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44 lines
1.7 KiB
Markdown
44 lines
1.7 KiB
Markdown
---
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title: Haystack
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weight: 400
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aliases: [ ../integrations/haystack/ ]
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---
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# Haystack
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[Haystack](https://haystack.deepset.ai/) serves as a comprehensive NLP framework, offering a modular methodology for constructing
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cutting-edge generative AI, QA, and semantic knowledge base search systems. A critical element in contemporary NLP systems is an
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efficient database for storing and retrieving extensive text data. Vector databases excel in this role, as they house vector
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representations of text and implement effective methods for swift retrieval. Thus, we are happy to announce the integration
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with Haystack - `QdrantDocumentStore`. This document store is unique, as it is maintained externally by the Qdrant team.
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The new document store comes as a separate package and can be updated independently of Haystack:
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```bash
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pip install qdrant-haystack
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```
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`QdrantDocumentStore` supports [all the configuration properties](/documentation/collections/#create-collection) available in
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the Qdrant Python client. If you want to customize the default configuration of the collection used under the hood, you can
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provide that settings when you create an instance of the `QdrantDocumentStore`. For example, if you'd like to enable the
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Scalar Quantization, you'd make that in the following way:
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```python
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from qdrant_haystack.document_stores import QdrantDocumentStore
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from qdrant_client.http import models
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document_store = QdrantDocumentStore(
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":memory:",
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index="Document",
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embedding_dim=512,
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recreate_index=True,
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quantization_config=models.ScalarQuantization(
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scalar=models.ScalarQuantizationConfig(
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type=models.ScalarType.INT8,
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quantile=0.99,
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always_ram=True,
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),
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),
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)
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```
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