---
title: "Anthropic Generative AI with Weaviate"
description: "Weaviate's integration with Anthropic's APIs allows you to access their models' capabilities directly from Weaviate."
url: https://docs.weaviate.io/weaviate/model-providers/anthropic/generative
---

# Anthropic Generative AI with Weaviate

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

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

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

![RAG integration illustration](https://docs.weaviate.io/assets/images/integration_anthropic_rag-195d648d2c61143f1fcf5bb4f16a35ba.png)

## Requirements

### Weaviate configuration

Your Weaviate instance must be configured with the Anthropic generative AI integration (`generative-anthropic`) 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 Anthropic API key to Weaviate for this integration. Go to [Anthropic](https://www.anthropic.com/api) to sign up and obtain an API key.

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

- Set the `ANTHROPIC_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
anthropic_key = os.getenv("ANTHROPIC_API_KEY")
headers = {
    "X-Anthropic-Api-Key": anthropic_key,
    "X-Anthropic-Baseurl": "https://api.anthropic.com",  # Optional; for providing a custom base URL
}

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 anthropicApiKey = process.env.ANTHROPIC_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-Anthropic-Api-Key': anthropicApiKey,
      'X-Anthropic-Baseurl': 'https://api.anthropic.com',  // Optional; for providing a custom base URL
    }
  }
)

// 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 an Anthropic generative model:

**Python**

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

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

**JavaScript/TypeScript**

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

### Select a model

You can specify one of the [available models](https://docs.weaviate.io/weaviate/model-providers/anthropic/generative#available-models) for Weaviate to use, as shown in the following configuration example:

**Python**

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

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.anthropic(
        model="claude-haiku-4-5"
    )
    # Additional parameters not shown
)
```

**JavaScript/TypeScript**

```ts
await client.collections.create({
  name: 'DemoCollection',
  generative: weaviate.configure.generative.anthropic({
    model: 'claude-haiku-4-5'
  }),
  // Additional parameters not shown
});
```

You can [specify](https://docs.weaviate.io/weaviate/model-providers/anthropic/generative#generative-parameters) one of the [available models](https://docs.weaviate.io/weaviate/model-providers/anthropic/generative#available-models) for Weaviate to use. The [default model](https://docs.weaviate.io/weaviate/model-providers/anthropic/generative#available-models) is used if no model is specified.

### 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.anthropic(
        # # These parameters are optional
        # base_url="https://api.anthropic.com",
        # model="claude-haiku-4-5",
        # max_tokens=512,
        # temperature=0.7,
        # stop_sequences=["\n\n"],
        # top_p=0.9,
        # top_k=5,
    )
    # Additional parameters not shown
)
```

**JavaScript/TypeScript**

```ts
await client.collections.create({
  name: 'DemoCollection',
  generative: weaviate.configure.generative.anthropic({
    // These parameters are optional
    // baseURL: 'https://api.anthropic.com',
    // model: 'claude-haiku-4-5',
    // maxTokens: 512,
    // temperature: 0.7,
    // stopSequences: ['\n\n'],
    // topP: 0.9,
    // topK: 5,
  }),
  // Additional parameters not shown
});
```

For further details on model parameters, see the [Anthropic API documentation](https://www.anthropic.com/docs).

## 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.anthropic(
        # # These parameters are optional
        # base_url="https://api.anthropic.com",
        # model="claude-haiku-4-5",
        # max_tokens=512,
        # temperature=0.7,
        # stop_sequences=["\n\n"],
        # top_p=0.9,
        # top_k=5,
    ),
    # 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.anthropic({
    // These parameters are optional
    // baseURL: "https://api.anthropic.com",
    // model: "claude-haiku-4-5",
    // maxTokens: 512,
    // temperature: 0.7,
    // stopSequences: ["\n\n"],
    // topP: 0.9,
    // topK: 5,
  }),
}, {
  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-Anthropic-Api-Key`: The Anthropic API key.
- `X-Anthropic-Baseurl`: The base URL to use (e.g. a proxy) instead of the default Anthropic 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/anthropic/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/anthropic/generative#single-prompt) or [grouped task](https://docs.weaviate.io/weaviate/model-providers/anthropic/generative#grouped-task) method.

### Single prompt

![Single prompt RAG integration generates individual outputs per search result](https://docs.weaviate.io/assets/images/integration_anthropic_rag_single-a15b2699c20eae88c2390af5d638ad3f.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_anthropic_rag_grouped-f392c38783f03b250588ed71a6556e60.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']);
}
```

### RAG with images

You can also supply images as a part of the input when performing retrieval augmented generation in both single prompts and grouped tasks.

**Python**

```py
import base64
import requests
from weaviate.classes.generate import GenerativeConfig, GenerativeParameters

src_img_path = "https://upload.wikimedia.org/wikipedia/commons/thumb/b/b0/Winter_forest_silver.jpg/960px-Winter_forest_silver.jpg"
base64_image = base64.b64encode(requests.get(src_img_path).content).decode('utf-8')

prompt = GenerativeParameters.grouped_task(
    prompt="Which movie is closest to the image in terms of atmosphere",
    images=[base64_image],      # A list of base64 encoded strings of the image bytes
    # image_properties=["img"], # Properties containing images in Weaviate
)

jeopardy = client.collections.use("DemoCollection")
response = jeopardy.generate.near_text(
    query="Movies",
    limit=5,
    grouped_task=prompt,
    generative_provider=GenerativeConfig.anthropic(
        max_tokens=1000
    ),
)

# Print the source property and the generated response
for o in response.objects:
    print(f"Title property: {o.properties['title']}")
print(f"Grouped task result: {response.generative.text}")
```

**JavaScript/TypeScript**

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

function arrayBufferToBase64(buffer: ArrayBuffer): string {
  const bytes = new Uint8Array(buffer);
  let binary = '';
  const chunkSize = 1024; // Process in chunks to avoid call stack issues

  for (let i = 0; i < bytes.length; i += chunkSize) {
    const chunk = bytes.slice(i, Math.min(i + chunkSize, bytes.length));
    binary += String.fromCharCode.apply(null, Array.from(chunk));
  }

  return btoa(binary);
}

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

const srcImgPath = "https://upload.wikimedia.org/wikipedia/commons/thumb/b/b0/Winter_forest_silver.jpg/960px-Winter_forest_silver.jpg"
const responseImg = await fetch(srcImgPath);
const image = await responseImg.arrayBuffer();

const base64String = arrayBufferToBase64(image);

const prompt = {
  prompt: "Which movie is closest to the image in terms of atmosphere",
  images: [base64String],      // A list of base64 encoded strings of the image bytes
  // imageProperties: ["img"], // Properties containing images in Weaviate
}

response = await myCollection.generate.nearText("Movies", {
  groupedTask: prompt,
  config: generativeParameters.anthropic({
    maxTokens: 1000
  }),
}, {
  limit: 5,
})

// Print the source property and the generated response
for (const item of response.objects) {
  console.log("Title property:", item.properties['title'])
}

console.log("Grouped task result:", response.generative?.text)
```

## References

#### Maximum output tokens

Use the `maxTokens` parameter to set the maximum number of output tokens for the Anthropic Generative AI models. This parameter is separate from the maximum allowable input tokens, also called a "context window".

For most models, the default `maxTokens` value is `4096`, which is the maximum, and the input (context window) size is `200,000`. The specific allowable values may vary between models. Refer to the [Anthropic documentation](https://docs.anthropic.com/en/docs/about-claude/models#model-comparison) for the latest information.

#### Base URL

Note that for Anthropic, you can provide a custom base URL for the API endpoint. This is useful for users who have a dedicated API endpoint, or is behind a proxy.

The custom base URL can be provided via the collection configuration as shown above, or in the header of the request. To provide it in the header, instantiate the Weaviate client using the `X-Anthropic-Baseurl` key and the custom base URL as the value.

The default base URL is `https://api.anthropic.com`.

### Available models

Any model available in the Anthropic API can be used with Weaviate. If you do not specify a model, Weaviate uses `claude-haiku-4-5` by default. That default was set in `v1.34.0`, and backported to `v1.31.20`, `v1.32.17`, and `v1.33.5`. Earlier releases on each of those lines default to `claude-3-5-sonnet-20240620`.

See the [Anthropic API documentation](https://docs.anthropic.com/en/docs/about-claude/models#model-names) for the most up-to-date list of available models.

## Further resources

### 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

- Anthropic [API documentation](https://www.anthropic.com/docs)