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34 lines
1.8 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](https://supabase.com/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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Currently vectors with up to 2,000 dimensions can be indexed.
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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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