KuzuQAChain

This notebook shows how to use LLMs to provide a natural language interface to Kùzu database.

Kùzu is an in-process property graph database management system. You can simply install it with pip:

pip install kuzu

Once installed, you can simply import it and start creating a database on the local machine and connect to it:

import kuzu

db = kuzu.Database("test_db")
conn = kuzu.Connection(db)

First, we create the schema for a simple movie database:

conn.execute("CREATE NODE TABLE Movie (name STRING, PRIMARY KEY(name))")
conn.execute(
    "CREATE NODE TABLE Person (name STRING, birthDate STRING, PRIMARY KEY(name))"
)
conn.execute("CREATE REL TABLE ActedIn (FROM Person TO Movie)")

<kuzu.query_result.QueryResult at 0x1066ff410>

Then we can insert some data.

conn.execute("CREATE (:Person {name: 'Al Pacino', birthDate: '1940-04-25'})")
conn.execute("CREATE (:Person {name: 'Robert De Niro', birthDate: '1943-08-17'})")
conn.execute("CREATE (:Movie {name: 'The Godfather'})")
conn.execute("CREATE (:Movie {name: 'The Godfather: Part II'})")
conn.execute(
    "CREATE (:Movie {name: 'The Godfather Coda: The Death of Michael Corleone'})"
)
conn.execute(
    "MATCH (p:Person), (m:Movie) WHERE p.name = 'Al Pacino' AND m.name = 'The Godfather' CREATE (p)-[:ActedIn]->(m)"
)
conn.execute(
    "MATCH (p:Person), (m:Movie) WHERE p.name = 'Al Pacino' AND m.name = 'The Godfather: Part II' CREATE (p)-[:ActedIn]->(m)"
)
conn.execute(
    "MATCH (p:Person), (m:Movie) WHERE p.name = 'Al Pacino' AND m.name = 'The Godfather Coda: The Death of Michael Corleone' CREATE (p)-[:ActedIn]->(m)"
)
conn.execute(
    "MATCH (p:Person), (m:Movie) WHERE p.name = 'Robert De Niro' AND m.name = 'The Godfather: Part II' CREATE (p)-[:ActedIn]->(m)"
)

<kuzu.query_result.QueryResult at 0x107016210>

Creating KuzuQAChain

We can now create the KuzuGraph and KuzuQAChain. To create the KuzuGraph we simply need to pass the database object to the KuzuGraph constructor.

from langchain.chains import KuzuQAChain
from langchain.chat_models import ChatOpenAI
from langchain.graphs import KuzuGraph

graph = KuzuGraph(db)

chain = KuzuQAChain.from_llm(ChatOpenAI(temperature=0), graph=graph, verbose=True)

Refresh graph schema information

If the schema of database changes, you can refresh the schema information needed to generate Cypher statements.

# graph.refresh_schema()

print(graph.get_schema)

Node properties: [{'properties': [('name', 'STRING')], 'label': 'Movie'}, {'properties': [('name', 'STRING'), ('birthDate', 'STRING')], 'label': 'Person'}]
Relationships properties: [{'properties': [], 'label': 'ActedIn'}]
Relationships: ['(:Person)-[:ActedIn]->(:Movie)']

Querying the graph

We can now use the KuzuQAChain to ask question of the graph

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

> Entering new  chain...
Generated Cypher:
MATCH (p:Person)-[:ActedIn]->(m:Movie {name: 'The Godfather: Part II'}) RETURN p.name
Full Context:
[{'p.name': 'Al Pacino'}, {'p.name': 'Robert De Niro'}]

> Finished chain.

'Al Pacino and Robert De Niro both played in The Godfather: Part II.'

chain.run("Robert De Niro played in which movies?")

> Entering new  chain...
Generated Cypher:
MATCH (p:Person {name: 'Robert De Niro'})-[:ActedIn]->(m:Movie)
RETURN m.name
Full Context:
[{'m.name': 'The Godfather: Part II'}]

> Finished chain.

'Robert De Niro played in The Godfather: Part II.'

chain.run("Robert De Niro is born in which year?")

> Entering new  chain...
Generated Cypher:
MATCH (p:Person {name: 'Robert De Niro'})-[:ActedIn]->(m:Movie)
RETURN p.birthDate
Full Context:
[{'p.birthDate': '1943-08-17'}]

> Finished chain.

'Robert De Niro was born on August 17, 1943.'

chain.run("Who is the oldest actor who played in The Godfather: Part II?")

> Entering new  chain...
Generated Cypher:
MATCH (p:Person)-[:ActedIn]->(m:Movie{name:'The Godfather: Part II'})
WITH p, m, p.birthDate AS birthDate
ORDER BY birthDate ASC
LIMIT 1
RETURN p.name
Full Context:
[{'p.name': 'Al Pacino'}]

> Finished chain.

'The oldest actor who played in The Godfather: Part II is Al Pacino.'