LegCo Reranker

RAG-powered document Q&A app. Upload PDFs, ask questions in Cantonese, get bullet-point answers with citations.

Quick Start (Dev)

# Backend
cd backend
cp .env.example .env    # edit .env with your LLM API key
pip install -r requirements.txt
uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

# Frontend
cd frontend
npm install
npm run dev

Backend → http://localhost:8000 | Frontend → http://localhost:5173

Deploy with Docker

Prerequisites

• Docker 24+ and Docker Compose v2
• OpenRouter API key (or compatible LLM provider)

Setup

# 1. Configure environment
cp backend/.env.example backend/.env
# Edit backend/.env with your API keys and model names

# 2. Build and start
docker compose up -d --build

# 3. Check health
curl http://localhost:8000/health

The app is served at http://localhost:8000 — both the API and the frontend UI.

Volumes

Volume	Purpose
chroma_data	ChromaDB vector store (persistent)
chunk_data	Extracted PDF page files
sqlite_data	Prompt templates and query history

Environment Variables

All configurable via backend/.env:

Variable	Default	Description
LLM_BASE_URL	https://openrouter.ai/api/v1	LLM API endpoint
LLM_API_KEY	—	API key for LLM provider
LLM_MODEL_NAME	qwen/qwen3.5-35b-a3b	Chat model
EMBEDDING_MODEL	qwen/qwen3-embedding-4b	Embedding model
EMBEDDING_API_KEY	—	API key for embeddings (falls back to LLM_API_KEY)
RETRIEVAL_N_RESULTS	10	Chunks per sub-question
RELEVANCE_THRESHOLD	7.0	Min relevance score (0-10)
LLM_TIMEOUT	60.0	LLM request timeout in seconds

Production: Nginx Reverse Proxy

# Include nginx.conf in your site config
# Key settings:
# - client_max_body_size 350M   (allow large PDF uploads)
# - proxy_read_timeout 300s     (LLM calls can take minutes)

# Install nginx
sudo apt install nginx

# Copy config
sudo cp nginx.conf /etc/nginx/sites-available/legco
sudo ln -s /etc/nginx/sites-available/legco /etc/nginx/sites-enabled/
sudo nginx -t && sudo systemctl reload nginx

Stopping

docker compose down

Updating

git pull
docker compose up -d --build

Architecture

User → Nginx (80) → Uvicorn (8000)
                         ├── FastAPI API (/api/v1/*)
                         └── Static Frontend (/*)
                              └── React 18 + Vite + Tailwind

RAG Pipeline (Per-Sub-Question)

User Question
  → [LLM] Decompose into 2-5 sub-questions
  → [ChromaDB] Retrieve 10 chunks per sub-question
  → [LLM] Score all chunks against their own sub-question (single call)
  → [LLM] Generate markdown response per sub-question
  → SSE stream with per-sub-question sources

Notes

• PDF upload limit: 300MB
• Desktop only (not mobile-optimized)
• No authentication (public demo)
• All LLM calls routed through configurable base URL