Integration: Anthropic
Use Anthropic Models with Haystack
Authors
deepset
Table of Contents
Overview
This integration supports Anthropic Claude models such as Claude Haiku 3.5, Claude Sonnet 3.7, and Claude Sonnet 4.5 through Anthropic’s inference infrastructure. For a complete list of available models, check out the Anthropic Claude documentation.
You can use Anthropic models with
AnthropicChatGenerator.
Installation
pip install anthropic-haystack
Usage
Before using, make sure to set the ANTHROPIC_API_KEY environment variable.
Using AnthropicChatGenerator
Below is an example RAG Pipeline where we answer a predefined question using the contents of the URL pointing to the Anthropic prompt engineering guide. We fetch the URL’s contents and generate an answer with the AnthropicChatGenerator.
# To run this example, you need to set the `ANTHROPIC_API_KEY` environment variable.
# !pip install trafilatura
from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.components.converters import HTMLToDocument
from haystack.components.fetchers import LinkContentFetcher
from haystack.components.generators.utils import print_streaming_chunk
from haystack.dataclasses import ChatMessage
from haystack.utils import Secret
from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
messages = [
ChatMessage.from_system("You are a prompt expert who answers questions based on the given documents."),
ChatMessage.from_user(
"Here are the documents:\n"
"{% for d in documents %} \n"
" {{d.content}} \n"
"{% endfor %}"
"\nAnswer: {{query}}"
),
]
rag_pipeline = Pipeline()
rag_pipeline.add_component("fetcher", LinkContentFetcher())
rag_pipeline.add_component("converter", HTMLToDocument())
rag_pipeline.add_component("prompt_builder", ChatPromptBuilder(variables=["documents"]))
rag_pipeline.add_component(
"llm",
AnthropicChatGenerator(
api_key=Secret.from_env_var("ANTHROPIC_API_KEY"),
streaming_callback=print_streaming_chunk,
),
)
rag_pipeline.connect("fetcher", "converter")
rag_pipeline.connect("converter", "prompt_builder")
rag_pipeline.connect("prompt_builder.prompt", "llm.messages")
question = "When should we use prompt engineering and when should we fine-tune?"
rag_pipeline.run(
data={
"fetcher": {"urls": ["https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview"]},
"prompt_builder": {"template_variables": {"query": question}, "template": messages},
}
)