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# neo4j_cypher
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This template allows you to interact with a Neo4j graph database in natural language, using an OpenAI LLM.
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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.
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[![Workflow diagram ](https://raw.githubusercontent.com/langchain-ai/langchain/master/templates/neo4j-cypher/static/workflow.png )](https://medium.com/neo4j/langchain-cypher-search-tips-tricks-f7c9e9abca4d)
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## Environment Setup
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Define the following environment variables:
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```
OPENAI_API_KEY=< YOUR_OPENAI_API_KEY >
NEO4J_URI=< YOUR_NEO4J_URI >
NEO4J_USERNAME=< YOUR_NEO4J_USERNAME >
NEO4J_PASSWORD=< YOUR_NEO4J_PASSWORD >
```
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## Neo4j database setup
There are a number of ways to set up a Neo4j database.
### Neo4j Aura
Neo4j AuraDB is a fully managed cloud graph database service.
Create a free instance on [Neo4j Aura ](https://neo4j.com/cloud/platform/aura-graph-database?utm_source=langchain&utm_content=langserve ).
When you initiate a free database instance, you'll receive credentials to access the database.
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## Populating with data
If you want to populate the DB with some example data, you can run `python ingest.py` .
This script will populate the database with sample movie data.
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## Usage
To use this package, you should first have the LangChain CLI installed:
```shell
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pip install -U langchain-cli
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```
To create a new LangChain project and install this as the only package, you can do:
```shell
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langchain app new my-app --package neo4j-cypher
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```
If you want to add this to an existing project, you can just run:
```shell
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langchain app add neo4j-cypher
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```
And add the following code to your `server.py` file:
```python
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 ](https://smith.langchain.com/ ).
If you don't have access, you can skip this section
```shell
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=< your-api-key >
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:
```shell
langchain serve
```
This will start the FastAPI app with a server is running locally at
[http://localhost:8000 ](http://localhost:8000 )
We can see all templates at [http://127.0.0.1:8000/docs ](http://127.0.0.1:8000/docs )
We can access the playground at [http://127.0.0.1:8000/neo4j_cypher/playground ](http://127.0.0.1:8000/neo4j_cypher/playground )
We can access the template from code with:
```python
from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/neo4j-cypher")
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```