pgvector—Vector search on top of the postgres sink.

What it does

Write both the structured archive (from the postgres sink) and frame embeddings into the same Postgres instance using the `pgvector` extension. One database for both metadata and RAG.

When to reach for it

• Single source of truth: SQL + vector search in the same `SELECT`
• Backup, replicate, and restore everything with one pg_dump
• Avoid running three services (sql + object store + vector DB) — one Postgres covers it

Install

npm i -g peepshow
npm i -g pg

Use it

DATABASE_URL=postgres://... \
peepshow ./clip.mp4 --sink pgvector

Make it automatic

Register the sink once — every run fires it afterward. Scope by--whenso it only runs for matching videos.

peepshow sinks add pgvector
peepshow sinks add pgvector --when extension=mp4,mov
peepshow sinks add pgvector --when path=/Volumes/Work/

Configuration

• DATABASE_URLPostgres connection string. The `vector` extension must be installed.required
• PEEPSHOW_PGVECTOR_DIMEmbedding dimension. Default 1536 (OpenAI).

Write your own

A sink is any executable that reads the--emit jsonpayload on stdin. Shell, Node, Python, Go — the spec's indocs/PLUGINS.md. Register persistent ones withpeepshow sinks add-cmd 'your-command'.