This template allows you to interact with a Neo4j graph database in natural language, using an OpenAI LLM.
It transforms a natural language question into a Cypher query (used to fetch data from Neo4j databases), executes the query, and provides a natural language response based on the query results.
Define the following environment variables:
Neo4j database setup
There are a number of ways to set up a Neo4j database.
Neo4j AuraDB is a fully managed cloud graph database service. Create a free instance on Neo4j Aura. When you initiate a free database instance, you'll receive credentials to access the database.
Populating with data
If you want to populate the DB with some example data, you can run
This script will populate the database with sample movie data.
To use this package, you should first have the LangChain CLI installed:
pip install -U langchain-cli
To create a new LangChain project and install this as the only package, you can do:
langchain app new my-app --package neo4j-cypher
If you want to add this to an existing project, you can just run:
langchain app add neo4j-cypher
And add the following code to your
from neo4j_cypher import chain as neo4j_cypher_chain
add_routes(app, neo4j_cypher_chain, path="/neo4j-cypher")
(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up here. If you don't have access, you can skip this section
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
If you are inside this directory, then you can spin up a LangServe instance directly by:
This will start the FastAPI app with a server is running locally at http://localhost:8000
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/neo4j-cypher")