在构建Neo4j知识图谱时，要在PropertyGraphIndex中自定义节点标签，可以定义严格的模式、实现自定义提取模块或使用LLM进行提取。以下是一般的方法：

1. 定义严格的模式:在模式中指定节点的标签。
2. 实现自定义提取模块:创建自己的模块以根据需要标记节点。
3. 使用LLM进行提取:允许LLM根据上下文提取和标记节点。

以下是定义严格模式的示例：

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)

此外，neo4j_property_graph.py模块提供了诸如refresh_schema之类的函数，以便您的模式保持最新，并提供预定义查询来管理节点和关系属性1(https://github.com/run-llama/llama_index/blob/main/docs/docs/module_guides/indexing/index_guide.md)2(https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/graph_stores/llama-index-graph-stores-neo4j/llama_index/graph_stores/neo4j/neo4j_property_graph.py)。

• 要继续与Dosu交谈，请提及@dosu。*

如果我想要为我提取的数据分配一个固定的标签，我应该怎么做？

要为使用PropertyGraphIndex构建Neo4j知识图谱时提取的数据分配固定标签，可以使用Node__标签存储节点标签。以下是在Neo4jPropertyGraphStore类的upsert_nodes方法中如何执行此操作的示例：

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)

在这段代码中，`Node__`标签用于存储节点标签，而`Entity`和`Chunk`标签用于存储相应节点类型的属性。这确保了每个节点都被分配了一个固定的标签。
此外，您还可以参考`MockKGExtractor`类中的`llama-index-core`测试，它演示了如何从文本中提取和标记实体和关系：

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],
            },
        ),
    ]

此示例展示了如何创建带有标签的节点和关系，然后将它们插入到属性图存储中。

* 要继续与[Dosu](https://dosu.dev)交谈，请提及@dosu。*

我需要更多的帮助，这不能解决我的问题。我如何通过标签进行搜索？

要使用LlamaIndex库在您的Neo4j知识图谱中基于标签执行搜索，您可以使用具有关键字实体检索功能的Neo4jQueryEnginePack查询引擎类型。以下是如何初始化和使用它的示例：

// 初始化 `Neo4jQueryEnginePack` 查询引擎类型，适用于基于关键字的实体检索

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)

然后，您可以根据标签执行搜索查询并打印结果。

此外，如果您需要直接与Neo4j数据库交互以根据标签检索节点和关系，您可以使用Neo4jGraphStore类。以下是如何使用它的示例：

// 使用 `Neo4jGraphStore` 类根据节点标签检索关系和关系Map

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)

这段代码展示了如何使用Neo4jGraphStore类根据节点标签检索关系和关系Map。

你的回复对我来说没有用。