Marquee

Turns one item in a media library into Plex-style rows of other items in that same library. Built for self-hosted media servers.

IN:  "The Sopranos"

OUT:
  Shows like The Sopranos      Gomorrah, Boardwalk Empire, The Wire, ZeroZeroZero
  More from Tim Van Patten     Game of Thrones, Black Mirror, Boardwalk Empire
  More with Edie Falco         Nurse Jackie, Oz
  Shot by Alik Sakharov        Game of Thrones, House of Cards

Two halves, both required

A retriever decides what goes in the rows, by joining your library on shared cast, crew and franchise and by embedding similarity. A small fine-tuned model decides which rows are worth showing, their order, and what to call them.

The model never sees item ids — it answers with index references into candidates it was handed, so it cannot invent a title that isn't in your library.

Install

pip install marquee-ai

Plus Ollama to run the model.

Download

hf download KernelMedia/marquee-ai --local-dir marquee
cd marquee && ollama create marquee -f Modelfile

~2.8 GB: the model plus a catalog of ~122,000 films and shows with full cast and crew.

Run

marquee serve --library catalog.jsonl --port 8080

That is the whole setup.

curl -X POST localhost:8080/recommend \
  -H 'Content-Type: application/json' \
  -d '{"title": "The Sopranos", "row_size": 20}'

Or from the command line:

marquee recommend --library catalog.jsonl --seed "Blade Runner 2049" --row-size 20
marquee person  --library catalog.jsonl --name "Roger Deakins"

API

POST /recommend   {"seed_id": "myserver-04471", "row_size": 20}
GET  /search?q=Scorsese          # matches titles AND people
GET  /person?name=Roger%20Deakins
GET  /health
DELETE /cache                    # after a library rescan

/search and /person are pure index lookups — no model call, effectively instant. Only /recommend uses the GPU.

Items carry an owned flag, so a UI can offer a Request button on anything not on the server. Ambiguous titles return 409 with the candidates rather than guessing — Godzilla matches three films. Narrow with year and media_type, or write "Top Gun (1986)" directly.

Use it as a library

The HTTP API is optional. If your server is Python, skip it:

from marquee import Library
from marquee.contract import messages, parse, repair, expand_rows

lib = Library.load("lib.jsonl")
lib.build_embeddings(cache=Path("lib.emb.npz"))     # once at startup

request = lib.build_request(seed_id, semantic_k=14, row_size=20)
raw = call_your_llm(messages(request))              # any OpenAI-compatible endpoint
rows = expand_rows(repair(parse(raw), request), request, 20)

Requirements

Full setup and troubleshooting: SETUP.md

Data

You harvest TMDB yourself with your own free key; no catalog is distributed. TMDB is free for non-commercial use and requires attribution — commercial use needs a written agreement with them.

This product uses the TMDB API but is not endorsed or certified by TMDB.

Licence

Apache-2.0.