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This component enables simple text completion using Anthropic Claude LLMs

Most common Position in a PipelineAfter a PromptBuilder
Mandatory Input variables“model”: the name of the model to use with this generator
Output variables“replies”: a list of strings with all the replies generated by the model


This component supports Anthropic Claude models provided through Anthropic’s own inferencing infrastructure. For a full list of available models, check out the Anthropic Claude documentation.

AnthropicGenerator needs an Anthropic API key to work. You can write this key in:

  • The api_key parameter
  • The ANTHROPIC_API_KEY environment variable (recommended)

Currently, available models are:

  • claude-2.1
  • claude-3-haiku-20240307
  • claude-3-sonnet-20240229 (default)
  • claude-3-opus-20240229

Although Anthropic natively supports a much richer messaging API, we have intentionally simplified it in this component so that the main input/output interface is string-based.
For more complete messaging support, consider using the AnthropicChatGenerator.

Refer to the Anthropic API documentation for more details on the parameters supported by the Anthropic API, which you can provide with generation_kwargs when running the component.


AnthropicGenerator supports streaming the tokens from the LLM directly in output. To do so, pass a function to the streaming_callback when initializing.


Install the anthropic-haystack package to use the AnthropicGenerator:

pip install anthropic-haystack

On its own

Basic usage:

import os
from haystack_integrations.components.generators.anthropic import AnthropicGenerator

os.environ["ANTHROPIC_API_KEY"] = "Your Anthropic API Key"

client = AnthropicGenerator(model="claude-2.1")
response ="What's Natural Language Processing? Be brief.")

# >>{'replies': ['Natural language processing (NLP) is a branch of artificial intelligence focused on enabling
# >>computers to understand, interpret, and manipulate human language. The goal of NLP is to read, decipher,
# >> understand, and make sense of the human languages in a manner that is valuable.'], 'meta': {'model':
# >> 'claude-2.1', 'index': 0, 'finish_reason': 'end_turn', 'usage': {'input_tokens': 18, 'output_tokens': 58}}}

In a pipeline

Below is an example RAG Pipeline where we answer a predefined question using the contents from the given URL pointing to the Anthropic prompt engineering guide. We fetch the contents of the URL and generate an answer with the AnthropicGenerator:

import os

from haystack import Pipeline
from import PromptBuilder
from haystack.components.converters import HTMLToDocument
from haystack.components.fetchers import LinkContentFetcher
from haystack.components.generators.utils import print_streaming_chunk

from haystack_integrations.components.generators.anthropic import AnthropicGenerator

# To run this example, you will need to set an `ANTHROPIC_API_KEY` environment variable.
os.environ["ANTHROPIC_API_KEY"] = "Your Anthropic API Key"

template = """
Given the following information, answer the question.

{% for document in documents %}
    {{ document.content }}
{% endfor %}

Question: {{ query }}

rag_pipeline = Pipeline()
rag_pipeline.add_component("fetcher", LinkContentFetcher())
rag_pipeline.add_component("converter", HTMLToDocument())
rag_pipeline.add_component("prompt_builder", PromptBuilder(template=template))

rag_pipeline.connect("fetcher", "converter")
rag_pipeline.connect("converter", "prompt_builder")
rag_pipeline.connect("prompt_builder", "llm")

question = "What are the best practices in prompt engineering?"
        "fetcher": {"urls": [""]},
        "prompt_builder": {"query": question},

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