Update change log, docs and examples

Author: NathalieCharbel

CHANGELOG.md

@@ -4,6 +4,7 @@
 
 ### Added
 - Added a new semantic match resolver to the KG Builder for entity resolution based on spaCy embeddings and cosine similarities so that nodes with similar textual properties get merged.
+- Added a new fuzzy match resolver to the KG Builder for entity resolution based on RapiFuzz string fuzzy matching.
 
 ## 1.6.0
 

docs/source/api.rst

@@ -105,11 +105,17 @@ SinglePropertyExactMatchResolver
     :members: run
 
 SpaCySemanticMatchResolver
-================================
+==========================
 
 .. autoclass:: neo4j_graphrag.experimental.components.resolver.SpaCySemanticMatchResolver
     :members: run
 
+FuzzyMatchResolver
+==================
+
+.. autoclass:: neo4j_graphrag.experimental.components.resolver.FuzzyMatchResolver
+    :members: run
+
 .. _pipeline-section:
 
 *********

docs/source/index.rst

@@ -101,6 +101,8 @@ List of extra dependencies:
     - Warning: this requires `pygraphviz`. Installation instructions can be found `here <https://pygraphviz.github.io/documentation/stable/install.html>`_.
 - nlp:
    - **spaCy**: load spaCy trained models for nlp pipelines, used by `SpaCySemanticMatchResolver` component from the Knowledge Graph creation pipelines.
+- fuzzy-matching:
+   - **rapidfuzz**: apply fuzzy matching using string similarity, used by `FuzzyMatchResolver` component from the Knowledge Graph creation pipelines.
 
 ********
 Examples

docs/source/user_guide_kg_builder.rst

@@ -1028,17 +1028,19 @@ without making assumptions about entity similarity. The Entity Resolver
 is responsible for refining the created knowledge graph by merging entity
 nodes that represent the same real-world object.
 
-In practice, this package implements two resolvers:
+In practice, this package implements three resolvers:
 
 - a simple resolver that merges nodes with the same label and identical "name" property;
-- a semantic match resolver that merges nodes with the same label and similar set of textual properties (by default it uses the "name" property).
-So far, the semantic matching is based on spaCy embeddings and cosine similarities of embedding vectors.
+- two similarity-based resolvers that merge nodes with the same label and similar set of textual properties (by default they use the "name" property):
+
+    - a semantic match resolver, which is based on spaCy embeddings and cosine similarities of embedding vectors;
+    - a fuzzy match resolver, which is based on RapidFuzz for Rapid fuzzy string matching using the Levenshtein Distance.
 
 .. warning::
 
-    - The `SinglePropertyExactMatchResolver` and `SpaCySemanticMatchResolver` **replace** the nodes created by the KG writer.
+    - The `SinglePropertyExactMatchResolver`, `SpaCySemanticMatchResolver`, and `FuzzyMatchResolver` **replace** the nodes created by the KG writer.
 
-    - Check the :ref:`installation` section to make sure you have the required dependencies installed when using `SpaCySemanticMatchResolver`.
+    - Check the :ref:`installation` section to make sure you have the required dependencies installed when using `SpaCySemanticMatchResolver`, and `FuzzyMatchResolver`.
 
 
 The resolvers can be used like this:
@@ -1047,9 +1049,12 @@ The resolvers can be used like this:
 
     from neo4j_graphrag.experimental.components.resolver import (
         SinglePropertyExactMatchResolver,
+        # SpaCySemanticMatchResolver,
+        # FuzzyMatchResolver,
     )
     resolver = SinglePropertyExactMatchResolver(driver)  # exact match resolver
     # resolver = SpaCySemanticMatchResolver(driver)  # semantic match with spaCy
+    # resolver = FuzzyMatchResolver(driver)  # fuzzy match with RapidFuzz
     res = await resolver.run()
 
 .. warning::

examples/README.md

@@ -126,6 +126,7 @@ are listed in [the last section of this file](#customize).
   - [Neo4j writer](./customize/build_graph/components/writers/neo4j_writer.py)
   - [Custom](./customize/build_graph/components/writers/custom_writer.py)
 - Entity Resolver:
+  - [FuzzyMatchResolver](./customize/build_graph/components/resolvers/fuzzy_match_entity_resolver_pre_filter.py)
   - [SinglePropertyExactMatchResolver with pre-filter](./customize/build_graph/components/resolvers/simple_entity_resolver_pre_filter.py)
   - [SpaCySemanticMatchResolver with pre-filter](./customize/build_graph/components/resolvers/spacy_entity_resolver_pre_filter.py)
   - [Custom resolver](./customize/build_graph/components/resolvers/custom_resolver.py)

examples/customize/build_graph/components/resolvers/fuzzy_match_entity_resolver_pre_filter.py

@@ -0,0 +1,36 @@
+"""The FuzzyMatchResolver merges nodes with same label
+and similar textual properties (by default using the "name" property) based on RapidFuzz
+for string matching.
+
+If the resolution is intended to be applied only on some nodes, for instance nodes that
+belong to a specific document, a "WHERE" query can be added. The only variable in the
+query scope is "entity".
+
+WARNING: this process is destructive, initial nodes are deleted and replaced
+by the resolved ones, but all relationships are kept.
+See apoc.refactor.mergeNodes documentation for more details.
+"""
+
+from neo4j_graphrag.experimental.components.resolver import (
+    FuzzyMatchResolver,
+)
+from neo4j_graphrag.experimental.components.types import ResolutionStats
+
+import neo4j
+
+
+async def main(driver: neo4j.Driver) -> None:
+    resolver = FuzzyMatchResolver(
+        driver,
+        # let's filter all entities that belong to a certain docId
+        filter_query="WHERE (entity)-[:FROM_CHUNK]->(:Chunk)-[:FROM_DOCUMENT]->(doc:"
+        "Document {id = 'docId'}",
+        # optionally, change the properties used for resolution (default is "name")
+        # resolve_properties=["name", "ssn"],
+        # the similarity threshold (default is 0.8)
+        # similarity_threshold=0.9
+        # and the neo4j database where data is updated
+        # neo4j_database="neo4j",
+    )
+    res: ResolutionStats = await resolver.run()
+    print(res)