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docs-website/docs/document-stores/qdrant-document-store.mdx

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+---
+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). 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=5,  # 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)