Glyph
Content intelligence without the baggage
What it does
Plot twist: there are no transformer models, GPU clusters, or vector embeddings here. We built Glyph because we needed fast content intelligence for some of our own products. Then we made it fast. Glyph converts text into fixed-size signatures using MinHash and uses those signatures for similarity estimation, ranking, search, recommendations, guided token chain autocompletions, and document matching. Glyph runs entirely in memory, with no embedding API or vector database required.
A lightweight toolkit for generating and comparing content fingerprints. - Koda-OSS/Glyph
Compare fast. Search in memory. Spotlight documents. Aggregate collections. Glyph turns text into glyphs — fixed-size MinHash fingerprints. Compare similarity, search an in-memory index, rank document chunks with spotlight, pre-aggregate labeled examples into one glyph, or suggest the next word with completions. import { Create , Compare } from "@koda.oss/glyph" ; const a = Create ( "the quick brown fox jumps over the lazy dog" ) ; const b = Create ( "the quick brown fox leaped over the lazy dog" ) ; console . log ( Compare ( a , b ) . similarity ) ; // ~0–1 Jaccard estimate Documentation Full doc index: docs/README.md · Changelog · Migration 0.4 → 1.0 A lightweight toolkit for generating…from github.com
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