---
title: "ArcadeDBDocumentStore"
id: arcadedbdocumentstore
slug: "/arcadedbdocumentstore"
---

# ArcadeDBDocumentStore

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

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

</div>

ArcadeDB is a multi-model database that supports vector search via its LSM_VECTOR (HNSW) index. The `ArcadeDBDocumentStore` uses ArcadeDB's HTTP/JSON API for all operations—no special drivers required. It supports dense embedding retrieval and SQL-based metadata filtering.

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

## Installation

Run ArcadeDB with Docker and update the password according to your setup:

```shell
docker run -d -p 2480:2480 \
  -e JAVA_OPTS="-Darcadedb.server.rootPassword=arcadedb" \
  arcadedata/arcadedb:latest
```

Install the Haystack integration:

```shell
pip install arcadedb-haystack
```

## Usage

Set credentials via environment variables (recommended) or pass them explicitly:

```shell
export ARCADEDB_USERNAME=root
export ARCADEDB_PASSWORD=arcadedb
```

Initialize the document store and write documents:

```python
from haystack import Document
from haystack_integrations.document_stores.arcadedb import ArcadeDBDocumentStore

document_store = ArcadeDBDocumentStore(
    url="http://localhost:2480",
    database="haystack",
    embedding_dimension=768,
    recreate_type=True,
)

document_store.write_documents([
    Document(content="This is first", embedding=[0.0] * 768),
    Document(content="This is second", embedding=[0.1, 0.2, 0.3] + [0.0] * 765),
])
print(document_store.count_documents())
```

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

Documents without embeddings or with a different dimension are stored with a zero-padded vector so they can be written and filtered; use an [Embedder](../pipeline-components/embedders/sentencetransformersdocumentembedder.mdx) for real embeddings.

### Supported Retrievers

- [ArcadeDBEmbeddingRetriever](../pipeline-components/retrievers/arcadedbembeddingretriever.mdx): An embedding-based Retriever that fetches documents from the Document Store by vector similarity (HNSW).
