# Getting Started

Invana Graph Analytics System can be deployed in your local or cloud using [docker compose](https://docs.docker.com/compose/).

### Features

1.

### Install using Docker

You can use the following templates to setup Invana with supported graph databases as graph processing engine.

{% tabs %}
{% tab title="Using JanusGraph" %}

```
git clone git@github.com:invanalabs/docker-templates.git
cd docker-templates/invana-with-janusgraph
docker-compose up
```

{% endtab %}

{% tab title="Using Neo4j" %}

```
git clone git@github.com:invanalabs/docker-templates.git
cd docker-templates/invana-with-neo4j
docker-compose up
```

{% endtab %}

{% tab title="Using ArcadeDB" %}

```
git clone git@github.com:invanalabs/docker-templates.git
cd docker-templates/invana-with-arcadedb
docker-compose up
```

{% endtab %}
{% endtabs %}

{% hint style="info" %}
In theory, you can run any Apache TinkerPop supported graph database as graph processing engine with Invana.
{% endhint %}

| Invana Engine           | http\://\<ip-address>:8200 |
| ----------------------- | -------------------------- |
| Invana Studio           | http\://\<ip-address>:8300 |
| Apache TinkerPop Server | http\://\<ip-address>:8182 |

Docker compose will expose the following services, that lets you visualise and browse through the graph data.

### Graph databases

1. [JanusGraph](https://janusgraph.org)

### Usage&#x20;

* [x] Python API&#x20;
* [ ] GraphQL API&#x20;
* [ ] REST API

Let's start with a story of graph.

{% content-ref url="/pages/-MZSxJ1iiEdG4HIA\_Sdp" %}
[Broken mention](broken://pages/-MZSxJ1iiEdG4HIA_Sdp)
{% endcontent-ref %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.invana.io/getting-started.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
