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feat: Qdrant vectorstore support (#4689)
* feat: Qdrant vectorstore support Signed-off-by: Anush008 <[email protected]> * chore: make build-ui again Signed-off-by: Anush008 <[email protected]> --------- Signed-off-by: Anush008 <[email protected]>
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# Qdrant online store (contrib) | ||
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## Description | ||
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[Qdrant](http://qdrant.tech) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage vectors with additional payload and extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications. | ||
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## Getting started | ||
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In order to use this online store, you'll need to run `pip install 'feast[qdrant]'`. | ||
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## Example | ||
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{% code title="feature_store.yaml" %} | ||
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```yaml | ||
project: my_feature_repo | ||
registry: data/registry.db | ||
provider: local | ||
online_store: | ||
type: qdrant | ||
host: localhost | ||
port: 6333 | ||
vector_len: 384 | ||
write_batch_size: 100 | ||
``` | ||
{% endcode %} | ||
The full set of configuration options is available in [QdrantOnlineStoreConfig](https://rtd.feast.dev/en/master/#feast.infra.online_stores.contrib.qdrant.QdrantOnlineStoreConfig). | ||
## Functionality Matrix | ||
| | Qdrant | | ||
| :-------------------------------------------------------- | :------- | | ||
| write feature values to the online store | yes | | ||
| read feature values from the online store | yes | | ||
| update infrastructure (e.g. tables) in the online store | yes | | ||
| teardown infrastructure (e.g. tables) in the online store | yes | | ||
| generate a plan of infrastructure changes | no | | ||
| support for on-demand transforms | yes | | ||
| readable by Python SDK | yes | | ||
| readable by Java | no | | ||
| readable by Go | no | | ||
| support for entityless feature views | yes | | ||
| support for concurrent writing to the same key | no | | ||
| support for ttl (time to live) at retrieval | no | | ||
| support for deleting expired data | no | | ||
| collocated by feature view | yes | | ||
| collocated by feature service | no | | ||
| collocated by entity key | no | | ||
To compare this set of functionality against other online stores, please see the full [functionality matrix](overview.md#functionality-matrix). | ||
## Retrieving online document vectors | ||
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 is the document vector. The document vector is a dense vector of floats. | ||
{% code title="python" %} | ||
```python | ||
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] | ||
top_k = 5 | ||
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# Retrieve the top k closest features to the query vector | ||
# Since Qdrant supports multiple vectors per entry, | ||
# the vector to use can be specified in the repo config. | ||
# Reference: https://qdrant.tech/documentation/concepts/vectors/#named-vectors | ||
feature_values = feature_store.retrieve_online_documents( | ||
feature="my_feature", | ||
query=query_vector, | ||
top_k=top_k | ||
) | ||
``` | ||
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{% endcode %} | ||
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These APIs are subject to change in future versions of Feast to improve performance and usability. |
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"thrift", | ||
"tpcds", | ||
"tpch", | ||
"qdrant", | ||
} | ||
CONNECTORS_WITHOUT_WITH_STATEMENTS: Set[str] = { | ||
"bigquery", | ||
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