arela

# Arela v4.2.0 Development Plan **Date:** 2025-11-15 **Status:** Planning **Goal:** Advanced Intelligence & Learning --- ## Current State (v4.1.0 - COMPLETE) ### ✅ What We Have - Meta-RAG Pipeline (QueryClassifier → MemoryRouter → FusionEngine → ContextRouter) - Hexi-Memory System (6 layers) - Multi-Agent Orchestration - CLI Commands - Performance: <2s, 73% token reduction - 40/40 tests passing --- ## v4.2.0 Features ### 1. Advanced Summarization 🎯 **Priority:** HIGH **Goal:** Code → Summary for better context **What it does:** - Summarizes large code files into concise descriptions - Extracts key functions, classes, and patterns - Reduces token usage by 50%+ for large files - Maintains semantic meaning **Implementation:** ```typescript class CodeSummarizer { // Summarize code file async summarize(code: string, options: { maxTokens: number; level: 'brief' | 'detailed'; }): Promise<string> { // 1. Parse AST // 2. Extract key elements (functions, classes, exports) // 3. Generate summary using LLM // 4. Validate summary maintains meaning } // Summarize multiple files async summarizeBatch(files: string[]): Promise<Map<string, string>> { // Parallel summarization } } ``` **Use Cases:** - Large utility files (>500 lines) - Third-party libraries - Generated code - Legacy code **Files to Create:** - `src/summarization/code-summarizer.ts` - `src/summarization/ast-extractor.ts` - `test/summarization/summarizer.test.ts` **Success Criteria:** - [ ] Summarizes 1000-line file to <100 tokens - [ ] Maintains 90%+ semantic accuracy - [ ] <3s per file - [ ] Works with TypeScript, JavaScript, Python, Go --- ### 2. Learning from Feedback 🎯 **Priority:** HIGH **Goal:** Improve routing based on user feedback **What it does:** - Tracks which context was actually useful - Learns from user corrections - Adapts layer weights over time - Improves classification accuracy **Implementation:** ```typescript class FeedbackLearner { // Record feedback async recordFeedback(query: string, routing: RoutingResult, feedback: { helpful: boolean; correctLayers?: string[]; correctType?: QueryType; }): Promise<void> { // Store in governance layer } // Adjust weights based on feedback async adjustWeights(): Promise<void> { // Analyze feedback patterns // Update layer weights // Retrain classifier (if needed) } // Get learning stats getStats(): { totalFeedback: number; accuracy: number; improvements: string[]; } } ``` **Use Cases:** - User says "This context wasn't helpful" - User corrects classification - User provides better layer selection **Files to Create:** - `src/learning/feedback-learner.ts` - `src/learning/weight-adjuster.ts` - `test/learning/feedback.test.ts` **Success Criteria:** - [ ] Accuracy improves by 10%+ after 100 feedbacks - [ ] Weights adapt to user patterns - [ ] Feedback stored in governance layer - [ ] CLI command: `arela feedback --helpful/--not-helpful` --- ### 3. Multi-Hop Reasoning 🎯 **Priority:** MEDIUM **Goal:** Answer complex queries requiring multiple steps **What it does:** - Breaks complex queries into sub-queries - Routes each sub-query independently - Combines results intelligently - Handles dependencies between sub-queries **Implementation:** ```typescript class MultiHopRouter { // Decompose complex query async decompose(query: string): Promise<SubQuery[]> { // Use LLM to break down query // Example: "How does auth work and where is it used?" // → ["How does auth work?", "Where is auth used?"] } // Route with dependencies async routeMultiHop(query: string): Promise<MultiHopResult> { // 1. Decompose query // 2. Route each sub-query // 3. Combine results // 4. Resolve dependencies } } ``` **Use Cases:** - "How does auth work and where is it used?" - "What are the dependencies of X and how do they interact?" - "Show me the data flow from API to database" **Files to Create:** - `src/multi-hop/decomposer.ts` - `src/multi-hop/router.ts` - `test/multi-hop/multi-hop.test.ts` **Success Criteria:** - [ ] Handles 2-3 hop queries - [ ] Combines results coherently - [ ] <5s for 2-hop query - [ ] 85%+ accuracy --- ### 4. Fine-Tuned Models (Optional) 🎯 **Priority:** LOW **Goal:** Custom models for classification **What it does:** - Fine-tune small models on user's codebase - Improve classification accuracy - Reduce latency - Reduce costs **Implementation:** ```typescript class ModelFineTuner { // Collect training data async collectTrainingData(): Promise<TrainingData[]> { // From feedback + manual labels } // Fine-tune model async fineTune(model: string, data: TrainingData[]): Promise<string> { // Use OpenAI fine-tuning API // Or local fine-tuning (Ollama) } // Use fine-tuned model async classify(query: string, model: string): Promise<Classification> { // Use custom model } } ``` **Use Cases:** - User has specific domain (e.g., medical, finance) - User wants faster classification - User wants lower costs **Files to Create:** - `src/fine-tuning/trainer.ts` - `src/fine-tuning/data-collector.ts` - `test/fine-tuning/fine-tune.test.ts` **Success Criteria:** - [ ] Collects 100+ training examples - [ ] Fine-tunes gpt-4o-mini or llama3.2 - [ ] Improves accuracy by 5%+ - [ ] Reduces latency by 20%+ --- ## Timeline **Week 1 (Days 1-3):** - ✅ Day 1: Advanced Summarization (implementation) - ✅ Day 2: Advanced Summarization (testing) - ✅ Day 3: Learning from Feedback (implementation) **Week 2 (Days 4-7):** - ✅ Day 4: Learning from Feedback (testing) - ✅ Day 5: Multi-Hop Reasoning (implementation) - ✅ Day 6: Multi-Hop Reasoning (testing) - ✅ Day 7: Integration & Polish **Optional (Week 3):** - Day 8-10: Fine-Tuned Models (if time permits) **Total:** 7-10 days --- ## Architecture ``` User Query ↓ Multi-Hop Decomposer (if complex) ↓ QueryClassifier (with fine-tuned model) ↓ MemoryRouter (with learned weights) ↓ Hexi-Memory (6 layers) ↓ FusionEngine (with summarization) ↓ Code Summarizer (for large files) ↓ Optimal Context ↓ User Feedback → Learning System ``` --- ## Success Criteria ### Performance - [ ] Summarization: <3s per file - [ ] Multi-hop: <5s for 2-hop query - [ ] Learning: 10%+ accuracy improvement after 100 feedbacks ### Accuracy - [ ] Summarization: 90%+ semantic accuracy - [ ] Multi-hop: 85%+ correct decomposition - [ ] Learning: Weights adapt to user patterns ### Efficiency - [ ] Token savings: 50%+ with summarization - [ ] Context quality: 20%+ improvement with learning - [ ] Latency: <20% increase with multi-hop --- ## Files Summary **New files (v4.2.0):** ``` src/ ├── summarization/ │ ├── code-summarizer.ts │ ├── ast-extractor.ts │ └── index.ts ├── learning/ │ ├── feedback-learner.ts │ ├── weight-adjuster.ts │ └── index.ts ├── multi-hop/ │ ├── decomposer.ts │ ├── router.ts │ └── index.ts └── fine-tuning/ (optional) ├── trainer.ts ├── data-collector.ts └── index.ts test/ ├── summarization/ │ └── summarizer.test.ts ├── learning/ │ └── feedback.test.ts ├── multi-hop/ │ └── multi-hop.test.ts └── fine-tuning/ (optional) └── fine-tune.test.ts ``` **Total:** ~1,500 lines of new code + tests --- ## Competitive Advantage **Current tools:** - Cursor/Windsurf: No summarization, no learning - Copilot: No context memory at all - Devin: Basic memory, no adaptation **Arela with v4.2.0:** - ✅ Intelligent summarization (50%+ token savings) - ✅ Learns from feedback (improves over time) - ✅ Multi-hop reasoning (handles complex queries) - ✅ Optional fine-tuning (custom models) **This makes Arela truly adaptive and intelligent.** --- ## Next Steps **Immediate (Today):** 1. 🎯 Start with Advanced Summarization 2. 🎯 Create code-summarizer.ts 3. 🎯 Write tests **This Week:** 1. 🎯 Complete Summarization 2. 🎯 Implement Learning from Feedback 3. 🎯 Build Multi-Hop Reasoning **Next Week (v5.0.0):** 1. 🎯 VS Code Extension 2. 🎯 Real-time collaboration 3. 🎯 Cloud sync --- ## Philosophy > "Make it work, make it right, make it fast." - Kent Beck **v4.1.0:** Make it work (basic Meta-RAG) ✅ **v4.2.0:** Make it right (add intelligence + learning) ← We are here **v4.3.0:** Make it fast (optimize performance) **v5.0.0:** Make it accessible (VS Code extension) **Ship working software. Iterate based on real usage.** 🚀