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zero-to-ai-fullstack
A Java backend engineer's attempt at AI full-stack. Building a RAG knowledge base from scratch, one week at a time.
What I'm building
A self-hostable RAG knowledge base: upload documents, ask questions, get answers with source citations.
┌─────────────────────────────────────────────┐
│ Next.js Frontend │
│ Upload · Manage · Chat · View Sources │
└──────────────────┬──────────────────────────┘
│ REST API / SSE
┌──────────────────┴──────────────────────────┐
│ Python Backend (FastAPI) │
│ ETL Pipeline · RAG Engine · LLM Router │
└────┬─────────────────┬───────────────────┬──┘
│ │ │
┌────┴─────┐ ┌───────┴──────┐ ┌────────┴───┐
│PostgreSQL│ │ pgvector │ │ LLM API │
│ metadata │ │ embeddings │ │ Claude/GPT │
└──────────┘ └──────────────┘ └────────────┘
Stack: Python · FastAPI · LangChain · PostgreSQL · pgvector · Next.js · Docker
8-Week Roadmap
| Week | Focus | Status |
|---|---|---|
| 1 | Python speed run + Claude API + Prompt Engineering | ✅ Done |
| 2 | LangChain + document processing pipeline | ✅ Done |
| 3 | PostgreSQL + pgvector + vector search | ✅ Done |
| 4 | Full RAG pipeline + FastAPI backend | ✅ Done |
| 5 | RAG optimization + evaluation | ⬜ |
| 6 | AI feature integration into production SaaS | ⬜ |
| 7 | Docker deployment + CI/CD | ⬜ |
| 8 | Interview prep + start applying | ⬜ |
Learning Log
Weekly notes on what clicked, what didn't, and how it maps to my Java background.
Recent entries:
- Week 3 — pgvector reduces to two things: store vectors in Postgres, find nearest neighbors with
<=>. The real catch is embedding consistency — query and document vectors must come from the same instance. Found that bug myself. Read →
Project Structure
zero-to-ai-fullstack/
├── backend/ # Python FastAPI service
│ ├── etl/ # ETL pipeline
│ │ ├── extractors/ # Document loaders (PDF, MD, TXT, HTML)
│ │ ├── transformers/ # Text cleaning & chunking
│ │ └── loaders/ # Database writers
│ ├── rag/ # RAG pipeline
│ │ ├── embeddings/ # Embedding model wrappers
│ │ ├── retrieval/ # Vector search
│ │ └── generation/ # RAG chain, LLM integration & prompts
│ ├── api/ # FastAPI routes
│ ├── models/ # SQLAlchemy ORM models
│ └── prompts/ # Prompt templates (versioned)
├── frontend/ # Next.js app
├── scripts/ # Weekly learning exercises
│ ├── week1/ # Python basics + Claude API chatbot
│ ├── week2/ # LangChain + document pipeline
│ ├── week3/ # PostgreSQL + pgvector + retrieval
│ └── week4/ # RAG chain end-to-end demo
├── sql/ # Alembic migrations
├── docker-compose.yml
├── .env.example
└── LEARNING_LOG.md
Quick Start
git clone https://github.com/yanhua1010/zero-to-ai-fullstack.git
cd zero-to-ai-fullstack
cp .env.example .env
# Start PostgreSQL + pgvector
docker compose up -d
# Install dependencies (requires uv)
uv sync
# Start the API — interactive docs at http://localhost:8000/docs
uv run uvicorn backend.api.main:app --reload
# Run the test suite
uv run pytest backend/
Install uv: curl -LsSf https://astral.sh/uv/install.sh | sh
Full-stack Docker setup (backend + frontend in containers) lands in Week 7.
About
8 years as a Java backend engineer. Built distributed systems, high-concurrency services, data pipelines — all in Java/Spring. Now doing the AI full-stack thing.
Following along? Hit Watch for weekly updates, or open an issue if you have questions.
MIT License