pgvector Dimension Limit: Cannot Index More Than 2000 Dimensions

How to fix it

// try these in order, the most common fix is first

1. FIX 1Use halfvec

   1. 01Store as halfvec(n) and index with halfvec ops, supports up to 4,000 dimensions
   Did this fix it? Yes No

2. FIX 2Request fewer dimensions

   Copy steps
   1. 01Ask the embedding model for a smaller size, one NVIDIA forum fix used 1536
   2. 02Or apply dimensionality reduction
   Did this fix it? Yes No

3. FIX 3Skip the index

   1. 01Exact search works without an index at 16,000 dimensions storage limit, but is slower on large tables
   Did this fix it? Yes No

Symptoms

CREATE INDEX fails for embeddings such as 3,072 dimensions.

Why it happens

• C-01vector index cap is 2,000
• C-02Embedding model output too large

// this guide also covers

• pgvector max dimensions
• column cannot have more than 2000 dimensions for hnsw index
• pgvector 3072 dimensions

The same problem in other apps

All plan limits, paywalls & watermarks guides →

• Pinecone Upsert Failing: Batch and Size Limits (Pinecone)
• Chroma "sqlite3.OperationalError: database is locked" (Chroma)

Other pgvector errors

• pgvector Error: extension "vector" is not available
• pgvector HNSW Index Build Slow
• pgvector "operator does not exist: vector <=>" Error
• pgvector "different vector dimensions" Error
• pgvector Query Returns Fewer Results Than LIMIT With Filter
• pgvector Index Not Being Used
• pgvector HNSW Build Out of Memory or Shared Memory Error
• How to Enable pgvector on Supabase