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",
        "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-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("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-haiku-4-5-20251001",
}).bindTools([calculatorTool]);

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

// 将提示词与模型链接起来
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-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("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
  }
}

let CACHED_TEXT = "...";

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

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

const LONG_TEXT = `You are a pirate. Always respond in pirate dialect.

Use the following as context when answering questions:

${CACHED_TEXT}`;

const messages = [
  {
    role: "system",
    content: [
      {
        type: "text",
        text: LONG_TEXT,
        // 告诉 Anthropic 缓存这个内容块
        cache_control: { type: "ephemeral" },
      },
    ],
  },
  {
    role: "user",
    content: "What types of messages are supported in LangChain?",
  },
];

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: "Location to get the weather for",
      },
      unit: {
        type: "string",
        description: "Temperature unit to return",
      },
    },
    required: ["location"],
  },
  // 告诉 Anthropic 缓存这个工具
  cache_control: { type: "ephemeral" },
}]

const modelWithCachedTools = modelWithCaching.bindTools(anthropicTools);

await modelWithCachedTools.invoke("what is the weather in SF?");

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: "Hello!" }]);

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: "The grass is green. The sky is blue.",
        },
        title: "My Document",
        context: "This is a trustworthy document.",
        citations: {
          enabled: true,
        },
      },
      {
        type: "text",
        text: "What color is the grass and sky?",
      },
    ],
  }
];

const responseWithCitations = await citationsModel.invoke(messagesWithCitations);

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

[
  {
    "type": "text",
    "text": "Based on the document, I can tell you that:\n\n- "
  },
  {
    "type": "text",
    "text": "The grass is green",
    "citations": [
      {
        "type": "char_location",
        "cited_text": "The grass is green. ",
        "document_index": 0,
        "document_title": "My Document",
        "start_char_index": 0,
        "end_char_index": 20
      }
    ]
  },
  {
    "type": "text",
    "text": "\n- "
  },
  {
    "type": "text",
    "text": "The sky is blue",
    "citations": [
      {
        "type": "char_location",
        "cited_text": "The sky is blue.",
        "document_index": 0,
        "document_title": "My Document",
        "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: "History of France",
        source: "https://some-uri.com",
        citations: { enabled: true },
        content: [
          {
            type: "text",
            text: "The capital of France is Paris.",
          },
          {
            type: "text",
            text: "The old capital of France was Lyon.",
          },
        ],
      },
      {
        type: "text",
        text: "What is the capital of France?",
      },
    ],
  },
];

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: "History of France",
      source: "https://some-uri.com",
      citations: { enabled: true },
      content: [
        {
          type: "text",
          text: "The capital of France is Paris.",
        },
        {
          type: "text",
          text: "The old capital of France was Lyon.",
        },
      ],
    },
    {
      type: "search_result",
      title: "Geography of France",
      source: "https://some-uri.com",
      citations: { enabled: true },
      content: [
        {
          type: "text",
          text: "France is a country in Europe.",
        },
        {
          type: "text",
          text: "The capital of France is Paris.",
        },
      ],
    },
  ],
  {
    name: "my_rag_tool",
    description: "Retrieval system that accesses my knowledge base.",
    schema: z.object({
      query: z.string().describe("query to search in the knowledge base"),
    }),
  }
);

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

const result = await model.invoke([
  {
    role: "user",
    content: "What is the capital of France?",
  },
]);

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 },
  };
}

// 拉取 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);

// 构建消息
const messageWithSplitDocuments = {
  role: "user",
  content: [
    formatToAnthropicDocuments(documents),
    { type: "text", text: "Give me a link to LangChain's tutorials. Cite your sources" },
  ],
};

// 查询大语言模型
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": "Based on the documentation, I can provide you with a link to LangChain's tutorials:\n\n"
  },
  {
    "type": "text",
    "text": "The tutorials can be found at: 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("Search for recent developments in AI");