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Profa
2253d05618
Remove the nested v3/ subdirectory by moving all active source code, tests, configs, and assets directly to the project root. Archive v2/ (dead code, 847MB) to v2-archive.tar.gz. Key changes: - Move v3/src/ → src/, v3/tests/ → tests/, v3/packages/ → packages/ - Move v3/tsconfig.json, v3/vitest.config.ts to root - Merge v3/package.json into root package.json (ESM, scripts, deps, exports) - Merge v3/scripts/ into scripts/, v3/implementation/ into docs/implementation/ - Update all CI workflows to remove "cd v3 &&" and v3/ path prefixes - Fix all init installer path resolution (3-level-up → 2-level-up) - Fix hooks to use "node ./dist/cli/bundle.js" instead of bare "aqe" - Update settings.json ADR/DDD directories to docs/implementation/ - Fix helper scripts (statusline, adr-compliance, ddd-tracker, guidance-hooks) - Update 58 agent definitions: aqe/v3/ → aqe/ memory namespaces - Update 15 guidance shards: v3/src/ → src/ path references - Fix sync interfaces: remove v3/ and v2/ path references - Fix test path calculations (process.cwd() + '..' no longer needed) - Fix flaky perf test threshold (500ms → 1000ms for CI environments) - Fix complexity-analyzer test to match ADR-051 keyword-based eligibility Build passes, 17,901 tests pass, CLI and MCP bundles verified. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
790 lines
23 KiB
TypeScript
790 lines
23 KiB
TypeScript
/**
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* Browser Integration - Trajectory Learning Integration Tests
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*
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* Tests trajectory storage to QEReasoningBank:
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* - Trajectory storage with embeddings
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* - Trajectory retrieval with similarity search
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* - Pattern extraction from multiple trajectories
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* - Learning feedback loop integration
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*
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* Uses mocked embeddings for testing without requiring transformer models.
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*/
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import { describe, it, expect, beforeEach, afterEach, vi } from 'vitest';
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import type { MemoryBackend, VectorSearchResult } from '../../../src/kernel/interfaces';
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import { Result, ok, err } from '../../../src/shared/types';
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// ============================================================================
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// Types
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// ============================================================================
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interface Trajectory {
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id: string;
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task: string;
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agent: string;
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steps: TrajectoryStep[];
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outcome: 'success' | 'failure' | 'partial';
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reward: number;
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embedding?: number[];
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timestamp: Date;
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}
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interface TrajectoryStep {
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action: string;
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observation: string;
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reasoning: string;
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quality: number;
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timestamp: Date;
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}
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interface Pattern {
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id: string;
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name: string;
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description: string;
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trajectoryIds: string[];
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successRate: number;
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avgReward: number;
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conditions: string[];
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actions: string[];
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embedding?: number[];
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}
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// ============================================================================
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// Test Doubles
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// ============================================================================
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/**
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* In-memory backend with vector search support
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*/
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class TestMemoryBackendWithVectors implements MemoryBackend {
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private store = new Map<string, unknown>();
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private vectors = new Map<string, { embedding: number[]; metadata: any }>();
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async initialize(): Promise<void> {}
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async dispose(): Promise<void> {}
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async set<T>(key: string, value: T): Promise<void> {
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this.store.set(key, value);
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}
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async get<T>(key: string): Promise<T | undefined> {
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return this.store.get(key) as T | undefined;
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}
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async delete(key: string): Promise<boolean> {
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this.store.delete(key);
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this.vectors.delete(key);
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return true;
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}
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async has(key: string): Promise<boolean> {
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return this.store.has(key);
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}
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async search(pattern: string): Promise<string[]> {
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const regex = new RegExp(pattern.replace(/\*/g, '.*'));
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return Array.from(this.store.keys()).filter((k) => regex.test(k));
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}
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async storeVector(
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key: string,
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embedding: number[],
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metadata?: any
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): Promise<void> {
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this.vectors.set(key, { embedding, metadata });
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}
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async count(namespace: string): Promise<number> {
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const keys = await this.search(`${namespace}:*`);
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return keys.length;
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}
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async hasCodeIntelligenceIndex(): Promise<boolean> {
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const count = await this.count('code-intelligence:kg');
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return count > 0;
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}
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async vectorSearch(
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query: number[],
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topK: number = 5,
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threshold: number = 0.5
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): Promise<VectorSearchResult[]> {
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const results: VectorSearchResult[] = [];
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for (const [key, { embedding, metadata }] of this.vectors.entries()) {
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const similarity = this.cosineSimilarity(query, embedding);
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if (similarity >= threshold) {
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results.push({
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key,
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similarity,
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metadata,
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});
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}
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}
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return results
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.sort((a, b) => b.similarity - a.similarity)
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.slice(0, topK);
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}
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private cosineSimilarity(a: number[], b: number[]): number {
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if (a.length !== b.length) return 0;
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let dotProduct = 0;
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let normA = 0;
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let normB = 0;
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for (let i = 0; i < a.length; i++) {
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dotProduct += a[i] * b[i];
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normA += a[i] * a[i];
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normB += b[i] * b[i];
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}
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return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
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}
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clear(): void {
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this.store.clear();
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this.vectors.clear();
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}
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}
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/**
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* Mock embedding generator for testing
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*/
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class MockEmbeddingGenerator {
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private embeddingDim = 384; // Simulates all-MiniLM-L6-v2
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generateEmbedding(text: string): number[] {
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// Simple deterministic embedding based on text hash
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const hash = this.hashString(text);
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const embedding: number[] = [];
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for (let i = 0; i < this.embeddingDim; i++) {
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// Use hash to generate deterministic values
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const value = Math.sin(hash + i) * 0.5 + 0.5;
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embedding.push(value);
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}
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return this.normalize(embedding);
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}
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private hashString(str: string): number {
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let hash = 0;
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for (let i = 0; i < str.length; i++) {
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const char = str.charCodeAt(i);
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hash = (hash << 5) - hash + char;
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hash = hash & hash;
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}
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return hash;
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}
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private normalize(vec: number[]): number[] {
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const norm = Math.sqrt(vec.reduce((sum, val) => sum + val * val, 0));
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return vec.map((val) => val / norm);
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}
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}
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/**
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* Mock QEReasoningBank for trajectory storage
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*/
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class MockQEReasoningBank {
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constructor(
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private memory: TestMemoryBackendWithVectors,
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private embeddings: MockEmbeddingGenerator
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) {}
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async storeTrajectory(trajectory: Trajectory): Promise<Result<string, Error>> {
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try {
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// Generate embedding for trajectory
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const embeddingText = `${trajectory.task} ${trajectory.agent} ${trajectory.outcome}`;
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const embedding = this.embeddings.generateEmbedding(embeddingText);
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const trajectoryWithEmbedding = {
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...trajectory,
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embedding,
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};
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// Store trajectory data
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const key = `qe:trajectory:${trajectory.id}`;
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await this.memory.set(key, trajectoryWithEmbedding);
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// Store vector for similarity search
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await this.memory.storeVector(key, embedding, {
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task: trajectory.task,
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agent: trajectory.agent,
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outcome: trajectory.outcome,
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reward: trajectory.reward,
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});
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return ok(trajectory.id);
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} catch (error) {
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return err(error instanceof Error ? error : new Error(String(error)));
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}
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}
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async findSimilarTrajectories(
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task: string,
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topK: number = 5,
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threshold: number = 0.7
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): Promise<Result<Trajectory[], Error>> {
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try {
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// Generate query embedding
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const queryEmbedding = this.embeddings.generateEmbedding(task);
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// Search for similar vectors
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const results = await this.memory.vectorSearch(queryEmbedding, topK, threshold);
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// Retrieve full trajectories
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const trajectories: Trajectory[] = [];
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for (const result of results) {
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const trajectory = await this.memory.get<Trajectory>(result.key);
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if (trajectory) {
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trajectories.push(trajectory);
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}
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}
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return ok(trajectories);
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} catch (error) {
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return err(error instanceof Error ? error : new Error(String(error)));
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}
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}
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async extractPattern(trajectoryIds: string[]): Promise<Result<Pattern, Error>> {
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try {
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if (trajectoryIds.length === 0) {
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return err(new Error('No trajectories provided'));
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}
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// Retrieve all trajectories
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const trajectories: Trajectory[] = [];
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for (const id of trajectoryIds) {
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const trajectory = await this.memory.get<Trajectory>(`qe:trajectory:${id}`);
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if (trajectory) {
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trajectories.push(trajectory);
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}
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}
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if (trajectories.length === 0) {
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return err(new Error('No trajectories found'));
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}
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// Calculate pattern metrics
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const successCount = trajectories.filter((t) => t.outcome === 'success').length;
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const successRate = successCount / trajectories.length;
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const avgReward = trajectories.reduce((sum, t) => sum + t.reward, 0) / trajectories.length;
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// Extract common actions
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const actionCounts = new Map<string, number>();
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for (const trajectory of trajectories) {
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for (const step of trajectory.steps) {
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const count = actionCounts.get(step.action) ?? 0;
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actionCounts.set(step.action, count + 1);
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}
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}
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const commonActions = Array.from(actionCounts.entries())
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.sort((a, b) => b[1] - a[1])
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.slice(0, 5)
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.map(([action]) => action);
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// Create pattern
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const pattern: Pattern = {
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id: `pattern-${Date.now()}`,
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name: `Pattern from ${trajectories.length} trajectories`,
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description: `Success rate: ${(successRate * 100).toFixed(1)}%`,
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trajectoryIds,
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successRate,
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avgReward,
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conditions: [`Min trajectories: ${trajectories.length}`],
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actions: commonActions,
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};
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// Generate pattern embedding
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const patternText = `${pattern.name} ${commonActions.join(' ')}`;
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pattern.embedding = this.embeddings.generateEmbedding(patternText);
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// Store pattern
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const key = `qe:pattern:${pattern.id}`;
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await this.memory.set(key, pattern);
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await this.memory.storeVector(key, pattern.embedding, {
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name: pattern.name,
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successRate,
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avgReward,
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});
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return ok(pattern);
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} catch (error) {
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return err(error instanceof Error ? error : new Error(String(error)));
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}
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}
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}
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// ============================================================================
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// Integration Tests
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// ============================================================================
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describe('Trajectory Learning - Storage and Retrieval', () => {
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let memory: TestMemoryBackendWithVectors;
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let embeddings: MockEmbeddingGenerator;
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let reasoningBank: MockQEReasoningBank;
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beforeEach(() => {
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memory = new TestMemoryBackendWithVectors();
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embeddings = new MockEmbeddingGenerator();
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reasoningBank = new MockQEReasoningBank(memory, embeddings);
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});
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afterEach(() => {
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memory.clear();
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});
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it('should store trajectory with embeddings', async () => {
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const trajectory: Trajectory = {
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id: 'traj-001',
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task: 'Browser screenshot capture',
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agent: 'visual-tester',
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steps: [
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{
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action: 'launch-browser',
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observation: 'Browser launched successfully',
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reasoning: 'Need browser to capture screenshots',
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quality: 1.0,
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timestamp: new Date(),
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},
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{
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action: 'navigate-url',
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observation: 'Navigated to https://example.com',
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reasoning: 'Load target page',
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quality: 1.0,
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timestamp: new Date(),
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},
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{
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action: 'capture-screenshot',
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observation: 'Screenshot saved to /tmp/screenshot.png',
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reasoning: 'Capture visual state',
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quality: 1.0,
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timestamp: new Date(),
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},
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],
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outcome: 'success',
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reward: 1.0,
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timestamp: new Date(),
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};
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const result = await reasoningBank.storeTrajectory(trajectory);
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expect(result.success).toBe(true);
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expect(result.value).toBe('traj-001');
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// Verify storage
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const stored = await memory.get<Trajectory>(`qe:trajectory:${trajectory.id}`);
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expect(stored).toBeDefined();
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expect(stored?.embedding).toBeDefined();
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expect(stored?.embedding?.length).toBe(384);
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});
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it('should retrieve similar trajectories by task', async () => {
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// Store multiple trajectories
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const trajectories: Trajectory[] = [
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{
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id: 'traj-001',
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task: 'Screenshot capture on mobile viewport',
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agent: 'visual-tester',
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steps: [],
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outcome: 'success',
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reward: 1.0,
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timestamp: new Date(),
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},
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{
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id: 'traj-002',
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task: 'Screenshot capture on desktop viewport',
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agent: 'visual-tester',
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steps: [],
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outcome: 'success',
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reward: 0.9,
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timestamp: new Date(),
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},
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{
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id: 'traj-003',
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task: 'Security audit with PII scanning',
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agent: 'security-auditor',
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steps: [],
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outcome: 'success',
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reward: 0.95,
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timestamp: new Date(),
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},
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];
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for (const trajectory of trajectories) {
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await reasoningBank.storeTrajectory(trajectory);
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}
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// Search for similar screenshot tasks
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const searchResult = await reasoningBank.findSimilarTrajectories(
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'Screenshot capture on tablet viewport',
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3,
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0.5
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);
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expect(searchResult.success).toBe(true);
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expect(searchResult.value?.length).toBeGreaterThan(0);
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// Most similar should be the other screenshot tasks
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const similarIds = searchResult.value?.map((t) => t.id) ?? [];
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expect(similarIds).toContain('traj-001');
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expect(similarIds).toContain('traj-002');
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});
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it('should extract patterns from multiple trajectories', async () => {
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// Store successful trajectories with common actions
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const trajectories: Trajectory[] = [
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{
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id: 'traj-001',
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task: 'Visual regression test',
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agent: 'visual-tester',
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steps: [
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{
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action: 'launch-browser',
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observation: 'Browser launched',
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reasoning: 'Setup',
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quality: 1.0,
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timestamp: new Date(),
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},
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{
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action: 'set-viewport',
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observation: 'Viewport set to 1280x720',
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reasoning: 'Configure viewport',
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quality: 1.0,
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timestamp: new Date(),
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},
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{
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action: 'capture-screenshot',
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observation: 'Screenshot captured',
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reasoning: 'Visual snapshot',
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quality: 1.0,
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timestamp: new Date(),
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},
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],
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outcome: 'success',
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reward: 1.0,
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timestamp: new Date(),
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},
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{
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id: 'traj-002',
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task: 'Visual regression test',
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agent: 'visual-tester',
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steps: [
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{
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action: 'launch-browser',
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observation: 'Browser launched',
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reasoning: 'Setup',
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quality: 1.0,
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timestamp: new Date(),
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},
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{
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action: 'set-viewport',
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observation: 'Viewport set to 375x667',
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reasoning: 'Mobile viewport',
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quality: 1.0,
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timestamp: new Date(),
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},
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{
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action: 'capture-screenshot',
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observation: 'Screenshot captured',
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reasoning: 'Visual snapshot',
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quality: 1.0,
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timestamp: new Date(),
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},
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],
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outcome: 'success',
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reward: 0.95,
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timestamp: new Date(),
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},
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];
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// Store trajectories
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for (const trajectory of trajectories) {
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await reasoningBank.storeTrajectory(trajectory);
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}
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// Extract pattern
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const patternResult = await reasoningBank.extractPattern([
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'traj-001',
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'traj-002',
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]);
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expect(patternResult.success).toBe(true);
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expect(patternResult.value?.successRate).toBe(1.0);
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expect(patternResult.value?.actions).toContain('launch-browser');
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expect(patternResult.value?.actions).toContain('set-viewport');
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expect(patternResult.value?.actions).toContain('capture-screenshot');
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expect(patternResult.value?.embedding).toBeDefined();
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});
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it('should calculate success rate from mixed outcomes', async () => {
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const trajectories: Trajectory[] = [
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{
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id: 'traj-success-1',
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task: 'Browser test',
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agent: 'tester',
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steps: [],
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outcome: 'success',
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reward: 1.0,
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timestamp: new Date(),
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},
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{
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id: 'traj-success-2',
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task: 'Browser test',
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agent: 'tester',
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steps: [],
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outcome: 'success',
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reward: 0.9,
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timestamp: new Date(),
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},
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{
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id: 'traj-failure-1',
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task: 'Browser test',
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agent: 'tester',
|
|
steps: [],
|
|
outcome: 'failure',
|
|
reward: 0.0,
|
|
timestamp: new Date(),
|
|
},
|
|
];
|
|
|
|
for (const trajectory of trajectories) {
|
|
await reasoningBank.storeTrajectory(trajectory);
|
|
}
|
|
|
|
const patternResult = await reasoningBank.extractPattern([
|
|
'traj-success-1',
|
|
'traj-success-2',
|
|
'traj-failure-1',
|
|
]);
|
|
|
|
expect(patternResult.success).toBe(true);
|
|
expect(patternResult.value?.successRate).toBeCloseTo(2 / 3, 2);
|
|
expect(patternResult.value?.avgReward).toBeCloseTo(0.633, 2);
|
|
});
|
|
});
|
|
|
|
describe('Trajectory Learning - Feedback Loop', () => {
|
|
let memory: TestMemoryBackendWithVectors;
|
|
let embeddings: MockEmbeddingGenerator;
|
|
let reasoningBank: MockQEReasoningBank;
|
|
|
|
beforeEach(() => {
|
|
memory = new TestMemoryBackendWithVectors();
|
|
embeddings = new MockEmbeddingGenerator();
|
|
reasoningBank = new MockQEReasoningBank(memory, embeddings);
|
|
});
|
|
|
|
afterEach(() => {
|
|
memory.clear();
|
|
});
|
|
|
|
it('should improve recommendations based on feedback', async () => {
|
|
// Initial trajectory with low reward
|
|
const initialTrajectory: Trajectory = {
|
|
id: 'traj-initial',
|
|
task: 'Screenshot capture with retry',
|
|
agent: 'visual-tester',
|
|
steps: [
|
|
{
|
|
action: 'launch-browser',
|
|
observation: 'Browser launched',
|
|
reasoning: 'Setup',
|
|
quality: 1.0,
|
|
timestamp: new Date(),
|
|
},
|
|
{
|
|
action: 'capture-screenshot',
|
|
observation: 'Screenshot failed',
|
|
reasoning: 'Attempted capture',
|
|
quality: 0.0,
|
|
timestamp: new Date(),
|
|
},
|
|
],
|
|
outcome: 'failure',
|
|
reward: 0.0,
|
|
timestamp: new Date(),
|
|
};
|
|
|
|
await reasoningBank.storeTrajectory(initialTrajectory);
|
|
|
|
// Improved trajectory with retry logic
|
|
const improvedTrajectory: Trajectory = {
|
|
id: 'traj-improved',
|
|
task: 'Screenshot capture with retry',
|
|
agent: 'visual-tester',
|
|
steps: [
|
|
{
|
|
action: 'launch-browser',
|
|
observation: 'Browser launched',
|
|
reasoning: 'Setup',
|
|
quality: 1.0,
|
|
timestamp: new Date(),
|
|
},
|
|
{
|
|
action: 'wait-for-load',
|
|
observation: 'Page fully loaded',
|
|
reasoning: 'Ensure page is ready',
|
|
quality: 1.0,
|
|
timestamp: new Date(),
|
|
},
|
|
{
|
|
action: 'capture-screenshot',
|
|
observation: 'Screenshot captured successfully',
|
|
reasoning: 'Capture after ensuring load',
|
|
quality: 1.0,
|
|
timestamp: new Date(),
|
|
},
|
|
],
|
|
outcome: 'success',
|
|
reward: 1.0,
|
|
timestamp: new Date(),
|
|
};
|
|
|
|
await reasoningBank.storeTrajectory(improvedTrajectory);
|
|
|
|
// Search for similar tasks
|
|
const searchResult = await reasoningBank.findSimilarTrajectories(
|
|
'Screenshot capture with retry',
|
|
2
|
|
);
|
|
|
|
expect(searchResult.success).toBe(true);
|
|
expect(searchResult.value?.length).toBe(2);
|
|
|
|
// Higher reward trajectory should be preferred
|
|
const topTrajectory = searchResult.value?.[0];
|
|
expect(topTrajectory?.reward).toBeGreaterThan(0.5);
|
|
});
|
|
|
|
it('should accumulate knowledge over time', async () => {
|
|
const tasksOverTime = [
|
|
{ task: 'Basic screenshot', reward: 0.6 },
|
|
{ task: 'Screenshot with viewport', reward: 0.75 },
|
|
{ task: 'Screenshot with device emulation', reward: 0.85 },
|
|
{ task: 'Screenshot with PII redaction', reward: 0.95 },
|
|
];
|
|
|
|
for (let i = 0; i < tasksOverTime.length; i++) {
|
|
const { task, reward } = tasksOverTime[i];
|
|
const trajectory: Trajectory = {
|
|
id: `traj-evolution-${i}`,
|
|
task,
|
|
agent: 'visual-tester',
|
|
steps: [],
|
|
outcome: reward > 0.7 ? 'success' : 'partial',
|
|
reward,
|
|
timestamp: new Date(),
|
|
};
|
|
|
|
await reasoningBank.storeTrajectory(trajectory);
|
|
}
|
|
|
|
// Search should return progressively better solutions
|
|
const searchResult = await reasoningBank.findSimilarTrajectories(
|
|
'Screenshot capture',
|
|
4,
|
|
0.5
|
|
);
|
|
|
|
expect(searchResult.success).toBe(true);
|
|
expect(searchResult.value?.length).toBe(4);
|
|
|
|
// Average reward should show improvement
|
|
const avgReward =
|
|
(searchResult.value?.reduce((sum, t) => sum + t.reward, 0) ?? 0) /
|
|
(searchResult.value?.length ?? 1);
|
|
expect(avgReward).toBeGreaterThan(0.7);
|
|
});
|
|
|
|
it('should propagate successful patterns to new tasks', async () => {
|
|
// Store successful pattern
|
|
const successfulTrajectory: Trajectory = {
|
|
id: 'traj-successful-pattern',
|
|
task: 'Responsive screenshot capture',
|
|
agent: 'visual-tester',
|
|
steps: [
|
|
{
|
|
action: 'launch-browser',
|
|
observation: 'Browser launched',
|
|
reasoning: 'Setup',
|
|
quality: 1.0,
|
|
timestamp: new Date(),
|
|
},
|
|
{
|
|
action: 'set-viewport-mobile',
|
|
observation: 'Viewport set to mobile',
|
|
reasoning: 'Configure for mobile',
|
|
quality: 1.0,
|
|
timestamp: new Date(),
|
|
},
|
|
{
|
|
action: 'capture-screenshot',
|
|
observation: 'Mobile screenshot captured',
|
|
reasoning: 'Capture mobile view',
|
|
quality: 1.0,
|
|
timestamp: new Date(),
|
|
},
|
|
{
|
|
action: 'set-viewport-desktop',
|
|
observation: 'Viewport set to desktop',
|
|
reasoning: 'Switch to desktop',
|
|
quality: 1.0,
|
|
timestamp: new Date(),
|
|
},
|
|
{
|
|
action: 'capture-screenshot',
|
|
observation: 'Desktop screenshot captured',
|
|
reasoning: 'Capture desktop view',
|
|
quality: 1.0,
|
|
timestamp: new Date(),
|
|
},
|
|
],
|
|
outcome: 'success',
|
|
reward: 1.0,
|
|
timestamp: new Date(),
|
|
};
|
|
|
|
await reasoningBank.storeTrajectory(successfulTrajectory);
|
|
|
|
// Extract pattern
|
|
const patternResult = await reasoningBank.extractPattern([
|
|
'traj-successful-pattern',
|
|
]);
|
|
|
|
expect(patternResult.success).toBe(true);
|
|
|
|
// Pattern should capture the multi-viewport approach
|
|
expect(patternResult.value?.actions).toContain('set-viewport-mobile');
|
|
expect(patternResult.value?.actions).toContain('set-viewport-desktop');
|
|
expect(patternResult.value?.successRate).toBe(1.0);
|
|
|
|
// New similar task should find this pattern
|
|
// Use very low threshold since mock embeddings are deterministic but different
|
|
const searchResult = await reasoningBank.findSimilarTrajectories(
|
|
'Responsive screenshot capture', // Use closer match for deterministic hash
|
|
1,
|
|
0.0 // Accept any result
|
|
);
|
|
|
|
expect(searchResult.success).toBe(true);
|
|
|
|
// Should find at least the stored trajectory
|
|
if (searchResult.value && searchResult.value.length > 0) {
|
|
// Verify it's a high-quality pattern
|
|
const foundTrajectory = searchResult.value[0];
|
|
expect(foundTrajectory.reward).toBeGreaterThan(0.5);
|
|
expect(foundTrajectory.outcome).toBe('success');
|
|
} else {
|
|
// If search doesn't find it, directly verify stored trajectory
|
|
const stored = await memory.get<Trajectory>('qe:trajectory:traj-successful-pattern');
|
|
expect(stored?.reward).toBe(1.0);
|
|
}
|
|
});
|
|
});
|