ArangoDocumentStore

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

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

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.

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.

To compute real embeddings for your documents, use a Document Embedder such as the SentenceTransformersDocumentEmbedder. The embedding dimension produced by the embedder must match the embedding_dimension configured on the store.

Authentication

Credentials are passed as Haystack Secret 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: Retrieves documents from the ArangoDocumentStore based on vector similarity using ArangoDB's AQL vector functions.