---
title: Qdrant
---

import { Image } from 'astro:assets';
import icon from '../../../assets/icon-qdrant.svg';

<span title="Label: Pro" data-view-component="true" class="Label Label--api text-uppercase">
  Unstable API
</span>
<span title="Label: Pro" data-view-component="true" class="Label Label--version text-uppercase">
  0.8.0
</span>
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  <a target="_blank" href="https://www.npmjs.com/package/@project-lakechain/qdrant-storage-connector">
    @project-lakechain/qdrant-storage-connector
  </a>
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<div style="margin-top: 26px"></div>

---

The Qdrant storage connector enables uploading vector embeddings produced by other middlewares to a [Qdrant](https://www.qdrant.tech/) collection.

> ℹ️ This middleware interacts with a third-party API outside of your AWS account.

---

### 🌲 Adding Documents

To use the Qdrant storage connector, you import it in your CDK stack, and connect it to a data source providing document embeddings.

> 💁 You need to specify a Qdrant API key to the connector, by specifying a reference to an [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/) secret containing the API key.

```typescript
import { QdrantStorageConnector } from '@project-lakechain/qdrant-storage-connector';
import { CacheStorage } from '@project-lakechain/core';

class Stack extends cdk.Stack {
  constructor(scope: cdk.Construct, id: string) {
    const cache = new CacheStorage(this, 'Cache');

    const qdrantApiKey = secrets.Secret.fromSecretNameV2(
      this,
      'QdrantApiKey',
      process.env.QDRANT_API_KEY_SECRET_NAME as string
    );

    // Create the Qdrant storage connector.
    const connector = new QdrantStorageConnector.Builder()
      .withScope(this)
      .withIdentifier('QdrantStorageConnector')
      .withCacheStorage(cache)
      .withSource(source) // 👈 Specify a data source
      .withApiKey(qdrantApiKey)
      .withCollectionName('{collection_name}')
      .withUrl('https://xyz-example.eu-central.aws.cloud.qdrant.io:6333')
      .build();
  }
}
```

<br />

---

#### Store Text

When the document being processed is a text document, you can choose to store the text of the document in the Qdrant payload. To do so, you can use the `withStoreText` and `withTextKey` options. If the document is not a text, this option is ignored.

> 💁 By default, the text is not included in the index.

```typescript
const connector = new QdrantStorageConnector.Builder()
  .withScope(this)
  .withIdentifier('QdrantStorageConnector')
  .withCacheStorage(cache)
  .withSource(source)
  .withApiKey(qdrantApiKey)
  .withCollectionName('{collection_name}')
  .withStoreText(true)
  .withTextKey('my-content')
  .withUrl('https://xyz-example.eu-central.aws.cloud.qdrant.io:6333')
  .build();
```

<br />

---

#### Configure Named vector

Since Qdrant supports [multiple vectors](https://qdrant.tech/documentation/concepts/vectors/#named-vectors) per entry, you can use the `withVectorName` option to specify one.
The connector defaults to unnamed (default) vector.

> 💁 By default, the text is not included in the index.

```typescript
const connector = new QdrantStorageConnector.Builder()
      .withScope(this)
      .withIdentifier('QdrantStorageConnector')
      .withCacheStorage(cache)
      .withSource(source)
      .withApiKey(qdrantApiKey)
      .withCollectionName('collection_name')
      .withVectorName('my-vector-name')
      .withUrl('https://xyz-example.eu-central.aws.cloud.qdrant.io:6333')
      .build();
```

<br />

---

### 🏷️ Properties

<br />

##### Supported Inputs

|  Mime Type  | Description |
| ----------- | ----------- |
| `*/*` | This middleware supports any type of documents. Note that if no embeddings are specified in the document metadata, the document is filtered out. |

##### Supported Outputs

*This middleware does not produce any output.*

##### Supported Compute Types

| Type  | Description |
| ----- | ----------- |
| `CPU` | This middleware only supports CPU compute. |

<br />

---

### 📖 Examples

- [Bedrock + Qdrant Pipeline](https://github.com/awslabs/project-lakechain/tree/main/examples/simple-pipelines/embedding-pipelines/bedrock-qdrant-pipeline) - An example showcasing an embedding pipeline using Amazon Bedrock and Qdrant.
