fn(),
+ onload: null,
+ };
+ vi.spyOn(window, "open").mockReturnValue(mockPrintWindow as unknown as Window);
+ });
+
+ afterEach(() => {
+ vi.restoreAllMocks();
+ });
+
+ it("should open a new popup window", () => {
+ exportMultiToPDF(makeMultiResult());
+ expect(window.open).toHaveBeenCalledWith("", "_blank");
+ });
+
+ it("should write HTML containing the report title", () => {
+ exportMultiToPDF(makeMultiResult());
+ const html = mockPrintWindow.document.write.mock.calls[0][0] as string;
+ expect(html).toContain("LLM Cost Estimate Report");
+ });
+
+ it("should include model name and provider in the generated HTML", () => {
+ exportMultiToPDF(makeMultiResult());
+ const html = mockPrintWindow.document.write.mock.calls[0][0] as string;
+ expect(html).toContain("gpt-4");
+ expect(html).toContain("openai");
+ });
+
+ it("should close the document after writing", () => {
+ exportMultiToPDF(makeMultiResult());
+ expect(mockPrintWindow.document.close).toHaveBeenCalledTimes(1);
+ });
+
+ it("should call print after the window finishes loading", () => {
+ exportMultiToPDF(makeMultiResult());
+ expect(mockPrintWindow.print).not.toHaveBeenCalled();
+ mockPrintWindow.onload!();
+ expect(mockPrintWindow.print).toHaveBeenCalledTimes(1);
+ });
+
+ it("should show the margin section when margin per request is greater than zero", () => {
+ const multiResult = makeMultiResult({
+ totals: {
+ cost_per_request: 0.06,
+ daily_cost: 5.0,
+ monthly_cost: 150.0,
+ margin_per_request: 0.01,
+ daily_margin: 1.0,
+ monthly_margin: 30.0,
+ },
+ });
+ exportMultiToPDF(multiResult);
+ const html = mockPrintWindow.document.write.mock.calls[0][0] as string;
+ expect(html).toContain("Margin/Request");
+ });
+
+ it("should not show the margin section when margin per request is zero", () => {
+ exportMultiToPDF(makeMultiResult());
+ const html = mockPrintWindow.document.write.mock.calls[0][0] as string;
+ expect(html).not.toContain("Margin/Request");
+ });
+
+ it("should alert when popup is blocked", () => {
+ vi.spyOn(window, "open").mockReturnValue(null);
+ const alertSpy = vi.spyOn(window, "alert").mockImplementation(() => {});
+ exportMultiToPDF(makeMultiResult());
+ expect(alertSpy).toHaveBeenCalledWith("Please allow popups to export PDF");
+ });
+
+ it("should only include entries that have a result", () => {
+ const multiResult: MultiModelResult = {
+ entries: [
+ { entry: { id: "e1", model: "gpt-4", input_tokens: 1000, output_tokens: 500 }, result: null, loading: false, error: null },
+ { entry: { id: "e2", model: "claude-3", input_tokens: 500, output_tokens: 250 }, result: makeCostResponse({ model: "claude-3", provider: "anthropic" }), loading: false, error: null },
+ ],
+ totals: { cost_per_request: 0.05, daily_cost: 5.0, monthly_cost: 150.0, margin_per_request: 0, daily_margin: null, monthly_margin: null },
+ };
+ exportMultiToPDF(multiResult);
+ const html = mockPrintWindow.document.write.mock.calls[0][0] as string;
+ expect(html).toContain("1 model configured");
+ expect(html).toContain("claude-3");
+ });
+
+ it("should show plural 'models' when multiple results are present", () => {
+ const multiResult: MultiModelResult = {
+ entries: [
+ { entry: { id: "e1", model: "gpt-4", input_tokens: 1000, output_tokens: 500 }, result: makeCostResponse(), loading: false, error: null },
+ { entry: { id: "e2", model: "claude-3", input_tokens: 500, output_tokens: 250 }, result: makeCostResponse({ model: "claude-3" }), loading: false, error: null },
+ ],
+ totals: { cost_per_request: 0.10, daily_cost: 10.0, monthly_cost: 300.0, margin_per_request: 0, daily_margin: null, monthly_margin: null },
+ };
+ exportMultiToPDF(multiResult);
+ const html = mockPrintWindow.document.write.mock.calls[0][0] as string;
+ expect(html).toContain("2 models configured");
+ });
+});
+
+describe("exportMultiToCSV", () => {
+ beforeEach(() => {
+ document.body.innerHTML = "";
+ window.URL.createObjectURL = vi.fn(() => "blob:mock-url");
+ window.URL.revokeObjectURL = vi.fn();
+ });
+
+ afterEach(() => {
+ vi.restoreAllMocks();
+ });
+
+ it("should create an object URL and revoke it after download", () => {
+ exportMultiToCSV(makeMultiResult());
+ expect(window.URL.createObjectURL).toHaveBeenCalledTimes(1);
+ expect(window.URL.revokeObjectURL).toHaveBeenCalledWith("blob:mock-url");
+ });
+
+ it("should set the download filename to include today's date", () => {
+ const createdAnchors: HTMLAnchorElement[] = [];
+ const originalCreate = document.createElement.bind(document);
+ vi.spyOn(document, "createElement").mockImplementation((tag: string) => {
+ const el = originalCreate(tag);
+ if (tag === "a") createdAnchors.push(el as HTMLAnchorElement);
+ return el;
+ });
+
+ const today = new Date().toISOString().split("T")[0];
+ exportMultiToCSV(makeMultiResult());
+
+ expect(createdAnchors[0].download).toBe(`cost_estimate_multi_model_${today}.csv`);
+ });
+
+ it("should generate CSV content containing a header row and model data", () => {
+ let csvContent = "";
+ const OriginalBlob = globalThis.Blob;
+ globalThis.Blob = class extends OriginalBlob {
+ constructor(parts?: BlobPart[], options?: BlobPropertyBag) {
+ super(parts, options);
+ if (typeof parts?.[0] === "string") csvContent = parts[0];
+ }
+ } as unknown as typeof Blob;
+
+ exportMultiToCSV(makeMultiResult());
+ globalThis.Blob = OriginalBlob;
+
+ expect(csvContent).toContain("Model");
+ expect(csvContent).toContain("Cost/Request");
+ expect(csvContent).toContain("gpt-4");
+ expect(csvContent).toContain("openai");
+ });
+
+ it("should include the combined totals section in CSV", () => {
+ let csvContent = "";
+ const OriginalBlob = globalThis.Blob;
+ globalThis.Blob = class extends OriginalBlob {
+ constructor(parts?: BlobPart[], options?: BlobPropertyBag) {
+ super(parts, options);
+ if (typeof parts?.[0] === "string") csvContent = parts[0];
+ }
+ } as unknown as typeof Blob;
+
+ exportMultiToCSV(makeMultiResult());
+ globalThis.Blob = OriginalBlob;
+
+ expect(csvContent).toContain("COMBINED TOTALS");
+ });
+
+ it("should create a blob with the correct CSV mime type", () => {
+ let capturedType = "";
+ const OriginalBlob = globalThis.Blob;
+ globalThis.Blob = class extends OriginalBlob {
+ constructor(parts?: BlobPart[], options?: BlobPropertyBag) {
+ super(parts, options);
+ if (options?.type) capturedType = options.type;
+ }
+ } as unknown as typeof Blob;
+
+ exportMultiToCSV(makeMultiResult());
+ globalThis.Blob = OriginalBlob;
+
+ expect(capturedType).toBe("text/csv;charset=utf-8;");
+ });
+
+ it("should skip entries with null results", () => {
+ const multiResult: MultiModelResult = {
+ entries: [
+ { entry: { id: "e1", model: "gpt-4", input_tokens: 1000, output_tokens: 500 }, result: null, loading: false, error: null },
+ ],
+ totals: { cost_per_request: 0, daily_cost: null, monthly_cost: null, margin_per_request: 0, daily_margin: null, monthly_margin: null },
+ };
+
+ let csvContent = "";
+ const OriginalBlob = globalThis.Blob;
+ globalThis.Blob = class extends OriginalBlob {
+ constructor(parts?: BlobPart[], options?: BlobPropertyBag) {
+ super(parts, options);
+ if (typeof parts?.[0] === "string") csvContent = parts[0];
+ }
+ } as unknown as typeof Blob;
+
+ exportMultiToCSV(multiResult);
+ globalThis.Blob = OriginalBlob;
+
+ // CSV should have metadata rows but no model data row for gpt-4
+ const lines = csvContent.split("\n").filter((l) => l.includes('"gpt-4"'));
+ expect(lines).toHaveLength(0);
+ });
+});
diff --git a/ui/litellm-dashboard/src/components/CostTrackingSettings/pricing_calculator/use_multi_cost_estimate.test.ts b/ui/litellm-dashboard/src/components/CostTrackingSettings/pricing_calculator/use_multi_cost_estimate.test.ts
new file mode 100644
index 00000000000..f5715194f58
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/CostTrackingSettings/pricing_calculator/use_multi_cost_estimate.test.ts
@@ -0,0 +1,342 @@
+import { describe, it, expect, vi, beforeEach, afterEach } from "vitest";
+import { renderHook, act } from "@testing-library/react";
+import { useMultiCostEstimate } from "./use_multi_cost_estimate";
+import type { ModelEntry } from "./types";
+import type { CostEstimateResponse } from "../types";
+
+vi.mock("@/components/networking", () => ({
+ getProxyBaseUrl: vi.fn(() => ""),
+ getGlobalLitellmHeaderName: vi.fn(() => "Authorization"),
+}));
+
+function makeEntry(overrides: Partial = {}): ModelEntry {
+ return {
+ id: "entry-1",
+ model: "gpt-4",
+ input_tokens: 1000,
+ output_tokens: 500,
+ ...overrides,
+ };
+}
+
+function makeApiResponse(overrides: Partial = {}): CostEstimateResponse {
+ return {
+ model: "gpt-4",
+ input_tokens: 1000,
+ output_tokens: 500,
+ num_requests_per_day: null,
+ num_requests_per_month: null,
+ cost_per_request: 0.05,
+ input_cost_per_request: 0.03,
+ output_cost_per_request: 0.02,
+ margin_cost_per_request: 0,
+ daily_cost: null,
+ daily_input_cost: null,
+ daily_output_cost: null,
+ daily_margin_cost: null,
+ monthly_cost: null,
+ monthly_input_cost: null,
+ monthly_output_cost: null,
+ monthly_margin_cost: null,
+ input_cost_per_token: 0.00003,
+ output_cost_per_token: 0.00004,
+ provider: "openai",
+ ...overrides,
+ };
+}
+
+describe("useMultiCostEstimate", () => {
+ beforeEach(() => {
+ vi.clearAllMocks();
+ vi.useFakeTimers();
+ });
+
+ afterEach(() => {
+ vi.useRealTimers();
+ });
+
+ describe("debouncedFetchForEntry", () => {
+ it("should not fetch when access token is null", async () => {
+ const fetchSpy = vi.spyOn(global, "fetch");
+ const { result } = renderHook(() => useMultiCostEstimate(null));
+
+ await act(async () => {
+ result.current.debouncedFetchForEntry(makeEntry());
+ await vi.runAllTimersAsync();
+ });
+
+ expect(fetchSpy).not.toHaveBeenCalled();
+ });
+
+ it("should not fetch when the model field is empty", async () => {
+ const fetchSpy = vi.spyOn(global, "fetch");
+ const { result } = renderHook(() => useMultiCostEstimate("token123"));
+
+ await act(async () => {
+ result.current.debouncedFetchForEntry(makeEntry({ model: "" }));
+ await vi.runAllTimersAsync();
+ });
+
+ expect(fetchSpy).not.toHaveBeenCalled();
+ });
+
+ it("should not fetch immediately — only after the debounce delay", async () => {
+ const fetchSpy = vi.spyOn(global, "fetch").mockResolvedValue({
+ ok: true,
+ json: async () => makeApiResponse(),
+ } as Response);
+
+ const { result } = renderHook(() => useMultiCostEstimate("token123"));
+
+ act(() => {
+ result.current.debouncedFetchForEntry(makeEntry());
+ });
+
+ expect(fetchSpy).not.toHaveBeenCalled();
+
+ await act(async () => {
+ await vi.runAllTimersAsync();
+ });
+
+ expect(fetchSpy).toHaveBeenCalledTimes(1);
+ });
+
+ it("should cancel an in-flight debounce when called again for the same entry", async () => {
+ const fetchSpy = vi.spyOn(global, "fetch").mockResolvedValue({
+ ok: true,
+ json: async () => makeApiResponse(),
+ } as Response);
+
+ const { result } = renderHook(() => useMultiCostEstimate("token123"));
+
+ await act(async () => {
+ result.current.debouncedFetchForEntry(makeEntry());
+ vi.advanceTimersByTime(200);
+ result.current.debouncedFetchForEntry(makeEntry());
+ vi.advanceTimersByTime(200);
+ result.current.debouncedFetchForEntry(makeEntry());
+ await vi.runAllTimersAsync();
+ });
+
+ expect(fetchSpy).toHaveBeenCalledTimes(1);
+ });
+
+ it("should store the API result after a successful fetch", async () => {
+ vi.spyOn(global, "fetch").mockResolvedValue({
+ ok: true,
+ json: async () => makeApiResponse(),
+ } as Response);
+
+ const { result } = renderHook(() => useMultiCostEstimate("token123"));
+ const entry = makeEntry();
+
+ await act(async () => {
+ result.current.debouncedFetchForEntry(entry);
+ await vi.runAllTimersAsync();
+ });
+
+ const multiResult = result.current.getMultiModelResult([entry]);
+ expect(multiResult.entries[0].result).not.toBeNull();
+ expect(multiResult.entries[0].result?.cost_per_request).toBe(0.05);
+ expect(multiResult.entries[0].loading).toBe(false);
+ expect(multiResult.entries[0].error).toBeNull();
+ });
+
+ it("should set an error message when the API returns a non-ok response", async () => {
+ vi.spyOn(global, "fetch").mockResolvedValue({
+ ok: false,
+ json: async () => ({ detail: { error: "Model not found" } }),
+ } as Response);
+
+ const { result } = renderHook(() => useMultiCostEstimate("token123"));
+ const entry = makeEntry();
+
+ await act(async () => {
+ result.current.debouncedFetchForEntry(entry);
+ await vi.runAllTimersAsync();
+ });
+
+ const multiResult = result.current.getMultiModelResult([entry]);
+ expect(multiResult.entries[0].result).toBeNull();
+ expect(multiResult.entries[0].error).toBe("Model not found");
+ });
+
+ it("should fall back to detail string when error has no nested error field", async () => {
+ vi.spyOn(global, "fetch").mockResolvedValue({
+ ok: false,
+ json: async () => ({ detail: "Bad request" }),
+ } as Response);
+
+ const { result } = renderHook(() => useMultiCostEstimate("token123"));
+ const entry = makeEntry();
+
+ await act(async () => {
+ result.current.debouncedFetchForEntry(entry);
+ await vi.runAllTimersAsync();
+ });
+
+ const multiResult = result.current.getMultiModelResult([entry]);
+ expect(multiResult.entries[0].error).toBe("Bad request");
+ });
+
+ it("should set 'Network error' when fetch throws", async () => {
+ vi.spyOn(global, "fetch").mockRejectedValue(new Error("connection refused"));
+
+ const { result } = renderHook(() => useMultiCostEstimate("token123"));
+ const entry = makeEntry();
+
+ await act(async () => {
+ result.current.debouncedFetchForEntry(entry);
+ await vi.runAllTimersAsync();
+ });
+
+ const multiResult = result.current.getMultiModelResult([entry]);
+ expect(multiResult.entries[0].error).toBe("Network error");
+ expect(multiResult.entries[0].result).toBeNull();
+ });
+ });
+
+ describe("removeEntry", () => {
+ it("should remove an entry's cached result", async () => {
+ vi.spyOn(global, "fetch").mockResolvedValue({
+ ok: true,
+ json: async () => makeApiResponse(),
+ } as Response);
+
+ const { result } = renderHook(() => useMultiCostEstimate("token123"));
+ const entry = makeEntry();
+
+ await act(async () => {
+ result.current.debouncedFetchForEntry(entry);
+ await vi.runAllTimersAsync();
+ });
+
+ // Confirm result was stored
+ expect(result.current.getMultiModelResult([entry]).entries[0].result).not.toBeNull();
+
+ act(() => {
+ result.current.removeEntry(entry.id);
+ });
+
+ // After removal, the entry should return as if it never fetched
+ const multiResult = result.current.getMultiModelResult([entry]);
+ expect(multiResult.entries[0].result).toBeNull();
+ });
+
+ it("should cancel a pending debounce for the removed entry", async () => {
+ const fetchSpy = vi.spyOn(global, "fetch").mockResolvedValue({
+ ok: true,
+ json: async () => makeApiResponse(),
+ } as Response);
+
+ const { result } = renderHook(() => useMultiCostEstimate("token123"));
+ const entry = makeEntry();
+
+ act(() => {
+ result.current.debouncedFetchForEntry(entry);
+ result.current.removeEntry(entry.id);
+ });
+
+ await act(async () => {
+ await vi.runAllTimersAsync();
+ });
+
+ expect(fetchSpy).not.toHaveBeenCalled();
+ });
+ });
+
+ describe("getMultiModelResult", () => {
+ it("should return zero totals when no entries have results", () => {
+ const { result } = renderHook(() => useMultiCostEstimate("token123"));
+ const multiResult = result.current.getMultiModelResult([makeEntry()]);
+
+ expect(multiResult.totals.cost_per_request).toBe(0);
+ expect(multiResult.totals.margin_per_request).toBe(0);
+ expect(multiResult.totals.daily_cost).toBeNull();
+ expect(multiResult.totals.monthly_cost).toBeNull();
+ });
+
+ it("should return an empty entries array for an empty input list", () => {
+ const { result } = renderHook(() => useMultiCostEstimate("token123"));
+ const multiResult = result.current.getMultiModelResult([]);
+
+ expect(multiResult.entries).toHaveLength(0);
+ expect(multiResult.totals.daily_cost).toBeNull();
+ expect(multiResult.totals.monthly_cost).toBeNull();
+ });
+
+ it("should sum cost_per_request across multiple loaded entries", async () => {
+ const entry1 = makeEntry({ id: "e1", model: "gpt-4" });
+ const entry2 = makeEntry({ id: "e2", model: "claude-3" });
+
+ let callIndex = 0;
+ const responses = [
+ makeApiResponse({ cost_per_request: 0.05, margin_cost_per_request: 0 }),
+ makeApiResponse({ model: "claude-3", cost_per_request: 0.10, margin_cost_per_request: 0 }),
+ ];
+
+ vi.spyOn(global, "fetch").mockImplementation(async () => ({
+ ok: true,
+ json: async () => responses[callIndex++],
+ } as Response));
+
+ const { result } = renderHook(() => useMultiCostEstimate("token123"));
+
+ await act(async () => {
+ result.current.debouncedFetchForEntry(entry1);
+ result.current.debouncedFetchForEntry(entry2);
+ await vi.runAllTimersAsync();
+ });
+
+ const multiResult = result.current.getMultiModelResult([entry1, entry2]);
+ expect(multiResult.totals.cost_per_request).toBeCloseTo(0.15);
+ });
+
+ it("should accumulate daily cost only when entries have a daily cost", async () => {
+ const entry1 = makeEntry({ id: "e1", model: "gpt-4" });
+ const entry2 = makeEntry({ id: "e2", model: "claude-3" });
+
+ let callIndex = 0;
+ const responses = [
+ makeApiResponse({ daily_cost: 5.0, daily_margin_cost: 0, monthly_cost: null, monthly_margin_cost: null }),
+ makeApiResponse({ model: "claude-3", daily_cost: 10.0, daily_margin_cost: 0, monthly_cost: null, monthly_margin_cost: null }),
+ ];
+
+ vi.spyOn(global, "fetch").mockImplementation(async () => ({
+ ok: true,
+ json: async () => responses[callIndex++],
+ } as Response));
+
+ const { result } = renderHook(() => useMultiCostEstimate("token123"));
+
+ await act(async () => {
+ result.current.debouncedFetchForEntry(entry1);
+ result.current.debouncedFetchForEntry(entry2);
+ await vi.runAllTimersAsync();
+ });
+
+ const multiResult = result.current.getMultiModelResult([entry1, entry2]);
+ expect(multiResult.totals.daily_cost).toBeCloseTo(15.0);
+ expect(multiResult.totals.monthly_cost).toBeNull();
+ });
+
+ it("should mark each entry's loading and error state from cached data", async () => {
+ vi.spyOn(global, "fetch").mockResolvedValue({
+ ok: false,
+ json: async () => ({ detail: "Not found" }),
+ } as Response);
+
+ const { result } = renderHook(() => useMultiCostEstimate("token123"));
+ const entry = makeEntry();
+
+ await act(async () => {
+ result.current.debouncedFetchForEntry(entry);
+ await vi.runAllTimersAsync();
+ });
+
+ const multiResult = result.current.getMultiModelResult([entry]);
+ expect(multiResult.entries[0].error).toBe("Not found");
+ expect(multiResult.entries[0].loading).toBe(false);
+ });
+ });
+});
diff --git a/ui/litellm-dashboard/src/components/page_metadata.ts b/ui/litellm-dashboard/src/components/page_metadata.ts
index 2459410d7a3..5e2c0ccc0b9 100644
--- a/ui/litellm-dashboard/src/components/page_metadata.ts
+++ b/ui/litellm-dashboard/src/components/page_metadata.ts
@@ -13,6 +13,7 @@ export const pageDescriptions: Record = {
guardrails: "Set up content moderation and safety guardrails",
policies: "Define access control and usage policies",
"search-tools": "Configure RAG search and retrieval tools",
+ "tool-policies": "Configure tool use policies and permissions",
"vector-stores": "Manage vector databases for embeddings",
new_usage: "View usage analytics and metrics",
logs: "Access request and response logs",