Improved schema definition in the SimpleKGPipeline

CHANGELOG.md

@@ -4,6 +4,7 @@
 
 ### Added
 - Introduced optional lexical graph configuration for SimpleKGPipeline, enhancing flexibility in customizing node labels and relationship types in the lexical graph.
+- Ability to provide description and list of properties for entities and relations in the SimpleKGPipeline constructor.
 
 ## 1.2.0
 

examples/build_graph/simple_kg_builder_from_text.py

@@ -11,6 +11,10 @@
 from neo4j_graphrag.embeddings import OpenAIEmbeddings
 from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline
 from neo4j_graphrag.experimental.pipeline.pipeline import PipelineResult
+from neo4j_graphrag.experimental.pipeline.types import (
+    EntityInputType,
+    RelationInputType,
+)
 from neo4j_graphrag.llm import LLMInterface
 from neo4j_graphrag.llm.openai_llm import OpenAILLM
 
@@ -21,12 +25,28 @@
 
 # Text to process
 TEXT = """The son of Duke Leto Atreides and the Lady Jessica, Paul is the heir of House Atreides,
-an aristocratic family that rules the planet Caladan."""
+an aristocratic family that rules the planet Caladan, the rainy planet, since 10191."""
 
 # Instantiate Entity and Relation objects. This defines the
 # entities and relations the LLM will be looking for in the text.
-ENTITIES = ["Person", "House", "Planet"]
-RELATIONS = ["PARENT_OF", "HEIR_OF", "RULES"]
+ENTITIES: list[EntityInputType] = [
+    # entities can be defined with a simple label...
+    "Person",
+    # ... or with a dict if more details are needed,
+    # such as a description:
+    {"label": "House", "description": "Family the person belongs to"},
+    # or a list of properties the LLM will try to attach to the entity:
+    {"label": "Planet", "properties": [{"name": "weather", "type": "STRING"}]},
+]
+# same thing for relationships:
+RELATIONS: list[RelationInputType] = [
+    "PARENT_OF",
+    {
+        "label": "HEIR_OF",
+        "description": "Used for inheritor relationship between father and sons",
+    },
+    {"label": "RULES", "properties": [{"name": "fromYear", "type": "INTEGER"}]},
+]
 POTENTIAL_SCHEMA = [
     ("Person", "PARENT_OF", "Person"),
     ("Person", "HEIR_OF", "House"),

src/neo4j_graphrag/experimental/pipeline/kg_builder.py

@@ -15,7 +15,7 @@
 
 from __future__ import annotations
 
-from typing import Any, List, Optional, Union
+from typing import Any, List, Optional, Sequence, Union
 
 import neo4j
 from pydantic import BaseModel, ConfigDict, Field
@@ -42,6 +42,10 @@
 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.experimental.pipeline.types import (
+    EntityInputType,
+    RelationInputType,
+)
 from neo4j_graphrag.generation.prompts import ERExtractionTemplate
 from neo4j_graphrag.llm.base import LLMInterface
 
@@ -74,8 +78,16 @@ class SimpleKGPipeline:
         llm (LLMInterface): An instance of an LLM to use for entity and relation extraction.
         driver (neo4j.Driver): A Neo4j driver instance for database connection.
         embedder (Embedder): An instance of an embedder used to generate chunk embeddings from text chunks.
-        entities (Optional[List[str]]): A list of entity labels as strings.
-        relations (Optional[List[str]]): A list of relation labels as strings.
+        entities (Optional[List[Union[str, dict[str, str], SchemaEntity]]]): A list of either:
+
+            - str: entity labels
+            - dict: following the SchemaEntity schema, ie with label, description and properties keys
+
+        relations (Optional[List[Union[str, dict[str, str], SchemaRelation]]]): A list of either:
+
+            - str: relation label
+            - dict: following the SchemaRelation schema, ie with label, description and properties keys
+
         potential_schema (Optional[List[tuple]]): A list of potential schema relationships.
         from_pdf (bool): Determines whether to include the PdfLoader in the pipeline.
                          If True, expects `file_path` input in `run` methods.
@@ -94,8 +106,8 @@ def __init__(
         llm: LLMInterface,
         driver: neo4j.Driver,
         embedder: Embedder,
-        entities: Optional[List[str]] = None,
-        relations: Optional[List[str]] = None,
+        entities: Optional[Sequence[EntityInputType]] = None,
+        relations: Optional[Sequence[RelationInputType]] = None,
         potential_schema: Optional[List[tuple[str, str, str]]] = None,
         from_pdf: bool = True,
         text_splitter: Optional[Any] = None,
@@ -106,9 +118,9 @@ def __init__(
         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 []]
-        self.potential_schema = potential_schema if potential_schema is not None else []
+        self.potential_schema = potential_schema or []
+        self.entities = [self.to_schema_entity(e) for e in entities or []]
+        self.relations = [self.to_schema_relation(r) for r in relations or []]
 
         try:
             on_error_enum = OnError(on_error)
@@ -150,6 +162,18 @@ def __init__(
 
         self.pipeline = self._build_pipeline()
 
+    @staticmethod
+    def to_schema_entity(entity: EntityInputType) -> SchemaEntity:
+        if isinstance(entity, dict):
+            return SchemaEntity.model_validate(entity)
+        return SchemaEntity(label=entity)
+
+    @staticmethod
+    def to_schema_relation(relation: RelationInputType) -> SchemaRelation:
+        if isinstance(relation, dict):
+            return SchemaRelation.model_validate(relation)
+        return SchemaRelation(label=relation)
+
     def _build_pipeline(self) -> Pipeline:
         pipe = Pipeline()
 

src/neo4j_graphrag/experimental/pipeline/types.py

@@ -14,6 +14,8 @@
 #  limitations under the License.
 from __future__ import annotations
 
+from typing import Union
+
 from pydantic import BaseModel, ConfigDict
 
 from neo4j_graphrag.experimental.pipeline.component import Component
@@ -35,3 +37,12 @@ class ConnectionConfig(BaseModel):
 class PipelineConfig(BaseModel):
     components: list[ComponentConfig]
     connections: list[ConnectionConfig]
+
+
+EntityInputType = Union[str, dict[str, Union[str, list[dict[str, str]]]]]
+RelationInputType = Union[str, dict[str, Union[str, list[dict[str, str]]]]]
+"""Types derived from the SchemaEntity and SchemaRelation types,
+ so the possible types for dict values are:
+- str (for label and description)
+- list[dict[str, str]] (for properties)
+"""

tests/unit/experimental/pipeline/test_kg_builder.py

@@ -19,7 +19,11 @@
 import pytest
 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.schema import (
+    SchemaEntity,
+    SchemaProperty,
+    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
@@ -379,3 +383,47 @@ async def test_knowledge_graph_builder_with_lexical_graph_config(_: Mock) -> Non
         assert pipe_inputs["extractor"] == {
             "lexical_graph_config": lexical_graph_config
         }
+
+
+def test_knowledge_graph_builder_to_schema_entity_method() -> None:
+    assert SimpleKGPipeline.to_schema_entity("EntityType") == SchemaEntity(
+        label="EntityType"
+    )
+    assert SimpleKGPipeline.to_schema_entity({"label": "EntityType"}) == SchemaEntity(
+        label="EntityType"
+    )
+    assert SimpleKGPipeline.to_schema_entity(
+        {"label": "EntityType", "description": "A special entity"}
+    ) == SchemaEntity(label="EntityType", description="A special entity")
+    assert SimpleKGPipeline.to_schema_entity(
+        {"label": "EntityType", "properties": []}
+    ) == SchemaEntity(label="EntityType")
+    assert SimpleKGPipeline.to_schema_entity(
+        {
+            "label": "EntityType",
+            "properties": [{"name": "entityProperty", "type": "DATE"}],
+        }
+    ) == SchemaEntity(
+        label="EntityType",
+        properties=[SchemaProperty(name="entityProperty", type="DATE")],
+    )
+
+
+def test_knowledge_graph_builder_to_schema_relation_method() -> None:
+    assert SimpleKGPipeline.to_schema_relation("REL_TYPE") == SchemaRelation(
+        label="REL_TYPE"
+    )
+    assert SimpleKGPipeline.to_schema_relation({"label": "REL_TYPE"}) == SchemaRelation(
+        label="REL_TYPE"
+    )
+    assert SimpleKGPipeline.to_schema_relation(
+        {"label": "REL_TYPE", "description": "A rel type"}
+    ) == SchemaRelation(label="REL_TYPE", description="A rel type")
+    assert SimpleKGPipeline.to_schema_relation(
+        {"label": "REL_TYPE", "properties": []}
+    ) == SchemaRelation(label="REL_TYPE")
+    assert SimpleKGPipeline.to_schema_relation(
+        {"label": "REL_TYPE", "properties": [{"name": "relProperty", "type": "DATE"}]}
+    ) == SchemaRelation(
+        label="REL_TYPE", properties=[SchemaProperty(name="relProperty", type="DATE")]
+    )