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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>
63 lines
1.8 KiB
Markdown
63 lines
1.8 KiB
Markdown
---
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title: Feast
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---
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## Feast
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[Feast (**Fe**ature **St**ore)](https://docs.feast.dev) 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.
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Qdrant is available as a supported vectorstore in Feast to integrate in your workflows.
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## Insatallation
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To use the Qdrant online store, you need to install Feast with the `qdrant` extra.
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```bash
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pip install 'feast[qdrant]'
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```
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## Usage
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An example config with Qdrant could look like:
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```yaml
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project: my_feature_repo
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registry: data/registry.db
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provider: local
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online_store:
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type: qdrant
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host: xyz-example.eu-central.aws.cloud.qdrant.io
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port: 6333
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api_key: <your-own-key>
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vector_len: 384
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# Reference: https://qdrant.tech/documentation/manage-data/vectors/#named-vectors
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# vector_name: text-vec
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write_batch_size: 100
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```
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You can refer to the Feast [documentation](https://docs.feast.dev/reference/alpha-vector-database#configuration-and-installation) for the full list of configuration options.
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## Retrieving Documents
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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.
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```python
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from feast import FeatureStore
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feature_store = FeatureStore(repo_path="feature_store.yaml")
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query_vector = [1.0, 2.0, 3.0, 4.0, 5.0]
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top_k = 5
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feature_values = feature_store.retrieve_online_documents(
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feature="my_feature",
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query=query_vector,
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top_k=top_k
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)
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```
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## 📚 Further Reading
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- [Feast Documentation](http://docs.feast.dev/)
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- [Source](https://github.com/feast-dev/feast/tree/master/sdk/python/feast/infra/online_stores/qdrant_online_store)
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