g1nation
98 lines
4.0 KiB
TypeScript
98 lines
4.0 KiB
TypeScript
import { parseModelPrefix, makeModelId, providerLabel } from '../src/features/providers/types';
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import { _internals } from '../src/features/providers/streamHelpers';
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describe('parseModelPrefix', () => {
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test('matches openrouter / anthropic / gemini prefixes', () => {
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expect(parseModelPrefix('openrouter:anthropic/claude-3.5-sonnet')).toEqual({
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provider: 'openrouter', model: 'anthropic/claude-3.5-sonnet',
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});
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expect(parseModelPrefix('anthropic:claude-3-5-sonnet-20241022')).toEqual({
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provider: 'anthropic', model: 'claude-3-5-sonnet-20241022',
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});
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expect(parseModelPrefix('gemini:gemini-2.0-flash-exp')).toEqual({
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provider: 'gemini', model: 'gemini-2.0-flash-exp',
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});
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});
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test('returns null for local engine model ids', () => {
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expect(parseModelPrefix('gemma4:e2b')).toBeNull();
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expect(parseModelPrefix('llama-3.2:8b')).toBeNull();
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expect(parseModelPrefix('google/gemma-4-e4b')).toBeNull();
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});
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test('returns null for empty / undefined input', () => {
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expect(parseModelPrefix('')).toBeNull();
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expect(parseModelPrefix(undefined as any)).toBeNull();
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});
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test('makeModelId round-trips with parseModelPrefix', () => {
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const id = makeModelId('openrouter', 'meta/llama-3.3-70b');
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expect(id).toBe('openrouter:meta/llama-3.3-70b');
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expect(parseModelPrefix(id)).toEqual({ provider: 'openrouter', model: 'meta/llama-3.3-70b' });
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});
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test('providerLabel returns Korean-friendly labels', () => {
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expect(providerLabel('openrouter')).toBe('OpenRouter');
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expect(providerLabel('anthropic')).toBe('Anthropic');
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expect(providerLabel('gemini')).toBe('Gemini');
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});
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});
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describe('Anthropic event → OpenAI SSE conversion', () => {
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const conv = _internals.anthropicEventToOpenAI;
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test('content_block_delta with text emits OpenAI chunk', () => {
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const out = conv('content_block_delta', '{"delta":{"type":"text_delta","text":"안녕"}}');
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expect(out).toBeTruthy();
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expect(out).toContain('"content":"안녕"');
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expect(out).toMatch(/^data: /);
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expect(out!.endsWith('\n\n')).toBe(true);
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});
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test('non-delta events (message_start, content_block_start, etc.) are ignored', () => {
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expect(conv('message_start', '{}')).toBeNull();
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expect(conv('content_block_start', '{}')).toBeNull();
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expect(conv('message_stop', '{}')).toBeNull();
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});
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test('malformed JSON returns null', () => {
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expect(conv('content_block_delta', 'not json')).toBeNull();
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expect(conv('content_block_delta', '{')).toBeNull();
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});
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test('empty text field returns null (no zero-length chunks)', () => {
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expect(conv('content_block_delta', '{"delta":{"type":"text_delta","text":""}}')).toBeNull();
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expect(conv('content_block_delta', '{"delta":{}}')).toBeNull();
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});
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});
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describe('Gemini event → OpenAI SSE conversion', () => {
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const conv = _internals.geminiEventToOpenAI;
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test('extracts text from candidates[0].content.parts', () => {
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const out = conv(null, '{"candidates":[{"content":{"parts":[{"text":"안녕"}],"role":"model"}}]}');
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expect(out).toBeTruthy();
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expect(out).toContain('"content":"안녕"');
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expect(out).toMatch(/^data: /);
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});
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test('joins multiple parts into one chunk', () => {
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const out = conv(null, '{"candidates":[{"content":{"parts":[{"text":"안"},{"text":"녕"}]}}]}');
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expect(out).toContain('"content":"안녕"');
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});
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test('returns null when candidates missing / empty', () => {
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expect(conv(null, '{}')).toBeNull();
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expect(conv(null, '{"candidates":[]}')).toBeNull();
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expect(conv(null, '{"candidates":[{"content":{}}]}')).toBeNull();
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});
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test('malformed JSON returns null', () => {
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expect(conv(null, 'not json')).toBeNull();
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});
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test('empty text returns null', () => {
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expect(conv(null, '{"candidates":[{"content":{"parts":[{"text":""}]}}]}')).toBeNull();
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});
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});
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