# > (40474, 384)

```

7. Download the saved vectors into a new file named `startup_vectors.npy`

```python
np.save("startup_vectors.npy", vectors, allow_pickle=False)

```

## [Anchor](https://qdrant.tech/documentation/beginner-tutorials/neural-search/\#run-qdrant-in-docker) Run Qdrant in Docker

Next, you need to manage all of your data using a vector engine. Qdrant lets you store, update or delete created vectors. Most importantly, it lets you search for the nearest vectors via a convenient API.

> **Note:** Before you begin, create a project directory and a virtual python environment in it.

1. Download the Qdrant image from DockerHub.

```bash
docker pull qdrant/qdrant

```

2. Start Qdrant inside of Docker.

```bash
docker run -p 6333:6333 \
    -v $(pwd)/qdrant_storage:/qdrant/storage \
    qdrant/qdrant

```

You should see output like this

```text
...
[2021-02-05T00:08:51Z INFO  actix_server::builder] Starting 12 workers
[2021-02-05T00:08:51Z INFO  actix_server::builder] Starting "actix-web-service-0.0.0.0:6333" service on 0.0.0.0:6333

```

Test the service by going to [http://localhost:6333/](http://localhost:6333/). You should see the Qdrant version info in your browser.

All data uploaded to Qdrant is saved inside the `./qdrant_storage` directory and will be persisted even if you recreate the container.

## [Anchor](https://qdrant.tech/documentation/beginner-tutorials/neural-search/\#upload-data-to-qdrant) Upload data to Qdrant

1. Install the official Python client to best interact with Qdrant.

```bash
pip install qdrant-client

```

At this point, you should have startup records in the `startups_demo.json` file, encoded vectors in `startup_vectors.npy` and Qdrant running on a local machine.

Now you need to write a script to upload all startup data and vectors into the search engine.

2. Create a client object for Qdrant.

```python