deepset-ai--haystack/docs-website/versioned_docs/version-2.30/document-stores/falkordbdocumentstore.mdx

---
title: "FalkorDBDocumentStore"
id: falkordbdocumentstore
slug: "/falkordbdocumentstore"
description: "Use the FalkorDB graph database with Haystack for GraphRAG workloads."
---

# FalkorDBDocumentStore

Use the FalkorDB graph database with Haystack for GraphRAG workloads.

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

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

</div>

FalkorDB is a high-performance graph database optimized for GraphRAG workloads. The `FalkorDBDocumentStore` stores documents as graph nodes and supports native vector search — no APOC is required. Documents and their `meta` fields are stored flat on each node, and all bulk writes use `UNWIND` + `MERGE` for safe OpenCypher upserts.

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

## Installation

Run FalkorDB with Docker:

```shell
docker run -d -p 6379:6379 falkordb/falkordb:latest
```

Install the Haystack integration:

```shell
pip install falkordb-haystack
```

## Usage

Initialize the document store and write documents:

```python
from haystack import Document
from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore

document_store = FalkorDBDocumentStore(
    host="localhost",
    port=6379,
    embedding_dim=768,
    recreate_graph=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-falkordb#falkordbdocumentstore).

To compute real embeddings for your documents, use a Document Embedder such as the [`SentenceTransformersDocumentEmbedder`](../pipeline-components/embedders/sentencetransformersdocumentembedder.mdx).

### Authentication

To connect to a password-protected FalkorDB instance, pass the password via `Secret`:

```python
from haystack.utils import Secret
from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore

document_store = FalkorDBDocumentStore(
    host="localhost",
    port=6379,
    password=Secret.from_env_var("FALKORDB_PASSWORD"),
)
```

### Similarity Functions

`FalkorDBDocumentStore` supports two similarity functions for vector search:

- `"cosine"` (default): cosine similarity, best for normalized embeddings.
- `"euclidean"`: Euclidean distance, useful when embedding magnitude matters.

```python
document_store = FalkorDBDocumentStore(
    host="localhost",
    port=6379,
    embedding_dim=768,
    similarity="euclidean",
)
```

### Supported Retrievers

- [`FalkorDBEmbeddingRetriever`](../pipeline-components/retrievers/falkordbembeddingretriever.mdx): Retrieves documents from the `FalkorDBDocumentStore` based on vector similarity using FalkorDB's native vector index.
- [`FalkorDBCypherRetriever`](../pipeline-components/retrievers/falkordbcypherretriever.mdx): Retrieves documents by executing arbitrary OpenCypher queries, enabling graph traversal and multi-hop queries for GraphRAG pipelines.