# Qdrant

## Description

[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.

## Getting started

In order to use this online store, you'll need to run `pip install 'feast[qdrant]'`.

## Example

{% code title="feature\_store.yaml" %}

```yaml
project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
    type: qdrant
    host: localhost
    port: 6333
    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.qdrant_online_store.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](/reference/online-stores/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

feature_store = FeatureStore(repo_path="feature_store.yaml")

query_vector = [1.0, 2.0, 3.0, 4.0, 5.0]
top_k = 5

# 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(
    features=["my_feature"],
    query=query_vector,
    top_k=top_k
)
```

{% endcode %}

These APIs are subject to change in future versions of Feast to improve performance and usability.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.feast.dev/reference/online-stores/qdrant.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.