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Update pgvecto.rs overview and remove comparison with pgvector (#21)
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Signed-off-by: Jinjing.Zhou <allenzhou@tensorchord.ai>
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VoVAllen authored Jan 17, 2024
1 parent e9952fe commit 6ba9402
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2 changes: 1 addition & 1 deletion .vitepress/config.mts
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Expand Up @@ -95,7 +95,7 @@ export default defineConfig({
text: 'FAQs',
collapsed: false,
items: [
{ text: 'pgvecto.rs vs. pgvector', link: '/faqs/comparison-pgvector' },
// { text: 'pgvecto.rs vs. pgvector', link: '/faqs/comparison-pgvector' },
{ text: 'pgvecto.rs vs. specialized vectordb', link: '/faqs/comparison-with-specialized-vectordb' },
]
},
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27 changes: 8 additions & 19 deletions src/getting-started/overview.md
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Expand Up @@ -8,25 +8,14 @@ pgvecto.rs is a Postgres extension that provides vector similarity search functi

## Why use pgvecto.rs

- 💃 **Easy to use**: pgvecto.rs is a Postgres extension, which means that you can use it directly within your existing database. This makes it easy to integrate into your existing workflows and applications.
- 🔗 **Async indexing**: pgvecto.rs's index is asynchronously constructed by the background threads and does not block insertions and always ready for new queries.
- 🥅 **Filtering**: pgvecto.rs supports filtering. You can set conditions when searching or retrieving points. This is the missing feature of other postgres extensions.
- 🧮 **Quantization**: pgvecto.rs supports scalar quantization and product qutization up to 64x.
- 🦀 **Rewrite in Rust**: Rust's strict compile-time checks ensure memory safety, reducing the risk of bugs and security issues commonly associated with C extensions.

## Comparison with pgvector

| | pgvecto.rs | pgvector |
| ------------------------------------------- | ------------------------------------------------------ | ----------------------- |
| Transaction support || ⚠️ |
| Sufficient Result with Delete/Update/Filter || ⚠️ |
| Vector Dimension Limit | 65535 | 2000 |
| Prefilter on HNSW |||
| Parallel HNSW Index build | ⚡️ Linearly faster with more cores | 🐌 Only single core used |
| Async Index build | Ready for queries anytime and do not block insertions. ||
| Quantization | Scalar/Product Quantization ||

More details at [📝pgvecto.rs vs. pgvector](/faqs/comparison-pgvector.md).
- 💃 **User-Friendly**: Effortlessly incorporate pgvecto.rs into your existing database as a Postgres extension, streamlining integration with your current workflows and applications.
- 🥅 **Join and Filter without Limitation**: Elevate your search capabilities in pgvecto.rs with VBASE filtering. Apply any filter conditions and join with other tables, achieving high recall and low latency, a distinctive edge over other vector databases.
- 🌓 **Efficient FP16 Support**: Optimize your data storage with pgvecto.rs, supporting FP16 vector type to cut memory and storage usage by half, and boosting throughput.
- 🧮 **Advanced Quantization**: Utilize scalar and product quantization in pgvecto.rs for up to 64x compression. Achieve up to 4x memory savings with less than 2% recall loss with scalar quantization.
- 🔍 **Hybrid Search**: Leverage the full-text search functionality in PostgreSQL with pgvecto.rs to search text and vector data within a single query.
- 🔗 **Async indexing**: The pgvecto.rs index is built asynchronously by background threads, allowing non-blocking inserts and always ready for new queries.
- ⬆️ **Extended Vector Length**: pgvecto.rs supports vector length up to 65535, suitable for the latest models.
- 🦀 **Rust-Powered Reliability**: Rust's strict compile-time checks ensure memory safety, reducing the risk of bugs and security issues commonly associated with C extensions.

## Quick start

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