# Weaviate and Cohere (Integration Guide)

> This page describes how to integrate Cohere with the Weaviate database.

<img src="https://files.buildwithfern.com/cohere.docs.buildwithfern.com/9edbf0b31bb02c014d41757668199da5943a593b011e299205df68904a9aaebc/assets/images/970c76b-weaviate-logo.svg" width="200px" height="auto" class="light-bg" />

[Weaviate](https://weaviate.io/) is an open source vector search engine that stores both objects and vectors, allowing for combining vector search with structured filtering. Here, we'll create a Weaviate Cluster to index your data with Cohere Embed, and process it with Rerank and Command.

Here are the steps involved:

* Create the Weaviate cluster (see [this post](https://weaviate.io/developers/wcs/quickstart) for more detail.)
* Once the cluster is created, you will receive the cluster URL and API key.
* Use the provided URL and API key to connect to your Weaviate cluster.
* Use the Weaviate Python client to create your collection to store data

## Getting Set up

First, let's handle the imports, the URLs, and the pip installs.

```python PYTHON
from google.colab import userdata

weaviate_url = userdata.get("WEAVIATE_ENDPOINT")
weaviate_key = userdata.get("WEAVIATE_API_KEY")
cohere_key = userdata.get("COHERE_API_KEY")
```

```python PYTHON
!pip install -U weaviate-client -q
```

```python PYTHON