deepset-ai--haystack/docs-website/versioned_docs/version-2.21/pipeline-components/generators/anthropicvertexchatgenerator.mdx

---
title: "AnthropicVertexChatGenerator"
id: anthropicvertexchatgenerator
slug: "/anthropicvertexchatgenerator"
description: "This component enables chat completions using AnthropicVertex API."
---

# AnthropicVertexChatGenerator

This component enables chat completions using AnthropicVertex API.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [`ChatPromptBuilder`](../builders/chatpromptbuilder.mdx) |
| **Mandatory init variables** | `region`: The region where the Anthropic model is deployed  <br /> <br />`project_id`: GCP project ID where the Anthropic model is deployed |
| **Mandatory run variables** | `messages`: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)   objects |
| **Output variables** | `replies`: A list of strings with all the replies generated by the LLM  <br /> <br />`meta`: A list of dictionaries with the metadata associated with each reply, such as token count, finish reason, and others |
| **API reference** | [Anthropic](/reference/integrations-anthropic) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/anthropic |

</div>

## Overview

`AnthropicVertexChatGenerator` enables text generation using state-of-the-art Claude 3 LLMs using the Anthropic Vertex AI API.
It supports   `Claude 3.5 Sonnet`, `Claude 3 Opus`, `Claude 3 Sonnet`, and `Claude 3 Haiku` models, that are accessible through the Vertex AI API endpoint. For more details about the models, refer to [Anthropic Vertex AI documentation](https://docs.anthropic.com/en/api/claude-on-vertex-ai).

### Parameters

To use the `AnthropicVertexChatGenerator`, ensure you have a GCP project with Vertex AI enabled. You need to specify your GCP `project_id` and `region`.

You can provide these keys in the following ways:

- The `REGION` and `PROJECT_ID` environment variables (recommended)
- The `region` and `project_id` init parameters

Before making requests, you may need to authenticate with GCP using `gcloud auth login`.

Set your preferred supported Anthropic model with the `model` parameter when initializing the component. Additionally, ensure that the desired Anthropic model is activated in the Vertex AI Model Garden.

`AnthropicVertexChatGenerator` requires a prompt to generate text, but you can pass any text generation parameters available in the Anthropic [Messaging API](https://docs.anthropic.com/en/api/messages) method directly to this component using the `generation_kwargs` parameter, both at initialization and when running the component. For more details on the parameters supported by the Anthropic API, see the [Anthropic documentation](https://docs.anthropic.com/).

Finally, the component needs a list of `ChatMessage` objects to operate. `ChatMessage` is a data class that contains a message, a role (who generated the message, such as `user`, `assistant`, `system`, `function`), and optional metadata.

Only text input modality is supported at this time.

### Streaming

You can stream output as it’s generated. Pass a callback to `streaming_callback`. Use the built-in `print_streaming_chunk` to print text tokens and tool events (tool calls and tool results).

```python
from haystack.components.generators.utils import print_streaming_chunk

## Configure any `Generator` or `ChatGenerator` with a streaming callback
component = SomeGeneratorOrChatGenerator(streaming_callback=print_streaming_chunk)

## If this is a `ChatGenerator`, pass a list of messages:
## from haystack.dataclasses import ChatMessage
## component.run([ChatMessage.from_user("Your question here")])

## If this is a (non-chat) `Generator`, pass a prompt:
## component.run({"prompt": "Your prompt here"})
```

:::info
Streaming works only with a single response. If a provider supports multiple candidates, set `n=1`.
:::

See our [Streaming Support](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) docs to learn more how `StreamingChunk` works and how to write a custom callback.

Give preference to `print_streaming_chunk` by default. Write a custom callback only if you need a specific transport (for example, SSE/WebSocket) or custom UI formatting.

### Prompt Caching

Prompt caching is a feature for Anthropic LLMs that stores large text inputs for reuse. It allows you to send a large text block once and then refer to it in later requests without resending the entire text.

This feature is particularly useful for coding assistants that need full codebase context and for processing large documents. It can help reduce costs and improve response times.

Here's an example of an instance of `AnthropicVertexChatGenerator` being initialized with prompt caching and tagging a message to be cached:

```python
from haystack_integrations.components.generators.anthropic import (
    AnthropicVertexChatGenerator,
)
from haystack.dataclasses import ChatMessage

generation_kwargs = {"extra_headers": {"anthropic-beta": "prompt-caching-2024-07-31"}}

claude_llm = AnthropicVertexChatGenerator(
    region="your_region",
    project_id="test_id",
    generation_kwargs=generation_kwargs,
)

system_message = ChatMessage.from_system(
    "Replace with some long text documents, code or instructions",
)
system_message.meta["cache_control"] = {"type": "ephemeral"}

messages = [
    system_message,
    ChatMessage.from_user("A query about the long text for example"),
]
result = claude_llm.run(messages)

## and now invoke again with

messages = [
    system_message,
    ChatMessage.from_user("Another query about the long text etc"),
]
result = claude_llm.run(messages)

## and so on, either invoking component directly or in the pipeline
```

For more details, refer to Anthropic's [documentation](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching) and integration [examples](https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/anthropic/example).

## Usage

Install the`anthropic-haystack` package to use the `AnthropicVertexChatGenerator`:

```shell
pip install anthropic-haystack
```

### On its own

```python
from haystack_integrations.components.generators.anthropic import (
    AnthropicVertexChatGenerator,
)
from haystack.dataclasses import ChatMessage

messages = [ChatMessage.from_user("What's Natural Language Processing?")]
client = AnthropicVertexChatGenerator(
    model="claude-3-sonnet@20240229",
    project_id="your-project-id",
    region="us-central1",
)

response = client.run(messages)
print(response)
```

### In a pipeline

You can also use `AnthropicVertexChatGenerator`with the Anthropic chat models in your pipeline.

```python
from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.anthropic import (
    AnthropicVertexChatGenerator,
)
from haystack.utils import Secret

pipe = Pipeline()
pipe.add_component("prompt_builder", ChatPromptBuilder())
pipe.add_component(
    "llm",
    AnthropicVertexChatGenerator(project_id="test_id", region="us-central1"),
)
pipe.connect("prompt_builder", "llm")

country = "Germany"
system_message = ChatMessage.from_system(
    "You are an assistant giving out valuable information to language learners.",
)
messages = [
    system_message,
    ChatMessage.from_user("What's the official language of {{ country }}?"),
]

res = pipe.run(
    data={
        "prompt_builder": {
            "template_variables": {"country": country},
            "template": messages,
        },
    },
)
print(res)
```