You Shouldn't Invest in Vector Databases?

1. txtai

   1 385 11,200 9.5 Python

   💡 All-in-one open-source AI framework for semantic search, LLM orchestration and language model workflows

   

   You can try txtai (https://github.com/neuml/txtai) with a Faiss backend.

   This Faiss wiki article might help (https://github.com/facebookresearch/faiss/wiki/Indexing-1G-v...).

   For example, a partial Faiss configuration with 4-bit PQ quantization and only using 5% of the data to train an IVF index is shown below.

   faiss={"components": "IVF,PQ384x4fs", "sample": 0.05}

2. Stream

   getstream.io featured

   Stream - Scalable APIs for Chat, Feeds, Moderation, & Video. Stream helps developers build engaging apps that scale to millions with performant and flexible Chat, Feeds, Moderation, and Video APIs and SDKs powered by a global edge network and enterprise-grade infrastructure.

   

3. faiss

   2 79 36,053 9.5 C++

   A library for efficient similarity search and clustering of dense vectors.

   

   You can try txtai (https://github.com/neuml/txtai) with a Faiss backend.

   This Faiss wiki article might help (https://github.com/facebookresearch/faiss/wiki/Indexing-1G-v...).

   For example, a partial Faiss configuration with 4-bit PQ quantization and only using 5% of the data to train an IVF index is shown below.

   faiss={"components": "IVF,PQ384x4fs", "sample": 0.05}

4. pgvecto.rs

   3 19 2,077 8.6 Rust

   Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database.

   

   It's kind of a tradeoff. Performance is just one factor when choosing the vector database. In pgvecto.rs https://github.com/tensorchord/pgvecto.rs, we store the index separately from PostgreSQL's internal storage, unlike pgvector's approach. This enable us to get multi-threaded indexing, async indexing without blocking the insertion, and faster search speed comparing to pgvector.

   I don't see any fundamental reason why the index in Postgres would be slower than a specialized vector database. The query pattern of the vector database is simply a point query using an index, similar to other queries in an OLTP system.

   The only limitation I see is scalability. It's not easy to make PostgreSQL distributed, but solutions like Citus exist, making it still possible.

   (I'm the author of pgvecto.rs)

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