ChatAnthropic 集成

export ANTHROPIC_API_KEY="your-api-key"

# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

import { ChatAnthropic } from "@langchain/anthropic"

const llm = new ChatAnthropic({
    model: "claude-haiku-4-5-20251001",
    temperature: 0,
    maxTokens: undefined,
    maxRetries: 2,
    // 其他参数...
});

const aiMsg = await llm.invoke([
    [
        "system",
        "你是一个将英语翻译成法语的有用助手。请翻译用户的句子。",
    ],
    ["human", "我爱编程。"],
])
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-haiku-4-5-20251001",
    "stop_reason": "end_turn",
    "stop_sequence": null,
    "usage": {
      "input_tokens": 29,
      "output_tokens": 20
    }
  },
  "response_metadata": {
    "id": "msg_013WBXXiggy6gMbAUY6NpsuU",
    "model": "claude-haiku-4-5-20251001",
    "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.

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

const calculatorSchema = z.object({
  operation: z
    .enum(["add", "subtract", "multiply", "divide"])
    .describe("要执行的操作类型。"),
  number1: z.number().describe("要操作的第一个数字。"),
  number2: z.number().describe("要操作的第二个数字。"),
});

const calculatorTool = {
  name: "calculator",
  description: "一个简单的计算器工具",
  input_schema: zodToJsonSchema(calculatorSchema),
};

const toolCallingLlm = new ChatAnthropic({
  model: "claude-haiku-4-5-20251001",
}).bindTools([calculatorTool]);

const toolPrompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    "你是一个总是需要使用计算器的有用助手。",
  ],
  ["human", "{input}"],
]);

// 将提示词和模型链接在一起
const toolCallChain = toolPrompt.pipe(toolCallingLlm);

await toolCallChain.invoke({
  input: "2 + 2 等于多少？",
});

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-haiku-4-5-20251001",
    "stop_reason": "tool_use",
    "stop_sequence": null,
    "usage": {
      "input_tokens": 449,
      "output_tokens": 100
    }
  },
  "response_metadata": {
    "id": "msg_01DZGs9DyuashaYxJ4WWpWUP",
    "model": "claude-haiku-4-5-20251001",
    "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
  }
}

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

const llmWithCustomHeaders = new ChatAnthropic({
  model: "claude-sonnet-4-6",
  maxTokens: 1024,
  clientOptions: {
    defaultHeaders: {
      "X-Api-Key": process.env.ANTHROPIC_API_KEY,
    },
  },
});

await llmWithCustomHeaders.invoke("为什么天空是蓝色的？");

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
  }
}

let CACHED_TEXT = "...";

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

const modelWithCaching = new ChatAnthropic({
  model: "claude-sonnet-4-6",
});

const LONG_TEXT = `你是一个海盗。始终用海盗方言回答。

回答问题时使用以下内容作为上下文：

${CACHED_TEXT}`;

const messages = [
  {
    role: "system",
    content: [
      {
        type: "text",
        text: LONG_TEXT,
        // 告诉 Anthropic 缓存这个块
        cache_control: { type: "ephemeral" },
      },
    ],
  },
  {
    role: "user",
    content: "LangChain 支持哪些类型的消息？",
  },
];

const res = await modelWithCaching.invoke(messages);

console.log("USAGE:", res.response_metadata.usage);

USAGE: {
  input_tokens: 18,
  cache_creation_input_tokens: 2960,
  cache_read_input_tokens: 0,
  cache_creation: { ephemeral_5m_input_tokens: 2960, ephemeral_1h_input_tokens: 0 },
  output_tokens: 433,
  service_tier: 'standard',
  inference_geo: 'global'
}

const res2 = await modelWithCaching.invoke(messages);

console.log("USAGE:", res2.response_metadata.usage);

USAGE: {
  input_tokens: 18,
  cache_creation_input_tokens: 0,
  cache_read_input_tokens: 2960,
  cache_creation: { ephemeral_5m_input_tokens: 0, ephemeral_1h_input_tokens: 0 },
  output_tokens: 393,
  service_tier: 'standard',
  inference_geo: 'global'
}

const SOME_LONG_DESCRIPTION = "...";

// Anthropic 格式的工具
const anthropicTools = [{
  name: "get_weather",
  description: SOME_LONG_DESCRIPTION,
  input_schema: {
    type: "object",
    properties: {
      location: {
        type: "string",
        description: "要获取天气的位置",
      },
      unit: {
        type: "string",
        description: "要返回的温度单位",
      },
    },
    required: ["location"],
  },
  // 告诉 Anthropic 缓存这个工具
  cache_control: { type: "ephemeral" },
}]

const modelWithCachedTools = modelWithCaching.bindTools(anthropicTools);

await modelWithCachedTools.invoke("旧金山的天气怎么样？");

import { AnthropicVertex } from "@anthropic-ai/vertex-sdk";

const customClient = new AnthropicVertex();

const modelWithCustomClient = new ChatAnthropic({
  modelName: "claude-sonnet-4-6",
  maxRetries: 0,
  createClient: () => customClient,
});

await modelWithCustomClient.invoke([{ role: "user", content: "你好！" }]);

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

const citationsModel = new ChatAnthropic({
  model: "claude-haiku-4-5-20251001",
});

const messagesWithCitations = [
  {
    role: "user",
    content: [
      {
        type: "document",
        source: {
          type: "text",
          media_type: "text/plain",
          data: "草是绿色的。天空是蓝色的。",
        },
        title: "我的文档",
        context: "这是一个可信的文档。",
        citations: {
          enabled: true,
        },
      },
      {
        type: "text",
        text: "草和天空是什么颜色？",
      },
    ],
  }
];

const responseWithCitations = await citationsModel.invoke(messagesWithCitations);

console.log(JSON.stringify(responseWithCitations.content, null, 2));

[
  {
    "type": "text",
    "text": "根据文档，我可以告诉你：\n\n- "
  },
  {
    "type": "text",
    "text": "草是绿色的",
    "citations": [
      {
        "type": "char_location",
        "cited_text": "草是绿色的。 ",
        "document_index": 0,
        "document_title": "我的文档",
        "start_char_index": 0,
        "end_char_index": 20
      }
    ]
  },
  {
    "type": "text",
    "text": "\n- "
  },
  {
    "type": "text",
    "text": "天空是蓝色的",
    "citations": [
      {
        "type": "char_location",
        "cited_text": "天空是蓝色的。",
        "document_index": 0,
        "document_title": "我的文档",
        "start_char_index": 20,
        "end_char_index": 36
      }
    ]
  }
]

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

const citationsModel = new ChatAnthropic({
  model: "claude-haiku-4-5-20251001",
});

const messagesWithCitations = [
  {
    type: "user",
    content: [
      {
        type: "search_result",
        title: "法国历史",
        source: "https://some-uri.com",
        citations: { enabled: true },
        content: [
          {
            type: "text",
            text: "法国的首都是巴黎。",
          },
          {
            type: "text",
            text: "法国的旧都是里昂。",
          },
        ],
      },
      {
        type: "text",
        text: "法国的首都是哪里？",
      },
    ],
  },
];

const responseWithCitations = await citationsModel.invoke(messagesWithCitations);

console.log(JSON.stringify(responseWithCitations.content, null, 2));

import { ChatAnthropic } from "@langchain/anthropic";
import { tool } from "@langchain/core/tools";

// 创建一个返回搜索结果的工具
const ragTool = tool(
  () => [
    {
      type: "search_result",
      title: "法国历史",
      source: "https://some-uri.com",
      citations: { enabled: true },
      content: [
        {
          type: "text",
          text: "法国的首都是巴黎。",
        },
        {
          type: "text",
          text: "法国的旧都是里昂。",
        },
      ],
    },
    {
      type: "search_result",
      title: "法国地理",
      source: "https://some-uri.com",
      citations: { enabled: true },
      content: [
        {
          type: "text",
          text: "法国是欧洲的一个国家。",
        },
        {
          type: "text",
          text: "法国的首都是巴黎。",
        },
      ],
    },
  ],
  {
    name: "my_rag_tool",
    description: "访问我的知识库的检索系统。",
    schema: z.object({
      query: z.string().describe("要在知识库中搜索的查询"),
    }),
  }
);

// 创建带有搜索结果 beta 头的模型
const model = new ChatAnthropic({
  model: "claude-haiku-4-5-20251001",
}).bindTools([ragTool]);

const result = await model.invoke([
  {
    role: "user",
    content: "法国的首都是哪里？",
  },
]);

console.log(JSON.stringify(result.content, null, 2));

import { ChatAnthropic } from "@langchain/anthropic";
import { MarkdownTextSplitter } from "@langchain/classic/text_splitter";

function formatToAnthropicDocuments(documents: string[]) {
  return {
    type: "document",
    source: {
      type: "content",
      content: documents.map((document) => ({ type: "text", text: document })),
    },
    citations: { enabled: true },
  };
}

// 获取 readme
const readmeResponse = await fetch(
  "https://raw.githubusercontent.com/langchain-ai/langchainjs/master/README.md"
);

const readme = await readmeResponse.text();

// 分割成块
const splitter = new MarkdownTextSplitter({
  chunkOverlap: 0,
  chunkSize: 50,
});
const documents = await splitter.splitText(readme);

// 构造消息
const messageWithSplitDocuments = {
  role: "user",
  content: [
    formatToAnthropicDocuments(documents),
    { type: "text", text: "给我一个 LangChain 教程的链接。请引用你的来源" },
  ],
};

// 查询 LLM
const citationsModelWithSplits = new ChatAnthropic({
  model: "claude-sonnet-4-6",
});
const resWithSplits = await citationsModelWithSplits.invoke([messageWithSplitDocuments]);

console.log(JSON.stringify(resWithSplits.content, null, 2));

[
  {
    "type": "text",
    "text": "根据文档，我可以为您提供 LangChain 教程的链接：\n\n"
  },
  {
    "type": "text",
    "text": "教程可以在以下网址找到：https://js.langchain.com/docs/tutorials/",
    "citations": [
      {
        "type": "content_block_location",
        "cited_text": "[Tutorial](https://js.langchain.com/docs/tutorials/) walkthroughs",
        "document_index": 0,
        "document_title": null,
        "start_block_index": 191,
        "end_block_index": 194
      }
    ]
  }
]

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

const llm = new ChatAnthropic({
  model: "claude-sonnet-4-6",
  clientOptions: {
    defaultHeaders: {
      "anthropic-beta": "context-management-2025-06-27",
    },
  },
  contextManagement: { edits: [{ type: "clear_tool_uses_20250919" }] },
)
const llmWithTools = llm.bindTools([{ type: "web_search_20250305", name: "web_search" }]);
const response = await llmWithTools.invoke("搜索人工智能的最新进展");