FalkorDB

FalkorDB is a low-latency Graph Database that delivers knowledge to GenAI.

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

Setting up

You can run the falkordb Docker container locally:

docker run -p 6379:6379 -it --rm falkordb/falkordb

Once launched, you create a database on the local machine and connect to it.

from langchain.chains import FalkorDBQAChain
from langchain_community.graphs import FalkorDBGraph
from langchain_openai import ChatOpenAI

API Reference:FalkorDBQAChain | FalkorDBGraph | ChatOpenAI

Create a graph connection and insert the demo data

graph = FalkorDBGraph(database="movies")

graph.query(
    """
    CREATE 
        (al:Person {name: 'Al Pacino', birthDate: '1940-04-25'}),
        (robert:Person {name: 'Robert De Niro', birthDate: '1943-08-17'}),
        (tom:Person {name: 'Tom Cruise', birthDate: '1962-07-3'}),
        (val:Person {name: 'Val Kilmer', birthDate: '1959-12-31'}),
        (anthony:Person {name: 'Anthony Edwards', birthDate: '1962-7-19'}),
        (meg:Person {name: 'Meg Ryan', birthDate: '1961-11-19'}),

        (god1:Movie {title: 'The Godfather'}),
        (god2:Movie {title: 'The Godfather: Part II'}),
        (god3:Movie {title: 'The Godfather Coda: The Death of Michael Corleone'}),
        (top:Movie {title: 'Top Gun'}),

        (al)-[:ACTED_IN]->(god1),
        (al)-[:ACTED_IN]->(god2),
        (al)-[:ACTED_IN]->(god3),
        (robert)-[:ACTED_IN]->(god2),
        (tom)-[:ACTED_IN]->(top),
        (val)-[:ACTED_IN]->(top),
        (anthony)-[:ACTED_IN]->(top),
        (meg)-[:ACTED_IN]->(top)
"""
)

[]

Creating FalkorDBQAChain

graph.refresh_schema()
print(graph.schema)

import os

os.environ["OPENAI_API_KEY"] = "API_KEY_HERE"

Node properties: [[OrderedDict([('label', None), ('properties', ['name', 'birthDate', 'title'])])]]
Relationships properties: [[OrderedDict([('type', None), ('properties', [])])]]
Relationships: [['(:Person)-[:ACTED_IN]->(:Movie)']]

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

Querying the graph

chain.run("Who played in Top Gun?")

> Entering new FalkorDBQAChain chain...
Generated Cypher:
MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
WHERE m.title = 'Top Gun'
RETURN p.name
Full Context:
[['Tom Cruise'], ['Val Kilmer'], ['Anthony Edwards'], ['Meg Ryan'], ['Tom Cruise'], ['Val Kilmer'], ['Anthony Edwards'], ['Meg Ryan']]

> Finished chain.

'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'

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

> Entering new FalkorDBQAChain chain...
Generated Cypher:
MATCH (p:Person)-[r:ACTED_IN]->(m:Movie)
WHERE m.title = 'The Godfather: Part II'
RETURN p.name
ORDER BY p.birthDate ASC
LIMIT 1
Full Context:
[['Al Pacino']]

> Finished chain.

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

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

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

> Finished chain.

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