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@@ -99,6 +99,44 @@ pip install "docarray[qdrant]"
More information can be found in [DocArray's documentations](https://docarray.jina.ai/advanced/document-store/qdrant/).
## Haystack
[Haystack](https://haystack.deepset.ai/) serves as a comprehensive NLP framework, offering a modular methodology for constructing
cutting-edge generative AI, QA, and semantic knowledge base search systems. A critical element in contemporary NLP systems is an
efficient database for storing and retrieving extensive text data. Vector databases excel in this role, as they house vector
representations of text and implement effective methods for swift retrieval. Thus, we are happy to announce the integration
with Haystack - `QdrantDocumentStore`. This document store is unique, as it is maintained externally by the Qdrant team.
The new document store comes as a separate package and can be updated independently of Haystack:
```bash
pip install qdrant-haystack
```
`QdrantDocumentStore` supports [all the configuration properties](/documentation/collections/#create-collection) available in
the Qdrant Python client. If you want to customize the default configuration of the collection used under the hood, you can
provide that settings when you create an instance of the `QdrantDocumentStore`. For example, if you'd like to enable the
Scalar Quantization, you'd make that in the following way:
```python
from qdrant_haystack.document_stores import QdrantDocumentStore
from qdrant_client.http import models
document_store = QdrantDocumentStore(
":memory:",
index="Document",
embedding_dim=512,
recreate_index=True,
quantization_config=models.ScalarQuantization(
scalar=models.ScalarQuantizationConfig(
type=models.ScalarType.INT8,
quantile=0.99,
always_ram=True,
),
),
)
```
## txtai
Qdrant might be also used as an embedding backend in [txtai](https://neuml.github.io/txtai/) semantic applications.
@@ -114,6 +152,23 @@ pip install qdrant-txtai
The examples and some more information might be found in [qdrant-txtai repository](https://github.com/qdrant/qdrant-txtai).
## FiftyOne
[FiftyOne](https://voxel51.com/) is an open-source toolkit designed to enhance computer vision workflows by optimizing dataset quality
and providing valuable insights about your models. FiftyOne 0.20, which includes a native integration with Qdrant, supporting workflows
like [image similarity search](https://docs.voxel51.com/user_guide/brain.html#image-similarity) and
[text search](https://docs.voxel51.com/user_guide/brain.html#text-similarity).
Qdrant helps FiftyOne to find the most similar images in the dataset using vector embeddings.
FiftyOne is available as a Python package that might be installed in the following way:
```bash
pip install fiftyone
```
Please check out the documentation of FiftyOne on [Qdrant integration](https://docs.voxel51.com/integrations/qdrant.html).
## Cohere
Qdrant is compatible with Cohere [co.embed API](https://docs.cohere.ai/reference/embed) and it's official Python SDK that
might be installed as any other package: