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FalkorDBQAChain

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

FalkorDB is a low latency property graph database management system. You can simply run its docker locally:

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

Once launched, you can simply start creating a database on the local machine and connect to it.

from langchain.chat_models import ChatOpenAI
from langchain.graphs import FalkorDBGraph
from langchain.chains import FalkorDBQAChain

Create a graph connection and insert some 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".'