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HugeGraph is a convenient, efficient, and adaptable graph database compatible with the Apache TinkerPop3 framework and the Gremlin query language. Gremlin is a graph traversal language and virtual machine developed by Apache TinkerPop of the Apache Software Foundation.
This notebook shows how to use LLMs to provide a natural language interface to HugeGraph database.

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Setting up

You will need to have a running HugeGraph instance. You can run a local docker container by running the executing the following script: 
docker run \
    --name=graph \
    -itd \
    -p 8080:8080 \
    hugegraph/hugegraph
If we want to connect HugeGraph in the application, we need to install python sdk: 
pip3 install hugegraph-python
If you are using the docker container, you need to wait a couple of second for the database to start, and then we need create schema and write graph data for the database. 
from hugegraph.connection import PyHugeGraph

client = PyHugeGraph("localhost", "8080", user="admin", pwd="admin", graph="hugegraph")
First, we create the schema for a simple movie database: 
"""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"}}\'"'
Then we can insert some data. 
"""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

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Creating HugeGraphQAChain

We can now create the HugeGraph and HugeGraphQAChain. To create the HugeGraph we simply need to pass the database object to the HugeGraph constructor. 
from langchain_classic.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",
)

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Refresh graph schema information

If the schema of database changes, you can refresh the schema information needed to generate Gremlin statements. 
# 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']

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Querying the graph

We can now use the graph Gremlin QA chain to ask question of the graph 
chain = HugeGraphQAChain.from_llm(ChatOpenAI(temperature=0), graph=graph, verbose=True)

chain.run("Who played in The Godfather?")

> Entering new  chain...
Generated gremlin:
g.V().has('Movie', 'name', 'The Godfather').in('ActedIn').valueMap(true)
Full Context:
[{'id': '1:Al Pacino', 'label': 'Person', 'name': ['Al Pacino'], 'birthDate': ['1940-04-25']}]

> Finished chain.

'Al Pacino played in The Godfather.'
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