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Database / pgvector basics Interview Questions

What is the pgvector maximum supported dimensions limit and how do you handle high-dimensional vectors?

pgvector has dimension limits that vary by data type. These limits are generous for current embedding models but may be a consideration for future or custom embeddings.

Dimension limits by type
TypeMax dimensionsNotes
vector(n)16,000Standard; covers OpenAI (1536/3072), most models
halfvec(n)16,000Half-precision; same dim limit, half storage
bit(n)64,000Binary quantised; very high dimension support
sparsevec(n)1,000,000Sparse vectors; only non-zero values stored
-- Check current dimension limit at runtime:
SELECT current_setting('vector.max_dimensions');

-- If you need > 16,000 dimensions (rare in practice):
-- Option 1: Use sparsevec if vector is sparse
CREATE TABLE items (
    id        BIGSERIAL PRIMARY KEY,
    embedding SPARSEVEC(100000)  -- up to 1M dims
);

-- Option 2: Dimensionality reduction before storage
-- Use PCA or UMAP to reduce from e.g. 65536-dim to 2048-dim
-- then store as vector(2048)

-- Option 3: Split vector across columns (workaround, ugly)
-- Not recommended but possible for extreme edge cases

-- Real-world dimension reference:
-- text-embedding-3-small: 1,536 dims (well within limit)
-- text-embedding-3-large: 3,072 dims (well within limit)
-- CLIP image embeddings: 512-768 dims (well within limit)
-- Custom deep models: may go up to 4096 dims
-- All well within the 16,000 dim limit for vector(n)

What is the maximum number of dimensions supported by the standard vector(n) type in pgvector?
Which pgvector type would you use to efficiently store vectors where most dimensions are zero?

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