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* added a table of content * wide layout for docs, styles for the table of content * fixes for docs layout * wide footer at the docs section * added support for nested docs, added toggling groups of links, delimiters, external links * external link icon * added active state for nested links, styles for the external link icon * styles fix * remove doc sync * update directory structure and doc titles * fix outstanding links * fix more links for merge * Revert "fix more links for merge" This reverts commit 46c9ccaf1b7765f2cda8dc85d625fa6b4e3f5436. * Revert "fix outstanding links" This reverts commit 28e6380b74f1ab74690c8184551f186656d6d4e9. * fix remaining broken links * move how-to tutorials in the different page * split tutorials * fix link * upd github edit link * skip empty index pages --------- Co-authored-by: Andrey Vasnetsov <andrey@vasnetsov.com> Co-authored-by: David Sertic <62056091+davidmyriel@users.noreply.github.com>
43 lines
1.7 KiB
Markdown
43 lines
1.7 KiB
Markdown
---
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title: Haystack
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weight: 400
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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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