b5f7047358
* feat: multi-turn context and conversation arc memory PR 2E - Section 2.9, 2.10: - Add multiTurnContext.ts with turn tracking and state preservation - Add conversationArc.ts with goal/decision/milestone tracking - Wire into query.ts after tool execution - Feature-flags: MULTI_TURN_CONTEXT, CONVERSATION_ARC - Add comprehensive tests (22 passing) * feat(memory): resolve review blockers and integrate native Knowledge Graph into Conversation Arcs - Fix: Extract text from production block arrays in phase detector\n- Fix: Ensure proper turn segmentation in query loop\n- Fix: Respect options in multi-turn context tracker\n- Feat: Add native Knowledge Graph (Entities/Relations) to ConversationArc architecture\n- Test: Comprehensive test suite for all fixes and new graph features * test(perf): add automated performance benchmarks for Knowledge Graph extraction and summary --------- Co-authored-by: LifeJiggy <Bloomtonjovish@gmail.com>
69 lines
2.2 KiB
TypeScript
69 lines
2.2 KiB
TypeScript
import { describe, expect, it, beforeEach } from 'bun:test'
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import {
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initializeArc,
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updateArcPhase,
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getArcSummary,
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resetArc
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} from './conversationArc.js'
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function createMessage(content: string): any {
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return {
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message: { role: 'user', content, id: 'test', type: 'message', created_at: Date.now() },
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sender: 'user',
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}
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}
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describe('Conversation Arc Performance Benchmarks', () => {
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beforeEach(() => {
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resetArc()
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initializeArc()
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})
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it('performs automatic fact extraction in sub-millisecond time', () => {
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const iterations = 100
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const complexContent = 'Deploying version v1.2.3 to /opt/prod/server on https://api.prod.local with JIRA_URL=https://jira.corp'
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const startTime = performance.now()
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for (let i = 0; i < iterations; i++) {
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updateArcPhase([createMessage(complexContent)])
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}
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const duration = performance.now() - startTime
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const averageTime = duration / iterations
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console.log(`[Benchmark] Avg extraction time: ${averageTime.toFixed(4)}ms`)
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// Performance guard: should definitely be under 0.5ms per message on any modern CI
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expect(averageTime).toBeLessThan(0.5)
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})
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it('generates summaries quickly even with a populated graph', () => {
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// Populate graph with 50 facts
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for (let i = 0; i < 50; i++) {
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updateArcPhase([createMessage(`Var_${i}=Value_${i} in /path/to/file_${i}`)])
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}
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const startTime = performance.now()
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const summary = getArcSummary()
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const duration = performance.now() - startTime
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console.log(`[Benchmark] Summary generation time (50 entities): ${duration.toFixed(4)}ms`)
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expect(summary).toContain('Knowledge Graph:')
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// Summary generation should be extremely fast
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expect(duration).toBeLessThan(10)
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})
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it('maintains a compact memory footprint', () => {
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const arc = initializeArc()
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for (let i = 0; i < 100; i++) {
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updateArcPhase([createMessage(`Fact_${i}=Value_${i}`)])
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}
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const serialized = JSON.stringify(arc)
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const sizeKB = serialized.length / 1024
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console.log(`[Benchmark] Memory footprint (100 facts): ${sizeKB.toFixed(2)}KB`)
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// Should be well under 100KB for 100 simple facts
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expect(sizeKB).toBeLessThan(100)
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})
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})
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