Ask HN: Semantic Search Demo

Live Demo

A single-page semantic search app over Hacker News, showcasing Rivestack pgvector in action.

Try the demo

Search 30 days of HN stories and comments with natural language.

Source code

Full source code: backend, frontend, and Kubernetes deployment.

What it does

Type a question, get semantically relevant Hacker News posts and comments, with real-time performance stats on every query.

~4,000 stories and ~40,000 chunks indexed from the last 30 days of HN
HNSW index for fast cosine similarity search
Performance stats displayed on every query: vector query time, embedding time, chunks searched, index type
Daily cron job keeps a rolling 30-day window of fresh data

Architecture

User query → OpenAI embedding → pgvector HNSW search → ranked results

Component	Stack
Database	Rivestack PostgreSQL + pgvector
Backend	Python, FastAPI, SQLAlchemy, asyncpg
Embeddings	OpenAI `text-embedding-3-small` (1536 dims)
Frontend	Vue 3, Vite, Tailwind CSS v4, shadcn-vue
Deployment	Kubernetes, Helm, GitHub Actions CI/CD

Performance

Typical query performance on a shared Rivestack instance:

Metric	Time
Vector query (pgvector)	~12ms
OpenAI embedding	~150ms
Total API response	~180ms

These numbers are from a shared Rivestack instance. Dedicated instances deliver significantly faster performance.

Run it yourself

1. Create a Rivestack database

Sign up at app.rivestack.io and create a database. pgvector is enabled by default on the initial appdb database. For new databases, add it through the dashboard or Terraform.

2. Clone and configure

git clone https://github.com/Rivestack/rag-starter.git
cd rag-starter
cp .env.example backend/.env

Edit backend/.env with your Rivestack DATABASE_URL and an OPENAI_API_KEY.

3. Run with Docker Compose

docker compose up

Backend: http://localhost:8000
Frontend: http://localhost:8080

4. Load data

curl -X POST http://localhost:8000/api/ingest/initial

This fetches 30 days of HN stories, generates embeddings, and stores them in your Rivestack database. The first load runs in the background and takes a few minutes.

5. Search

Open http://localhost:8080 and start searching.

Getting Started

Features

Demos

Resources

Changelog

Ask HN: Semantic Search Demo

Live Demo

Try the demo

Source code

What it does

Architecture

Performance

Run it yourself

1. Create a Rivestack database

2. Clone and configure

3. Run with Docker Compose

4. Load data

5. Search

​Live Demo

Try the demo

Source code

​What it does

​Architecture

​Performance

​Run it yourself

​1. Create a Rivestack database

​2. Clone and configure

​3. Run with Docker Compose

​4. Load data

​5. Search

Live Demo

What it does

Architecture

Performance

Run it yourself

1. Create a Rivestack database

2. Clone and configure

3. Run with Docker Compose

4. Load data

5. Search