# Neo4j

This page covers how to use the Neo4j ecosystem within LangChain.

What is Neo4j?

**Neo4j in a nutshell:**

- Neo4j is an open-source database management system that specializes in graph database technology.
- Neo4j allows you to represent and store data in nodes and edges, making it ideal for handling connected data and relationships.
- Neo4j provides a Cypher Query Language, making it easy to interact with and query your graph data.
- With Neo4j, you can achieve high-performance graph traversals and queries, suitable for production-level systems.
- Get started quickly with Neo4j by visiting [their website](https://neo4j.com/).

## Installation and Setup

- Install the Python SDK with `pip install neo4j`

## Wrappers

### VectorStore

There exists a wrapper around Neo4j vector index, allowing you to use it as a vectorstore,
whether for semantic search or example selection.

To import this vectorstore:

```python
from langchain.vectorstores import Neo4jVector
```

For a more detailed walkthrough of the Neo4j vector index wrapper, see [documentation](/docs/integrations/vectorstores/neo4jvector)

### GraphCypherQAChain

There exists a wrapper around Neo4j graph database that allows you to generate Cypher statements based on the user input
and use them to retrieve relevant information from the database.

```python
from langchain.graphs import Neo4jGraph
from langchain.chains import GraphCypherQAChain
```

For a more detailed walkthrough of Cypher generating chain, see [documentation](/docs/use_cases/graph/graph_cypher_qa)

### Constructing a knowledge graph from text

Text data often contain rich relationships and insights that can be useful for various analytics, recommendation engines, or knowledge management applications.
Diffbot's NLP API allows for the extraction of entities, relationships, and semantic meaning from unstructured text data.
By coupling Diffbot's NLP API with Neo4j, a graph database, you can create powerful, dynamic graph structures based on the information extracted from text.
These graph structures are fully queryable and can be integrated into various applications.

```python
from langchain.graphs import Neo4jGraph
from langchain_experimental.graph_transformers.diffbot import DiffbotGraphTransformer
```

For a more detailed walkthrough generating graphs from text, see [documentation](/docs/use_cases/graph/diffbot_graphtransformer)
