Lantern Archive: find a remembered story in its original words

A developer built Lantern Archive, an offline semantic search tool that uses Xenova's quantized all-MiniLM-L6-v2 model via Transformers.js to match natural-language questions to passages in letters, journals and notes while returning the original text with file and line references. The tool splits documents into passages with character offsets, embeds each into a 384-dimensional vector, and selects up to three passages by cosine similarity above a threshold, verifying source SHA-256 hashes before each search. In a recorded demo it indexed twelve passages in 23 ms and answered three queries in 1.6–3.8 ms each on macOS arm64 with fetch disabled, and nine core tests plus a real-model paraphrase test pass.

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.