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langchain/templates/neo4j-cypher/README.md

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# neo4j_cypher
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.
[![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)
## Environment Setup
Define the following environment variables:
```
OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
NEO4J_URI=<YOUR_NEO4J_URI>
NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
```
## 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.
## 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.
## Usage
To use this package, you should first have the LangChain CLI installed:
```shell
pip install -U langchain-cli
```
To create a new LangChain project and install this as the only package, you can do:
```shell
langchain app new my-app --package neo4j-cypher
```
If you want to add this to an existing project, you can just run:
```shell
langchain app add neo4j-cypher
```
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
runnable = RemoteRunnable("http://localhost:8000/neo4j-cypher")
```