---
title: "ArangoDocumentStore"
id: arangodocumentstore
slug: "/arangodocumentstore"
description: "Use the ArangoDB multi-model database with Haystack for embedding retrieval and GraphRAG workloads."
---

# ArangoDocumentStore

Use the ArangoDB multi-model database with Haystack for embedding retrieval and GraphRAG workloads.

<div className="key-value-table">

|  |  |
| --- | --- |
| API reference | [ArangoDB](/reference/integrations-arangodb) |
| GitHub link   | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/arangodb |

</div>

ArangoDB is a multi-model database that combines documents, graphs, and key-value data in a single engine. The `ArangoDocumentStore` stores documents in an ArangoDB collection and runs vector similarity search using AQL (ArangoDB Query Language) vector functions. Because documents and their relationships live in the same database, ArangoDB is a good fit for GraphRAG pipelines that combine semantic search with graph traversal.

Vector search requires **ArangoDB 3.12 or later** with the vector index feature enabled (the `--vector-index` startup flag).

For more information, see the [ArangoDB documentation](https://docs.arangodb.com/).

## Installation

Run ArangoDB with Docker, enabling the vector index and setting a root password:

```shell
docker run -d -p 8529:8529 \
  -e ARANGO_ROOT_PASSWORD=test-password \
  arangodb:3.12 arangod --vector-index
```

Install the Haystack integration:

```shell
pip install arangodb-haystack
```

## Usage

The store reads its credentials from the `ARANGO_USERNAME` and `ARANGO_PASSWORD` environment variables by default. `ARANGO_USERNAME` falls back to `root` if it is not set, so you typically only need to provide the password:

```shell
export ARANGO_PASSWORD=test-password
```

Initialize the document store and write documents:

```python
from haystack import Document
from haystack_integrations.document_stores.arangodb import ArangoDocumentStore

document_store = ArangoDocumentStore(
    host="http://localhost:8529",
    database="haystack",
    collection_name="documents",
    embedding_dimension=768,
    recreate_collection=True,
)

document_store.write_documents(
    [
        Document(
            content="There are over 7,000 languages spoken around the world today.",
        ),
        Document(
            content="Elephants have been observed to recognize themselves in mirrors.",
        ),
    ],
)
print(document_store.count_documents())
```

To learn more about the initialization parameters, see the [API docs](/reference/integrations-arangodb#arangodocumentstore).

To compute real embeddings for your documents, use a Document Embedder such as the [`SentenceTransformersDocumentEmbedder`](../pipeline-components/embedders/sentencetransformersdocumentembedder.mdx). The embedding dimension produced by the embedder must match the `embedding_dimension` configured on the store.

### Authentication

Credentials are passed as Haystack [`Secret`](../concepts/secret-management.mdx) objects. By default they are read from environment variables, but you can also pass them explicitly:

```python
from haystack.utils import Secret
from haystack_integrations.document_stores.arangodb import ArangoDocumentStore

document_store = ArangoDocumentStore(
    host="http://localhost:8529",
    database="haystack",
    username=Secret.from_env_var("ARANGO_USERNAME", strict=False),
    password=Secret.from_env_var("ARANGO_PASSWORD"),
)
```

### Similarity Functions

`ArangoDocumentStore` supports three similarity functions for vector search, configured at initialization with the `similarity_function` parameter:

- `"cosine"` (default): cosine similarity, best for normalized embeddings.
- `"dot_product"`: dot product, useful when embedding magnitude carries meaning.
- `"l2"`: Euclidean (L2) distance.

```python
document_store = ArangoDocumentStore(
    host="http://localhost:8529",
    embedding_dimension=768,
    similarity_function="dot_product",
)
```

### Supported Retrievers

- [`ArangoEmbeddingRetriever`](../pipeline-components/retrievers/arangoembeddingretriever.mdx): Retrieves documents from the `ArangoDocumentStore` based on vector similarity using ArangoDB's AQL vector functions.