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