Vector Search Limitations
This document describes the known limitations of TiDB Vector Search.
Vector data type limitations
- Each vector supports up to 16383 dimensions.
- Vector data types cannot store
NaN,Infinity, or-Infinityvalues. - Vector data types cannot store double-precision floating-point numbers. If you insert or store double-precision floating-point numbers in vector columns, TiDB converts them to single-precision floating-point numbers.
- Vector columns cannot be used in primary keys, unique indexes, or partition keys. To accelerate the vector search performance, use Vector Search Index.
- A table can have multiple vector columns. However, there is a limit on the total number of columns in a table.
- Currently, TiDB does not support dropping a vector column with a vector index. To drop such a column, drop the vector index first, then drop the vector column.
- Currently, TiDB does not support modifying a vector column to other data types such as
JSONandVARCHAR.
Vector index limitations
- Vector index is used for vector search. It cannot accelerate other queries like range queries or equality queries. Thus, it is not possible to create a vector index on a non-vector column, or on multiple vector columns.
- A table can have multiple vector indexes. However, there is a limit on the total number of indexes in a table.
- Creating multiple vector indexes on the same column is allowed only if they use different distance functions.
- Currently, only
VEC_COSINE_DISTANCE()andVEC_L2_DISTANCE()are supported as the distance functions for vector indexes. - Currently, TiDB does not support dropping a vector column with a vector index. To drop such a column, drop the vector index first, then drop the vector column.
- Currently, TiDB does not support setting vector index as invisible.
Compatibility with TiDB tools
- The Data Migration feature in the TiDB Cloud console does not support migrating or replicating MySQL 9.0 vector data types to TiDB Cloud.
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