Unverified50% confidenceFactExact time
Extensions such as pgvector enable Postgres to natively store and retrieve high-dimensional vectors.
1
Sources
50%
Confidence
Long-term
Relevance
10/1/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedUsing Postgres with pgvector allows structured data and vector data to be placed in the same storage layer, reusing transactions, permissions, and backup strategies, thereby reducing operational overhead.79% similarVerifiedThe pgvector extension adds vector storage and similarity search capabilities to PostgreSQL, enabling Supabase to serve simultaneously as a traditional database and a vector database.76% similarUnverifiedImmich 借助 pgvector 将 CLIP 特征向量持久化存储，pgvector 是 PostgreSQL 的向量相似度搜索扩展，于 2021 年首次发布69% similarUnverifiedTypical examples of the "Just use Postgres" philosophy include using pgvector instead of a vector database, using pg_cron instead of Celery/Redis for scheduled tasks, and using LISTEN/NOTIFY instead of a message queue.69% similarUnverifiedThe core advantage of vector stores (such as Pinecone, Weaviate, and pgvector) is efficient approximate nearest neighbor retrieval, but their data model is not designed for relational aggregation queries61% similar
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