> ## Documentation Index
> Fetch the complete documentation index at: https://docs.unstructured.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Qdrant
If you're new to Unstructured, read this note first.
Before you can create a destination connector, you must first sign in to your Unstructured account:
* If you do not already have an Unstructured account, [sign up for free](https://unstructured.io/?modal=try-for-free). After you sign up, you are automatically signed in to your new Unstructured **Let's Go** account, at [https://platform.unstructured.io](https://platform.unstructured.io).
To sign up for a **Business** account instead, [contact Unstructured Sales](https://unstructured.io/?modal=contact-sales), or [learn more](/ui/overview#how-am-i-billed%3F).
* If you already have an Unstructured **Let's Go**, **Pay-As-You-Go**, or **Business SaaS** account and are not already signed in, sign in to your account at
[https://platform.unstructured.io](https://platform.unstructured.io). For other types of **Business** accounts, see your Unstructured account administrator for sign-in instructions,
or email Unstructured Support at [support@unstructured.io](mailto:support@unstructured.io).
After you sign in, the [Unstructured user interface](/ui/overview) (UI) appears, which you use to create your destination connector.
After you create the destination connector, add it along with a
[source connector](/ui/sources/overview) to a [workflow](/ui/workflows). Then run the worklow as a
[job](/ui/jobs). To learn how, try out the [hands-on UI quickstart](/ui/quickstart#remote-quickstart) or watch the 4-minute
[video tutorial](https://www.youtube.com/watch?v=Wn2FfHT6H-o).
You can also create destination connectors with the Unstructured API.
[Learn how](/api-reference/workflow/destinations/overview).
If you need help, email Unstructured Support at [support@unstructured.io](mailto:support@unstructured.io).
You are now ready to start creating a destination connector! Keep reading to learn how.
Send processed data from Unstructured to Qdrant.
The requirements are as follows.
* For the [Unstructured UI](/ui/overview) or the [Unstructured API](/api-reference/overview), only [Qdrant Cloud](https://qdrant.tech/documentation/cloud-intro/) is supported.
* For [Unstructured Ingest](/open-source/ingestion/overview), Qdrant Cloud,
[Qdrant local](https://github.com/qdrant/qdrant), and [Qdrant client-server](https://qdrant.tech/documentation/quickstart/) are supported.
The following video shows how to set up Qdrant Cloud:
* For Qdrant local, the path to the local Qdrant installation, for example: `/qdrant/local`
* For Qdrant client-server, the Qdrant server URL, for example: `http://localhost:6333`
* For Qdrant Cloud:
* A [Qdrant account](https://cloud.qdrant.io/login).
* A [Qdrant cluster](https://qdrant.tech/documentation/cloud/create-cluster/).
* The cluster's URL. To get this URL, do the following:
1. Sign in to your Qdrant Cloud account.
2. On the sidebar, under **Dashboard**, click **Clusters**.
3. Click the cluster's name.
4. Note the value of the **Endpoint** field, for example: `https://...cloud.qdrant.io`.
* A [Qdrant API key](https://qdrant.tech/documentation/cloud/authentication/#create-api-keys).
* The name of the target [collection](https://qdrant.tech/documentation/concepts/collections) on the Qdrant local installation,
Qdrant server, or Qdrant Cloud cluster.
Qdrant requires the target collection to exist before Unstructured can write to the collection.
The following example code demonstrates the use of the [Python Qdrant Client](https://pypi.org/project/qdrant-client/) to create
a collection on a Qdrant Cloud cluster, configuring the collection for vectors with 3072 dimensions:
```python Python theme={null}
from qdrant_client import QdrantClient, models
import os
client = QdrantClient(
url=os.getenv("QDRANT_URL"),
api_key=os.getenv("QDRANT_API_KEY")
)
client.create_collection(
collection_name=os.getenv("QDRANT_COLLECTION"),
vectors_config=models.VectorParams(
size=3072,
distance=models.Distance.COSINE
)
)
collection = client.get_collection(
collection_name=os.getenv("QDRANT_COLLECTION")
)
print(f"The collection named '{os.getenv("QDRANT_COLLECTION")}' exists and " +
f"has a status of '{collection.status}'.")
```
To create the destination connector:
1. On the sidebar, click **Connectors**.
2. Click **Destinations**.
3. Cick **New** or **Create Connector**.
4. Give the connector some unique **Name**.
5. In the **Provider** area, click **Qdrant**.
6. Click **Continue**.
7. Follow the on-screen instructions to fill in the fields as described later on this page.
8. Click **Save and Test**.
Fill in the following fields:
* **Name** (*required*): A unique name for this connector.
* **URL** (*required*): The target Qdrant cluster's URL.
* **Collection Name** (*required*): The name of the target collection on the cluster.
* **Batch Size** The maximum number of records to be transmitted per batch. The default is `50` if not otherwise specified.
* **API Key** (*required*): The Qdrant API key value.
## Learn more
* [Qdrant Integration in the Unstructured Platform](https://unstructured.io/blog/streamlining-rag-pipeline-development-qdrant-integration-in-the-unstructured-platform)
* [How to go from S3 to Qdrant with no code using Unstructured](https://unstructured.io/blog/how-to-go-from-s3-to-qdrant-with-no-code-using-unstructured)