feat(intelligence): complete migration to Rust backend
- Unify all intelligence modules to use intelligenceClient - Delete legacy TS implementations (agent-memory, reflection-engine, heartbeat-engine, context-compactor, agent-identity, memory-index) - Update all consumers to use snake_case backend types - Remove deprecated llm-integration.test.ts This eliminates code duplication between frontend and backend, resolves localStorage limitations, and enables persistent intelligence features. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
iven
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/**
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* LLM Integration Tests - Phase 2 Engine Upgrades
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*
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* Tests for LLM-powered features:
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* - ReflectionEngine with LLM semantic analysis
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* - ContextCompactor with LLM summarization
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* - MemoryExtractor with LLM importance scoring
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*/
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import { describe, it, expect, vi, beforeEach, afterEach } from 'vitest';
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import {
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ReflectionEngine,
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} from '../reflection-engine';
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import {
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ContextCompactor,
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} from '../context-compactor';
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import {
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MemoryExtractor,
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} from '../memory-extractor';
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import {
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type LLMProvider,
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} from '../llm-service';
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// === Mock LLM Adapter ===
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const mockLLMAdapter = {
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complete: vi.fn(),
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isAvailable: vi.fn(() => true),
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getProvider: vi.fn(() => 'mock' as LLMProvider),
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};
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vi.mock('../llm-service', () => ({
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getLLMAdapter: vi.fn(() => mockLLMAdapter),
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resetLLMAdapter: vi.fn(),
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llmReflect: vi.fn(async () => JSON.stringify({
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patterns: [
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{
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observation: '用户经常询问代码优化问题',
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frequency: 5,
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sentiment: 'positive',
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evidence: ['多次讨论性能优化'],
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},
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],
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improvements: [
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{
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area: '代码解释',
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suggestion: '可以提供更详细的代码注释',
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priority: 'medium',
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},
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],
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identityProposals: [],
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})),
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llmCompact: vi.fn(async () => '[LLM摘要]\n讨论主题: 代码优化\n关键决策: 使用缓存策略\n待办事项: 完成性能测试'),
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llmExtract: vi.fn(async () => JSON.stringify([
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{ content: '用户偏好简洁的回答', type: 'preference', importance: 7, tags: ['style'] },
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{ content: '项目使用 TypeScript', type: 'fact', importance: 6, tags: ['tech'] },
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])),
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}));
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// === ReflectionEngine Tests ===
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describe('ReflectionEngine with LLM', () => {
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let engine: ReflectionEngine;
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beforeEach(() => {
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vi.clearAllMocks();
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engine = new ReflectionEngine({ useLLM: true });
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});
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afterEach(() => {
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engine?.updateConfig({ useLLM: false });
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});
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it('should initialize with LLM config', () => {
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const config = engine.getConfig();
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expect(config.useLLM).toBe(true);
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});
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it('should have llmFallbackToRules enabled by default', () => {
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const config = engine.getConfig();
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expect(config.llmFallbackToRules).toBe(true);
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});
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it('should track conversations for reflection trigger', () => {
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engine.recordConversation();
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engine.recordConversation();
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expect(engine.shouldReflect()).toBe(false);
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// After 5 conversations (default trigger)
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for (let i = 0; i < 4; i++) {
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engine.recordConversation();
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}
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expect(engine.shouldReflect()).toBe(true);
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});
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it('should use LLM when enabled and available', async () => {
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mockLLMAdapter.isAvailable.mockReturnValue(true);
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const result = await engine.reflect('test-agent', { forceLLM: true });
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expect(result.patterns.length).toBeGreaterThan(0);
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expect(result.timestamp).toBeDefined();
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});
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it('should fallback to rules when LLM fails', async () => {
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mockLLMAdapter.isAvailable.mockReturnValue(false);
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const result = await engine.reflect('test-agent');
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// Should still work with rule-based approach
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expect(result).toBeDefined();
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expect(result.timestamp).toBeDefined();
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});
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});
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// === ContextCompactor Tests ===
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describe('ContextCompactor with LLM', () => {
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let compactor: ContextCompactor;
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beforeEach(() => {
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vi.clearAllMocks();
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compactor = new ContextCompactor({ useLLM: true });
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});
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it('should initialize with LLM config', () => {
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const config = compactor.getConfig();
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expect(config.useLLM).toBe(true);
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});
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it('should have llmFallbackToRules enabled by default', () => {
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const config = compactor.getConfig();
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expect(config.llmFallbackToRules).toBe(true);
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});
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it('should check threshold correctly', () => {
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const messages = [
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{ role: 'user', content: 'Hello'.repeat(1000) },
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{ role: 'assistant', content: 'Response'.repeat(1000) },
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];
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const check = compactor.checkThreshold(messages);
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expect(check.shouldCompact).toBe(false);
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expect(check.urgency).toBe('none');
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});
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it('should trigger soft threshold', () => {
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// Create enough messages to exceed 15000 soft threshold but not 20000 hard threshold
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// estimateTokens: CJK chars ~1.5 tokens each
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// 20 messages × 600 CJK chars × 1.5 = ~18000 tokens (between soft and hard)
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const messages = Array(20).fill(null).map((_, i) => ({
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role: i % 2 === 0 ? 'user' : 'assistant',
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content: '测试内容'.repeat(150), // 600 CJK chars ≈ 900 tokens each
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}));
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const check = compactor.checkThreshold(messages);
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expect(check.shouldCompact).toBe(true);
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expect(check.urgency).toBe('soft');
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});
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});
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// === MemoryExtractor Tests ===
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describe('MemoryExtractor with LLM', () => {
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let extractor: MemoryExtractor;
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beforeEach(() => {
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vi.clearAllMocks();
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extractor = new MemoryExtractor({ useLLM: true });
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});
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it('should initialize with LLM config', () => {
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// MemoryExtractor doesn't expose config directly, but we can test behavior
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expect(extractor).toBeDefined();
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});
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it('should skip extraction with too few messages', async () => {
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const messages = [
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{ role: 'user', content: 'Hi' },
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{ role: 'assistant', content: 'Hello!' },
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];
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const result = await extractor.extractFromConversation(messages, 'test-agent');
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expect(result.saved).toBe(0);
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});
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it('should extract with enough messages', async () => {
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const messages = [
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{ role: 'user', content: '我喜欢简洁的回答' },
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{ role: 'assistant', content: '好的，我会简洁一些' },
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{ role: 'user', content: '我的项目使用 TypeScript' },
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{ role: 'assistant', content: 'TypeScript 是个好选择' },
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{ role: 'user', content: '继续' },
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{ role: 'assistant', content: '继续...' },
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];
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const result = await extractor.extractFromConversation(messages, 'test-agent');
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expect(result.items.length).toBeGreaterThanOrEqual(0);
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});
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});
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// === Integration Test ===
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describe('LLM Integration Full Flow', () => {
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it('should work end-to-end with all engines', async () => {
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// Setup all engines with LLM
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const engine = new ReflectionEngine({ useLLM: true, llmFallbackToRules: true });
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const compactor = new ContextCompactor({ useLLM: true, llmFallbackToRules: true });
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const extractor = new MemoryExtractor({ useLLM: true, llmFallbackToRules: true });
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// Verify they all have LLM support
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expect(engine.getConfig().useLLM).toBe(true);
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expect(compactor.getConfig().useLLM).toBe(true);
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// All should work without throwing
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await expect(engine.reflect('test-agent')).resolves;
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await expect(compactor.compact([], 'test-agent')).resolves;
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await expect(extractor.extractFromConversation([], 'test-agent')).resolves;
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});
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});
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