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ChatAnthropic

Anthropic is an AI safety and research company. They are the creator of Claude.

This will help you getting started with Anthropic chat models. For detailed documentation of all ChatAnthropic features and configurations head to the API reference.

Overview​

Integration details​

ClassPackageLocalSerializablePY supportPackage downloadsPackage latest
ChatAnthropic@langchain/anthropicβŒβœ…βœ…NPM - DownloadsNPM - Version

Model features​

See the links in the table headers below for guides on how to use specific features.

Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingToken usageLogprobs
βœ…βœ…βŒβœ…βŒβŒβœ…βœ…βŒ

Setup​

You’ll need to sign up and obtain an Anthropic API key, and install the @langchain/anthropic integration package.

Credentials​

Head to Anthropic’s website to sign up to Anthropic and generate an API key. Once you’ve done this set the ANTHROPIC_API_KEY environment variable:

export ANTHROPIC_API_KEY="your-api-key"

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

# export LANGCHAIN_TRACING_V2="true"
# export LANGCHAIN_API_KEY="your-api-key"

Installation​

The LangChain ChatAnthropic integration lives in the @langchain/anthropic package:

yarn add @langchain/anthropic

Instantiation​

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

import { ChatAnthropic } from "@langchain/anthropic";

const llm = new ChatAnthropic({
model: "claude-3-haiku-20240307",
temperature: 0,
maxTokens: undefined,
maxRetries: 2,
// other params...
});

Invocation​

const aiMsg = await llm.invoke([
[
"system",
"You are a helpful assistant that translates English to French. Translate the user sentence.",
],
["human", "I love programming."],
]);
aiMsg;
AIMessage {
"id": "msg_013WBXXiggy6gMbAUY6NpsuU",
"content": "Voici la traduction en français :\n\nJ'adore la programmation.",
"additional_kwargs": {
"id": "msg_013WBXXiggy6gMbAUY6NpsuU",
"type": "message",
"role": "assistant",
"model": "claude-3-haiku-20240307",
"stop_reason": "end_turn",
"stop_sequence": null,
"usage": {
"input_tokens": 29,
"output_tokens": 20
}
},
"response_metadata": {
"id": "msg_013WBXXiggy6gMbAUY6NpsuU",
"model": "claude-3-haiku-20240307",
"stop_reason": "end_turn",
"stop_sequence": null,
"usage": {
"input_tokens": 29,
"output_tokens": 20
},
"type": "message",
"role": "assistant"
},
"tool_calls": [],
"invalid_tool_calls": [],
"usage_metadata": {
"input_tokens": 29,
"output_tokens": 20,
"total_tokens": 49
}
}
console.log(aiMsg.content);
Voici la traduction en français :

J'adore la programmation.

Chaining​

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

import { ChatPromptTemplate } from "@langchain/core/prompts";

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

const chain = prompt.pipe(llm);
await chain.invoke({
input_language: "English",
output_language: "German",
input: "I love programming.",
});
AIMessage {
"id": "msg_01Ca52fpd1mcGRhH4spzAWr4",
"content": "Ich liebe das Programmieren.",
"additional_kwargs": {
"id": "msg_01Ca52fpd1mcGRhH4spzAWr4",
"type": "message",
"role": "assistant",
"model": "claude-3-haiku-20240307",
"stop_reason": "end_turn",
"stop_sequence": null,
"usage": {
"input_tokens": 23,
"output_tokens": 11
}
},
"response_metadata": {
"id": "msg_01Ca52fpd1mcGRhH4spzAWr4",
"model": "claude-3-haiku-20240307",
"stop_reason": "end_turn",
"stop_sequence": null,
"usage": {
"input_tokens": 23,
"output_tokens": 11
},
"type": "message",
"role": "assistant"
},
"tool_calls": [],
"invalid_tool_calls": [],
"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 calls a tool, the tool invocation is part of the message content (as well as being exposed in the standardized AIMessage.tool_calls field):

import { ChatAnthropic } from "@langchain/anthropic";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

const calculatorSchema = z.object({
operation: z
.enum(["add", "subtract", "multiply", "divide"])
.describe("The type of operation to execute."),
number1: z.number().describe("The first number to operate on."),
number2: z.number().describe("The second number to operate on."),
});

const calculatorTool = {
name: "calculator",
description: "A simple calculator tool",
input_schema: zodToJsonSchema(calculatorSchema),
};

const toolCallingLlm = new ChatAnthropic({
model: "claude-3-haiku-20240307",
}).bindTools([calculatorTool]);

const toolPrompt = ChatPromptTemplate.fromMessages([
[
"system",
"You are a helpful assistant who always needs to use a calculator.",
],
["human", "{input}"],
]);

// Chain your prompt and model together
const toolCallChain = toolPrompt.pipe(toolCallingLlm);

await toolCallChain.invoke({
input: "What is 2 + 2?",
});
AIMessage {
"id": "msg_01DZGs9DyuashaYxJ4WWpWUP",
"content": [
{
"type": "text",
"text": "Here is the calculation for 2 + 2:"
},
{
"type": "tool_use",
"id": "toolu_01SQXBamkBr6K6NdHE7GWwF8",
"name": "calculator",
"input": {
"number1": 2,
"number2": 2,
"operation": "add"
}
}
],
"additional_kwargs": {
"id": "msg_01DZGs9DyuashaYxJ4WWpWUP",
"type": "message",
"role": "assistant",
"model": "claude-3-haiku-20240307",
"stop_reason": "tool_use",
"stop_sequence": null,
"usage": {
"input_tokens": 449,
"output_tokens": 100
}
},
"response_metadata": {
"id": "msg_01DZGs9DyuashaYxJ4WWpWUP",
"model": "claude-3-haiku-20240307",
"stop_reason": "tool_use",
"stop_sequence": null,
"usage": {
"input_tokens": 449,
"output_tokens": 100
},
"type": "message",
"role": "assistant"
},
"tool_calls": [
{
"name": "calculator",
"args": {
"number1": 2,
"number2": 2,
"operation": "add"
},
"id": "toolu_01SQXBamkBr6K6NdHE7GWwF8",
"type": "tool_call"
}
],
"invalid_tool_calls": [],
"usage_metadata": {
"input_tokens": 449,
"output_tokens": 100,
"total_tokens": 549
}
}

Custom headers​

You can pass custom headers in your requests like this:

import { ChatAnthropic } from "@langchain/anthropic";

const llmWithCustomHeaders = new ChatAnthropic({
model: "claude-3-sonnet-20240229",
maxTokens: 1024,
clientOptions: {
defaultHeaders: {
"X-Api-Key": process.env.ANTHROPIC_API_KEY,
},
},
});

await llmWithCustomHeaders.invoke("Why is the sky blue?");
AIMessage {
"id": "msg_019z4nWpShzsrbSHTWXWQh6z",
"content": "The sky appears blue due to a phenomenon called Rayleigh scattering. Here's a brief explanation:\n\n1) Sunlight is made up of different wavelengths of visible light, including all the colors of the rainbow.\n\n2) As sunlight passes through the atmosphere, the gases (mostly nitrogen and oxygen) cause the shorter wavelengths of light, such as violet and blue, to be scattered more easily than the longer wavelengths like red and orange.\n\n3) This scattering of the shorter blue wavelengths occurs in all directions by the gas molecules in the atmosphere.\n\n4) Our eyes are more sensitive to the scattered blue light than the scattered violet light, so we perceive the sky as having a blue color.\n\n5) The scattering is more pronounced for light traveling over longer distances through the atmosphere. This is why the sky appears even darker blue when looking towards the horizon.\n\nSo in essence, the selective scattering of the shorter blue wavelengths of sunlight by the gases in the atmosphere is what causes the sky to appear blue to our eyes during the daytime.",
"additional_kwargs": {
"id": "msg_019z4nWpShzsrbSHTWXWQh6z",
"type": "message",
"role": "assistant",
"model": "claude-3-sonnet-20240229",
"stop_reason": "end_turn",
"stop_sequence": null,
"usage": {
"input_tokens": 13,
"output_tokens": 236
}
},
"response_metadata": {
"id": "msg_019z4nWpShzsrbSHTWXWQh6z",
"model": "claude-3-sonnet-20240229",
"stop_reason": "end_turn",
"stop_sequence": null,
"usage": {
"input_tokens": 13,
"output_tokens": 236
},
"type": "message",
"role": "assistant"
},
"tool_calls": [],
"invalid_tool_calls": [],
"usage_metadata": {
"input_tokens": 13,
"output_tokens": 236,
"total_tokens": 249
}
}

API reference​

For detailed documentation of all ChatAnthropic features and configurations head to the API reference: https://api.js.langchain.com/classes/langchain_anthropic.ChatAnthropic.html


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