langchain/templates/neo4j-cypher-ft/README.md

# Neo4j Cypher full-text index

This template allows you to interact with a `Neo4j` graph database using natural language, leveraging OpenAI's LLM. 

Its main function is to convert natural language questions into `Cypher` queries (the language used to query Neo4j databases), execute these queries, and provide natural language responses based on the query's results. 

The package utilizes a `full-text index` for efficient mapping of text values to database entries, thereby enhancing the generation of accurate Cypher statements. 

In the provided example, the full-text index is used to map names of people and movies from the user's query to corresponding database entries.

![Workflow diagram showing the process from a user asking a question to generating an answer using the Neo4j knowledge graph and full-text index.](https://raw.githubusercontent.com/langchain-ai/langchain/master/templates/neo4j-cypher-ft/static/workflow.png) "Neo4j Cypher Workflow Diagram"

## Environment Setup

The following environment variables need to be set:

```
OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
NEO4J_URI=<YOUR_NEO4J_URI>
NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
```

Additionally, if you wish to populate the DB with some example data, you can run `python ingest.py`.
This script will populate the database with sample movie data and create a full-text index named `entity`, which is used to map person and movies from user input to database values for precise Cypher statement generation.


## 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-ft
```

If you want to add this to an existing project, you can just run:

```shell
langchain app add neo4j-cypher-ft
```

And add the following code to your `server.py` file:
```python
from neo4j_cypher_ft import chain as neo4j_cypher_ft_chain

add_routes(app, neo4j_cypher_ft_chain, path="/neo4j-cypher-ft")
```

(Optional) Let's now configure LangSmith. 
LangSmith will help us trace, monitor and debug LangChain applications. 
You can sign up for LangSmith [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 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-ft/playground](http://127.0.0.1:8000/neo4j-cypher-ft/playground)  

We can access the template from code with:

```python
from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/neo4j-cypher-ft")
```