[Question]: I use PropertyGraphIndex to build a noe4j knowledge graph and want to customize node labels to isolate data. How should I do it?

[cxycxm]

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Question

I use PropertyGraphIndex to build a noe4j knowledge graph and want to customize node labels to isolate data. How should I do it?

[dosubot[bot]]

To customize node labels in PropertyGraphIndex when building a Neo4j knowledge graph, you can define a strict schema, implement custom extraction modules, or use the LLM for extraction. Here's a general approach:

1. Define a Strict Schema: Specify the labels for nodes in a schema.
2. Implement Custom Extraction Modules: Create your own modules to label nodes as needed.
3. Use LLM for Extraction: Allow the LLM to extract and label nodes based on context.

Here's an example of defining a strict schema:

from llama_index import PropertyGraphIndex

# Define your strict schema for node labels
schema = {
    "Person": ["name", "age", "occupation"],
    "Location": ["name", "coordinates"],
    "Event": ["name", "date", "location"]
}

# Initialize the PropertyGraphIndex with the schema
graph_index = PropertyGraphIndex(schema=schema)

# Add documents to the index
documents = [
    {"text": "Alice is a software engineer living in San Francisco."},
    {"text": "Bob is a data scientist from New York."}
]

for doc in documents:
    graph_index.add_document(doc)

# Optionally, connect to an existing Neo4j knowledge graph
graph_index.connect_to_neo4j(uri="bolt://localhost:7687", user="neo4j", password="password")

# Query the graph
results = graph_index.query("Find all software engineers in San Francisco.")
print(results)

Additionally, the neo4j_property_graph.py module provides functions like refresh_schema to keep your schema up-to-date and predefined queries to manage node and relationship properties ^[1][2].

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[cxycxm]

If I want to assign a fixed label to the data I extract, what should I do?

[dosubot[bot]]

To assign a fixed label to the data you extract when using PropertyGraphIndex to build a Neo4j knowledge graph, you can use the Node__ tag to store the node label. Here is an example of how you can do this in the upsert_nodes method of the Neo4jPropertyGraphStore class:

def upsert_nodes(self, nodes: List[LabelledNode]) -> None:
    # Lists to hold separated types
    entity_dicts: List[dict] = []
    chunk_dicts: List[dict] = []

    # Sort by type
    for item in nodes:
        if isinstance(item, EntityNode):
            entity_dicts.append({**item.dict(), "id": item.id})
        elif isinstance(item, ChunkNode):
            chunk_dicts.append({**item.dict(), "id": item.id})
        else:
            # Log that we do not support these types of nodes
            # Or raise an error?
            pass

    if chunk_dicts:
        self.structured_query(
            """
            UNWIND $data AS row
            MERGE (c:Chunk {id: row.id})
            SET c.text = row.text
            WITH c, row
            SET c += row.properties
            WITH c, row.embedding AS embedding
            WHERE embedding IS NOT NULL
            CALL db.create.setNodeVectorProperty(c, 'embedding', embedding)
            RETURN count(*)
            """,
            param_map={"data": chunk_dicts},
        )

    if entity_dicts:
        self.structured_query(
            """
            UNWIND $data AS row
            MERGE (e:Entity {id: row.id})
            SET e.name = row.name
            WITH e, row
            SET e += row.properties
            RETURN count(*)
            """,
            param_map={"data": entity_dicts},
        )

    # Create tags for each LabelledNode
    for node in nodes:
        stmt = f'CREATE (n:Node__ {{id: "{node.id}", label: "{node.label}"}})'
        self.structured_query(stmt)

In this code, the Node__ tag is used to store the node label, and the Entity and Chunk tags are used to store the properties of the respective node types. This ensures that each node has a fixed label assigned to it ^[1].

Additionally, you can refer to the MockKGExtractor class from the llama-index-core tests, which demonstrates how to extract and label entities and relations from text:

class MockKGExtractor(TransformComponent):
    """A mock knowledge graph extractor that extracts a simple relation from a text."""

    def __call__(self, nodes: List[BaseNode], **kwargs: Any) -> List[BaseNode]:
        entity1 = EntityNode(name="Logan", label="PERSON")
        entity2 = EntityNode(name="Canada", label="LOCATION")
        relation = Relation(label="BORN_IN", source_id=entity1.id, target_id=entity2.id)

        return [
            TextNode(
                id_="test",
                text="Logan was born in Canada",
                metadata={
                    KG_NODES_KEY: [entity1, entity2],
                    KG_RELATIONS_KEY: [relation],
                },
            ),
        ]

This example shows how to create labeled nodes and relations, which can then be inserted into the property graph store ^[2].

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[cxycxm]

i need more help, thhis doesn't solve my problem,How can I search by label ?

[dosubot[bot]]

To perform searches based on labels in your Neo4j knowledge graph using the LlamaIndex library, you can utilize the Neo4jQueryEnginePack with the KG_KEYWORD query engine type for keyword-based entity retrieval. Here's an example of how to initialize and use it:

from llama_index.packs.neo4j_query_engine.base import Neo4jQueryEnginePack, Neo4jQueryEngineType
from llama_index.core.schema import Document

# Define your Neo4j connection parameters
username = "your_neo4j_username"
password = "your_neo4j_password"
url = "your_neo4j_url"
database = "your_neo4j_database"

# Define your documents
docs = [
    Document(text="Your document text here"),
    # Add more documents as needed
]

# Initialize the Neo4jQueryEnginePack with KG_KEYWORD type
query_engine_pack = Neo4jQueryEnginePack(
    username=username,
    password=password,
    url=url,
    database=database,
    docs=docs,
    query_engine_type=Neo4jQueryEngineType.KG_KEYWORD
)

# Perform a search query
query = "your search query based on labels"
results = query_engine_pack.run(query)

# Print the results
for result in results:
    print(result)

This code initializes the Neo4jQueryEnginePack with the KG_KEYWORD query engine type, which is suitable for keyword-based entity retrieval. You can then perform a search query based on labels and print the results ^[1].

Additionally, if you need to directly interact with the Neo4j database to retrieve nodes and relationships based on labels, you can use the Neo4jGraphStore class. Here is an example of how to use it:

from llama_index.legacy.graph_stores.neo4j import Neo4jGraphStore

# Define your Neo4j connection parameters
username = "your_neo4j_username"
password = "your_neo4j_password"
url = "your_neo4j_url"
database = "your_neo4j_database"

# Initialize the Neo4jGraphStore
graph_store = Neo4jGraphStore(
    username=username,
    password=password,
    url=url,
    database=database,
    node_label="YourNodeLabel"
)

# Example function to get relationships for a specific node
def get_relationships(node_id):
    return graph_store.get(node_id)

# Example function to get a relationship map
def get_relationship_map(node_ids, depth=2, limit=30):
    return graph_store.get_rel_map(subjs=node_ids, depth=depth, limit=limit)

# Retrieve relationships for a specific node
node_id = "your_node_id"
relationships = get_relationships(node_id)
print(relationships)

# Retrieve a relationship map for a list of nodes
node_ids = ["node_id_1", "node_id_2"]
relationship_map = get_relationship_map(node_ids)
print(relationship_map)

This code demonstrates how to use the Neo4jGraphStore class to retrieve relationships and relationship maps for nodes based on their labels ^[2].

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[cxycxm]

Your reply is useless to me.