e7dd72a8d7
Non-streaming codex traffic recorded cached_tokens: 0 even when upstream prompt caching worked. The Claude-format branch (which OpenAI Responses usage also matches) never read input_tokens_details, and the OpenAI branch ignored a top-level flat cached_tokens. Read both in both branches; Responses prompts are cache-inclusive so canonicalizeUsage passes the value through without folding. 5 new regression tests.
75 lines
3.1 KiB
JavaScript
75 lines
3.1 KiB
JavaScript
import { describe, it, expect, vi } from "vitest";
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// sever the DB import chain (usageDb -> @/lib/db/*) — not under test
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vi.mock("@/lib/usageDb.js", () => ({
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saveRequestUsage: vi.fn(),
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appendRequestLog: vi.fn(),
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saveRequestDetail: vi.fn(),
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}));
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// and the stream/console-coloring utils that drag in the translator graph
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vi.mock("../../open-sse/utils/stream.js", () => ({
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COLORS: {},
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formatSSE: vi.fn(),
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}));
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import { extractUsageFromResponse } from "../../open-sse/handlers/chatCore/requestDetail.js";
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import { canonicalizeUsage } from "../../open-sse/utils/usageTracking.js";
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// The three real-world usage shapes and how extractUsageFromResponse() must
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// surface their cache-read count so canonicalizeUsage() produces a correct
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// cached_tokens. Regression for non-streaming codex/Responses traffic, where
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// cache reads were silently dropped and usage recorded cached_tokens: 0.
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describe("extractUsageFromResponse cache surfaces", () => {
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it("surfaces OpenAI Responses input_tokens_details.cached_tokens", () => {
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// codex / /v1/responses shape: prompt is cache-INCLUSIVE
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const out = extractUsageFromResponse({
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usage: { input_tokens: 25421, output_tokens: 5, total_tokens: 25426,
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input_tokens_details: { cached_tokens: 24320 } },
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});
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expect(out.cached_tokens).toBe(24320);
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expect(out.prompt_tokens).toBe(25421);
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expect(out.cache_read_input_tokens).toBeUndefined();
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});
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it("canonicalizes Responses usage without double-counting the prompt", () => {
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const extracted = extractUsageFromResponse({
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usage: { input_tokens: 25421, output_tokens: 5,
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input_tokens_details: { cached_tokens: 24320 } },
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});
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const out = canonicalizeUsage(extracted);
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// inclusive prompt passes through unchanged; cache reported as subset
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expect(out.prompt_tokens).toBe(25421);
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expect(out.cached_tokens).toBe(24320);
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expect(out.total_tokens).toBe(25426);
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expect(out.cache_creation_input_tokens).toBe(0);
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});
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it("still folds genuine Claude exclusive cache (regression)", () => {
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const extracted = extractUsageFromResponse({
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usage: { input_tokens: 100, output_tokens: 50,
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cache_read_input_tokens: 200, cache_creation_input_tokens: 30 },
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});
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expect(extracted.cached_tokens).toBeUndefined();
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const out = canonicalizeUsage(extracted);
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expect(out.prompt_tokens).toBe(330); // 100 + 200 + 30
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expect(out.cached_tokens).toBe(200);
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expect(out.cache_creation_input_tokens).toBe(30);
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});
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it("surfaces flat cached_tokens on the OpenAI branch (SSE-to-JSON shape)", () => {
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const out = extractUsageFromResponse({
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usage: { prompt_tokens: 300, completion_tokens: 10, cached_tokens: 240 },
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});
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expect(out.cached_tokens).toBe(240);
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});
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it("keeps nested prompt_tokens_details.cached_tokens working (regression)", () => {
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const out = extractUsageFromResponse({
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usage: { prompt_tokens: 300, completion_tokens: 10,
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prompt_tokens_details: { cached_tokens: 240 } },
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});
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expect(out.cached_tokens).toBe(240);
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expect(canonicalizeUsage(out).cached_tokens).toBe(240);
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});
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});
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