More ruff formatting

examples/customize/build_graph/pipeline/kg_builder_from_pdf.py

@@ -39,7 +39,9 @@
 logging.basicConfig(level=logging.INFO)
 
 
-async def define_and_run_pipeline(neo4j_driver: neo4j.Driver, llm: LLMInterface) -> PipelineResult:
+async def define_and_run_pipeline(
+    neo4j_driver: neo4j.Driver, llm: LLMInterface
+) -> PipelineResult:
     from neo4j_graphrag.experimental.pipeline import Pipeline
 
     # Instantiate Entity and Relation objects
@@ -56,7 +58,9 @@ async def define_and_run_pipeline(neo4j_driver: neo4j.Driver, llm: LLMInterface)
         ),
     ]
     relations = [
-        SchemaRelation(label="SITUATED_AT", description="Indicates the location of a person."),
+        SchemaRelation(
+            label="SITUATED_AT", description="Indicates the location of a person."
+        ),
         SchemaRelation(
             label="LED_BY",
             description="Indicates the leader of an organization.",
@@ -65,7 +69,9 @@ async def define_and_run_pipeline(neo4j_driver: neo4j.Driver, llm: LLMInterface)
             label="OWNS",
             description="Indicates the ownership of an item such as a Horcrux.",
         ),
-        SchemaRelation(label="INTERACTS", description="The interaction between two people."),
+        SchemaRelation(
+            label="INTERACTS", description="The interaction between two people."
+        ),
     ]
     potential_schema = [
         ("PERSON", "SITUATED_AT", "LOCATION"),
@@ -78,7 +84,8 @@ async def define_and_run_pipeline(neo4j_driver: neo4j.Driver, llm: LLMInterface)
     pipe = Pipeline()
     pipe.add_component(PdfLoader(), "pdf_loader")
     pipe.add_component(
-        FixedSizeSplitter(chunk_size=4000, chunk_overlap=200, approximate=False), "splitter"
+        FixedSizeSplitter(chunk_size=4000, chunk_overlap=200, approximate=False),
+        "splitter",
     )
     pipe.add_component(SchemaBuilder(), "schema")
     pipe.add_component(
@@ -126,7 +133,9 @@ async def main() -> PipelineResult:
             "response_format": {"type": "json_object"},
         },
     )
-    driver = neo4j.GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))
+    driver = neo4j.GraphDatabase.driver(
+        "bolt://localhost:7687", auth=("neo4j", "password")
+    )
     res = await define_and_run_pipeline(driver, llm)
     driver.close()
     await llm.async_client.close()

examples/customize/build_graph/pipeline/kg_builder_from_text.py

@@ -39,7 +39,9 @@
 import neo4j
 
 
-async def define_and_run_pipeline(neo4j_driver: neo4j.Driver, llm: LLMInterface) -> PipelineResult:
+async def define_and_run_pipeline(
+    neo4j_driver: neo4j.Driver, llm: LLMInterface
+) -> PipelineResult:
     """This is where we define and run the KG builder pipeline, instantiating a few
     components:
     - Text Splitter: in this example we use the fixed size text splitter
@@ -74,7 +76,9 @@ async def define_and_run_pipeline(neo4j_driver: neo4j.Driver, llm: LLMInterface)
     # and how the output of previous components must be used
     pipe.connect("splitter", "chunk_embedder", input_config={"text_chunks": "splitter"})
     pipe.connect("schema", "extractor", input_config={"schema": "schema"})
-    pipe.connect("chunk_embedder", "extractor", input_config={"chunks": "chunk_embedder"})
+    pipe.connect(
+        "chunk_embedder", "extractor", input_config={"chunks": "chunk_embedder"}
+    )
     pipe.connect(
         "extractor",
         "writer",
@@ -145,7 +149,9 @@ async def main() -> PipelineResult:
             "response_format": {"type": "json_object"},
         },
     )
-    driver = neo4j.GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))
+    driver = neo4j.GraphDatabase.driver(
+        "bolt://localhost:7687", auth=("neo4j", "password")
+    )
     res = await define_and_run_pipeline(driver, llm)
     driver.close()
     await llm.async_client.close()

examples/customize/build_graph/pipeline/lexical_graph_builder_from_text.py

@@ -79,5 +79,7 @@ async def main(neo4j_driver: neo4j.Driver) -> PipelineResult:
 
 
 if __name__ == "__main__":
-    with neo4j.GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password")) as driver:
+    with neo4j.GraphDatabase.driver(
+        "bolt://localhost:7687", auth=("neo4j", "password")
+    ) as driver:
         print(asyncio.run(main(driver)))

examples/customize/build_graph/pipeline/text_to_lexical_graph_to_entity_graph_single_pipeline.py

@@ -93,7 +93,9 @@ async def define_and_run_pipeline(
     )
     # define the execution order of component
     # and how the output of previous components must be used
-    pipe.connect("chunk_embedder", "extractor", input_config={"chunks": "chunk_embedder"})
+    pipe.connect(
+        "chunk_embedder", "extractor", input_config={"chunks": "chunk_embedder"}
+    )
     pipe.connect("schema", "extractor", input_config={"schema": "schema"})
     pipe.connect(
         "extractor",
@@ -188,5 +190,7 @@ async def main(driver: neo4j.Driver) -> PipelineResult:
 
 
 if __name__ == "__main__":
-    with neo4j.GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password")) as driver:
+    with neo4j.GraphDatabase.driver(
+        "bolt://localhost:7687", auth=("neo4j", "password")
+    ) as driver:
         print(asyncio.run(main(driver)))

examples/customize/build_graph/pipeline/text_to_lexical_graph_to_entity_graph_two_pipelines.py

@@ -201,11 +201,15 @@ async def main(driver: neo4j.Driver) -> PipelineResult:
         },
     )
     await build_lexical_graph(driver, lexical_graph_config, text=text)
-    res = await read_chunk_and_perform_entity_extraction(driver, llm, lexical_graph_config)
+    res = await read_chunk_and_perform_entity_extraction(
+        driver, llm, lexical_graph_config
+    )
     await llm.async_client.close()
     return res
 
 
 if __name__ == "__main__":
-    with neo4j.GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password")) as driver:
+    with neo4j.GraphDatabase.driver(
+        "bolt://localhost:7687", auth=("neo4j", "password")
+    ) as driver:
         print(asyncio.run(main(driver)))

tests/unit/experimental/components/text_splitters/test_fixed_size_splitter.py

@@ -103,7 +103,9 @@ def test_invalid_chunk_size() -> None:
         ("Hello World", 0, 0),
     ],
 )
-def test_adjust_chunk_start(text: str, approximate_start: int, expected_start: int) -> None:
+def test_adjust_chunk_start(
+    text: str, approximate_start: int, expected_start: int
+) -> None:
     """
     Test that the _adjust_chunk_start function correctly shifts
     the start index to avoid breaking words, unless no whitespace is found.
@@ -127,7 +129,9 @@ def test_adjust_chunk_start(text: str, approximate_start: int, expected_start: i
         ("Hello World", 6, 15, 15),
     ],
 )
-def test_adjust_chunk_end(text: str, start: int, approximate_end: int, expected_end: int) -> None:
+def test_adjust_chunk_end(
+    text: str, start: int, approximate_end: int, expected_end: int
+) -> None:
     """
     Test that the _adjust_chunk_end function correctly shifts
     the end index to avoid breaking words, unless no whitespace is found.
@@ -183,7 +187,11 @@ def test_adjust_chunk_end(text: str, start: int, approximate_end: int, expected_
     ],
 )
 async def test_fixed_size_splitter_run(
-    text: str, chunk_size: int, chunk_overlap: int, approximate: bool, expected_chunks: list[str]
+    text: str,
+    chunk_size: int,
+    chunk_overlap: int,
+    approximate: bool,
+    expected_chunks: list[str],
 ) -> None:
     """
     Test that 'FixedSizeSplitter.run' returns the expected chunks