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title, weight
| title | weight |
|---|---|
| Integrations | 52 |
Qdrant is a vector database performing an approximate nearest neighbours search on neural embeddings. It can work perfectly fine as a standalone system, yet, in some cases, you may find it easier to implement your semantic search application using some higher-level libraries. Some of such projects provide ready-to-go integrations and here is a curated list of them.
DocArray
You can use Qdrant natively in DocArray, where Qdrant serves as a high-performance document store to enable scalable vector search.
DocArray is a library from Jina AI for nested, unstructured data in transit, including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process, embed, search, recommend, store, and transfer the data with a Pythonic API.
To install DocArray with Qdrant support, please do
pip install "docarray[qdrant]"
More information can be found in DocArray's documentations.
txtai
Qdrant might be also used as an embedding backend in txtai semantic applications.
txtai simplifies building AI-powered semantic search applications using Transformers. It leverages the neural embeddings and their properties to encode high-dimensional data in a lower-dimensional space and allows to find similar objects based on their embeddings' proximity.
Qdrant is not built-in txtai backend and requires installing an additional dependency:
pip install qdrant-txtai
The examples and some more information might be found in qdrant-txtai repository.
Cohere
Qdrant is compatible with Cohere co.embed API and it's official Python SDK that might be installed as any other package:
pip install cohere
The embeddings returned by co.embed API might be used directly in the Qdrant client's calls:
import cohere
import qdrant_client
from qdrant_client.http.models import Batch
cohere_client = cohere.Client("<< your_api_key >>")
qdrant_client = qdrant_client.QdrantClient()
qdrant_client.upsert(
collection_name="MyCollection",
points=Batch(
ids=[1],
vectors=cohere_client.embed(
model="large",
texts=["The best vector database"],
).embeddings,
)
)
If you are interested in seeing an end-to-end project created with co.embed API and Qdrant, please check out the "Question Answering as a Service with Cohere and Qdrant" article.