1a40961d62
* initial commit; fixed anchor links on internal docs pages * add back in absolute paths for links in code comments * fix: update linkchecker include filter to match server port 1314 PR #1629 changed the Hugo server to port 1314 but forgot to update the --include filter, which still matched port 1313. This caused all links to be excluded, making the checker a no-op (0 checked, 82277 excluded). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fix url rewrite regex so images are not impacted * Fix links from non-documentation pages * Fix broken links * more broken links * more broken links * broken link * Add srcset width descriptor to .lycheeignore * Ignore URLs that contain a % character * Anchor regex so it matches the entire URL --------- Co-authored-by: kanungle <neil.kanungo@gmail.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>
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title, aliases
| title | aliases | ||
|---|---|---|---|
| Haystack |
|
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 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,
),
),
)