Carlos Florêncio
74 lines
2.1 KiB
TypeScript
74 lines
2.1 KiB
TypeScript
import { describe, expect, it } from "vitest";
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import type { Message, Turn } from "@/lib/types/http";
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import { buildMessageDebugEntries, hasMessageDebugMetadata } from "./message-debug-metadata";
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const TEST_TIMESTAMP = "2026-06-13T19:00:00Z";
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function message(overrides: Partial<Message> = {}): Message {
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return {
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id: "m1",
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session_id: "s1",
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task_id: "t1",
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turn_id: "turn1",
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author_type: "agent",
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content: "hello",
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type: "agent",
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created_at: TEST_TIMESTAMP,
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...overrides,
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} as Message;
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}
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function turn(overrides: Partial<Turn> = {}): Turn {
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return {
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id: "turn1",
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session_id: "s1",
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task_id: "t1",
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started_at: TEST_TIMESTAMP,
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created_at: TEST_TIMESTAMP,
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updated_at: TEST_TIMESTAMP,
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...overrides,
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} as Turn;
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}
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describe("message debug metadata", () => {
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it("hides the debug affordance when message and turn metadata are empty", () => {
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expect(hasMessageDebugMetadata(message(), turn())).toBe(false);
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});
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it("shows the debug affordance when turn metadata exists", () => {
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expect(
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hasMessageDebugMetadata(
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message(),
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turn({ metadata: { model: "gpt-5.5", prompt_usage: { total_tokens: 42 } } }),
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),
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).toBe(true);
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});
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it("shows the debug affordance when context metadata exists", () => {
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expect(hasMessageDebugMetadata(message(), turn(), { usageMultiplier: "3x" })).toBe(true);
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});
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it("builds a compact summary while preserving raw metadata", () => {
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const entries = buildMessageDebugEntries(
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message({ metadata: { tool_call_id: "tool1" } }),
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turn({
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metadata: {
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model: "gpt-5.5",
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agent_type: "codex-acp",
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prompt_usage: { total_tokens: 42, provider_reported_cost_subcents: 123 },
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},
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}),
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{ usageMultiplier: "3x" },
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);
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expect(entries.model).toBe("gpt-5.5");
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expect(entries.usage_multiplier).toBe("3x");
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expect(entries.agent_type).toBe("codex-acp");
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expect(entries.prompt_usage).toEqual({
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total_tokens: 42,
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provider_reported_cost_subcents: 123,
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});
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expect(entries.message_metadata).toEqual({ tool_call_id: "tool1" });
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});
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});
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