ChatAnthropic

This notebook provides a quick overview for getting started with Anthropic chat models. For detailed documentation of all ChatAnthropic features and configurations head to the API reference.

Anthropic has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the Anthropic docs.

AWS Bedrock and Google VertexAI

Note that certain Anthropic models can also be accessed via AWS Bedrock and Google VertexAI. See the ChatBedrock and ChatVertexAI integrations to use Anthropic models via these services.

Overview

Integration details

Class	Package	Local	Serializable	JS support	Package downloads	Package latest
ChatAnthropic	langchain-anthropic	❌	beta	✅

Model features

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
✅	✅	❌	✅	❌	❌	✅	✅	✅	❌

Setup

To access Anthropic models you'll need to create an Anthropic account, get an API key, and install the langchain-anthropic integration package.

Credentials

Head to https://console.anthropic.com/ to sign up for Anthropic and generate an API key. Once you've done this set the ANTHROPIC_API_KEY environment variable:

import getpass
import os

os.environ["anthropic_API_KEY"] = getpass.getpass("Enter your Anthropic API key: ")

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

Installation

The LangChain Anthropic integration lives in the langchain-anthropic package:

%pip install -qU langchain-anthropic

Instantiation

Now we can instantiate our model object and generate chat completions:

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(
    model="claude-3-sonnet-20240229",
    temperature=0,
    max_tokens=1024,
    timeout=None,
    max_retries=2,
    # other params...
)

API Reference:ChatAnthropic

Invocation

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg

AIMessage(content="Voici la traduction en français :\n\nJ'aime la programmation.", response_metadata={'id': 'msg_013qztabaFADNnKsHR1rdrju', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 29, 'output_tokens': 21}}, id='run-a22ab30c-7e09-48f5-bc27-a08a9d8f7fa1-0', usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50})

print(ai_msg.content)

Voici la traduction en français :

J'aime la programmation.

Chaining

We can chain our model with a prompt template like so:

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ),
        ("human", "{input}"),
    ]
)

chain = prompt | llm
chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love programming.",
    }
)

API Reference:ChatPromptTemplate

AIMessage(content='Ich liebe Programmieren.', response_metadata={'id': 'msg_01FWrA8w9HbjqYPTQ7VryUnp', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 23, 'output_tokens': 11}}, id='run-b749bf20-b46d-4d62-ac73-f59adab6dd7e-0', usage_metadata={'input_tokens': 23, 'output_tokens': 11, 'total_tokens': 34})

Content blocks

One key difference to note between Anthropic models and most others is that the contents of a single Anthropic AI message can either be a single string or a list of content blocks. For example when an Anthropic model invokes a tool, the tool invocation is part of the message content (as well as being exposed in the standardized AIMessage.tool_calls):

from langchain_core.pydantic_v1 import BaseModel, Field


class GetWeather(BaseModel):
    """Get the current weather in a given location"""

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")


llm_with_tools = llm.bind_tools([GetWeather])
ai_msg = llm_with_tools.invoke("Which city is hotter today: LA or NY?")
ai_msg.content

[{'text': "Okay, let's use the GetWeather tool to check the current temperatures in Los Angeles and New York City.",
  'type': 'text'},
 {'id': 'toolu_01Tnp5tL7LJZaVyQXKEjbqcC',
  'input': {'location': 'Los Angeles, CA'},
  'name': 'GetWeather',
  'type': 'tool_use'}]

ai_msg.tool_calls

[{'name': 'GetWeather',
  'args': {'location': 'Los Angeles, CA'},
  'id': 'toolu_01Tnp5tL7LJZaVyQXKEjbqcC'}]

API reference

For detailed documentation of all ChatAnthropic features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html