Search

To retrieve the nearest neighbors to a vector,

SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5;

These operators are used for distance metrics:

Name	Description
<->	squared Euclidean distance
<#>	negative dot product
<=>	cosine distance

To retrieve the nearest neighbors to a vector within a given category,

SELECT 1 FROM items WHERE category_id = 1 ORDER BY embedding <#> '[0.5,0.5,0.5]' limit 10

Reference

Search Parameters

vchordrq.probes

• Description: This GUC parameter vchordrq.probes controls how the vector space assists in query pruning. The more probes, the more accurate the search, but also the slower it is.
• Type: list of integers
• Default:
  • since v0.3.0
  • 10 until v0.2.2: the default value was 10 before `lists` defaulted to empty
• Example:
  • SET vchordrq.probes = 1 means that only one probe is used.
  • SET vchordrq.probes = 10 means that ten probes are used.
• Note: The default value is an empty list. The length of this option must match the length of lists.
  • If lists = [], then probes must be .
  • If lists = [11, 22], then probes can be 2,4 or 4,8. It must not be incorrectly shaped, for example, , 3, 7,8,9, 5,5,5,5.
• See also: Performance Tuning

vchordrq.epsilon

• Description: Even after pruning, the number of retrieved vectors remains substantial. The index employs the RaBitQ algorithm to quantize vectors into bit vectors, which require just the memory of single-precision floating-point vectors. Most computations are integer-based, leading to faster processing. Unlike typical quantization algorithms, RaBitQ not only estimates distances but also their lower bounds. The index computes the lower bound for each vector and dynamically adjusts the number of vectors needing recalculated distances, based on the query count, thus balancing performance and accuracy. The GUC parameter vchordrq.epsilon controls the conservativeness of the lower bounds of distances. The higher the value, the higher the accuracy, but the worse the performance. The default value provides unnecessarily high accuracy for most indexes, so you can try lowering this parameter to achieve better performance.
• Type: float
• Default: 1.9
• Domain: [0.0, 4.0]
• Example:
  • SET vchordrq.epsilon = 0.1 indicates you are using a very optimistic lower bound estimation. You set it this way because your dataset is not sensitive to the lower bound estimation, for the precision you need.
  • SET vchordrq.epsilon = 4.0 indicates you are using a very pessimistic lower bound estimation. You set it this way because your dataset is not very sensitive to the lower bound estimation, for the precision you need.
• Note: The default value is 1.9. The acceptable range is from 0.0 to 4.0.
• See also: Performance Tuning

vchordrq.prewarm_dim deprecated in v0.4.0

• Description: The vchordrq.prewarm_dim GUC parameter is used to precompute the RaBitQ projection matrix for the specified dimensions. This can help to reduce the latency of the first query after the PostgreSQL cluster is started.
• Type: list of integers
• Default: 64,128,256,384,512,768,1024,1536
• Example:
  • ALTER SYSTEM SET vchordrq.prewarm_dim = '64,128' means that the projection matrix will be precomputed for dimensions 64 and 128.
• Note:
  • This setting requires a database restart to take effect.
  • Since v0.4.0, new algorithm made this option obsolete.