postgres-impl-pgvector-similarity

What problem does it solve?

This Skill addresses the challenges of storing and querying vector embeddings in PostgreSQL, ensuring efficient and accurate semantic search and similarity comparisons.

Core Features & Use Cases

• Vector Storage and Indexing: Facilitates the storage and indexing of vector embeddings in PostgreSQL tables.
• Semantic Search: Enables semantic search by comparing vector embeddings to find semantically similar rows.
• Use Case: For a knowledge base, this Skill can be used to quickly find documents that are most relevant to a given query, based on the similarity of their vector representations.

Quick Start

Install the skill and run the following query to find the most similar document based on a given embedding:

SELECT id, embedding <=> $1 AS distance
FROM documents
ORDER BY embedding <=> $1
LIMIT 10;