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]*5),
Document(content="This is second", embedding=[0.1, 0.2, 0.3, 0.4, 0.5])
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
You can find more ways to initialize and use QdrantDocumentStore on our [integration page](https://haystack.deepset.ai/integrations/qdrant-document-store).
- [`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.
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.
