Langchain includes functionality to integrate directly with Neo4j, including allowing you to run Cypher statements, query vector indexes and use Neo4j as a vector store.
In this lesson, you will learn how to connect to a Neo4j instance and run Cypher statement.
Connecting to a Neo4j instance
You must install the neo4j Python package to connect to a Neo4j database.
pip install neo4jThe following code will connect to a Neo4j database and run a simple query.
from langchain_community.graphs import Neo4jGraph
graph = Neo4jGraph(
url="bolt://localhost:7687",
username="neo4j",
password="pleaseletmein"
)
result = graph.query("""
MATCH (m:Movie{title: 'Toy Story'})
RETURN m.title, m.plot, m.poster
""")
print(result)You can connect to the Neo4j sandbox created for you when you joined the course.
Update the code above to use the url, username and password of your Neo4j sandbox.
- Connection URL
-
bolt://44.204.225.40:7687
- Username
-
neo4j
- Password
-
rotations-raincoat-breezes
Run the query - you should see data about the movie Toy Story.
The Neo4jGraph class is a wrapper to the Neo4j Python driver. It simplifies connecting to Neo4j and integrating with the Langchain framework.
Schema
When you connect to the Neo4j database, the object loads the database schema into memory - this enables Langchain to access the schema information without having to query the database.
You can access the schema information using the schema property.
print(graph.schema)View schema
Node properties are the following:
[{'properties': [{'property': 'url', 'type': 'STRING'}, {'property': 'runtime', 'type': 'INTEGER'}, {'property': 'revenue', 'type': 'INTEGER'}, {'property': 'budget', 'type': 'INTEGER'}, {'property': 'imdbRating', 'type': 'FLOAT'}, {'property': 'released', 'type': 'STRING'}, {'property': 'countries', 'type': 'LIST'}, {'property': 'languages', 'type': 'LIST'}, {'property': 'plot', 'type': 'STRING'}, {'property': 'imdbVotes', 'type': 'INTEGER'}, {'property': 'imdbId', 'type': 'STRING'}, {'property': 'year', 'type': 'INTEGER'}, {'property': 'poster', 'type': 'STRING'}, {'property': 'movieId', 'type': 'STRING'}, {'property': 'tmdbId', 'type': 'STRING'}, {'property': 'title', 'type': 'STRING'}], 'labels': 'Movie'}, {'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Genre'}, {'properties': [{'property': 'userId', 'type': 'STRING'}, {'property': 'name', 'type': 'STRING'}], 'labels': 'User'}, {'properties': [{'property': 'url', 'type': 'STRING'}, {'property': 'name', 'type': 'STRING'}, {'property': 'tmdbId', 'type': 'STRING'}, {'property': 'bornIn', 'type': 'STRING'}, {'property': 'bio', 'type': 'STRING'}, {'property': 'died', 'type': 'DATE'}, {'property': 'born', 'type': 'DATE'}, {'property': 'imdbId', 'type': 'STRING'}, {'property': 'poster', 'type': 'STRING'}], 'labels': 'Actor'}, {'properties': [{'property': 'url', 'type': 'STRING'}, {'property': 'bornIn', 'type': 'STRING'}, {'property': 'born', 'type': 'DATE'}, {'property': 'died', 'type': 'DATE'}, {'property': 'tmdbId', 'type': 'STRING'}, {'property': 'imdbId', 'type': 'STRING'}, {'property': 'name', 'type': 'STRING'}, {'property': 'poster', 'type': 'STRING'}, {'property': 'bio', 'type': 'STRING'}], 'labels': 'Director'}, {'properties': [{'property': 'url', 'type': 'STRING'}, {'property': 'bornIn', 'type': 'STRING'}, {'property': 'bio', 'type': 'STRING'}, {'property': 'died', 'type': 'DATE'}, {'property': 'born', 'type': 'DATE'}, {'property': 'imdbId', 'type': 'STRING'}, {'property': 'name', 'type': 'STRING'}, {'property': 'poster', 'type': 'STRING'}, {'property': 'tmdbId', 'type': 'STRING'}], 'labels': 'Person'}]
Relationship properties are the following:
[{'type': 'RATED', 'properties': [{'property': 'rating', 'type': 'FLOAT'}, {'property': 'timestamp', 'type': 'INTEGER'}]}, {'type': 'ACTED_IN', 'properties': [{'property': 'role', 'type': 'STRING'}]}, {'type': 'DIRECTED', 'properties': [{'property': 'role', 'type': 'STRING'}]}]
The relationships are the following:
['(:Movie)-[:IN_GENRE]->(:Genre)', '(:User)-[:RATED]->(:Movie)', '(:Actor)-[:ACTED_IN]->(:Movie)', '(:Actor)-[:DIRECTED]->(:Movie)', '(:Director)-[:DIRECTED]->(:Movie)', '(:Director)-[:ACTED_IN]->(:Movie)', '(:Person)-[:DIRECTED]->(:Movie)', '(:Person)-[:ACTED_IN]->(:Movie)']
Refreshing the schema
You can refresh the schema by calling thegraph.refresh_schema() method.Check Your Understanding
Neo4j Langchain integration
Select all statements about the Langchain Neo4j integration that are True.
-
✓ You can connect to a Neo4j database
-
✓ The database schema is loaded automatically
-
✓ You can run Cypher queries
-
✓ You can use Neo4j as a vector store
Hint
The key goal is to simplify connecting to Neo4j and integrating with the Langchain framework.
Solution
All the statements are true.
Summary
In this lesson, you learned how to connect to a Neo4j database and run Cypher statements.
In the next lesson, you will learn how to use Neo4j as a vector store using Langchain Receivers.