zero-to-ai-fullstack
[中文](README.zh.md) | **English** # 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. → [Read the full Learning Log](LEARNING_LOG.en.md) **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 →](LEARNING_LOG.en.md#week-3) --- ## 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 ```bash 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. → [GitHub: @yanhua1010](https://github.com/yanhua1010) --- MIT License