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chore: import upstream snapshot with attribution
2026-07-13 13:24:08 +08:00

207 lines
6.0 KiB
TypeScript

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);
}
});
});