---
title: "AnthropicChatGenerator"
id: anthropicchatgenerator
slug: "/anthropicchatgenerator"
description: "This component enables chat completions using Anthropic large language models (LLMs)."
---

# AnthropicChatGenerator

This component enables chat completions using Anthropic large language models (LLMs).

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx)  |
| **Mandatory init variables** | "api_key": An Anthropic API key. Can be set with `ANTHROPIC_API_KEY` env var. |
| **Mandatory run variables** | “messages” A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  objects |
| **Output variables** | "replies": A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  objects  <br /> <br />”meta”: A list of dictionaries with the metadata associated with each reply, such as token count, finish reason, and so on |
| **API reference** | [Anthropic](/reference/integrations-anthropic) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/anthropic |

## Overview

This integration supports Anthropic `chat` models such as `claude-3-5-sonnet-20240620`,`claude-3-opus-20240229`, `claude-3-haiku-20240307`, and similar. Check out the most recent full list in [Anthropic documentation](https://docs.anthropic.com/en/docs/about-claude/models).

### Parameters

`AnthropicChatGenerator` needs an Anthropic API key to work. You can provide this key in:

- The `ANTHROPIC_API_KEY` environment variable (recommended)
- The `api_key` init parameter and Haystack [Secret](../../concepts/secret-management.mdx) API: `Secret.from_token("your-api-key-here")`

Set your preferred Anthropic model with the `model` parameter when initializing the component.

`AnthropicChatGenerator` 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"})
```

:::note
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 `AnthropicChatGenerator` being initialized with prompt caching and tagging a message to be cached:

```python python
from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
from haystack.dataclasses import ChatMessage

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

claude_llm = AnthropicChatGenerator(
    api_key=Secret.from_env_var("ANTHROPIC_API_KEY"), 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 `AnthropicChatGenerator`:

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

### On its own

```python
from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
from haystack.dataclasses import ChatMessage

generator = AnthropicChatGenerator()
message = ChatMessage.from_user("What's Natural Language Processing? Be brief.")
print(generator.run([message]))
```

### In a pipeline

You can also use `AnthropicChatGenerator`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 AnthropicChatGenerator
from haystack.utils import Secret

pipe = Pipeline()
pipe.add_component("prompt_builder", ChatPromptBuilder())
pipe.add_component(
    "llm",
    AnthropicChatGenerator(Secret.from_env_var("ANTHROPIC_API_KEY")),
)
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)
```

## Additional References

🧑‍🍳 Cookbook: [Advanced Prompt Customization for Anthropic](https://haystack.deepset.ai/cookbook/prompt_customization_for_anthropic)
