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docs/source/user_guide_kg_builder.rst

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@@ -1033,8 +1033,8 @@ In practice, this package implements three resolvers:
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- a simple resolver that merges nodes with the same label and identical "name" property;
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- two similarity-based resolvers that merge nodes with the same label and similar set of textual properties (by default they use the "name" property):
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- a semantic match resolver, which is based on spaCy embeddings and cosine similarities of embedding vectors;
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- a fuzzy match resolver, which is based on RapidFuzz for Rapid fuzzy string matching using the Levenshtein Distance.
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- a semantic match resolver, which is based on spaCy embeddings and cosine similarities of embedding vectors. This resolver is ideal for higher quality KG resolution using static embeddings.
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- a fuzzy match resolver, which is based on RapidFuzz for Rapid fuzzy string matching using the Levenshtein Distance. This resolver offers faster ingestion speeds by using string similarity measures, at the potential cost of resolution precision.
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.. warning::
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src/neo4j_graphrag/experimental/components/resolver.py

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@@ -168,7 +168,7 @@ class BasePropertySimilarityResolver(EntityResolver, abc.ABC):
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Args:
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driver (neo4j.Driver): The Neo4j driver to connect to the database.
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filter_query (Optional[str]): Optional Cypher WHERE clause to reduce the resolution scope.
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resolve_properties (Optional[List[str]]): The list of properties to consider for embeddings Defaults to ["name"].
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resolve_properties (Optional[List[str]]): The list of properties to consider for similarity. Defaults to ["name"].
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similarity_threshold (float): The similarity threshold above which nodes are merged. Defaults to 0.8.
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neo4j_database (Optional[str]): The name of the Neo4j database. If not provided, this defaults to the server's default database ("neo4j" by default) (`see reference to documentation <https://neo4j.com/docs/operations-manual/current/database-administration/#manage-databases-default>`_).
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