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add test and docs
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@ -32,13 +32,18 @@ Approximate Nearest Neighbor Search Indexes (`ANNIndexes`) are similar to skip i
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LIMIT N
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```
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In these queries, `DistanceFunction` is selected from [distance functions](../../../sql-reference/functions/distance-functions). `Point` is a known vector (something like `(0.1, 0.1, ... )`). `Value` - a float value that will bound the neighbourhood.
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In these queries, `DistanceFunction` is selected from [distance functions](../../../sql-reference/functions/distance-functions). `Point` is a known vector (something like `(0.1, 0.1, ... )`). To avoid writing large vectors, use [client parameters](../../../interfaces/cli.md#queries-with-parameters-cli-queries-with-parameters). `Value` - a float value that will bound the neighbourhood.
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!!! note "Note"
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ANN index can't speed up query that satisfies both types(`where + order by`, only one of them). All queries must have the limit, as algorithms are used to find nearest neighbors and need a specific number of them.
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!!! note "Note"
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Indexes are applied only to queries with a limit less than the `max_limit_for_ann_queries` setting. This helps to avoid memory overflows in queries with a large limit. `max_limit_for_ann_queries` setting can be changed if you know you can provide enough memory. The default value is `1000000`.
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Both types of queries are handled the same way. The indexes get `n` neighbors (where `n` is taken from the `LIMIT` clause) and work with them. In `ORDER BY` query they remember the numbers of all parts of the granule that have at least one of neighbor. In `WHERE` query they remember only those parts that satisfy the requirements.
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## Create table with ANNIndex
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```sql
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@ -6,3 +6,11 @@
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1 [0,0,10]
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5 [0,0,10.2]
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4 [0,0,9.7]
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1 [0,0,10]
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2 [0,0,10.5]
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3 [0,0,9.5]
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4 [0,0,9.7]
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5 [0,0,10.2]
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1 [0,0,10]
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5 [0,0,10.2]
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4 [0,0,9.7]
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@ -24,6 +24,18 @@ FROM 02354_annoy
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ORDER BY L2Distance(embedding, [0.0, 0.0, 10.0])
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LIMIT 3;
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SET param_02354_target_vector='[0.0, 0.0, 10.0]';
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SELECT *
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FROM 02354_annoy
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WHERE L2Distance(embedding, {02354_target_vector: Array(Float32)}) < 1.0
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LIMIT 5;
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SELECT *
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FROM 02354_annoy
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ORDER BY L2Distance(embedding, {02354_target_vector: Array(Float32)})
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LIMIT 3;
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SELECT *
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FROM 02354_annoy
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ORDER BY L2Distance(embedding, [0.0, 0.0])
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