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Powering next gen
AI apps with Postgres

Build and scale transformative LLM applications with vector indexes and similarity search in Postgres

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Fast and Accurate

Scale your AI apps to millions of users with Neon

Fully open-source
Scales to10M+rows

Speed up your queries with HNSW

Query execution time (ms) at 99% recall

13.2ms
262.3ms
HNSWIVFFlat

HNSW indexes bring 20x the speed for 99% accuracy to graph-based approximate nearest neighbor search in your Postgres database.

20xFaster than IVFFlat
Uses HNSW
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Simple to use,
scales automatically

Store vector embeddings and perform similarity search

CREATE EXTENSION vector;
CREATE TABLE items (id BIGSERIAL PRIMARY KEY, embedding VECTOR(3));
INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]');
SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 1;

Store embeddings and perform vector similarity search in Postgres with pgvector.Learn moreabout pgvector

Benefits

Vector search with Neon

Use the power of HNSW indexes to unlock new levels of efficiency in high-dimensional vector similarity search in Postgres

  • Reliable & actively maintained

    The pgvector extension is open-source and actively maintained

  • Amazing scalability

    Grow your vector stores without impacting search performance

  • Blazingly fast search

    Use HNSW indexes for fast and scalable vector similarity search in Postgres

  • Highly compatible

    Use Neon with pgvector in your Postgres and LangChain projects

Build your next AI app now with Neon

Neon offers flexible usage and volume-based plans. Contact our Sales team to learn more.