Qdrant

Last updated 3 months ago

Was this helpful?

Qdrant

This document provides clear steps on how to use and integrate Qdrant with Superlinked.

Configuring your existing managed Qdrant

To use Superlinked with Qdrant, start a managed instance provided by Qdrant (a free-tier is available). For detailed steps on initializing a managed instance, refer to the section below.

Once your Qdrant instance is up and running, ensure it is accessible from the server that will use it. Additionally, configure the necessary authentication settings as described below.

Modifications in your configuration

To integrate Qdrant, you need to add the QdrantVectorDatabase class and include it in the executor. Here’s how you can do it:

from superlinked import framework as sl

vector_database = sl.QdrantVectorDatabase(
    "<your_qdrant_url>", # (Mandatory) This is your qdrant URL generally with a port but without any extra fields
    "<your_api_key>", # (Mandatory) This is the api key to your qdrant cluster
    # The following params must be in a form of kwarg params. Here you can specify anything that the official python client enables. For more details visit:
    # https://python-client.qdrant.tech/qdrant_client.qdrant_client.
    default_query_limit=10, # This optional parameter specifies the maximum number of query results returned. If not set, it defaults to 10.
)

Once you have configured the vector database just simply pass it to the executor.

...
executor = sl.RestExecutor(
    sources=[source],
    indices=[index],
    queries=[sl.RestQuery(sl.RestDescriptor("query"), query)],
    vector_database=vector_database,
)
...

Start a Managed Qdrant Instance

Example app with Qdrant

PreviousMongo DB NextOverview

Last updated 3 months ago

Was this helpful?

Configuring your existing managed Qdrant

To use Superlinked with Qdrant, start a managed instance provided by Qdrant (a free-tier is available). For detailed steps on initializing a managed instance, refer to the section below.

Once your Qdrant instance is up and running, ensure it is accessible from the server that will use it. Additionally, configure the necessary authentication settings as described below.

Modifications in your configuration

To integrate Qdrant, you need to add the QdrantVectorDatabase class and include it in the executor. Here’s how you can do it:

from superlinked import framework as sl

vector_database = sl.QdrantVectorDatabase(
    "<your_qdrant_url>", # (Mandatory) This is your qdrant URL generally with a port but without any extra fields
    "<your_api_key>", # (Mandatory) This is the api key to your qdrant cluster
    # The following params must be in a form of kwarg params. Here you can specify anything that the official python client enables. For more details visit:
    # https://python-client.qdrant.tech/qdrant_client.qdrant_client.
    default_query_limit=10, # This optional parameter specifies the maximum number of query results returned. If not set, it defaults to 10.
)

Once you have configured the vector database just simply pass it to the executor.

...
executor = sl.RestExecutor(
    sources=[source],
    indices=[index],
    queries=[sl.RestQuery(sl.RestDescriptor("query"), query)],
    vector_database=vector_database,
)
...

Start a Managed Qdrant Instance

To initialize a managed Qdrant instance, navigate to , sign in then click on "Overview" on the left side of the page. Here, you can create a free-tier or production-ready clusters. A free-tier offers 0.5 vCPU, 1GB memory, 4GB disk space running on 1 node. You can customize these parameters with a paid plan. You can also choose your prefered platform, location and whether high-availability (HA) is a necessity. After the cluster was created, generate an API key and save it to a secure place, you won't be able to see it again. This key is part of the QdrantVectorDatabase configuration.