# Overview

> vchord is a PostgreSQL extension designed for scalable, high-performance, and disk-efficient vector similarity search.

---

LLMS index: [llms.txt](/llms.txt)

---

`vchord` is a PostgreSQL extension designed for scalable, high-performance, and disk-efficient vector similarity search.

With `vchord`, you can store 400,000 vectors for just $1, enabling significant savings: 6x more vectors compared to Pinecone's optimized storage and 26x more than pgvector/pgvecto.rs for the same price[^1]. For further insights, check out our [launch blog post](/blog/post/vectorchord-store-400k-vectors-for-1-in-postgresql/).

<a id="Features"></a>
## Features

`vchord` introduces remarkable enhancements over pgvecto.rs and pgvector:

**⚡ Enhanced Performance**: Delivering optimized operations with up to 5x faster queries, 16x higher insert throughput, and 16x quicker[^1] index building compared to pgvector's HNSW implementation.

[^1]: Based on [MyScale Benchmark](https://myscale.github.io/benchmark/) with 768-dimensional vectors and 95% recall. Please check out our [blog post](/blog/post/vectorchord-store-400k-vectors-for-1-in-postgresql/) for more details.

**💰 Affordable Vector Search**: Query 100M 768-dimensional vectors using just 32GB of memory, achieving 35ms P50 latency with top10 recall@95%, helping you keep infrastructure costs down while maintaining high search quality.

**🔌 Seamless Integration**: Fully compatible with pgvector data types and syntax while providing optimal defaults out of the box - no manual parameter tuning needed. Just drop in `vchord` for enhanced performance.

**🔧 Accelerated Index Build**: Leverage IVF to build indexes externally (e.g., on GPU) for faster KMeans clustering, combined with RaBitQ[^2] compression to efficiently store vectors while maintaining search quality through autonomous reranking.

[^2]: Gao, Jianyang, and Cheng Long. "RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search." Proceedings of the ACM on Management of Data 2.3 (2024): 1-27.

**📏 Long Vector Support**: Store and search vectors up to 60,000[^3] dimensions, enabling the use of the best high-dimensional models like text-embedding-3-large with ease.

[^3]: There is a [limitation](https://github.com/pgvector/pgvector#vector-type) at pgvector of 16,000 dimensions now. If you really have a large dimension(`16,000<dim<60,000`), consider changing [VECTOR_MAX_DIM](https://github.com/pgvector/pgvector/blob/fef635c9e5512597621e5669dce845c744170822/src/vector.h#L4) and compile pgvector yourself.

**🌐 Scale As You Want**: Based on horizontal expansion, the query of 5M / 100M 768-dimensional vectors can be easily scaled to 10000+ QPS with top10 recall@90% at a competitive cost[^4]

[^4]: Please check our [blog post](/blog/post/vector-search-at-10000-qps-in-postgresql-with-vectorchord/)  for more details, the PostgreSQL scalability is powered by [CloudNative-PG](https://github.com/cloudnative-pg/cloudnative-pg).

## Quick Start

For new users, we recommend using the Docker image to get started quickly. If you do not prefer Docker, please read [installation guide](/docs/vchord/getting-started/installation/) for other installation methods.

```bash
docker run \
  --name vectorchord-demo \
  -e POSTGRES_PASSWORD=mysecretpassword \
  -p 5432:5432 \
  -d tensorchord/vchord-postgres:pg18-v1.1.1
```
> In addition to the base image with the VectorChord extension, we provide an all-in-one image, `tensorchord/vchord-suite:pg18-latest`. This comprehensive image includes all official TensorChord extensions. Developers should select an image tag that is compatible with their extension's version, as indicated in [the support matrix](https://github.com/tensorchord/VectorChord-images?tab=readme-ov-file#support-matrix).

Then you can connect to the database using the `psql` command line tool. The default username is `postgres`, and the default password is `mysecretpassword`.

```bash
psql -h localhost -p 5432 -U postgres
```

Now you can play with VectorChord!

VectorChord depends on pgvector, including the vector representation. Since you can use them directly, your application can be easily migrated without pain!

```sql
CREATE EXTENSION IF NOT EXISTS vchord CASCADE;
```

Similar to pgvector, you can create a table with vector column and insert some rows to it.

```sql
CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3));
INSERT INTO items (embedding) SELECT ARRAY[random(), random(), random()]::real[] FROM generate_series(1, 1000);
```

With VectorChord, you can create `vchordrq` indexes.

```SQL
CREATE INDEX ON items USING vchordrq (embedding vector_l2_ops);
```

And then perform a vector search using `SELECT ... ORDER BY ... LIMIT ...`.

```SQL
SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5;
```

For more usage, please read:

- [Indexing](/docs/vchord/usage/indexing/)
- [Multi-Vector Retrieval](/docs/vchord/usage/multi-vector-retrieval/)
- [Graph Index](/docs/vchord/usage/graph-index/)
- [Quantization Types](/docs/vchord/usage/quantization-types/)
- [Similarity Filter](/docs/vchord/usage/similarity-filter/)
- [PostgreSQL Tuning](/docs/vchord/usage/postgresql-tuning/)
- [Monitoring](/docs/vchord/usage/monitoring/)
- [Fallback Parameters](/docs/vchord/usage/fallback-parameters/)
- [Measure Recall](/docs/vchord/usage/measure-recall/)
- [Prewarm](/docs/vchord/usage/prewarm/)
- [Prefilter](/docs/vchord/usage/prefilter/)
- [Prefetch](/docs/vchord/usage/prefetch/)
- [Rerank in Table](/docs/vchord/usage/rerank-in-table/)
- [Partitioning Tuning](/docs/vchord/usage/partitioning-tuning/)
- [External Build](/docs/vchord/usage/external-build/)

<a id="License"></a>
## License

`vchord` is licensed under the **GNU Affero General Public License v3.0 ([`AGPL-3.0-only`](https://github.com/pgsty/vchord/blob/main/LICENSE))**. We welcome any commercial collaboration or support inquiries—please email us at [rh@vonng.com](mailto:rh@vonng.com) with any questions.

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Backlinks:

- [Kubernetes](/docs/vchord/admin/kubernetes/)
- [pgvecto.rs](/docs/vectors/)