arela

# 🎉 v4.0.2 SHIPPED + v4.1.0 READY TO BUILD **Date:** 2025-11-15 **Status:** v4.0.2 LIVE on NPM, v4.1.0 tickets created --- ## ✅ v4.0.2 - SHIPPED! ### What We Shipped - **OpenAI Integration** - gpt-4o-mini as primary classifier - **700-1500ms classification** - Fast and reliable - **Auto-fallback** - Uses Ollama if OpenAI unavailable - **Simple .env setup** - Just add OPENAI_API_KEY ### Package Info - **Version:** 4.0.2 - **Size:** 1.3 MB - **Files:** 990 - **Status:** LIVE on npm registry ### Install It ```bash npm install -g arela@latest arela init echo "OPENAI_API_KEY=sk-proj-..." >> .env ``` ### Documentation Updated - ✅ README.md - v4.0.2 features - ✅ CHANGELOG.md - Full release notes - ✅ QUICKSTART.md - OpenAI setup guide - ✅ package.json - Version 4.0.2 - ✅ src/cli.ts - Version 4.0.2 --- ## 🎯 v4.1.0 - READY TO BUILD ### Goal **Complete Meta-RAG Context Routing System** Integrate: Classifier → Router → Fusion → Context Router ### Architecture ``` User Query ↓ QueryClassifier (OpenAI/Ollama) ✅ DONE ↓ MemoryRouter (layer selection) ⏳ NEXT ↓ FusionEngine (dedup + merge) ⏳ NEXT ↓ ContextRouter (orchestrator) ⏳ NEXT ↓ MCP Server (arela_search) ⏳ INTEGRATION ↓ Cascade/Agents ``` ### Tickets Created **1. META-RAG-002: Memory Router** - **Agent:** Claude - **Time:** 2-3 hours - **Status:** Ready to start - **File:** `.arela/tickets/claude/META-RAG-002-v4.1.0-memory-router.md` **What it does:** - Routes queries to appropriate memory layers - Parallel execution (Promise.all) - Timeout handling (50ms per layer) - Result aggregation **2. FUSION-001: Result Fusion** - **Agent:** Claude - **Time:** 2-3 hours - **Status:** Depends on META-RAG-002 - **File:** `.arela/tickets/claude/FUSION-001-v4.1.0-result-fusion.md` **What it does:** - Scores results by relevance - Semantic deduplication - Layer weighting - Token limiting **3. CONTEXT-001: Context Router Integration** - **Agent:** Cascade - **Time:** 2-3 hours - **Status:** Depends on FUSION-001 - **File:** `.arela/tickets/cascade/CONTEXT-001-v4.1.0-context-router.md` **What it does:** - End-to-end orchestration - MCP integration - CLI command (`arela route`) - Performance tracking ### Timeline **Total Time:** 6-9 hours (2-3 days) **Day 1:** - META-RAG-002 (Memory Router) - 2-3 hours **Day 2:** - FUSION-001 (Result Fusion) - 2-3 hours **Day 3:** - CONTEXT-001 (Context Router) - 2-3 hours - Testing + Integration - Ship v4.1.0 ### Success Criteria - [ ] End-to-end routing working - [ ] <3s total query time - [ ] >90% routing accuracy - [ ] Token limits respected - [ ] MCP integration complete - [ ] CLI command working - [ ] All tests passing ### Expected Performance **Query: "Continue working on authentication"** ``` Classification: 700-1500ms (OpenAI) Retrieval: 100-200ms (parallel layers) Fusion: 50-100ms (dedup + merge) Total: <2s ✅ ``` ### User Experience **Before v4.1.0:** ``` Agent: arela_search "auth code" Result: All 6 layers queried (slow, expensive) ``` **After v4.1.0:** ``` Agent: arela_search "Continue working on auth" Arela: - Classifies as PROCEDURAL - Routes to Session + Project + Graph only - Deduplicates results - Returns optimal context Result: 3 layers queried (fast, cheap, relevant) ``` ### CLI Command ```bash # Test routing arela route "Continue working on authentication" --debug # Output: 🔍 Routing query: "Continue working on authentication" 📊 Classification: procedural (1.0) 🎯 Layers: session, project, graph 🔄 Routed to 3 layers 🔥 Fused 47 → 12 items 💾 Estimated tokens: 8543 ✅ Total: 1479ms ``` ### Files to Create **Memory Router:** - `src/meta-rag/router.ts` - `test/meta-rag/router.test.ts` **Fusion Engine:** - `src/fusion/scorer.ts` - `src/fusion/dedup.ts` - `src/fusion/merger.ts` - `src/fusion/index.ts` - `test/fusion/fusion.test.ts` **Context Router:** - `src/context-router.ts` - `test/context-router.test.ts` - Update `src/mcp/server.ts` - Update `src/cli.ts` --- ## 🚀 How to Execute ### Option 1: Orchestration (Recommended) ```bash # Start with Memory Router arela orchestrate --tickets META-RAG-002-v4.1.0-memory-router # Then Fusion Engine arela orchestrate --tickets FUSION-001-v4.1.0-result-fusion # Finally Context Router arela orchestrate --tickets CONTEXT-001-v4.1.0-context-router ``` ### Option 2: Manual Delegation 1. Assign META-RAG-002 to Claude 2. Wait for completion 3. Assign FUSION-001 to Claude 4. Wait for completion 5. Assign CONTEXT-001 to Cascade 6. Review and ship ### Option 3: Parallel (Risky) - META-RAG-002 and FUSION-001 can be built in parallel - CONTEXT-001 must wait for both --- ## 📊 Impact ### Token Savings **Current:** Query all 6 layers every time - Session: 2k tokens - Project: 3k tokens - User: 1k tokens - Vector: 5k tokens - Graph: 2k tokens - Governance: 1k tokens - **Total: 14k tokens per query** **After v4.1.0:** Query only relevant layers - PROCEDURAL: Session + Project + Graph = 7k tokens (50% savings) - FACTUAL: Vector only = 5k tokens (64% savings) - USER: User only = 1k tokens (93% savings) **Average savings: 50-70%** ### Performance **Current:** Sequential queries = 600ms+ (6 layers × 100ms) **After v4.1.0:** Parallel queries = 100-200ms (3 layers in parallel) **Speedup: 3-6x faster** ### Quality **Current:** Dump all memory, hope LLM finds relevant bits **After v4.1.0:** - Smart routing (right layers) - Relevance scoring (best results first) - Deduplication (no redundancy) - Token limiting (fits context window) **Result: Better context, better responses** --- ## 🎓 What This Enables ### For Cascade (You) - Faster context gathering (<2s vs 5s+) - More relevant results - Lower token costs - Better responses ### For Users - Faster AI responses - Lower API costs - More accurate answers - Better memory utilization ### For Arela - Intelligent context routing - Scalable to millions of files - Production-ready memory system - Foundation for v5.0 (VS Code extension) --- ## 🔮 After v4.1.0 **v4.2.0 (Optional):** - TOON compression (50-70% token savings) - Adaptive routing (learn from usage) - Streaming results **v5.0.0 (Big One):** - VS Code Extension - Direct IDE integration - No MCP dependency - Reliable context routing --- ## 📝 Summary **v4.0.2:** ✅ SHIPPED - OpenAI integration working **v4.1.0:** 🎯 READY - 3 tickets created, 6-9 hours of work **Next Step:** Start with META-RAG-002 (Memory Router) **The Goal:** Complete intelligent context routing system **The Vision:** Arela understands your queries and delivers the perfect context, every time. --- **Let's build v4.1.0!** 🚀