Vector search

Vector search retrieves the most semantically similar items to a query vector using Approximate Nearest Neighbor (ANN) algorithms (e.g., HNSW via Lucene).

CrateDB supports native vector search, enabling you to perform similarity-based retrieval directly in SQL, without needing a separate vector database or search engine.

Whether you're powering semantic search, recommendation engines, anomaly detection, or AI-enhanced applications, CrateDB lets you store, manage, and search vector embeddings at scale right alongside your structured, JSON, and full-text data.

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Why CrateDB for Vector Search?

FLOAT_VECTOR	Store embeddings up to 2048 dimensions
KNN_MATCH	SQL-native k-nearest neighbor function with _score support
VECTOR_SIMILARITY	Compute similarity scores between vectors in queries
Real-time indexing	Fresh vectors are immediately searchable
Hybrid queries	Combine vector search with filters, full-text, and JSON

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Common Query Patterns

K-Nearest Neighbors (KNN) Search

SELECT text, _score
FROM word_embeddings
WHERE KNN_MATCH(embedding, [0.3, 0.6, 0.0, 0.9], 3)
ORDER BY _score DESC;

Returns top 3 most similar embeddings.

Combine with Filters

SELECT product_name, _score
FROM products
WHERE category = 'shoes'
  AND KNN_MATCH(features, [0.2, 0.1, …], 5)
ORDER BY _score DESC;

Compute Similarity Score

SELECT id, VECTOR_SIMILARITY(emb, [q_vector]) AS score
FROM items
WHERE KNN_MATCH(emb, [q_vector], 10)
ORDER BY score DESC;

Useful if combining scoring logic manually.

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Real-World Examples

Semantic Document Search

SELECT id, title
FROM documents
WHERE KNN_MATCH(embedding, [query_emb], 5)
ORDER BY _score DESC;

E-commerce Recommendations

SELECT id, name
FROM product_vecs
WHERE in_stock
  AND KNN_MATCH(feature_vec, [user_emb], 4)
ORDER BY _score DESC;

Chat Memory Recall

SELECT message
FROM chat_history
WHERE KNN_MATCH(vec, [query_emb], 3)
ORDER BY _score DESC;

Anomaly Detection

SELECT *
FROM events
WHERE type = 'sensor'
  AND KNN_MATCH(vector_repr, [normal_pattern_emb], 1)
ORDER BY _score ASC
LIMIT 1;

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Further Learning & Resources

• Vector Search Guide

• KNN_MATCH & VECTOR_SIMILARITY reference

• Intro Blog: Vector support & KNN search in CrateDB

• LangChain & Vector Store integration