Class ChatAnthropicMessages<CallOptions>

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

const llm = new ChatAnthropic({
  model: "claude-3-5-sonnet-20240620",
  temperature: 0,
  maxTokens: undefined,
  maxRetries: 2,
  // apiKey: "...",
  // baseUrl: "...",
  // other params...
});
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const input = `Translate "I love programming" into French.`;

// Models also accept a list of chat messages or a formatted prompt
const result = await llm.invoke(input);
console.log(result);
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AIMessage {
  "id": "msg_01QDpd78JUHpRP6bRRNyzbW3",
  "content": "Here's the translation to French:\n\nJ'adore la programmation.",
  "response_metadata": {
    "id": "msg_01QDpd78JUHpRP6bRRNyzbW3",
    "model": "claude-3-5-sonnet-20240620",
    "stop_reason": "end_turn",
    "stop_sequence": null,
    "usage": {
      "input_tokens": 25,
      "output_tokens": 19
    },
    "type": "message",
    "role": "assistant"
  },
  "usage_metadata": {
    "input_tokens": 25,
    "output_tokens": 19,
    "total_tokens": 44
  }
}
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for await (const chunk of await llm.stream(input)) {
  console.log(chunk);
}
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AIMessageChunk {
  "id": "msg_01N8MwoYxiKo9w4chE4gXUs4",
  "content": "",
  "additional_kwargs": {
    "id": "msg_01N8MwoYxiKo9w4chE4gXUs4",
    "type": "message",
    "role": "assistant",
    "model": "claude-3-5-sonnet-20240620"
  },
  "usage_metadata": {
    "input_tokens": 25,
    "output_tokens": 1,
    "total_tokens": 26
  }
}
AIMessageChunk {
  "content": "",
}
AIMessageChunk {
  "content": "Here",
}
AIMessageChunk {
  "content": "'s",
}
AIMessageChunk {
  "content": " the translation to",
}
AIMessageChunk {
  "content": " French:\n\nJ",
}
AIMessageChunk {
  "content": "'adore la programmation",
}
AIMessageChunk {
  "content": ".",
}
AIMessageChunk {
  "content": "",
  "additional_kwargs": {
    "stop_reason": "end_turn",
    "stop_sequence": null
  },
  "usage_metadata": {
    "input_tokens": 0,
    "output_tokens": 19,
    "total_tokens": 19
  }
}
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import { AIMessageChunk } from '@langchain/core/messages';
import { concat } from '@langchain/core/utils/stream';

const stream = await llm.stream(input);
let full: AIMessageChunk | undefined;
for await (const chunk of stream) {
  full = !full ? chunk : concat(full, chunk);
}
console.log(full);
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AIMessageChunk {
  "id": "msg_01SBTb5zSGXfjUc7yQ8EKEEA",
  "content": "Here's the translation to French:\n\nJ'adore la programmation.",
  "additional_kwargs": {
    "id": "msg_01SBTb5zSGXfjUc7yQ8EKEEA",
    "type": "message",
    "role": "assistant",
    "model": "claude-3-5-sonnet-20240620",
    "stop_reason": "end_turn",
    "stop_sequence": null
  },
  "usage_metadata": {
    "input_tokens": 25,
    "output_tokens": 20,
    "total_tokens": 45
  }
}
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import { z } from 'zod';

const GetWeather = {
  name: "GetWeather",
  description: "Get the current weather in a given location",
  schema: z.object({
    location: z.string().describe("The city and state, e.g. San Francisco, CA")
  }),
}

const GetPopulation = {
  name: "GetPopulation",
  description: "Get the current population in a given location",
  schema: z.object({
    location: z.string().describe("The city and state, e.g. San Francisco, CA")
  }),
}

const llmWithTools = llm.bindTools([GetWeather, GetPopulation]);
const aiMsg = await llmWithTools.invoke(
  "Which city is hotter today and which is bigger: LA or NY?"
);
console.log(aiMsg.tool_calls);
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[
  {
    name: 'GetWeather',
    args: { location: 'Los Angeles, CA' },
    id: 'toolu_01WjW3Dann6BPJVtLhovdBD5',
    type: 'tool_call'
  },
  {
    name: 'GetWeather',
    args: { location: 'New York, NY' },
    id: 'toolu_01G6wfJgqi5zRmJomsmkyZXe',
    type: 'tool_call'
  },
  {
    name: 'GetPopulation',
    args: { location: 'Los Angeles, CA' },
    id: 'toolu_0165qYWBA2VFyUst5RA18zew',
    type: 'tool_call'
  },
  {
    name: 'GetPopulation',
    args: { location: 'New York, NY' },
    id: 'toolu_01PGNyP33vxr13tGqr7i3rDo',
    type: 'tool_call'
  }
]
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import { z } from 'zod';

const Joke = z.object({
  setup: z.string().describe("The setup of the joke"),
  punchline: z.string().describe("The punchline to the joke"),
  rating: z.number().optional().describe("How funny the joke is, from 1 to 10")
}).describe('Joke to tell user.');

const structuredLlm = llm.withStructuredOutput(Joke, { name: "Joke" });
const jokeResult = await structuredLlm.invoke("Tell me a joke about cats");
console.log(jokeResult);
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{
  setup: "Why don't cats play poker in the jungle?",
  punchline: 'Too many cheetahs!',
  rating: 7
}
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import { HumanMessage } from '@langchain/core/messages';

const imageUrl = "https://example.com/image.jpg";
const imageData = await fetch(imageUrl).then(res => res.arrayBuffer());
const base64Image = Buffer.from(imageData).toString('base64');

const message = new HumanMessage({
  content: [
    { type: "text", text: "describe the weather in this image" },
    {
      type: "image_url",
      image_url: { url: `data:image/jpeg;base64,${base64Image}` },
    },
  ]
});

const imageDescriptionAiMsg = await llm.invoke([message]);
console.log(imageDescriptionAiMsg.content);
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The weather in this image appears to be beautiful and clear. The sky is a vibrant blue with scattered white clouds, suggesting a sunny and pleasant day. The clouds are wispy and light, indicating calm conditions without any signs of storms or heavy weather. The bright green grass on the rolling hills looks lush and well-watered, which could mean recent rainfall or good growing conditions. Overall, the scene depicts a perfect spring or early summer day with mild temperatures, plenty of sunshine, and gentle breezes - ideal weather for enjoying the outdoors or for plant growth.
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const aiMsgForMetadata = await llm.invoke(input);
console.log(aiMsgForMetadata.usage_metadata);
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{ input_tokens: 25, output_tokens: 19, total_tokens: 44 }
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const streamForMetadata = await llm.stream(
  input,
  {
    streamUsage: true
  }
);
let fullForMetadata: AIMessageChunk | undefined;
for await (const chunk of streamForMetadata) {
  fullForMetadata = !fullForMetadata ? chunk : concat(fullForMetadata, chunk);
}
console.log(fullForMetadata?.usage_metadata);
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{ input_tokens: 25, output_tokens: 20, total_tokens: 45 }
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const aiMsgForResponseMetadata = await llm.invoke(input);
console.log(aiMsgForResponseMetadata.response_metadata);
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{
  id: 'msg_01STxeQxJmp4sCSpioD6vK3L',
  model: 'claude-3-5-sonnet-20240620',
  stop_reason: 'end_turn',
  stop_sequence: null,
  usage: { input_tokens: 25, output_tokens: 19 },
  type: 'message',
  role: 'assistant'
}
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