Add flexibility for lexical graph config to SimpleKGPipeline (#209)

* Add flexibility for lexical graph config to SimpleKGPipeline

* Update CHANGELOG and update E2E test

* Revert LLMEntityRelationExtractor changes and move lexical_graph_config to pipe_inputs in SimpleKGPipeline

CHANGELOG.md

@@ -2,6 +2,9 @@
 
 ## Next
 
+### Added
+- Introduced optional lexical graph configuration for SimpleKGPipeline, enhancing flexibility in customizing node labels and relationship types in the lexical graph.
+
 ## 1.2.0
 
 ### Added

src/neo4j_graphrag/experimental/pipeline/kg_builder.py

@@ -39,6 +39,7 @@
 from neo4j_graphrag.experimental.components.text_splitters.fixed_size_splitter import (
     FixedSizeSplitter,
 )
+from neo4j_graphrag.experimental.components.types import LexicalGraphConfig
 from neo4j_graphrag.experimental.pipeline.exceptions import PipelineDefinitionError
 from neo4j_graphrag.experimental.pipeline.pipeline import Pipeline, PipelineResult
 from neo4j_graphrag.generation.prompts import ERExtractionTemplate
@@ -59,6 +60,7 @@ class SimpleKGPipelineConfig(BaseModel):
     on_error: OnError = OnError.RAISE
     prompt_template: Union[ERExtractionTemplate, str] = ERExtractionTemplate()
     perform_entity_resolution: bool = True
+    lexical_graph_config: Optional[LexicalGraphConfig] = None
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
@@ -84,6 +86,7 @@ class SimpleKGPipeline:
         on_error (str): Error handling strategy for the Entity and relation extractor. Defaults to "IGNORE", where chunk will be ignored if extraction fails. Possible values: "RAISE" or "IGNORE".
         perform_entity_resolution (bool): Merge entities with same label and name. Default: True
         prompt_template (str): A custom prompt template to use for extraction.
+        lexical_graph_config (Optional[LexicalGraphConfig], optional): Lexical graph configuration to customize node labels and relationship types in the lexical graph.
     """
 
     def __init__(
@@ -101,6 +104,7 @@ def __init__(
         on_error: str = "IGNORE",
         prompt_template: Union[ERExtractionTemplate, str] = ERExtractionTemplate(),
         perform_entity_resolution: bool = True,
+        lexical_graph_config: Optional[LexicalGraphConfig] = None,
     ):
         self.entities = [SchemaEntity(label=label) for label in entities or []]
         self.relations = [SchemaRelation(label=label) for label in relations or []]
@@ -127,6 +131,7 @@ def __init__(
             prompt_template=prompt_template,
             embedder=embedder,
             perform_entity_resolution=perform_entity_resolution,
+            lexical_graph_config=lexical_graph_config,
         )
 
         self.from_pdf = config.from_pdf
@@ -141,6 +146,7 @@ def __init__(
         )
         self.prompt_template = config.prompt_template
         self.perform_entity_resolution = config.perform_entity_resolution
+        self.lexical_graph_config = config.lexical_graph_config
 
         self.pipeline = self._build_pipeline()
 
@@ -252,4 +258,9 @@ def _prepare_inputs(
         else:
             pipe_inputs["splitter"] = {"text": text}
 
+        if self.lexical_graph_config:
+            pipe_inputs["extractor"] = {
+                "lexical_graph_config": self.lexical_graph_config
+            }
+
         return pipe_inputs

tests/e2e/test_simplekgpipeline_e2e.py

@@ -20,6 +20,7 @@
 import neo4j
 import pytest
 from neo4j_graphrag.embeddings.base import Embedder
+from neo4j_graphrag.experimental.components.types import LexicalGraphConfig
 from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline
 from neo4j_graphrag.llm import LLMInterface, LLMResponse
 
@@ -111,6 +112,11 @@ async def test_pipeline_builder_happy_path(
         ("ORGANIZATION", "LED_BY", "PERSON"),
     ]
 
+    # Additional arguments
+    lexical_graph_config = LexicalGraphConfig(chunk_node_label="chunkNodeLabel")
+    from_pdf = False
+    on_error = "RAISE"
+
     # Create an instance of the SimpleKGPipeline
     kg_builder_text = SimpleKGPipeline(
         llm=llm,
@@ -119,8 +125,9 @@ async def test_pipeline_builder_happy_path(
         entities=entities,
         relations=relations,
         potential_schema=potential_schema,
-        from_pdf=False,
-        on_error="RAISE",
+        from_pdf=from_pdf,
+        on_error=on_error,
+        lexical_graph_config=lexical_graph_config,
     )
 
     # Run the knowledge graph building process with text input

tests/unit/experimental/pipeline/test_kg_builder.py

@@ -20,6 +20,7 @@
 from neo4j_graphrag.embeddings import Embedder
 from neo4j_graphrag.experimental.components.entity_relation_extractor import OnError
 from neo4j_graphrag.experimental.components.schema import SchemaEntity, SchemaRelation
+from neo4j_graphrag.experimental.components.types import LexicalGraphConfig
 from neo4j_graphrag.experimental.pipeline.exceptions import PipelineDefinitionError
 from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline
 from neo4j_graphrag.experimental.pipeline.pipeline import PipelineResult
@@ -316,3 +317,65 @@ def test_simple_kg_pipeline_no_entity_resolution(_: Mock) -> None:
     )
 
     assert "resolver" not in kg_builder.pipeline
+
+
+@mock.patch(
+    "neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter._get_version",
+    return_value=(5, 23, 0),
+)
+@pytest.mark.asyncio
+def test_simple_kg_pipeline_lexical_graph_config_attribute(_: Mock) -> None:
+    llm = MagicMock(spec=LLMInterface)
+    driver = MagicMock(spec=neo4j.Driver)
+    embedder = MagicMock(spec=Embedder)
+
+    lexical_graph_config = LexicalGraphConfig()
+    kg_builder = SimpleKGPipeline(
+        llm=llm,
+        driver=driver,
+        embedder=embedder,
+        on_error="IGNORE",
+        lexical_graph_config=lexical_graph_config,
+    )
+
+    assert kg_builder.lexical_graph_config == lexical_graph_config
+
+
+@mock.patch(
+    "neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter._get_version",
+    return_value=(5, 23, 0),
+)
+@pytest.mark.asyncio
+async def test_knowledge_graph_builder_with_lexical_graph_config(_: Mock) -> None:
+    llm = MagicMock(spec=LLMInterface)
+    driver = MagicMock(spec=neo4j.Driver)
+    embedder = MagicMock(spec=Embedder)
+
+    chunk_node_label = "TestChunk"
+    document_nodel_label = "TestDocument"
+    lexical_graph_config = LexicalGraphConfig(
+        chunk_node_label=chunk_node_label, document_node_label=document_nodel_label
+    )
+
+    kg_builder = SimpleKGPipeline(
+        llm=llm,
+        driver=driver,
+        embedder=embedder,
+        from_pdf=False,
+        lexical_graph_config=lexical_graph_config,
+    )
+
+    text_input = "May thy knife chip and shatter."
+
+    with patch.object(
+        kg_builder.pipeline,
+        "run",
+        return_value=PipelineResult(run_id="test_run", result=None),
+    ) as mock_run:
+        await kg_builder.run_async(text=text_input)
+
+        pipe_inputs = mock_run.call_args[0][0]
+        assert "extractor" in pipe_inputs
+        assert pipe_inputs["extractor"] == {
+            "lexical_graph_config": lexical_graph_config
+        }