WIP: Add basic Parquet Writer component

examples/customize/build_graph/components/writers/parquet_writer.py

@@ -0,0 +1,92 @@
+import asyncio
+
+from neo4j_graphrag.experimental.components.kg_writer import (
+    KGWriterModel,
+    ParquetWriter,
+)
+from neo4j_graphrag.experimental.components.schema import GraphSchema
+from neo4j_graphrag.experimental.components.types import (
+    Neo4jGraph,
+    Neo4jNode,
+    Neo4jRelationship,
+)
+
+OUTPUT_FOLDER = "output"
+
+
+async def main(graph: Neo4jGraph) -> KGWriterModel:
+    writer = ParquetWriter(output_folder=OUTPUT_FOLDER)
+    result = await writer.run(graph=graph)
+    return result
+
+
+if __name__ == "__main__":
+    graph = Neo4jGraph(
+        nodes=[
+            Neo4jNode(
+                id="p-0",
+                label="Person",
+                properties={
+                    "name": "Alice",
+                    "eyeColor": "blue",
+                },
+            ),
+            Neo4jNode(
+                id="p-1",
+                label="Person",
+                properties={
+                    "name": "Robert",
+                    "eyeColor": "brown",
+                    "nickName": "Bob",
+                },
+            ),
+            Neo4jNode(
+                id="l-0",
+                label="Location",
+                properties={
+                    "name": "Wonderland",
+                },
+            ),
+        ],
+        relationships=[
+            Neo4jRelationship(
+                type="KNOWS",
+                start_node_id="p-0",
+                end_node_id="p-1",
+                properties={"reason": "Cryptography"},
+            )
+        ],
+    )
+
+    schema = GraphSchema.model_validate(
+        {
+            "node_types": [
+                "Location",
+                {
+                    "label": "Person",
+                    "properties": [
+                        {
+                            "name": "name",
+                            "type": "STRING",
+                            "required": True,
+                        }
+                    ],
+                    "additional_properties": True,
+                },
+            ],
+            "relationship_types": [
+                "KNOWS",
+            ],
+        }
+    )
+
+    res = asyncio.run(main(graph=graph))
+    print(res)
+    # import pandas as pd
+    #
+    # df = pd.read_parquet(f'{OUTPUT_FOLDER}/nodes/Person.parquet')
+    # print(df.head(10))
+    # df = pd.read_parquet(f'{OUTPUT_FOLDER}/nodes/Location.parquet')
+    # print(df.head(10))
+    # df = pd.read_parquet(f'{OUTPUT_FOLDER}/relationships/KNOWS.parquet')
+    # print(df.head(10))

pyproject.toml

@@ -61,6 +61,7 @@ scipy = [
   { version = "^1.15.0", python = ">=3.13,<3.14" }
 ]
 tenacity = "^9.1.2"
+pyarrow = "^21.0.0"
 
 [tool.poetry.group.dev.dependencies]
 urllib3 = "<2"

src/neo4j_graphrag/experimental/components/kg_writer.py

@@ -13,9 +13,12 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 from __future__ import annotations
+import pandas as pd
 
 import logging
 from abc import abstractmethod
+from collections import defaultdict
+from pathlib import Path
 from typing import Any, Generator, Literal, Optional
 
 import neo4j
@@ -83,6 +86,100 @@ async def run(
         pass
 
 
+class ParquetWriter(KGWriter):
+    """Writes a knowledge graph to Parquet files.
+
+    Args:
+        node_file_path (str): The file path to write the nodes Parquet file.
+        relationship_file_path (str): The file path to write the relationships Parquet file.
+    """
+
+    def __init__(
+        self,
+        output_folder: str,
+    ):
+        self._output_folder = Path(output_folder)
+
+    @staticmethod
+    def _nodes_to_rows(
+        nodes: list[Neo4jNode],
+        label_to_rows: dict[str, list[dict[str, Any]]],
+        lexical_graph_config: LexicalGraphConfig,
+    ) -> None:
+        for node in nodes:
+            row: dict[str, Any] = dict()
+
+            labels = [node.label]
+            if node.label not in lexical_graph_config.lexical_graph_node_labels:
+                labels.append("__Entity__")
+
+            row["id"] = node.id
+            row["labels"] = labels
+            row.update(node.properties)
+            if node.embedding_properties is not None:
+                row.update(node.embedding_properties)
+
+            label_to_rows[node.label].append(row)
+
+    @staticmethod
+    def _relationships_to_rows(
+        relationships: list[Neo4jRelationship],
+        type_to_rows: dict[str, list[dict[str, Any]]],
+    ) -> None:
+        for rel in relationships:
+            row: dict[str, Any] = dict()
+
+            row["from"] = rel.start_node_id
+            row["to"] = rel.end_node_id
+            row["type"] = rel.type
+            row.update(rel.properties)
+            if rel.embedding_properties is not None:
+                row.update(rel.embedding_properties)
+
+            type_to_rows[rel.type].append(row)
+
+    @validate_call
+    async def run(
+        self,
+        graph: Neo4jGraph,
+        lexical_graph_config: LexicalGraphConfig = LexicalGraphConfig(),
+    ) -> KGWriterModel:
+        """Writes a knowledge graph to Parquet files.
+
+        Args:
+            graph (Neo4jGraph): The knowledge graph to write to Parquet files.
+            lexical_graph_config (LexicalGraphConfig): Node labels and relationship types for the lexical graph.
+        """
+        (self._output_folder / "nodes").mkdir(parents=True, exist_ok=True)
+        (self._output_folder / "relationships").mkdir(parents=True, exist_ok=True)
+
+        label_to_rows: defaultdict[str, list[dict[str, Any]]] = defaultdict(list)
+        type_to_rows: defaultdict[str, list[dict[str, Any]]] = defaultdict(list)
+
+        # TODO: Parallelize?
+        self._nodes_to_rows(graph.nodes, label_to_rows, lexical_graph_config)
+        self._relationships_to_rows(graph.relationships, type_to_rows)
+
+        for label, node_df in label_to_rows.items():
+            pd.DataFrame(node_df).to_parquet(
+                self._output_folder / "nodes" / f"{label}.parquet", index=False
+            )
+
+        for rtype, rel_df in type_to_rows.items():
+            pd.DataFrame(rel_df).to_parquet(
+                self._output_folder / "relationships" / f"{rtype}.parquet", index=False
+            )
+
+        return KGWriterModel(
+            status="SUCCESS",
+            metadata={
+                "node_count": len(graph.nodes),
+                "relationship_count": len(graph.relationships),
+                "output_folder": str(self._output_folder),
+            },
+        )
+
+
 class Neo4jWriter(KGWriter):
     """Writes a knowledge graph to a Neo4j database.