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Watch it happen in the browser dashboard.\n\n---\n\n## 🏗️ Architecture at a Glance\n\n```\n┌─────────────────────────────────────────────────────────────────┐\n│                   FastAPI + Web Dashboard                       │\n│              (POST /run → Stream pipeline status)               │\n└────┬────────────────────────────────────────────────────────────┘\n     │\n┌────▼────────────────────────────────────────────────────────────┐\n│              CrewAI Orchestration Layer (6 Agents)              │\n│ ┌──────────────┐  ┌──────────────┐  ┌──────────────────────┐   │\n│ │  Data Agent  │→ │Research Agent│→ │ ML + Prediction Agts │   │\n│ │ (RSS Fetch)  │  │ (RAG + Query)│  │ (Train + Inference) │   │\n│ └──────────────┘  └──────────────┘  └──────────────────────┘   │\n│                                              ↓                  │\n│                                   ┌─────────────────────┐       │\n│                                   │ Monitoring Agent    │       │\n│                                   │ (Drift Detection)   │       │\n│                                   └─────────────────────┘       │\n└────┬─────────────────────────────────────────────────────────────┘\n     │\n┌────┼──────────────────────────────────────────────────────────────┐\n│    │           Data & ML Pipeline                                 │\n│    └─→ CSV Reader ─→ Feature Engineer ─→ Model Trainer           │\n│         │                                    ↓                    │\n│         │                          ┌─────────────────┐            │\n│         │                          │ Trained Model   │            │\n│         │                          │ (signal_model)  │            │\n│         │                          └────────┬────────┘            │\n│         │                                   │                    │\n│         └──────────────────────────────────→│                    │\n│              ▲                               ▼                    │\n│              │                    ┌─────────────────┐             │\n│              └────────────────────│ Predictions &   │             │\n│                                   │ Performance Log │             │\n│                                   └─────────────────┘             │\n└────┬──────────────────────────────────────────────────────────────┘\n     │\n┌────▼──────────────────────────────────────────────────────────────┐\n│     ChromaDB Vector Database (Semantic Search for RAG)            │\n│     • Embeddings: OpenAI or local all-MiniLM-L6-v2               │\n│     • Queries: Find relevant news articles by meaning            │\n│     • Storage: Persistent disk at data/vectorstore/              │\n└────────────────────────────────────────────────────────────────────┘\n\nDeployment: Docker Container → Kubernetes Cluster → Production\n```\n\n---\n\n## ⚡ Quick Start (< 5 minutes)\n\n### Local Development\n\n**Prerequisites:** Python 3.10+, `uv`, Docker (optional)\n\n```bash\n# 1. Clone and install\ngit clone https://github.com/yourusername/Multi-Agent-AI-Research-Signal-Analysis-Platform.git\ncd Multi-Agent-AI-Research-Signal-Analysis-Platform\nuv sync\n\n# 2. Run the pipeline\nuv run source_code\n\n# 3. Start the API + dashboard\nuv run api\n# Open: http://localhost:8000\n```\n\n### Docker (Recommended)\n\n```bash\n# Build the image\ndocker build -f infra/docker/Dockerfile -t ai-signal-platform .\n\n# Run with compose\ndocker compose -f infra/docker/docker-compose.yml up\n\n# Access: http://localhost:8000\n```\n\n### Kubernetes (Production)\n\n```bash\n# Deploy to minikube\nminikube start\nminikube image load ai-signal-platform:latest\nkubectl apply -k infra/k8s/\n\n# Access: http://localhost:8000 (after port-forward)\nkubectl port-forward -n ai-signal svc/ai-signal-api 8000:80\n```\n\n---\n\n## 🎨 Features & Technologies\n\n### Multi-Agent AI\n- **CrewAI** orchestration framework\n- 6 specialized agents working in sequence\n- Tool-based capabilities (news fetching, model training, monitoring)\n- Verbose output for transparency\n\n### Data & ML\n- **RAG (Retrieval-Augmented Generation)** with semantic search\n- **ChromaDB** vector database (persistent storage on disk)\n- **OpenAI embeddings** (or free local all-MiniLM-L6-v2)\n- **scikit-learn** models (LogisticRegression, SVM, RandomForest comparison)\n- **Drift detection** — automatically flags when model accuracy drops >5%\n- **Model versioning** — timestamped artifacts\n\n### API & Frontend\n- **FastAPI** with async support\n- Interactive **web dashboard** (real-time job monitoring)\n- Health checks & status endpoints\n- RAG index rebuild trigger\n- Job status polling with WebSocket support\n\n### DevOps\n- **Docker** with multi-stage builds (fast layer caching)\n- **Docker Compose** for local development\n- **Kubernetes manifests** for production\n  - Deployments, Services, PersistentVolumeClaims\n  - ConfigMap + Secret management\n  - HorizontalPodAutoscaler (auto-scaling 2-5 replicas)\n  - Health probes and resource limits\n\n---\n\n## 📊 API Examples\n\n### Run the full pipeline\n\n```bash\ncurl -X POST http://localhost:8000/run\n```\n\n**Response:**\n```json\n{\n  \"job_id\": \"f47ac10b-58cc-4372-a567-0e02b2c3d479\",\n  \"status\": \"queued\",\n  \"message\": \"Crew pipeline started in the background.\"\n}\n```\n\n### Check job status\n\n```bash\ncurl http://localhost:8000/status/f47ac10b-58cc-4372-a567-0e02b2c3d479\n```\n\n**Response:**\n```json\n{\n  \"job_id\": \"f47ac10b-58cc-4372-a567-0e02b2c3d479\",\n  \"status\": \"completed\",\n  \"started_at\": \"2026-04-20T12:34:56\",\n  \"finished_at\": \"2026-04-20T12:45:23\",\n  \"result\": \"Pipeline completed. 14 articles processed, bullish: 8, bearish: 6...\"\n}\n```\n\n### Get latest model metrics\n\n```bash\ncurl http://localhost:8000/metrics\n```\n\n**Response:**\n```json\n{\n  \"timestamp\": \"2026-04-20T12:45:23\",\n  \"metrics\": {\n    \"best_model\": \"RandomForest\",\n    \"accuracy\": 0.8571,\n    \"precision\": 0.8333,\n    \"recall\": 0.8333,\n    \"f1_score\": 0.8333\n  }\n}\n```\n\n### Rebuild the vector database\n\n```bash\ncurl -X POST http://localhost:8000/rag/rebuild\n```\n\n**Response:**\n```json\n{\n  \"status\": \"built\",\n  \"collection\": \"financial_news\",\n  \"count\": 14,\n  \"strategy\": \"chromadb:openai\",\n  \"persist_dir\": \"/app/data/vectorstore\"\n}\n```\n\n---\n\n## 🗂️ Project Structure\n\n```\n├── source_code/              # Core implementation (stable runtime)\n│   ├── crew.py              # 6-agent orchestration + task definitions\n│   ├── main.py              # CLI entrypoint\n│   ├── api.py               # FastAPI app entrypoint\n│   ├── runtime.py           # Environment loading + pipeline kickoff\n│   ├── paths.py             # Centralized path management\n│   ├── tools/               # CrewAI tools (news fetch, model training, etc.)\n│   ├── pipelines/           # Data pipelines (ingestion, feature engineering)\n│   ├── monitoring/          # MLOps monitoring (drift, retraining, logging)\n│\n├── services/                 # Refactored domain layers (migration in progress)\n│   ├── data/                # Data ingestion + validation\n│   ├── rag/                 # RAG layer (ChromaDB + retrieval)\n│   ├── ml/                  # ML layer (training, inference, monitoring)\n│\n├── apps/                     # Application layer\n│   ├── api/                 # FastAPI application with routes\n│   └── dashboard/           # Frontend (HTML/JS dashboard)\n│\n├── infra/                    # Infrastructure as Code\n│   ├── docker/              # Dockerfile + docker-compose.yml\n│   └── k8s/                 # Kubernetes manifests (deployment, service, HPA, PVC)\n│\n├── data/                     # Data directory\n│   ├── raw/                 # Input CSV/RSS data\n│   ├── processed/           # Cleaned dataset\n│   └── vectorstore/         # ChromaDB persistent storage\n│\n├── models/                   # ML artifacts\n│   └── signal_model.pkl     # Trained model\n│\n├── logs/                     # Monitoring & metrics\n│   └── model_performance.json\n│\n├── deployed/                 # Production deployment\n│   └── deployment_manifest.json\n│\n├── docs/                     # Architecture & guides\n│   ├── final-architecture.md\n│   ├── implementation-order.md\n│   └── cloud-architecture.md\n│\n└── pyproject.toml            # Dependencies (uv-managed)\n```\n\n---\n\n## 🎓 What This Demonstrates \n\n### Multi-Agent AI\n- **Understand agent design:** Each agent has a specific role, goal, and backstory\n- **Orchestration complexity:** Sequential task dependencies, context passing, tool selection\n- **Prompt engineering:** How to define agent behavior through goal/backstory\n\n### RAG & Vector Databases\n- **Why vector DBs matter:** Semantic search > keyword matching\n- **ChromaDB in production:** Persistent storage, subPath organization in Kubernetes\n- **Embedding strategies:** OpenAI vs. free local models trade-offs\n- **Fallback patterns:** TF-IDF when primary unavailable (production resilience)\n\n### MLOps\n- **Full ML lifecycle:** Data ingestion → feature engineering → training → monitoring → retraining\n- **Drift detection:** Comparing model accuracy across time\n- **Model versioning:** Timestamped artifacts, deployment manifest\n- **Experiment tracking:** Logging metrics to JSON (easily extensible to MLflow)\n\n### FastAPI\n- **Async patterns:** Background job execution with threading\n- **Pydantic validation:** Request/response schemas\n- **Dependency injection:** Clean separation of concerns\n- **WebSocket-ready:** Dashboard polling pattern scales to real-time\n\n### DevOps & Deployment\n- **Docker best practices:** Multi-stage builds, layer caching, minimal base images\n- **Kubernetes manifests:** Production patterns (Deployment, Service, HPA, PVC, ConfigMap, Secret)\n- **High availability:** Pod anti-affinity, readiness/liveness probes, resource limits\n- **Auto-scaling:** HPA based on metrics\n- **Secret management:** Environment variables + Kubernetes Secrets\n\n---\n\n## 📈 Performance\n\n| Metric | Result |\n|--------|--------|\n| **Model Accuracy** | 85.7% (RandomForest) |\n| **Embedding Strategy** | OpenAI text-embedding-3-small or free local |\n| **API Response Time** | <100ms (health), <5min (full pipeline) |\n| **Docker Build Time** | ~2min (with cache: <10s) |\n| **Container Size** | 1.42 GB (includes Python, dependencies, models) |\n| **K8s Deployment Time** | <30s (rolling update) |\n\n---\n\n## 🚀 Deployment Options\n\n| Environment | Command | Notes |\n|---|---|---|\n| **Local Dev** | `uv run api` | Fast iteration, single process |\n| **Docker** | `docker compose up` | Reproducible, volume mounts for persistence |\n| **Minikube** | `kubectl apply -k infra/k8s/` | Local K8s testing, full production patterns |\n| **Production (GKE/EKS/AKS)** | Push image + update manifests | Auto-scaling, managed storage, CDN ready |\n\n---\n\n## 📚 Learning Path\n\n**New to these topics?** Start here:\n\n1. **Agent-Based AI:** Read `source_code/crew.py` — understand how agents define roles, goals, tasks\n2. **RAG System:** Check `services/rag/rag.py` — see how ChromaDB queries and embeddings work\n3. **FastAPI:** Look at `apps/api/app.py` — trace a request from client → dashboard update\n4. **MLOps:** Review `source_code/monitoring/` — observe drift detection and metrics logging\n5. **Docker:** Run `docker build -f infra/docker/Dockerfile -t test .` — understand layer caching\n6. **Kubernetes:** Deploy locally `kubectl apply -k infra/k8s/` — see manifests in action\n\nDetailed guides:\n- `docs/final-architecture.md` — System design\n- `docs/implementation-order.md` — Build sequence\n- `infra/docker/docker-compose.yml` — Development setup\n- `infra/k8s/README.md` — Kubernetes deployment\n\n---\n\n## 🔧 Configuration\n\n### Environment Variables\n\n```bash\n# Optional: Use OpenAI embeddings (recommended for production)\nexport OPENAI_API_KEY=\"sk-...\"\nexport OPENAI_EMBEDDING_MODEL=\"text-embedding-3-small\"\n\n# Without OPENAI_API_KEY, the system uses:\n# • Local all-MiniLM-L6-v2 (free, ~80MB download)\n# • TF-IDF fallback if ChromaDB unavailable\n```\n\n### Data Format\n\nPlace CSV files in `data/raw/` with columns:\n- `headline` — article title\n- `content` — article body\n- `timestamp` — publication date\n- `source` — news source name\n\n---\n\n\n## 📦 Dependencies\n\n**Core:**\n- `crewai[tools]==1.9.3` — Multi-agent orchestration\n- `fastapi>=0.115` — Web framework\n- `chromadb>=1.0` — Vector database\n- `scikit-learn>=1.3` — ML models\n- `pandas>=2.0` — Data manipulation\n\n**Full list:** See `pyproject.toml`\n\n---\n\n## 📝 License\n\nMIT — Use freely in portfolio, production, or learning projects.\n\n---\n\n## 🎯 Next Steps\n\n1. **Run locally:** `uv run api` → http://localhost:8000\n2. **Trigger pipeline:** Click \"Run Pipeline\" or `curl -X POST http://localhost:8000/run`\n3. **Explore dashboard:** Watch agents work in real-time\n4. **Deploy to Docker:** `docker compose up`\n5. **Deploy to Kubernetes:** `kubectl apply -k infra/k8s/`\n6. 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 ┌──────────────┐  ┌──────────────────────┐   │\n│ │  Data Agent  │→ │Research Agent│→ │ ML + Prediction Agts │   │\n│ │ (RSS Fetch)  │  │ (RAG + Query)│  │ (Train + Inference) │   │\n│ └──────────────┘  └──────────────┘  └──────────────────────┘   │\n│                                              ↓                  │\n│                                   ┌─────────────────────┐       │\n│                                   │ Monitoring Agent    │       │\n│                                   │ (Drift Detection)   │       │\n│                                   └─────────────────────┘       │\n└────┬─────────────────────────────────────────────────────────────┘\n     │\n┌────┼──────────────────────────────────────────────────────────────┐\n│    │           Data & ML Pipeline                                 │\n│    └─→ CSV Reader ─→ Feature Engineer ─→ Model Trainer           │\n│         │                                    ↓                    │\n│         │                          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