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OpenAI's chat completions response includes prompt_tokens_details. cached_tokens when prompt caching kicks in - previously dropped entirely. Now extracted in LlmClient and forwarded to Langfuse as the flat cache_read_input_tokens usage key. Used the flat key rather than OpenAI's nested prompt_tokens_details shape: the installed @langfuse/tracing SDK's usageDetails type is Record<string, number> | OpenAiUsage (a 3-field camelCase shape, no _details nesting) even though the wire schema in @langfuse/core supports nesting - a gap in the SDK's public types, not something worth fighting with a cast. Verified: full suite (82/82) passing, tsc/eslint clean.
66 lines
2.1 KiB
TypeScript
66 lines
2.1 KiB
TypeScript
import { describe, expect, it, vi } from 'vitest';
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const createMock = vi.fn();
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vi.mock('openai', () => ({
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default: vi.fn().mockImplementation(() => ({
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chat: { completions: { create: createMock } },
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})),
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}));
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import { LlmClient } from '../../../src/core/llm/LlmClient';
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describe('LlmClient', () => {
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it('sends messages/options to the OpenAI-compatible endpoint and returns content + usage', async () => {
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createMock.mockResolvedValue({
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choices: [{ message: { content: 'hello' } }],
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usage: { prompt_tokens: 10, completion_tokens: 5, total_tokens: 15 },
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});
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const client = new LlmClient({ baseUrl: 'http://localhost:11434/v1', apiKey: 'key' });
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const result = await client.chat([{ role: 'user', content: 'hi' }], { model: 'llama3', temperature: 0.7 });
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expect(result).toEqual({
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content: 'hello',
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usage: { promptTokens: 10, completionTokens: 5, totalTokens: 15 },
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});
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expect(createMock).toHaveBeenCalledWith({
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model: 'llama3',
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temperature: 0.7,
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max_tokens: undefined,
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messages: [{ role: 'user', content: 'hi' }],
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});
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});
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it('extracts cachedTokens from prompt_tokens_details when present', async () => {
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createMock.mockResolvedValue({
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choices: [{ message: { content: 'hello' } }],
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usage: {
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prompt_tokens: 100,
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completion_tokens: 20,
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total_tokens: 120,
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prompt_tokens_details: { cached_tokens: 80 },
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},
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});
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const client = new LlmClient({ baseUrl: 'http://localhost:11434/v1' });
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const result = await client.chat([{ role: 'user', content: 'hi' }], { model: 'gpt-4o-mini' });
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expect(result.usage).toEqual({
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promptTokens: 100,
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completionTokens: 20,
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totalTokens: 120,
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cachedTokens: 80,
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});
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});
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it('throws when the response has no choices', async () => {
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createMock.mockResolvedValue({ choices: [] });
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const client = new LlmClient({ baseUrl: 'http://localhost:11434/v1' });
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await expect(client.chat([{ role: 'user', content: 'hi' }], { model: 'llama3' })).rejects.toThrow(
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/no choices/
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);
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});
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});
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