3215985d1d
* 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>
1.7 KiB
title, weight
| title | weight |
|---|---|
| Haystack | 400 |
Haystack
Haystack 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:
pip install qdrant-haystack
QdrantDocumentStore supports all the configuration properties 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:
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,
),
),
)