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* 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
| title |
|---|
| Feast |
Feast
Feast (Feature Store) is an open-source feature store that helps teams operate production ML systems at scale by allowing them to define, manage, validate, and serve features for production AI/ML.
Qdrant is available as a supported vectorstore in Feast to integrate in your workflows.
Insatallation
To use the Qdrant online store, you need to install Feast with the qdrant extra.
pip install 'feast[qdrant]'
Usage
An example config with Qdrant could look like:
project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: qdrant
host: xyz-example.eu-central.aws.cloud.qdrant.io
port: 6333
api_key: <your-own-key>
vector_len: 384
# Reference: https://qdrant.tech/documentation/manage-data/vectors/#named-vectors
# vector_name: text-vec
write_batch_size: 100
You can refer to the Feast documentation for the full list of configuration options.
Retrieving Documents
The Qdrant online store supports retrieving document vectors for a given list of entity keys. The document vectors are returned as a dictionary where the key is the entity key and the value being the vector.
from feast import FeatureStore
feature_store = FeatureStore(repo_path="feature_store.yaml")
query_vector = [1.0, 2.0, 3.0, 4.0, 5.0]
top_k = 5
feature_values = feature_store.retrieve_online_documents(
feature="my_feature",
query=query_vector,
top_k=top_k
)