HugeGraph
HugeGraph 是一个方便、高效且适应性强的图数据库，兼容
Apache TinkerPop3框架和Gremlin查询语言。Gremlin 是一种图遍历语言和虚拟机，由
Apache TinkerPop的Apache Software Foundation开发。
本笔记本展示了如何使用LLMs为HugeGraph数据库提供自然语言接口。
设置
您需要有一个正在运行的HugeGraph实例。 您可以通过运行以下脚本来启动一个本地docker容器：
docker run \
--name=graph \
-itd \
-p 8080:8080 \
hugegraph/hugegraph
如果我们想在应用程序中连接HugeGraph，我们需要安装python sdk：
pip3 install hugegraph-python
如果您使用的是docker容器，您需要等待几秒钟让数据库启动，然后我们需要为数据库创建模式并写入图数据。
from hugegraph.connection import PyHugeGraph
client = PyHugeGraph("localhost", "8080", user="admin", pwd="admin", graph="hugegraph")
首先，我们为简单的电影数据库创建模式：
"""schema"""
schema = client.schema()
schema.propertyKey("name").asText().ifNotExist().create()
schema.propertyKey("birthDate").asText().ifNotExist().create()
schema.vertexLabel("Person").properties(
"name", "birthDate"
).usePrimaryKeyId().primaryKeys("name").ifNotExist().create()
schema.vertexLabel("Movie").properties("name").usePrimaryKeyId().primaryKeys(
"name"
).ifNotExist().create()
schema.edgeLabel("ActedIn").sourceLabel("Person").targetLabel(
"Movie"
).ifNotExist().create()
'create EdgeLabel success, Detail: "b\'{"id":1,"name":"ActedIn","source_label":"Person","target_label":"Movie","frequency":"SINGLE","sort_keys":[],"nullable_keys":[],"index_labels":[],"properties":[],"status":"CREATED","ttl":0,"enable_label_index":true,"user_data":{"~create_time":"2023-07-04 10:48:47.908"}}\'"'
然后我们可以插入一些数据。
"""graph"""
g = client.graph()
g.addVertex("Person", {"name": "Al Pacino", "birthDate": "1940-04-25"})
g.addVertex("Person", {"name": "Robert De Niro", "birthDate": "1943-08-17"})
g.addVertex("Movie", {"name": "The Godfather"})
g.addVertex("Movie", {"name": "The Godfather Part II"})
g.addVertex("Movie", {"name": "The Godfather Coda The Death of Michael Corleone"})
g.addEdge("ActedIn", "1:Al Pacino", "2:The Godfather", {})
g.addEdge("ActedIn", "1:Al Pacino", "2:The Godfather Part II", {})
g.addEdge(
"ActedIn", "1:Al Pacino", "2:The Godfather Coda The Death of Michael Corleone", {}
)
g.addEdge("ActedIn", "1:Robert De Niro", "2:The Godfather Part II", {})
1:Robert De Niro--ActedIn-->2:The Godfather Part II
创建 HugeGraphQAChain
我们现在可以创建HugeGraph和HugeGraphQAChain。要创建HugeGraph，我们只需要将数据库对象传递给HugeGraph构造函数。
from langchain.chains import HugeGraphQAChain
from langchain_community.graphs import HugeGraph
from langchain_openai import ChatOpenAI
graph = HugeGraph(
username="admin",
password="admin",
address="localhost",
port=8080,
graph="hugegraph",
)
刷新图模式信息
如果数据库的模式发生变化，您可以刷新生成Gremlin语句所需的模式信息。
# graph.refresh_schema()
print(graph.get_schema)
Node properties: [name: Person, primary_keys: ['name'], properties: ['name', 'birthDate'], name: Movie, primary_keys: ['name'], properties: ['name']]
Edge properties: [name: ActedIn, properties: []]
Relationships: ['Person--ActedIn-->Movie']
查询图
我们现在可以使用图Gremlin QA链来向图提问
chain = HugeGraphQAChain.from_llm(ChatOpenAI(temperature=0), graph=graph, verbose=True)
chain.run("Who played in The Godfather?")
[1m> Entering new chain...[0m
Generated gremlin:
[32;1m[1;3mg.V().has('Movie', 'name', 'The Godfather').in('ActedIn').valueMap(true)[0m
Full Context:
[32;1m[1;3m[{'id': '1:Al Pacino', 'label': 'Person', 'name': ['Al Pacino'], 'birthDate': ['1940-04-25']}][0m
[1m> Finished chain.[0m
'Al Pacino played in The Godfather.'