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dikshant
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dikshant
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src/current/_includes/v25.2/known-limitations/vector-limitations.md
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andy-kimball
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@dikshant @andy-kimball TFTRs! I've incorporated your comments. |
DrewKimball
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src/current/_includes/v25.2/known-limitations/vector-limitations.md
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| - {% include {{ page.version.version }}/sql/vector-batch-inserts.md %} | ||
| - Creating a vector index through a backfill disables mutations ([`INSERT`]({% link {{ page.version.version }}/insert.md %}), [`UPSERT`]({% link {{ page.version.version }}/upsert.md %}), [`UPDATE`]({% link {{ page.version.version }}/update.md %}), [`DELETE`]({% link {{ page.version.version }}/delete.md %})) on the table. [#144443](https://github.com/cockroachdb/cockroach/issues/144443) | ||
| - `IMPORT INTO` is not supported on tables with vector indexes. You can import the vectors first and create the index after import is complete. [#145227](https://github.com/cockroachdb/cockroach/issues/145227) | ||
| - Only L2 distance (`<->`) searches are accelerated. [#144016](https://github.com/cockroachdb/cockroach/issues/144016) |
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What do you mean by "accelerated"? Should "accelerated" be "supported"?
| @@ -88,7 +90,10 @@ SELECT category, vector FROM items WHERE category = 'electronics' ORDER BY vecto | |||
| electronics | [0.9,0.1,0] | |||
| ~~~ | |||
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| You can use a [vector index]({% link {{ page.version.version }}/vector-indexes.md %}) to make searches on large numbers of high-dimensional [`VECTOR`]({% link {{ page.version.version }}/vector.md %}) rows more efficient. | |||
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remove link to same page
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| You can use a [vector index]({% link {{ page.version.version }}/vector-indexes.md %}) to make searches on large numbers of high-dimensional [`VECTOR`]({% link {{ page.version.version }}/vector.md %}) rows more efficient. | |
| You can use a [vector index]({% link {{ page.version.version }}/vector-indexes.md %}) to make searches on large numbers of high-dimensional `VECTOR` rows more efficient. |
| (14 rows) | ||
| ~~~ | ||
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| We also have other resources on indexes: | ||
| [Learn more about indexes]({% link {{ page.version.version }}/indexes.md %}). |
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This sentence does not seem necessary here and could be removed. Maybe "Secondary indexes" in line 262 should have a link to https://www.cockroachlabs.com/docs/v25.2/schema-design-indexes.
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| CREATE TABLE items ( | ||
| id uuid DEFAULT gen_random_uuid(), | ||
| embedding VECTOR (1536), | ||
| VECTOR INDEX (embedding) | ||
| ); |
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If cluster setting is not enabled, it gives an error.
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| CREATE TABLE items ( | |
| id uuid DEFAULT gen_random_uuid(), | |
| embedding VECTOR (1536), | |
| VECTOR INDEX (embedding) | |
| ); | |
| SET CLUSTER SETTING feature.vector_index.enabled = true; | |
| CREATE TABLE items ( | |
| id uuid DEFAULT gen_random_uuid(), | |
| embedding VECTOR (1536), | |
| VECTOR INDEX (embedding) | |
| ); |
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| ### Specify an opclass | ||
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| You can optionally specify an opclass. If not specified, the default is `vector_l2_ops`: |
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For readers like me who do not know what an opclass is:
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| You can optionally specify an opclass. If not specified, the default is `vector_l2_ops`: | |
| You can optionally specify an opclass (short for operator class) that defines how a `VECTOR` data type is handled by the index. If not specified, the default is `vector_l2_ops`: |
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| Vector indexes on `VECTOR` columns support the following comparison operator: | ||
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| - **L2 distance**: [`<->`]({% link {{ page.version.version }}/functions-and-operators.md %}#operators) |
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I think this might be a more informative link:
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| - **L2 distance**: [`<->`]({% link {{ page.version.version }}/functions-and-operators.md %}#operators) | |
| - **L2 distance**: [`<->`]({% link {{ page.version.version }}/vector.md %}#syntax) |
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| Partition size and beam size interact to control both the precision of nearest neighbor search and the cost of maintaining the index. You can improve the accuracy of vector searches by increasing either the search beam size or partition size: | ||
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| - A larger search beam improves accuracy by exploring more partitions, which increases the number of candidate vectors evaluated. |
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For parallelism:
Suggested change
| - A larger search beam improves accuracy by exploring more partitions, which increases the number of candidate vectors evaluated. | |
| - A larger search beam size improves accuracy by exploring more partitions, which increases the number of candidate vectors evaluated. |
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