goldenmatch-kg

Drop-in goldenmatch entity resolution for knowledge-graph frameworks.

What this is: goldenmatch is an entity-resolution engine, not a KG builder. KG pipelines (neo4j-graphrag, LlamaIndex, Graphiti, ...) ingest text, extract entities and relationships, resolve/dedupe the entities, and write a graph. This package drops goldenmatch in as that resolve stage, where each framework exposes a seam:

• neo4j-graphrag -- a real GoldenMatchResolver you pass into the pipeline (true in-pipeline plugin; replaces the built-in FuzzyMatchResolver).
• LlamaIndex PropertyGraphIndex -- a GoldenMatchEntityResolver transform that canonicalizes entity names before upsert. LlamaIndex ships no fuzzy resolver of its own (its default is exact name+label upsert), so this is additive: exact-only to real ER.
• Graphiti -- a post-ingestion propose_entity_merges pass over the graph's existing entity nodes (Graphiti exposes no public resolver seam, so this runs as a maintenance step, not in-line).

One framework-agnostic core (resolve_entities) does all the goldenmatch work; each adapter just marshals its framework's entities in and the merge decision out.

Install

pip install goldenmatch-kg                        # core only
pip install "goldenmatch-kg[neo4j-graphrag]"      # + neo4j-graphrag adapter
pip install "goldenmatch-kg[llamaindex]"          # + LlamaIndex adapter
pip install "goldenmatch-kg[graphiti]"            # + Graphiti adapter

The lift (measured, not asserted)

These adapters deliver goldenmatch's resolution through each framework's pipeline. The size of the win is measured by ER-KG-Bench, which scores each framework's own default entity resolution against goldenmatch on real corpora. On the self-sourced ghsuite corpus (RESULTS_ghsuite.md):

goldenmatch (F1) is the goldenmatch(auto+fields) row (P 0.963 / R 0.975 / F1 0.969), deterministic, zero LLM calls. The lift is the resolution you get by swapping goldenmatch in as the ER stage; it is read off the existing bench board, not re-scored here. The same shape holds on the external Wikidata/RxNorm corpus (RESULTS.md).

This is an ER-stage win, not "goldenmatch builds your KG": you still run the rest of your pipeline (extraction, relationship building, graph store). goldenmatch resolves the entities.

Per-framework guides

• neo4j-graphrag
• LlamaIndex
• Graphiti

License

MIT