⚡ Fullstack AI Platform Development Syllabus (Inspired by ChatGPT/DeepSeek)

🚀 PHASE 1: FastAPI REST API Development

📦 Module 1: FastAPI Setup & Architecture

• ASGI vs WSGI

• Install FastAPI + Uvicorn

• Project structure (modular & scalable)

• .env + python-dotenv

• App factory pattern

🌐 Module 2: Routing & Request Lifecycle

• Path/query parameters

• Dependency injection with Depends

• Pydantic response models

• Custom responses, headers, cookies

📤 Module 3: Request Handling

• Methods: GET, POST, PUT, DELETE, PATCH

• JSON body validation

• File upload (form-data, multipart)

• Versioned API strategy

💾 PHASE 2: Database + Repository Layer

🧩 Module 4: SQLAlchemy ORM Integration

• SQLAlchemy 2.0 ORM (declarative)

• DB config via settings.yml

• Pydantic ↔ SQLAlchemy bridge

• Sync/Async engine setup

🧱 Module 5: Repository Pattern & Services

• Entity → Repository → Service → Controller

• AbstractBaseRepository (generic CRUD)

• Use session in service layer only

• Auto rollback/commit flow

🔌 Module 6: Multi-DB & Redis Integration

• Postgres + Redis + SQLite bindings

• DB router logic

• Redis caching (fastapi-cache2)

• Alembic migrations per environment

🔐 PHASE 3: Core Backend Features for Microservices

🔒 Module 7: Auth & Security

• OAuth2 + JWT (access + refresh)

• Password hashing (bcrypt/passlib)

• Role-based auth

• 3rd-party login (Google, GitHub)

🌍 Module 8: External API Communication

• httpx.AsyncClient usage

• Circuit breaker with Tenacity

• Call Spring Boot APIs or external LLMs

• Internal auth tokens

🧭 Module 9: Background Tasks & Scheduling

• BackgroundTasks in FastAPI

• APScheduler (cron + interval jobs)

• Event-driven architecture intro

📦 PHASE 4: Production-Ready API

🛠️ Module 10: Middleware & Error Handling

• CORS, GZip, logging middleware

• Scoped DB session via middleware

• Custom exceptions & handlers

🧪 Module 11: Testing + CI/CD

• Pytest with FastAPI test client

• Unit + Integration tests

• Mock DB/HTTP

• GitHub Actions CI pipeline

🧱 PHASE 5: Microservices, Docker & K8s

🧬 Module 12: Microservices Architecture

• Build Auth/User/Feed services

• Shared schema package (Pydantic)

• Service discovery (DNS/Consul)

• Inter-service comm with tokens

🐳 Module 13: Dockerization

• Multi-stage Dockerfile

• docker-compose.yml with FastAPI + DB + Redis

• Environment-specific overrides

☸️ Module 14: Kubernetes Deployment

• Helm chart for each service

• YAMLs for Deployment, Ingress, ConfigMap

• Health checks, rolling updates

⚙️ PHASE 6: Async, Messaging, Observability

🧵 Module 15: Async FastAPI Mastery

• Async routes, httpx, asyncpg, Redis

• Async SQLAlchemy 2.0

• Concurrency patterns

📩 Module 16: Event Bus Integration

• Kafka / RabbitMQ setup

• Pydantic schemas for messages

• Producer/Consumer services

📊 Module 17: Monitoring + Admin

• FastAPI Admin dashboard

• Prometheus + Grafana setup

• Logs, metrics, uptime checks

🧠 PHASE 7: AI System - LLM & RAG (ChatGPT/DeepSeek Style)

🧬 Module 18: LLM Core Foundations

• Tokens, context windows, embeddings

• Prompt engineering (zero-shot, few-shot, CoT)

• OpenAI, Ollama (LLaMA, Mistral), HuggingFace models

• API vs self-hosted comparison

🔍 Module 19: Retrieval-Augmented Generation (RAG)

• Embed text via Sentence Transformers/OpenAI

• Store in Chroma, Pinecone, Weaviate

• Search + merge results into LLM prompt

📡 Module 20: FastAPI LLM Gateway

• Endpoints: /chat, /embed, /agent, /upload

• Prompt router + memory handler (Redis)

• Token/session tracking + rate limiting

🔧 Module 21: LangChain Integration

• LangChain for tools, memory, agents

• Chain: User → Embed → Retrieval → LLM → Output

• WebSocket / SSE streaming

🧩 PHASE 8: Event-Driven Microservices + Analytics

🛰️ Module 22: Kafka & Event Tracking

• Topics: query_event, feedback_event, usage_event

• FastAPI → Kafka logs

• Spring Boot Kafka consumers

🔁 Module 23: ETL & Data Pipeline

• Kafka → Spark / Beam

• Real-time intent tagging, sentiment, analytics

• Store in S3 + Data Warehouse

🏛️ Module 24: Data Warehouse & Dashboards

• DWH: BigQuery / Snowflake / Redshift

• Tables: query_fact, user_dim, model_usage_fact

• BI tools: Superset, Metabase, Grafana dashboards

🎙️ PHASE 9: Add-ons (Personalization + Voice + Monetization)

🗣️ Voice Assistant

• Whisper / Deepgram STT

• ElevenLabs / Coqui TTS

🧠 Memory + Personalization

• Per-user vector memory

• Session profiles + preferred config

💳 Billing & Access Control

• JWT with rate limits

• Stripe / Razorpay billing

• Usage metering + tiered plans

🎓 FINAL: Capstone AI Project

• ✅ Auth Service (FastAPI)

• ✅ LLM Gateway (FastAPI)

• ✅ Spring Boot Services (Billing, Profile, Config)

• ✅ VectorDB RAG Engine

• ✅ Kafka + Spark Stream Analytics

• ✅ Frontend: Vue.js (chat + upload)

• ✅ Docker + K8s Deployment (Helm)

• ✅ Monitoring + Dashboards (Grafana + Metabase)