Files
sugarr/test/core/llm/LlmClient.test.ts
Fran Jurmanović cb5e7085bf Support OpenRouter's real per-request cost reporting
Send usage: { include: true } on every chat completion request (an
OpenRouter-specific extension - requires Usage Accounting enabled on
the OpenRouter account; other OpenAI-compatible providers generally
ignore unknown top-level request fields). When the response includes
OpenRouter's own usage.cost, forward it to Langfuse as
costDetails.total, so cost is the real per-request figure OpenRouter
billed rather than a guess from Langfuse's model price list (which
has no entry for proxied/aggregated OpenRouter model names anyway).

Verified: full suite (84/84) passing, tsc/eslint clean, docker
compose build succeeds. Per Langfuse's OpenRouter integration guide:
https://langfuse.com/integrations/gateways/openrouter
2026-08-12 23:43:30 +02:00

84 lines
2.7 KiB
TypeScript

import { describe, expect, it, vi } from 'vitest';
const createMock = vi.fn();
vi.mock('openai', () => ({
default: vi.fn().mockImplementation(() => ({
chat: { completions: { create: createMock } },
})),
}));
import { LlmClient } from '../../../src/core/llm/LlmClient';
describe('LlmClient', () => {
it('sends messages/options to the OpenAI-compatible endpoint and returns content + usage', async () => {
createMock.mockResolvedValue({
choices: [{ message: { content: 'hello' } }],
usage: { prompt_tokens: 10, completion_tokens: 5, total_tokens: 15 },
});
const client = new LlmClient({ baseUrl: 'http://localhost:11434/v1', apiKey: 'key' });
const result = await client.chat([{ role: 'user', content: 'hi' }], { model: 'llama3', temperature: 0.7 });
expect(result).toEqual({
content: 'hello',
usage: { promptTokens: 10, completionTokens: 5, totalTokens: 15 },
});
expect(createMock).toHaveBeenCalledWith({
model: 'llama3',
temperature: 0.7,
max_tokens: undefined,
messages: [{ role: 'user', content: 'hi' }],
usage: { include: true },
});
});
it('extracts OpenRouter cost from the response usage when present', async () => {
createMock.mockResolvedValue({
choices: [{ message: { content: 'hello' } }],
usage: { prompt_tokens: 10, completion_tokens: 5, total_tokens: 15, cost: 0.00042 },
});
const client = new LlmClient({ baseUrl: 'https://openrouter.ai/api/v1', apiKey: 'key' });
const result = await client.chat([{ role: 'user', content: 'hi' }], { model: 'openrouter/auto' });
expect(result.usage).toEqual({
promptTokens: 10,
completionTokens: 5,
totalTokens: 15,
cost: 0.00042,
});
});
it('extracts cachedTokens from prompt_tokens_details when present', async () => {
createMock.mockResolvedValue({
choices: [{ message: { content: 'hello' } }],
usage: {
prompt_tokens: 100,
completion_tokens: 20,
total_tokens: 120,
prompt_tokens_details: { cached_tokens: 80 },
},
});
const client = new LlmClient({ baseUrl: 'http://localhost:11434/v1' });
const result = await client.chat([{ role: 'user', content: 'hi' }], { model: 'gpt-4o-mini' });
expect(result.usage).toEqual({
promptTokens: 100,
completionTokens: 20,
totalTokens: 120,
cachedTokens: 80,
});
});
it('throws when the response has no choices', async () => {
createMock.mockResolvedValue({ choices: [] });
const client = new LlmClient({ baseUrl: 'http://localhost:11434/v1' });
await expect(client.chat([{ role: 'user', content: 'hi' }], { model: 'llama3' })).rejects.toThrow(
/no choices/
);
});
});