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Maksym Ionutsa
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## I have read the [CONTRIBUTING.md](https://github.com/supabase/supabase/blob/master/CONTRIBUTING.md) file. YES ## What kind of change does this PR introduce? docs update - Upgrade project button and Postgres/PostgREST version checks moved from Infrastructure to General settings - Updated links across 13 docs pages to match <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Documentation** - Updated dashboard links throughout the documentation to direct users to **General Settings** instead of **Infrastructure Settings**. - Corrected guidance for Postgres, pgvector, pg_net, and PostgREST upgrades, configuration, and version checks. - Updated monitoring, Grafana, and Log Drains links to current documentation paths. - Fixed troubleshooting links, CLI project path examples, pg_cron terminology, and Markdown formatting. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
42 lines
2.3 KiB
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42 lines
2.3 KiB
Plaintext
---
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id: 'ai-vector-indexes'
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title: 'Vector indexes'
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description: 'Understanding vector indexes'
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sidebar_label: 'Vector indexes'
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---
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Once your vector table starts to grow, you will likely want to add an index to speed up queries. Without indexes, you'll be performing a sequential scan which can be a resource-intensive operation when you have many records.
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## Choosing an index
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Today `pgvector` supports two types of indexes:
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- [HNSW](/docs/guides/ai/vector-indexes/hnsw-indexes)
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- [IVFFlat](/docs/guides/ai/vector-indexes/ivf-indexes)
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In general we recommend using [HNSW](/docs/guides/ai/vector-indexes/hnsw-indexes) because of its [performance](/blog/increase-performance-pgvector-hnsw#hnsw-performance-1536-dimensions) and [robustness against changing data](/docs/guides/ai/vector-indexes/hnsw-indexes#when-should-you-create-hnsw-indexes).
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## Distance operators
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Indexes can be used to improve performance of nearest neighbor search using various distance measures. `pgvector` includes 3 distance operators:
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| Operator | Description | [**Operator class**](https://www.postgresql.org/docs/current/sql-createopclass.html) |
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| -------- | ---------------------- | ------------------------------------------------------------------------------------ |
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| `<->` | Euclidean distance | `vector_l2_ops` |
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| `<#>` | negative inner product | `vector_ip_ops` |
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| `<=>` | cosine distance | `vector_cosine_ops` |
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For pgvector versions 0.7.0 and above, it's possible to create indexes on vectors with the following maximum dimensions:
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- vector: up to 2,000 dimensions
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- halfvec: up to 4,000 dimensions
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- bit: up to 64,000 dimensions
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You can check your current pgvector version by running: `SELECT * FROM pg_extension WHERE extname = 'vector';` or by navigating to the [Extensions](/dashboard/project/_/database/extensions) tab in your Supabase project dashboard.
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If you are on an earlier version of pgvector, you should [upgrade your project here](/dashboard/project/_/settings/general).
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## Resources
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Read more about indexing on `pgvector`'s [GitHub page](https://github.com/pgvector/pgvector#indexing).
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