---
title: "Deploy vchord-suite"
description: "One-click PostgreSQL with VectorChord, BM25, and hybrid search."
category: "Storage"
url: https://railway.com/deploy/vchord-suite
---

# Deploy vchord-suite

One-click PostgreSQL with VectorChord, BM25, and hybrid search.

**[Deploy vchord-suite on Railway](https://railway.com/template/vchord-suite)**

- **Creator:** Nilum
- **Category:** Storage
- **Total deploys:** 1

## Template content

### vchord-suite-railway https://docs.vectorchord.ai/logo.svg

- **Source:** nilum-ai/vchord-suite-railway

## Documentation

# Deploy and Host vchord-suite on Railway

VectorChord Suite is a PostgreSQL distribution with built-in vector search and BM25 full-text search. It includes `vchord` for IVF+RaBitQ vector indexes that scale to billions of vectors, `pg_tokenizer` for text tokenization, and `vchord_bm25` for hybrid search—all with pgvector compatibility.

## About Hosting vchord-suite

Hosting VectorChord Suite requires a PostgreSQL-compatible environment with persistent storage for your vector data. This template pre-configures all four extensions (vchord, pg_tokenizer, vchord_bm25, vector) and initializes them on first boot. Unlike standard pgvector HNSW indexes which require significant RAM, VectorChord's IVF+RaBitQ indexes use disk-based quantization, making it cost-effective to scale to millions of vectors without expensive memory upgrades.

## Common Use Cases

- **AI/ML applications** — Semantic search, RAG (retrieval-augmented generation), recommendation systems
- **Hybrid search** — Combine vector similarity with BM25 keyword search for better relevance
- **Large-scale embeddings** — Store and query 1M+ vectors without hitting memory limits

## Dependencies for vchord-suite Hosting

- PostgreSQL 17
- Persistent volume for database storage

### Deployment Dependencies

- [VectorChord Documentation](https://docs.vectorchord.ai/)
- [TensorChord Docker Hub](https://hub.docker.com/r/tensorchord/vchord-suite)
- [VectorChord GitHub](https://github.com/tensorchord/VectorChord)

### Implementation Details

```sql
-- Create a scalable vector index
CREATE INDEX ON items USING vchordrq (embedding vector_ip_ops)
WITH (options = $$
residual_quantization = true
[build.internal]
lists = [1000]
$$);

-- Configure recall vs latency (set probes to 3-10% of lists)
ALTER DATABASE railway SET vchordrq.probes = 50;
```

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