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A high-performance entity resolution engine written in Rust. Matches messy vendor records against clean reference data using embeddings, BM25, fuzzy matching, and a few clever tricks.
Same scoring pipeline, different execution models.
meld run — Load both datasets, match every B record against the A-side pool in parallel, write results.
meld serve — HTTP server with both datasets preloaded. New records matched instantly via API.
Build and run in under a minute.
Combine multiple comparison strategies with configurable weights.
| Method | What it does | Best for |
|---|---|---|
embedding | Neural semantic similarity | Primary name field |
bm25 | IDF-weighted token overlap | Suppressing common-word noise |
fuzzy | Edit-distance similarity | Names, free text with typos |
exact | Binary string equality | Identifiers, codes, country |
synonym | Acronym/abbreviation detection | "HSBC" vs "Hongkong and Shanghai Banking Corp" |
numeric | Numeric equality | Numeric identifiers |
Everything you need to configure, run, and understand Melder.
Complete annotated YAML reference. Every field, option, and default.
Detailed reference for each comparison method with trade-offs.
Measuring match quality, using meld tune, worked threshold example.
Running meld run — output files, SQLite mode, data formats.
Storage backends, persistence, WAL, crash recovery.
Single-pool entity resolution for deduplication and graph-based ER.
HTTP endpoints — add, remove, match, query, crossmap, review.
All subcommands — validate, run, serve, enroll, tune, cache, review.
Run commands on match, review, no-match, and break events.
Benchmarks, scaling characteristics, running your own tests.
Embedding caches, staleness detection, incremental encoding.
Build instructions, feature flags, Windows notes.
New to the project? Start with the interactive guide.
A self-contained walkthrough of the architecture, scoring pipeline, and engineering patterns — with animated diagrams, code explanations, and quizzes.
Open the Interactive Guide