---
title: "Cohere Generative AI with Weaviate"
description: "Cohere Generative Model Provider"
url: https://docs.weaviate.io/weaviate/model-providers/cohere/generative
---

# Cohere Generative AI with Weaviate

Weaviate's integration with Cohere's APIs allows you to access their models' capabilities directly from Weaviate.

[Configure a Weaviate collection](https://docs.weaviate.io/weaviate/model-providers/cohere/generative#configure-collection) to use a generative AI model with Cohere. Weaviate will perform retrieval augmented generation (RAG) using the specified model and your Cohere API key.

More specifically, Weaviate will perform a search, retrieve the most relevant objects, and then pass them to the Cohere generative model to generate outputs.

![RAG integration illustration](https://docs.weaviate.io/assets/images/integration_cohere_rag-b17639b1cc7a07fab00f6e0c90549585.png)

## Requirements

### Weaviate configuration

Your Weaviate instance must be configured with the Cohere generative AI integration (`generative-cohere`) module.

**For Weaviate Cloud (WCD) users**

This integration is enabled by default on Weaviate Cloud (WCD) instances.

**For self-hosted users**

- Check the [cluster metadata](https://docs.weaviate.io/deploy/configuration/status#cluster-metadata) to verify if the module is enabled.
- Follow the [how-to configure modules](https://docs.weaviate.io/weaviate/configuration/modules) guide to enable the module in Weaviate.

### API credentials

You must provide a valid Cohere API key to Weaviate for this integration. Go to [Cohere](https://cohere.com/) to sign up and obtain an API key.

Provide the API key to Weaviate using one of the following methods:

- Set the `COHERE_APIKEY` environment variable that is available to Weaviate.
- Provide the API key at runtime, as shown in the examples below.

**Python**

```py
import weaviate
from weaviate.classes.init import Auth
import os

# Recommended: save sensitive data as environment variables
cohere_key = os.getenv("COHERE_API_KEY")
headers = {
    "X-Cohere-Api-Key": cohere_key,
}

client = weaviate.connect_to_weaviate_cloud(
    cluster_url=weaviate_url,                       # `weaviate_url`: your Weaviate URL
    auth_credentials=Auth.api_key(weaviate_key),      # `weaviate_key`: your Weaviate API key
    headers=headers
)

# Work with Weaviate

client.close()
```

**JavaScript/TypeScript**

```ts
import weaviate from '@weaviate/node'

const cohereApiKey = process.env.COHERE_API_KEY || '';  // Replace with your inference API key

const client = await weaviate.connectToWeaviateCloud(
  'WEAVIATE_INSTANCE_URL',  // Replace with your instance URL
  {
    authCredentials: new weaviate.ApiKey('WEAVIATE_INSTANCE_API_KEY'),
    headers: {
      'X-Cohere-Api-Key': cohereApiKey,
    }
  }
)

// Work with Weaviate

client.close()
```

## Configure collection

> [!NOTE]
>
> **Generative model integration mutability**
>
> A collection's `generative` model integration configuration is mutable from `v1.25.23`, `v1.26.8` and `v1.27.1`. See [this section](https://docs.weaviate.io/weaviate/manage-collections/generative-reranker-models#update-the-generative-model-integration) for details on how to update the collection configuration.

[Configure a Weaviate index](https://docs.weaviate.io/weaviate/manage-collections/generative-reranker-models#specify-a-generative-model-integration) as follows to use a Cohere generative AI model:

**Python**

```py
from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.cohere()
    # Additional parameters not shown
)
```

**JavaScript/TypeScript**

```ts
await client.collections.create({
  name: 'DemoCollection',
  generative: weaviate.configure.generative.cohere(),
  // Additional parameters not shown
});
```

### Select a model

You can specify which [model](https://docs.weaviate.io/weaviate/model-providers/cohere/generative#available-models) Weaviate uses, as shown in the following configuration example:

**Python**

```py
from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.cohere(
        model="command-r-plus"
    )
    # Additional parameters not shown
)
```

**JavaScript/TypeScript**

```ts
await client.collections.create({
  name: 'DemoCollection',
  generative: weaviate.configure.generative.cohere({
    model: 'command-r-plus'
  }),
  // Additional parameters not shown
});
```

You can [specify](https://docs.weaviate.io/weaviate/model-providers/cohere/generative#generative-parameters) which [model](https://docs.weaviate.io/weaviate/model-providers/cohere/generative#available-models) Weaviate uses. If you do not specify a model, Weaviate uses the server default.

### Generative parameters

Configure the following generative parameters to customize the model behavior.

**Python**

```py
from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.cohere(
        # # These parameters are optional
        # model="command-r",
        # temperature=0.7,
        # max_tokens=500,
        # k=5,
        # stop_sequences=["\n\n"],
    )
    # Additional parameters not shown
)
```

**JavaScript/TypeScript**

```ts
await client.collections.create({
  name: 'DemoCollection',
  generative: weaviate.configure.generative.cohere({
    // These parameters are optional
    // model: 'command-r',
    // temperature: 0.7,
    // maxTokens: 500,
    // k: 5,
    // stopSequences: ['\n\n'],
  }),
  // Additional parameters not shown
});
```

For further details on model parameters, see the [Cohere API documentation](https://docs.cohere.com/reference/chat).

## Select a model at runtime

Aside from setting the default model provider when creating the collection, you can also override it at query time.

**Python**

```py
from weaviate.classes.config import Configure
from weaviate.classes.generate import GenerativeConfig

collection = client.collections.use("DemoCollection")
response = collection.generate.near_text(
    query="A holiday film",
    limit=2,
    grouped_task="Write a tweet promoting these two movies",
    generative_provider=GenerativeConfig.cohere(
        # # These parameters are optional
        # model="command-r",
        # temperature=0.7,
        # max_tokens=500,
        # k=5,
        # stop_sequences=["\n\n"],
    ),
    # Additional parameters not shown
)
```

**JavaScript/TypeScript**

```ts
import { generativeParameters } from '@weaviate/node';

let response;
const myCollection = client.collections.use("DemoCollection");

response = await myCollection.generate.nearText("A holiday film", {
  groupedTask: "Write a tweet promoting these two movies",
  config: generativeParameters.cohere({
    // These parameters are optional
    // model: 'command-r',
    // temperature: 0.7,
    // maxTokens: 500,
    // k: 5,
    // stopSequences: ['\n\n'],
  }),
}, {
  limit: 2,
}
  // Additional parameters not shown
)
```

## Header parameters

You can provide the API key as well as some optional parameters at runtime through additional headers in the request. The following headers are available:

- `X-Cohere-Api-Key`: The Cohere API key.
- `X-Cohere-Baseurl`: The base URL to use (e.g. a proxy) instead of the default Cohere URL.

Any additional headers provided at runtime will override the existing Weaviate configuration.

Provide the headers as shown in the [API credentials examples](https://docs.weaviate.io/weaviate/model-providers/cohere/generative#api-credentials) above.

## Retrieval augmented generation

After configuring the generative AI integration, perform RAG operations, either with the [single prompt](https://docs.weaviate.io/weaviate/model-providers/cohere/generative#single-prompt) or [grouped task](https://docs.weaviate.io/weaviate/model-providers/cohere/generative#grouped-task) method.

### Single prompt

![Single prompt RAG integration generates individual outputs per search result](https://docs.weaviate.io/assets/images/integration_cohere_rag_single-fde82f072d530bdff0b567666831a2a7.png)

To generate text for each object in the search results, use the single prompt method.

The example below generates outputs for each of the `n` search results, where `n` is specified by the `limit` parameter.

When creating a single prompt query, use braces `{}` to interpolate the object properties you want Weaviate to pass on to the language model. For example, to pass on the object's `title` property, include `{title}` in the query.

**Python**

```py
collection = client.collections.use("DemoCollection")

response = collection.generate.near_text(
    query="A holiday film",  # The model provider integration will automatically vectorize the query
    single_prompt="Translate this into French: {title}",
    limit=2
)

for obj in response.objects:
    print(obj.properties["title"])
    print(f"Generated output: {obj.generated}")  # Note that the generated output is per object
```

**JavaScript/TypeScript**

```ts
let response;
const myCollection = client.collections.use("DemoCollection");

let myCollection = client.collections.use('DemoCollection');

const singlePromptResults = await myCollection.generate.nearText('A holiday film', {
  singlePrompt: `Translate this into French: {title}`,
}, {
  limit: 2,
});

for (const obj of singlePromptResults.objects) {
  console.log(obj.properties['title']);
  console.log(`Generated output: ${obj.generative?.text}`);  // Note that the generated output is per object
}
```

### Grouped task

![Grouped task RAG integration generates one output for the set of search results](https://docs.weaviate.io/assets/images/integration_cohere_rag_grouped-206f5859759436963a5fd5d2cdfb5f04.png)

To generate one text for the entire set of search results, use the grouped task method.

In other words, when you have `n` search results, the generative model generates one output for the entire group.

**Python**

```py
collection = client.collections.use("DemoCollection")

response = collection.generate.near_text(
    query="A holiday film",  # The model provider integration will automatically vectorize the query
    grouped_task="Write a fun tweet to promote readers to check out these films.",
    limit=2
)

print(f"Generated output: {response.generative.text}")  # Note that the generated output is per query
for obj in response.objects:
    print(obj.properties["title"])
```

**JavaScript/TypeScript**

```ts
let response;
const myCollection = client.collections.use("DemoCollection");

let myCollection = client.collections.use('DemoCollection');

const groupedTaskResults = await myCollection.generate.nearText('A holiday film', {
  groupedTask: `Write a fun tweet to promote readers to check out these films.`,
}, {
  limit: 2,
});

console.log(`Generated output: ${groupedTaskResults.generative?.text}`);  // Note that the generated output is per query
for (const obj of groupedTaskResults.objects) {
  console.log(obj.properties['title']);
}
```

## References

### Available models

Weaviate does not validate the model name, so you can set any model that your Cohere account can reach. Name validation was removed in `v1.33.0`, and backported to `v1.31.17` and `v1.32.10`.

The server default is `command-a-03-2025`. It changed in `v1.33.0`, and was backported to `v1.31.17` and `v1.32.10`. Earlier releases on each of those lines default to `command-r`.

See the [Cohere model documentation](https://docs.cohere.com/docs/models) for the list of available models.

## Further resources

### Other integrations

- [Cohere text embedding models + Weaviate](https://docs.weaviate.io/weaviate/model-providers/cohere/embeddings).
- [Cohere multimodal embedding models + Weaviate](https://docs.weaviate.io/weaviate/model-providers/cohere/embeddings-multimodal).
- [Cohere reranker models + Weaviate](https://docs.weaviate.io/weaviate/model-providers/cohere/reranker).

### Code examples

Once the integrations are configured at the collection, the data management and search operations in Weaviate work identically to any other collection. See the following model-agnostic examples:

- The [How-to: Manage collections](https://docs.weaviate.io/weaviate/manage-collections) and [How-to: Manage objects](https://docs.weaviate.io/weaviate/manage-objects) guides show how to perform data operations (i.e. create, read, update, delete collections and objects within them).
- The [How-to: Query & Search](https://docs.weaviate.io/weaviate/search) guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation.

### References

- Cohere [Chat API documentation](https://docs.cohere.com/reference/chat)