Bringing real-time to innovative teams

NVIDIA-VGoldman SachsLG-new BrandAdobeSiemensDell TechnologiesCignaSTCDisney+ HuluGEKrogerComcast6senseAkamaiAnt MoneyArculesBank of KoreaCARFAXCiscoCometlyDBSDentsuErnst & Young EYFactors.AIFathom AnalyticsFirstEnergyGameFam-Silicon DigitalHeap | by ContentsquareIMAXimpact.comLiveRampOpenDialogOutreachPalo Alto NetworksPicPayRBC-new BrandRecartSamsungSegMetricsSiriusXMSiteimprove-new BrandSK TelecomSkaiSonySunRunTATAZebra-AntuitZoomInfo
Vector search on SingleStore

Unparalleled ingest and search performance

Support your real-time search, recommendation, or AI agent workflows—vector embeddings inserted to SingleStore become immediately searchable, powered by out native vector support.

SingleStore combines competitive vector search performance with fast SQL analytics, joins and aggregations across petabytes of structured and semi-structured data to power intelligent applications.

Powerful hybrid search for your analytical and AI applications

Bring powerful search to your application with SingleStore's native vector and full-text search.

Combine the benefits of fine-grained control provided by full-text search with semantic vector search in one easy-to-use SQL interface.

1

SET @query_vec =

2

('[0.44, 0.554, 0.34, 0.62]') :> VECTOR(4);

3

4

SELECT

5

    SUBSTRING(comment, 0, 20) AS comment,

6

    FORMAT(

7

@query_vec <*> comment_embedding,

8

4

9

) AS v_score,

10

    FORMAT(

11

MATCH(comment) AGAINST ("restaurant"),

12

4

13

) AS ft_score,

14

    FORMAT(

15

(v_score + ft_score) / 2,

16

4

17

) AS total

18

FROM comments

19

WHERE category = "Food"

20

ORDER BY total DESC

21

LIMIT 10;
+--------------------+---------+----------+--------+
| comment | v_score | ft_score | total |
+--------------------+---------+----------+--------+
| The B24 restaurant | 0.8993 | 0.1151 | 0.5072 |
| The cafeteria in b | 0.9810 | 0.0000 | 0.4905 |
+--------------------+---------+----------+--------+
Vector search

Vector search

Native vector search support

Vector search typically involves a three-way trade-off between accuracy, performance and cost. With SingleStore, you get what you need, when you need it.

Chart Scatter 3 D Icon

k-Nearest Neighbor (kNN) algorithms

Chart Scatter 3 D Icon

Approximate Nearest Neighbor (ANN) algorithms

Arrow Up Right And Arrow Down Left From Center Icon

Dot product and Euclidean distance metrics

Align Right Icon

IVF-PQ for low index build time and small index size

Align Right Icon

HNSW for excellent recall and high dimensionality

Full-text search

Lightning-fast full-text search

SingleStore's full-text search is a robust search solution that leverages the Lucene library to deliver high-quality, fast search results across your data.

Bars Staggered Icon

Native support for inverted indexes

Magnifying Glass Icon

Support for fuzzy search, wildcard search, proximity search, range search, and regex search

Puzzle Icon

SingleStore full-text search is based on the active, open-source Lucene

Laptop Code Icon

Integrated full-text search directly with analytical queries, allowing you to combine search operations with aggregations and other SQL operations

Full-text search

Fast, enterprise-grade SQL database

Gauge Max Icon

Unparalleled speed

Fast ingest for vector embeddings and new vectors makes data immediately searchable.

Code Icon

Familiar, powerful SQL interface

Execute searches in SQL with support for filters, aggregates + joins. No need to learn a new language.

Chart Mixed Icon

Scalability, performance and reliability

Handle complex text and vector search queries as your data volume grows — without significant degradation in performance.

Explore some sample code

View some of our Jupyter Notebooks showing how to hybrid search works on SingleStore

Real-world use cases of SingleStore hybrid search