VectorChord

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Overview ​

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

With VectorChord, 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.

Features ​

VectorChord 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[1:1] index building compared to pgvector's HNSW implementation.

💰 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 VectorChord 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.

📏 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.

🌐 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]

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 for other installation methods.

bash
docker run \
  --name vectorchord-demo \
  -e POSTGRES_PASSWORD=mysecretpassword \
  -p 5432:5432 \
  -d tensorchord/vchord-postgres:pg17-v0.2.2

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!

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) WITH (options = $$
residual_quantization = true
[build.internal]
lists = []
$$);

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

SQL
SET vchordrq.probes TO '';
SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5;

For more usage, please read:

License ​

This software is licensed under a dual license model:

  1. GNU Affero General Public License v3 (AGPLv3): You may use, modify, and distribute this software under the terms of the AGPLv3.

  2. Elastic License v2 (ELv2): You may also use, modify, and distribute this software under the Elastic License v2, which has specific restrictions.

You may choose either license based on your needs. We welcome any commercial collaboration or support, so please email us vectorchord-inquiry@tensorchord.ai with any questions or requests regarding the licenses.

  1. Based on MyScale Benchmark with 768-dimensional vectors and 95% recall. Please checkout our blog post for more details. ↩ī¸Ž ↩ī¸Ž

  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. ↩ī¸Ž

  3. There is a limitation at pgvector of 16,000 dimensions now. If you really have a large dimension(16,000<dim<60,000), consider changing VECTOR_MAX_DIM and compile pgvector yourself. ↩ī¸Ž

  4. Please check our blog post for more details, the PostgreSQL scalability is powered by CloudNative-PG. ↩ī¸Ž