# Lantern Archive: find a remembered story in its original words > Source: > Published: 2026-10-29 22:36:41+00:00 *This is a submission for the [Hacktoberfest Open-Source AI Challenge: Ghost in the Machine](https://dev.to/challenges/hacktoberfest-week4-2026-10-26).* Lantern Archive is a small offline search tool for letters, journals and notes. You can remember the meaning of a story without remembering the words that would find it. Lantern uses an open embedding model to connect a question to the relevant passage, then shows the original words and where to find them. In the included fictional archive, “Who fixed the broken timepiece?” finds Elsie's repair of the station clock. “What gave the homemade loaf its citrus fragrance?” finds the letter about orange peel rubbed into sugar. The “ghost” is a trace already present in the source: a small detail waiting to be found again. Every result includes its file and line numbers so the reader can return to the full story. [Watch the 48-second walkthrough](https://github.com/soundcake/lantern-archive-hacktoberfest-20261029/blob/main/demo/walkthrough.mp4) · [Read the exact search results](https://github.com/soundcake/lantern-archive-hacktoberfest-20261029/blob/main/demo/transcript.txt) The walkthrough shows three searches against four fictional letters. Its captions come from an actual local inference run. The saved JSON and transcript are included alongside the video in the repository's `demo/` folder. To run the same demonstration: ``` npm ci npm run setup:model npm run demo ``` Setup downloads the pinned model once. The demonstration then disables fetch requests while the model loads, indexes the archive and searches it. This run recorded zero attempted requests. [Source code and setup instructions](https://github.com/soundcake/lantern-archive-hacktoberfest-20261029) The repository contains the CLI, source-indexing logic, a model setup script, fictional example letters, tests and the recorded demonstration. The app and examples use the MIT license. ``` node src/cli.mjs index examples/letters memories.lantern.json node src/cli.mjs ask memories.lantern.json "Who fixed the broken timepiece?" ``` Lantern runs [Xenova's quantized all-MiniLM-L6-v2 model](https://huggingface.co/Xenova/all-MiniLM-L6-v2) through [Transformers.js](https://github.com/huggingface/transformers.js). The model builds on the [Sentence Transformers all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) sentence-embedding work. The model weights and Transformers.js use Apache-2.0. The code pins both the model revision and the library version. Documents are split into passages with their original character offsets and line numbers. Each passage becomes a 384-dimensional vector. A question becomes another vector, and cosine similarity selects up to three passages above a threshold. Returned text comes directly from those source passages. Source SHA-256 hashes are checked before each search. If a document changes, Lantern asks for a rebuilt index, keeping quotations and line references tied to the current document. An explicit no-match result handles questions whose scores fall below the threshold. Nine core tests pass, covering quotation accuracy, line references, changed sources, passage splitting, ranking, no-match behavior and index handling. A separate real-model test passes four paraphrase searches with fetch disabled. The demonstration indexed twelve passages in 23 ms and answered its three queries in 1.6–3.8 ms each on macOS arm64; the saved report records the runtime and measurements for this small archive. A personal archive can outlast the service used to create it. Open weights let Lantern search locally, without a hosted inference account or a subscription. The model, its revision and the retrieval code are available for inspection. Someone can repeat the same experiment, change the threshold or replace the model as their archive grows. That matters for the theme: keeping a story accessible includes keeping the tools needed to find it accessible. Lantern brings an old passage back into view, with its original wording intact.