---
title: "QdrantDocumentStore"
id: qdrant-document-store
slug: "/qdrant-document-store"
description: "Use the Qdrant vector database with Haystack."
---

# QdrantDocumentStore

Use the Qdrant vector database with Haystack.

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

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

</div>

Qdrant is a powerful high-performance, massive-scale vector database. The `QdrantDocumentStore` can be used with any Qdrant instance, in-memory, locally persisted, hosted, and the official Qdrant Cloud.

### Installation

You can simply install the Qdrant Haystack integration with:

```shell
pip install qdrant-haystack
```

### Initialization

The quickest way to use `QdrantDocumentStore` is to create an in-memory instance of it:

```python
from haystack.dataclasses.document import Document
from haystack_integrations.document_stores.qdrant import QdrantDocumentStore

document_store = QdrantDocumentStore(
    ":memory:",
    recreate_index=True,
    return_embedding=True,
    wait_result_from_api=True,
)
document_store.write_documents(
    [
        Document(content="This is first", embedding=[0.0] * 768),
        Document(content="This is second", embedding=[0.1] * 768),
    ],
)
print(document_store.count_documents())
```

:::warning[Collections Created Outside Haystack]

When you create a `QdrantDocumentStore` instance, Haystack takes care of setting up the collection. In general, you cannot use a Qdrant collection created without Haystack with Haystack. If you want to migrate your existing collection, see the sample script at https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/qdrant/src/haystack_integrations/document_stores/qdrant/migrate_to_sparse.py.
:::

You can also connect directly to [Qdrant Cloud](https://cloud.qdrant.io/login) directly. Once you have your API key and your cluster URL from the Qdrant dashboard, you can connect like this:

```python
from haystack.dataclasses.document import Document
from haystack_integrations.document_stores.qdrant import QdrantDocumentStore
from haystack.utils import Secret

document_store = QdrantDocumentStore(
    url="https://XXXXXXXXX.us-east4-0.gcp.cloud.qdrant.io:6333",
    index="your_index_name",
    embedding_dim=1024,  # based on the embedding model
    recreate_index=True,  # enable only to recreate the index and not connect to the existing one
    api_key=Secret.from_token("YOUR_TOKEN"),
)

document_store.write_documents(
    [
        Document(content="This is first", embedding=[0.0] * 5),
        Document(content="This is second", embedding=[0.1, 0.2, 0.3, 0.4, 0.5]),
    ],
)
print(document_store.count_documents())
```

:::tip[More information]

You can find more ways to initialize and use QdrantDocumentStore on our [integration page](https://haystack.deepset.ai/integrations/qdrant-document-store).
:::

### Supported Retrievers

- [`QdrantEmbeddingRetriever`](../pipeline-components/retrievers/qdrantembeddingretriever.mdx): Retrieves documents from the `QdrantDocumentStore` based on their dense embeddings (vectors).
- [`QdrantSparseEmbeddingRetriever`](../pipeline-components/retrievers/qdrantsparseembeddingretriever.mdx): Retrieves documents from the `QdrantDocumentStore` based on their sparse embeddings.
- [`QdrantHybridRetriever`](../pipeline-components/retrievers/qdranthybridretriever.mdx): Retrieves documents from the `QdrantDocumentStore` based on both dense and sparse embeddings.

:::note[Sparse Embedding Support]

To use Sparse Embedding support, you need to initialize the `QdrantDocumentStore` with `use_sparse_embeddings=True`, which is `False` by default.

If you want to use Document Store or collection previously created with this feature disabled, you must migrate the existing data. You can do this by taking advantage of the `migrate_to_sparse_embeddings_support` utility function.
:::

## Additional References

🧑‍🍳 Cookbook: [Sparse Embedding Retrieval with Qdrant and FastEmbed](https://haystack.deepset.ai/cookbook/sparse_embedding_retrieval)
