ARCHE: Behavioral Simulation & Intelligent Recommendation Engine

✅ PRODUCTION READY
Test Status: 41/41 tests passing (100%)
Scenario Validation: 20/20 checks passing (100%)
Deployment Status: Ready for live deployment

Executive Summary

ARCHE is a production-grade AI system that demonstrates behavioral simulation and context-aware recommendation intelligence. It solves the core hackathon challenge: given a user's behavioral history, predict their review ratings/text (Task A) and deliver personalized recommendations with reasoning (Task B).

The system achieves this through:

• Unified Behavioral Engine — Single brain powers both Task A and Task B
• LLM-Driven Intelligence — Groq (Llama) + Anthropic Claude for inference; OpenAI integration optional
• Live Web Search — Serper + DuckDuckGo for real-time contextual recommendations
• Privacy by Design — SHA256 token hashing, context redaction, no raw PII storage
• Production Logging — Structured instrumentation (provider, model, latency, success/error)
• Memory Persistence — SQLite + optional embeddings for behavior history replay

---

Product Overview

Problem

E-commerce and rating platforms struggle with:

1. Cold-start problem — No interaction history → generic recommendations
2. Behavioral prediction — Can't predict what users will review or how they'll rate
3. Context blindness — Ignoring time-of-day, device, region when personalizing
4. Lack of reasoning — Recommendations appear arbitrary to users

Solution

ARCHE provides two complementary APIs that work in tandem:

Task A: Review Generation (POST /v1/simulate-review)

• Input: User persona (review history) + unseen product
• Output: Predicted 1-5 star rating + authentic review text in user's voice
• Mechanism: Reviews generated fresh from behavioral snapshot (no historical copy)
• Quality Metrics: BERTScore/ROUGE for text, RMSE for rating accuracy

Task B: Personalized Recommendations (POST /v1/recommend)

• Input: User behavioral history + optional real-time context
• Output: Top-10 ranked items with explanation types (precision/adjacent/discovery)
• Mechanism: LLM-scored simulation → ranking with exploration factor tuning
• Quality Metrics: NDCG@10, Hit Rate@10, Precision@10, contextual relevance

---

Key Features

🎯 Intelligent User Simulation

• Behavioral snapshot building from review history (rating distribution, category affinity, style metrics)
• Context-aware simulation (time-of-day, device, region, session depth)
• Heuristic fallback when LLM unavailable (deterministic, reproducible)

📝 Fresh Review Generation

• Extracts writing style (vocabulary diversity, sentence length, formality register) NOT raw text
• Generates completely fresh reviews using LLM or heuristic templates
• No content leakage — validates against historical text corpus
• Pidgin, formal, technical, casual registers supported

🔍 Contextual Recommendations

• Precision recommendations — Top matches for stated interests
• Adjacent exploration — Related categories to current interests
• Discovery recommendations — New items at appropriate price tier
• Live web search for real-time catalog expansion

🔐 Privacy Architecture

• Token hashing (SHA256 with app-level salt)
• Context redaction (email, phone, location stripped before persistence)
• Privacy layer transparent to API consumers (original tokens returned)
• GDPR-ready design (no user PII in memory logs)

📊 Observability & Instrumentation

• LLM instrumentation metadata in all responses (provider, model used, latency)
• Live search provider tracking (Serper vs DuckDuckGo)
• Structured logging (agent operations, HTTP calls, latency tracking)
• Performance trace support for debugging

🌐 Live Data Integration

• Serper API for Google Search results → real-time product discovery
• DuckDuckGo JSON fallback for resilience
• Query planning via LLM (what to search for given user interests)
• Seamless merging of memory-based + live results

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Architecture

Layered Design

┌─────────────────────────────────────────┐
│     API Routes (task_a, task_b, etc)    │
├─────────────────────────────────────────┤
│  Orchestrator (Pipeline, LLM wiring)    │
├─────────────────────────────────────────┤
│  Core Agents (Review Gen, Simulation)   │
├─────────────────────────────────────────┤
│  Memory Layer (SQLite + Local Vector)   │
├─────────────────────────────────────────┤
│  Privacy Abstraction (Hashing + Redaction)  │
└─────────────────────────────────────────┘

Key Components

Component	File(s)	Purpose
API Entrypoint	api/main.py	FastAPI app, routes, logging config
Task A Route	api/routes/task_a.py	/v1/simulate-review endpoint
Task B Route	api/routes/task_b.py	/v1/recommend endpoint
Review Agent	agents/review_generation_agent.py	Generates review text + rating
Simulation Agent	agents/simulation_agent.py	Builds behavioral snapshot
Recommendation Scoring	agents/recommendation_scoring.py	Ranks items, generates explanations
Live Search	api/live_search.py	Serper + DuckDuckGo integration
Memory Manager	memory/memory_manager.py	SQLite CRUD for behavior signals
Local Vector Store	memory/local_vector_store.py	In-memory embeddings fallback

---

API Endpoints

Health & Metadata

GET /v1/health
Returns: { "status": "ok" }

Task A: Review Simulation

POST /v1/simulate-review
Content-Type: application/json

Request:
{
  "user_token": "user-123",
  "review_history": [
    {
      "rating": 5.0,
      "item_category": "electronics",
      "review_text": "Great product!",
      "context": { "time_of_day": "evening", "day_type": "weekend" }
    }
  ],
  "unseen_item": {
    "product_id": "item-456",
    "category": "electronics",
    "price": 199.99,
    "description": "Bluetooth Speaker"
  },
  "context": {
    "time_of_day": "morning",
    "day_type": "weekday",
    "device_class": "mobile",
    "region_tier": "urban"
  }
}

Response:
{
  "user_token": "user-123",
  "predicted_rating": 4.5,
  "review_text": "Solid speaker, great sound quality but pricey.",
  "behavioral_basis": "Similar electronics purchases, evening preference",
  "llm_instrumentation": {
    "used": true,
    "provider": "groq",
    "model": "llama-3.1-70b-versatile"
  }
}

Task B: Recommendations

POST /v1/recommend
Content-Type: application/json

Request:
{
  "user_token": "user-123",
  "review_history": [
    {
      "rating": 5.0,
      "item_category": "books",
      "review_text": "Amazing sci-fi novel!",
      "context": { "time_of_day": "evening" }
    }
  ],
  "context": {
    "time_of_day": "evening",
    "day_type": "weekday",
    "exploration_factor": 0.3
  },
  "n": 10,
  "live_data_enabled": true
}

Response:
{
  "user_token": "user-123",
  "recommendations": [
    {
      "rank": 1,
      "item_id": "b-999",
      "title": "Dune Messiah",
      "category": "books",
      "score": 0.92,
      "recommendation_type": "precision",
      "explanation": "Matches your interest in epic sci-fi novels.",
      "rationale": "High engagement with sci-fi; category affinity 5.0"
    },
    {
      "rank": 2,
      "item_id": "g-456",
      "title": "Foundation",
      "category": "books",
      "score": 0.88,
      "recommendation_type": "adjacent_exploration",
      "explanation": "Related to your sci-fi interest, explores AI themes.",
      "rationale": "Adjacent category; similar price tier"
    }
  ],
  "live_search_provider": "serper",
  "llm_instrumentation": {
    "used": true,
    "provider": "groq",
    "model": "llama-3.1-70b-versatile",
    "query_planning_latency_ms": 120
  }
}

Full Endpoint Reference

• GET /v1/health — Health check
• POST /v1/ingest — Ingest behavioral signals (internal use)
• POST /v1/simulate — Build behavioral snapshot (internal)
• POST /v1/simulate-review — Task A (review generation)
• POST /v1/recommend — Task B (recommendations)
• POST /v1/explain — Get explanation trace for a recommendation

---

Local Setup & Development

1. Prerequisites

• Python 3.11+
• Virtual environment (venv, conda, or pyenv)
• Git

2. Clone & Install

# Clone repo
git clone https://github.com/yourusername/ARCHE.git
cd ARCHE

# Create virtual environment
python -m venv .venv
.\.venv\Scripts\Activate.ps1

# Install dependencies
python -m pip install -r requirements.txt

3. Environment Variables

Create .env in project root:

# Required
GROQ_API_KEY=gsk_xxxxx
ANTHROPIC_API_KEY=sk-ant-xxxxx

# Optional (live search)
SERPER_API_KEY=xxxxx
ENABLE_FALLBACK_WEBSEARCH=true

# Logging
ARCHE_LOG_LEVEL=INFO

4. Run Backend

# With auto-reload for development
python -m uvicorn api.main:app --reload --port 8000

# Production mode
python -m uvicorn api.main:app --host 0.0.0.0 --port 8000 --workers 4

Visit: http://localhost:8000/docs (Swagger UI)

5. Run Frontend

cd frontend
npm install
npm run dev

Visit: http://localhost:5173

---

Testing & Validation

Run Full Test Suite

# All tests (41 tests, ~30 seconds)
pytest -q

# Specific test file
pytest tests/test_task_a.py -v
pytest tests/test_task_b.py -v

# With coverage
pytest --cov=api --cov=agents --cov=memory -q

Generate Comprehensive Report

# 7-scenario validation (covers all major flows)
python scripts/generate_7_scenario_report.py

Current Status:

• ✅ 41/41 tests passing (100%)
• ✅ 20/20 scenario validation checks passing (100%)
• ✅ No content leakage (Task A)
• ✅ Intelligent explanations (Task B)
• ✅ Memory persistence working
• ✅ LLM instrumentation captured
• ✅ Live search integration validated

Sample Test Scenarios

1. Task A (Formal Persona) — Generates formal, detailed reviews
2. Task A (Pidgin Persona) — Generates casual, conversational reviews
3. Task B (Cold Start) — Recommends with empty history + time-of-day context
4. Task B (Personalized) — Top recommendations match stated interests
5. Task B (Live Search) — Merges memory + real-time results
6. Task B (Manual Override) — Respects user query directives
7. Task B (Explain Trace) — Returns reasoning for recommendations

---

Deployment

Quick Deploy (Recommended)

Backend → Render (5 min)

1. Push code to GitHub
2. Create Render Web Service
3. Set environment variables (GROQ_API_KEY, etc.)
4. Deploy (auto-scales)

Frontend → Vercel (5 min)

1. Update vercel.json with backend URL
2. Push code to GitHub
3. Create Vercel project
4. Deploy (auto-builds from git)

See: DEPLOYMENT_GUIDE.md for detailed step-by-step instructions, troubleshooting, and rollback procedures.

Docker Compose (Local)

docker compose up --build
# Runs API on :8000, frontend on :5173

---

Performance & Scale

Metric	Value
Task A Latency	800ms–2s (includes LLM call + generation)
Task B Latency	500ms–1.5s (ranking ~8600 items)
Memory Cold Start	<100ms (heuristic fallback)
Live Search Query	200–600ms (Serper API + LLM planning)
Throughput	10+ req/sec per instance
Storage	<5MB for 10k user signals (SQLite)

---

What's Next

For Deployment

• Set up .env with API keys
• Deploy backend to Render or Railway
• Deploy frontend to Vercel with backend URL
• Test live URLs for CORS and API connectivity
• Monitor logs on dashboard

For Judges & Submission

• Review JUDGES_GUIDE.md for evaluation path
• Review SESSION_SUMMARY.md for complete context
• Test demo flows locally before submitting
• Share live URLs + Swagger docs (/docs endpoint)

Future Enhancements (Out of Scope)

• Native LangGraph DAG orchestration
• Vector embeddings for semantic search
• A/B testing framework for exploration factor tuning
• Multi-model ensemble (Groq + Anthropic + Claude)
• Redis caching for frequently accessed items

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Documentation

Document	Purpose
DEPLOYMENT_GUIDE.md	Step-by-step deployment to Render + Vercel
JUDGES_GUIDE.md	Quick evaluation path and success criteria
SESSION_SUMMARY.md	Complete recap of all work and fixes
BuildDocs/ARCHE_Hackathon_PRD.md	Original product requirements
BuildDocs/ARCHE_Hackathon_Architecture_Dev_Plan.md	Architecture deep-dive

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Tech Stack

Backend

• FastAPI 0.120+ (Python async web framework)
• Pydantic (request/response validation)
• SQLite3 (behavior signal persistence)
• httpx (async HTTP for LLM + web search)
• Groq API (Llama 3.1 70B inference)
• Anthropic Claude (fallback LLM)
• Serper (Google Search) + DuckDuckGo (web search)

Frontend

• Vite + React (UI framework)
• Tailwind CSS (styling)
• Responsive mobile-first design

Testing

• pytest (test framework)
• FastAPI TestClient (in-process API testing)
• Mock data fixtures

Deployment

• Docker + Docker Compose (containerization)
• Render or Railway (backend hosting)
• Vercel (frontend hosting)
• GitHub (version control)

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Quick Links

• Live Demo: [Frontend URL] (after deployment)
• API Swagger: [Backend URL]/docs
• Hackathon: DSN x BCT LLM Agent Challenge 3.0
• Submission Deadline: May 24, 2026

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Status as of May 26, 2026: ✅ Production ready, all tests passing, deployment guides complete.