Platform
Hybrid
Search
Get the best of both worlds with vector and full-text search
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Bringing real-time to innovative teams
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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.
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
SELECT5
SUBSTRING(comment, 0, 20) AS comment,6
FORMAT(7
@query_vec <*> comment_embedding,8
49
) AS v_score,10
FORMAT(11
MATCH(comment) AGAINST ("restaurant"),12
413
) AS ft_score,14
FORMAT(15
(v_score + ft_score) / 2,16
417
) AS total18
FROM comments19
WHERE category = "Food"20
ORDER BY total DESC21
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 |
+--------------------+---------+----------+--------+.png?width=1024&disable=upscale&auto=webp)
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.
k-Nearest Neighbor (kNN) algorithms
Approximate Nearest Neighbor (ANN) algorithms
Dot product and Euclidean distance metrics
IVF-PQ for low index build time and small index size
HNSW for excellent recall and high dimensionality
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.
Native support for inverted indexes
Support for fuzzy search, wildcard search, proximity search, range search, and regex search
SingleStore full-text search is based on the active, open-source Lucene
Integrated full-text search directly with analytical queries, allowing you to combine search operations with aggregations and other SQL operations
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Unparalleled speed
Fast ingest for vector embeddings and new vectors makes data immediately searchable.
Familiar, powerful SQL interface
Execute searches in SQL with support for filters, aggregates + joins. No need to learn a new language.
Scalability, performance and reliability
Handle complex text and vector search queries as your data volume grows — without significant degradation in performance.
View some of our Jupyter Notebooks showing how to hybrid search works on SingleStore
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Sentiment analysis on employee survey responses
Siemens drives sentiment analysis using vector capabilities within SingleStore to analyze and gain deeper insights into the responses from company-wide HR surveys across 200,000 employees worldwide.
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Real-time image search and object recognition for search and analytics
nyris.io, a leading AI-based visual search engine for retailers powers its platform (including catalog search, visual search and analytics) using dot_product vector similarity capabilities in SingleStore.
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Catalog, customer 360 and personalization matching job seekers with roles
DirectlyApply, a job discovery platform and vertical search engine that connects job seekers with employers, uses SingleStore to store vector embeddings generated from job titles and run vector similarity search (using dot_product) to match embeddings with job openings and standard ISCO job titles.
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AI-driven video monitoring and surveillance for safety and security
Lumana, a SaaS visual intelligence platform for real-time video monitoring and surveillance uses SingleStore's vector functionality to perform image and video matching to monitor occupational safety, surveillance footage and more.
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