import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest'; import { calculateHyperbolicCost, createHyperbolicProvider, HYPERBOLIC_CHAT_MODELS, HYPERBOLIC_REASONING_MODELS, HyperbolicProvider, } from '../../../src/providers/hyperbolic/chat'; import { OpenAiChatCompletionProvider } from '../../../src/providers/openai/chat'; describe('HyperbolicProvider', () => { let provider: HyperbolicProvider; const modelName = 'deepseek-ai/DeepSeek-R1'; const options = { config: { config: { apiKey: 'test-key', }, }, }; beforeEach(() => { provider = new HyperbolicProvider(modelName, options); }); afterEach(() => { vi.restoreAllMocks(); }); it('should create provider with correct config', () => { expect(provider.id()).toBe(`hyperbolic:${modelName}`); expect(provider.toString()).toBe(`[Hyperbolic Provider ${modelName}]`); }); it('should convert to JSON correctly', () => { const json = provider.toJSON(); expect(json).toEqual({ provider: 'hyperbolic', model: modelName, config: { apiKeyEnvar: 'HYPERBOLIC_API_KEY', apiBaseUrl: 'https://api.hyperbolic.xyz/v1', config: expect.any(Object), }, }); }); it('should process API response correctly for reasoning model', async () => { const mockResponse = { raw: { usage: { completion_tokens_details: { reasoning_tokens: 100, accepted_prediction_tokens: 50, rejected_prediction_tokens: 25, }, }, }, tokenUsage: { prompt: 200, completion: 150, }, }; vi.spyOn(OpenAiChatCompletionProvider.prototype, 'callApi').mockResolvedValue(mockResponse); const result = await provider.callApi('test prompt'); expect(result.tokenUsage.completionDetails).toEqual({ reasoning: 100, acceptedPrediction: 50, rejectedPrediction: 25, }); }); it('should handle raw response as string', async () => { const mockResponse = { raw: JSON.stringify({ usage: { completion_tokens_details: { reasoning_tokens: 100, accepted_prediction_tokens: 50, rejected_prediction_tokens: 25, }, }, }), tokenUsage: { prompt: 200, completion: 150, }, }; vi.spyOn(OpenAiChatCompletionProvider.prototype, 'callApi').mockResolvedValue(mockResponse); const result = await provider.callApi('test prompt'); expect(result.tokenUsage.completionDetails).toEqual({ reasoning: 100, acceptedPrediction: 50, rejectedPrediction: 25, }); }); it('should handle error response', async () => { const mockResponse = { error: 'API error' }; vi.spyOn(OpenAiChatCompletionProvider.prototype, 'callApi').mockResolvedValue(mockResponse); const result = await provider.callApi('test prompt'); expect(result.error).toBe('API error'); }); it('should handle invalid JSON in raw response', async () => { const mockResponse = { raw: 'invalid json', tokenUsage: { prompt: 200, completion: 150, }, }; vi.spyOn(OpenAiChatCompletionProvider.prototype, 'callApi').mockResolvedValue(mockResponse); const result = await provider.callApi('test prompt'); expect(result.tokenUsage.completionDetails).toBeUndefined(); }); it('should calculate cost for non-cached response', async () => { const mockResponse = { raw: 'test response', tokenUsage: { prompt: 1000, completion: 500, }, cached: false, }; vi.spyOn(OpenAiChatCompletionProvider.prototype, 'callApi').mockResolvedValue(mockResponse); const result = await provider.callApi('test prompt'); expect(result.cost).toBe((0.5 / 1e6) * 1000 + (2.18 / 1e6) * 500); }); }); describe('calculateHyperbolicCost', () => { it('should calculate cost correctly for known model', () => { const cost = calculateHyperbolicCost('deepseek-ai/DeepSeek-R1', {}, 1000, 500); expect(cost).toBe((0.5 / 1e6) * 1000 + (2.18 / 1e6) * 500); }); it('should calculate cost correctly for model alias', () => { const cost = calculateHyperbolicCost('DeepSeek-R1', {}, 1000, 500); expect(cost).toBe((0.5 / 1e6) * 1000 + (2.18 / 1e6) * 500); }); it('should return undefined for unknown model', () => { const cost = calculateHyperbolicCost('unknown-model', {}, 1000, 500); expect(cost).toBeUndefined(); }); it('should return undefined if tokens are missing', () => { const cost = calculateHyperbolicCost('deepseek-ai/DeepSeek-R1', {}, undefined, 500); expect(cost).toBeUndefined(); }); it('should use custom cost from config if provided', () => { const config = { cost: 0.001, }; const cost = calculateHyperbolicCost('deepseek-ai/DeepSeek-R1', config, 1000, 500); expect(cost).toBe(0.001 * 1000 + 0.001 * 500); }); }); describe('createHyperbolicProvider', () => { it('should create provider with correct model name', () => { const provider = createHyperbolicProvider('hyperbolic:deepseek-ai/DeepSeek-R1'); expect(provider.id()).toBe('hyperbolic:deepseek-ai/DeepSeek-R1'); }); it('should throw error if model name is missing', () => { expect(() => createHyperbolicProvider('hyperbolic:')).toThrow('Model name is required'); }); }); describe('HYPERBOLIC_CHAT_MODELS', () => { it('should contain valid model definitions', () => { for (const model of HYPERBOLIC_CHAT_MODELS) { expect(model).toHaveProperty('id'); expect(model).toHaveProperty('cost.input'); expect(model).toHaveProperty('cost.output'); expect(model).toHaveProperty('aliases'); expect(Array.isArray(model.aliases)).toBe(true); } }); }); describe('HYPERBOLIC_REASONING_MODELS', () => { it('should be a subset of HYPERBOLIC_CHAT_MODELS', () => { for (const modelId of HYPERBOLIC_REASONING_MODELS) { const found = HYPERBOLIC_CHAT_MODELS.some( (m) => m.id === modelId || m.aliases.includes(modelId), ); expect(found).toBe(true); } }); });