PMPSTI RAG

A Retrieval-Augmented Generation (RAG) assistant for PMPSTI academic documents at Universitas Gadjah Mada, built with Rust (Actix-web), SvelteKit, PostgreSQL and pgvector.

• Production: https://pmpsti-rag.vercel.app (frontend on Vercel, backend on Render/Fly, database on Neon)

---

Architecture

Frontend (SvelteKit :5173)
    ↕ /api/* proxy
Rust backend (Actix-web :8080)
    ↕ SQLx
PostgreSQL + pgvector (:5432 locally, Neon in the cloud)   ←   Python ingest pipeline

---

Prerequisites

• Podman (or Docker), to run PostgreSQL locally (optional if you use Neon)
• Rust + Cargo, for the backend
• Node.js 18–22, for the frontend (the Vercel adapter rejects newer versions for local builds)
• Python 3.10+, for document ingestion (optional)
• A Gemini API key, used for embeddings and the LLM

---

1. Start the database (PostgreSQL + pgvector)

Skip this section if your DATABASE_URL points to Neon.

First time (create the container)

podman run -d \
  --name pmpsti-db \
  -e POSTGRES_USER=admin \
  -e POSTGRES_PASSWORD=admin \
  -e POSTGRES_DB=pmpsti_rag \
  -p 5432:5432 \
  docker.io/pgvector/pgvector:pg16

Afterwards (start the existing container)

podman start pmpsti-db

Check status

podman ps

Enable the pgvector extension (once)

podman exec -it pmpsti-db psql -U admin -d pmpsti_rag -c "CREATE EXTENSION IF NOT EXISTS vector;"

---

2. Configure the environment

Create a .env file in the project root. It is git-ignored and must never be committed.

# Database: local Podman, or a Neon branch (see "Code and database workflow")
DATABASE_URL=postgres://admin:admin@localhost:5432/pmpsti_rag

# Server (the frontend dev proxy expects 8080; the default is 7860)
HOST=0.0.0.0
PORT=8080

# Auth: required, at least 32 characters (the server refuses to start without it).
# Generate with: openssl rand -hex 32
JWT_SECRET=replace-with-a-long-random-secret

# Production only: shared with the Vercel proxy so the backend can trust the visitor IP
# (login and question limits are per visitor). Set the same value on Vercel and Render.
# PROXY_SECRET=...
# Guests together: questions per day (default 150)
# GUEST_DAILY_LIMIT=150

# Optional: create or update an admin account on startup
# [email protected]
# ADMIN_PASSWORD=...

# Gemini: required for embeddings (gemini-embedding-001), also used as the LLM
GEMINI_API_KEY=...
LLM_BACKEND=gemini
GEMINI_MODEL=gemini-3.5-flash-lite
# Used in order when the model above hits its quota (429); default gemini-3.1-flash-lite
# LLM_FALLBACK_MODELS=gemini-3.1-flash-lite

# Alternative LLMs:
# Ollama (local)
# LLM_BACKEND=ollama
# LLM_API_URL=http://localhost:11434/api/chat
# LLM_MODEL=llama3.2
# Groq or any OpenAI-compatible API
# LLM_BACKEND=openai
# LLM_API_URL=https://api.groq.com/openai/v1/chat/completions
# LLM_API_KEY=gsk_...
# LLM_MODEL=llama-3.3-70b-versatile

# Email (verification and password reset)
# RESEND_API_KEY=...
# APP_BASE_URL=http://localhost:5173

---

3. Run the Rust backend

cargo run

Wait for the startup banner (PMPSTI API, database, LLM, listen address) followed by Server siap di http://0.0.0.0:8080.

The backend needs GEMINI_API_KEY to embed questions with Gemini gemini-embedding-001.

---

4. Run the SvelteKit frontend

In a new terminal (keep the backend running):

cd frontend
npm install        # first time only
npm run dev

Open http://localhost:5173.

The frontend always calls the backend through its own /api route (the Vite dev proxy locally, src/routes/api/[...path]/+server.ts on Vercel), so no API URL needs to be configured.

---

5. Set up an admin account

Option A (automatic): set ADMIN_EMAIL and ADMIN_PASSWORD in .env and restart the backend.

Option B (manual):

1. Register at http://localhost:5173/register.

2. Promote the account in psql:

   podman exec -it pmpsti-db psql -U admin -d pmpsti_rag
   

   UPDATE users SET role = 'admin' WHERE email = '[email protected]';
   SELECT id, email, role FROM users;
   \q
   

3. Sign in again so the session picks up the new role.

---

6. Ingest documents (Python)

cd scripts
python -m venv .venv && source .venv/bin/activate   # first time only
pip install -r requirements.txt                      # first time only

# Put PDF/DOCX files in docs/, then:
python ingest.py

Ingested documents appear under Admin → Documents. You can also add web pages there with Add from URL.

ingest.py needs GEMINI_API_KEY (read from the root .env). Choose the target database with the DATABASE_URL env var or --db.

Re-embed all documents (after changing the embedding model)

Document embeddings must use the same model as the backend (Gemini gemini-embedding-001).

cd scripts
python regen_embeddings.py --all --dry-run   # count rows only
python regen_embeddings.py --all             # overwrite every embedding
python regen_embeddings.py                   # fill only rows whose embedding is NULL

Copy embeddings between databases (no Gemini calls)

If embeddings were already generated on another Neon branch (e.g. test-embedding), copy them to production instead of regenerating:

# Root .env: DATABASE_URL = source, PROD_DATABASE_URL = destination
cd scripts
python copy_embeddings.py --dry-run   # report only, writes nothing
python copy_embeddings.py             # copy (asks you to type 'ya' to confirm)

Rows are matched by id and content (title + content). Rows that differ, or exist on only one side, are skipped and reported. Create a production backup branch in Neon first, and remove PROD_DATABASE_URL from .env when you are done.

---

This is the beginning of the README. Read the rest at the original. Read the full README ↗