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* Update document embedding values in example * Update document embeddings of Qdrant example to 768 dimensions * Remove spaces in embedding initialization * Update URL in agent output example to remove the 403 error
100 lines
4.1 KiB
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100 lines
4.1 KiB
Plaintext
---
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title: "QdrantDocumentStore"
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id: qdrant-document-store
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slug: "/qdrant-document-store"
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description: "Use the Qdrant vector database with Haystack."
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---
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# QdrantDocumentStore
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Use the Qdrant vector database with Haystack.
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<div className="key-value-table">
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| --- | --- |
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| API reference | [Qdrant](/reference/integrations-qdrant) |
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| GitHub link | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/qdrant |
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</div>
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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.
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### Installation
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You can simply install the Qdrant Haystack integration with:
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```shell
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pip install qdrant-haystack
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```
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### Initialization
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The quickest way to use `QdrantDocumentStore` is to create an in-memory instance of it:
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```python
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from haystack.dataclasses.document import Document
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from haystack_integrations.document_stores.qdrant import QdrantDocumentStore
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document_store = QdrantDocumentStore(
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":memory:",
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recreate_index=True,
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return_embedding=True,
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wait_result_from_api=True,
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)
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document_store.write_documents([
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Document(content="This is first", embedding=[0.0]*768),
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Document(content="This is second", embedding=[0.1]*768)
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])
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print(document_store.count_documents())
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```
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:::warning Collections Created Outside Haystack
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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.
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:::
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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:
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```python
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from haystack.dataclasses.document import Document
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from haystack_integrations.document_stores.qdrant import QdrantDocumentStore
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from haystack.utils import Secret
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document_store = QdrantDocumentStore(
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url="https://XXXXXXXXX.us-east4-0.gcp.cloud.qdrant.io:6333",
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index="your_index_name",
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embedding_dim=1024, # based on the embedding model
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recreate_index=True, # enable only to recreate the index and not connect to the existing one
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api_key = Secret.from_token("YOUR_TOKEN")
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)
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document_store.write_documents([
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Document(content="This is first", embedding=[0.0]*5),
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Document(content="This is second", embedding=[0.1, 0.2, 0.3, 0.4, 0.5])
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])
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print(document_store.count_documents())
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```
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:::tip More information
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You can find more ways to initialize and use QdrantDocumentStore on our [integration page](https://haystack.deepset.ai/integrations/qdrant-document-store).
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:::
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### Supported Retrievers
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- [`QdrantEmbeddingRetriever`](../pipeline-components/retrievers/qdrantembeddingretriever.mdx): Retrieves documents from the `QdrantDocumentStore` based on their dense embeddings (vectors).
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- [`QdrantSparseEmbeddingRetriever`](../pipeline-components/retrievers/qdrantsparseembeddingretriever.mdx): Retrieves documents from the `QdrantDocumentStore` based on their sparse embeddings.
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- [`QdrantHybridRetriever`](../pipeline-components/retrievers/qdranthybridretriever.mdx): Retrieves documents from the `QdrantDocumentStore` based on both dense and sparse embeddings.
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:::note Sparse Embedding Support
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To use Sparse Embedding support, you need to initialize the `QdrantDocumentStore` with `use_sparse_embeddings=True`, which is `False` by default.
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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.
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:::
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## Additional References
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🧑🍳 Cookbook: [Sparse Embedding Retrieval with Qdrant and FastEmbed](https://haystack.deepset.ai/cookbook/sparse_embedding_retrieval)
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