ChatAnthropic 集成

Anthropic 是一家人工智能安全与研究公司。他们是 Claude 的创造者。这将帮助你开始使用 ChatAnthropic 聊天模型。有关所有 ChatAnthropic 功能和配置的详细文档，请访问 API 参考。

概述

集成详情

类	包	可序列化	Python 支持	下载量	版本
`ChatAnthropic`	`@langchain/anthropic`	✅	✅

模型功能

有关如何使用特定功能的指南，请参阅下表标题中的链接。

工具调用	结构化输出	图像输入	音频输入	视频输入	令牌级流式传输	令牌使用量	对数概率
✅	✅	✅	❌	❌	✅	✅	❌

设置

你需要注册并获取 Anthropic API 密钥，并安装 @langchain/anthropic 集成包。

凭证

前往 Anthropic 网站注册 Anthropic 并生成 API 密钥。完成此操作后，设置 ANTHROPIC_API_KEY 环境变量：

export ANTHROPIC_API_KEY="your-api-key"

如果你想获得模型调用的自动跟踪，也可以取消注释以下内容来设置你的 LangSmith API 密钥：

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

安装

LangChain 的 ChatAnthropic 集成位于 @langchain/anthropic 包中：

npm install @langchain/anthropic @langchain/core

yarn add @langchain/anthropic @langchain/core

pnpm add @langchain/anthropic @langchain/core

实例化

现在我们可以实例化模型对象并生成聊天补全：

import { ChatAnthropic } from "@langchain/anthropic"

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

调用

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.

内容块

需要注意 Anthropic 模型与大多数其他模型之间的一个关键区别：单个 Anthropic AIMessage 的内容可以是单个字符串，也可以是内容块列表。例如，当 Anthropic 模型调用工具时，工具调用是消息内容的一部分（同时也暴露在标准化的 AIMessage.tool_calls 字段中）：

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}"],
]);

// 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-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
  }
}

自定义请求头

你可以像这样在请求中传递自定义请求头：

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
  }
}

提示缓存

Anthropic 支持缓存提示的某些部分，以降低需要长上下文的用例的成本。你可以缓存工具以及整个消息或单个块。包含一个或多个块或工具定义且带有 "cache_control": { "type": "ephemeral" } 字段的初始请求将自动缓存该部分提示。此初始缓存步骤会产生额外费用，但后续请求将以降低的费率计费。缓存的生命周期为 5 分钟，但每次命中缓存时都会刷新。对于更长的缓存时间，请在 cache_control 字段中指定 "ttl": "1h"。存在一个最小可缓存提示长度，根据模型而异。有关更多信息，请参阅提示缓存详情。以下是缓存包含 LangChain 概念文档的系统消息部分的示例：

let CACHED_TEXT = "...";

// @lc-docs-hide-cell

CACHED_TEXT = `## Components

LangChain provides standard, extendable interfaces and external integrations for various components useful for building with LLMs.
Some components LangChain implements, some components we rely on third-party integrations for, and others are a mix.

### Chat models

<span data-heading-keywords="chat model,chat models"></span>

Language models that use a sequence of messages as inputs and return chat messages as outputs (as opposed to using plain text).
These are generally newer models (older models are generally \`LLMs\`, see below).
Chat models support the assignment of distinct roles to conversation messages, helping to distinguish messages from the AI, users, and instructions such as system messages.

Although the underlying models are messages in, message out, the LangChain wrappers also allow these models to take a string as input.
This gives them the same interface as LLMs (and simpler to use).
When a string is passed in as input, it will be converted to a \`HumanMessage\` under the hood before being passed to the underlying model.

LangChain does not host any Chat Models, rather we rely on third party integrations.

We have some standardized parameters when constructing ChatModels:

- \`model\`: the name of the model

Chat Models also accept other parameters that are specific to that integration.

<Warning>
**Some chat models have been fine-tuned for **tool calling** and provide a dedicated API for it.**

Generally, such models are better at tool calling than non-fine-tuned models, and are recommended for use cases that require tool calling.
Please see the [tool calling section](/oss/javascript/langchain/tools) for more information.
</Warning>

For specifics on how to use chat models, see the [relevant how-to guides here](/oss/javascript/langchain/models).

#### Multimodality

Some chat models are multimodal, accepting images, audio and even video as inputs.
These are still less common, meaning model providers haven't standardized on the "best" way to define the API.
Multimodal outputs are even less common. As such, we've kept our multimodal abstractions fairly light weight
and plan to further solidify the multimodal APIs and interaction patterns as the field matures.

In LangChain, most chat models that support multimodal inputs also accept those values in OpenAI's content blocks format.
So far this is restricted to image inputs. For models like Gemini which support video and other bytes input, the APIs also support the native, model-specific representations.

For specifics on how to use multimodal models, see the [relevant how-to guides here](/oss/javascript/how-to/#multimodal).

### LLMs

<span data-heading-keywords="llm,llms"></span>

<Warning>
**Pure text-in/text-out LLMs tend to be older or lower-level. Many popular models are best used as [chat completion models](/oss/javascript/langchain/models),**

even for non-chat use cases.

You are probably looking for [the section above instead](/oss/javascript/langchain/models).
</Warning>

Language models that takes a string as input and returns a string.
These are traditionally older models (newer models generally are [Chat Models](/oss/javascript/langchain/models), see above).

Although the underlying models are string in, string out, the LangChain wrappers also allow these models to take messages as input.
This gives them the same interface as [Chat Models](/oss/javascript/langchain/models).
When messages are passed in as input, they will be formatted into a string under the hood before being passed to the underlying model.

LangChain does not host any LLMs, rather we rely on third party integrations.

For specifics on how to use LLMs, see the [relevant how-to guides here](/oss/javascript/langchain/models).

### Message types

Some language models take an array of messages as input and return a message.
There are a few different types of messages.
All messages have a \`role\`, \`content\`, and \`response_metadata\` property.

The \`role\` describes WHO is saying the message.
LangChain has different message classes for different roles.

The \`content\` property describes the content of the message.
This can be a few different things:

- A string (most models deal this type of content)
- A List of objects (this is used for multi-modal input, where the object contains information about that input type and that input location)

#### HumanMessage

This represents a message from the user.

#### AIMessage

This represents a message from the model. In addition to the \`content\` property, these messages also have:

**\`response_metadata\`**

The \`response_metadata\` property contains additional metadata about the response. The data here is often specific to each model provider.
This is where information like log-probs and token usage may be stored.

**\`tool_calls\`**

These represent a decision from an language model to call a tool. They are included as part of an \`AIMessage\` output.
They can be accessed from there with the \`.tool_calls\` property.

This property returns a list of \`ToolCall\`s. A \`ToolCall\` is an object with the following arguments:

- \`name\`: The name of the tool that should be called.
- \`args\`: The arguments to that tool.
- \`id\`: The id of that tool call.

#### SystemMessage

This represents a system message, which tells the model how to behave. Not every model provider supports this.

#### ToolMessage

This represents the result of a tool call. In addition to \`role\` and \`content\`, this message has:

- a \`tool_call_id\` field which conveys the id of the call to the tool that was called to produce this result.
- an \`artifact\` field which can be used to pass along arbitrary artifacts of the tool execution which are useful to track but which should not be sent to the model.

#### (Legacy) FunctionMessage

This is a legacy message type, corresponding to OpenAI's legacy function-calling API. \`ToolMessage\` should be used instead to correspond to the updated tool-calling API.

This represents the result of a function call. In addition to \`role\` and \`content\`, this message has a \`name\` parameter which conveys the name of the function that was called to produce this result.

### Prompt templates

<span data-heading-keywords="prompt,prompttemplate,chatprompttemplate"></span>

Prompt templates help to translate user input and parameters into instructions for a language model.
This can be used to guide a model's response, helping it understand the context and generate relevant and coherent language-based output.

Prompt Templates take as input an object, where each key represents a variable in the prompt template to fill in.

Prompt Templates output a PromptValue. This PromptValue can be passed to an LLM or a ChatModel, and can also be cast to a string or an array of messages.
The reason this PromptValue exists is to make it easy to switch between strings and messages.

There are a few different types of prompt templates:

#### String PromptTemplates

These prompt templates are used to format a single string, and generally are used for simpler inputs.
For example, a common way to construct and use a PromptTemplate is as follows:

\`\`\`typescript
import { PromptTemplate } from "@langchain/core/prompts";

const promptTemplate = PromptTemplate.fromTemplate(
  "Tell me a joke about {topic}"
);

await promptTemplate.invoke({ topic: "cats" });
\`\`\`

#### ChatPromptTemplates

These prompt templates are used to format an array of messages. These "templates" consist of an array of templates themselves.
For example, a common way to construct and use a ChatPromptTemplate is as follows:

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

const promptTemplate = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant"],
  ["user", "Tell me a joke about {topic}"],
]);

await promptTemplate.invoke({ topic: "cats" });
\`\`\`

In the above example, this ChatPromptTemplate will construct two messages when called.
The first is a system message, that has no variables to format.
The second is a HumanMessage, and will be formatted by the \`topic\` variable the user passes in.

#### MessagesPlaceholder

<span data-heading-keywords="messagesplaceholder"></span>

This prompt template is responsible for adding an array of messages in a particular place.
In the above ChatPromptTemplate, we saw how we could format two messages, each one a string.
But what if we wanted the user to pass in an array of messages that we would slot into a particular spot?
This is how you use MessagesPlaceholder.

\`\`\`typescript
import {
  ChatPromptTemplate,
  MessagesPlaceholder,
} from "@langchain/core/prompts";
import { HumanMessage } from "@langchain/core/messages";

const promptTemplate = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant"],
  new MessagesPlaceholder("msgs"),
]);

promptTemplate.invoke({ msgs: [new HumanMessage({ content: "hi!" })] });
\`\`\`

This will produce an array of two messages, the first one being a system message, and the second one being the HumanMessage we passed in.
If we had passed in 5 messages, then it would have produced 6 messages in total (the system message plus the 5 passed in).
This is useful for letting an array of messages be slotted into a particular spot.

An alternative way to accomplish the same thing without using the \`MessagesPlaceholder\` class explicitly is:

\`\`\`typescript
const promptTemplate = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant"],
  ["placeholder", "{msgs}"], // <-- This is the changed part
]);
\`\`\`

For specifics on how to use prompt templates, see the [relevant how-to guides here](/oss/javascript/how-to/#prompt-templates).

### Example Selectors

One common prompting technique for achieving better performance is to include examples as part of the prompt.
This gives the language model concrete examples of how it should behave.
Sometimes these examples are hardcoded into the prompt, but for more advanced situations it may be nice to dynamically select them.
Example Selectors are classes responsible for selecting and then formatting examples into prompts.

For specifics on how to use example selectors, see the [relevant how-to guides here](/oss/javascript/how-to/#example-selectors).

### Output parsers

<span data-heading-keywords="output parser"></span>

<Note>
**此处的信息指的是接收模型文本输出并尝试将其解析为更结构化表示形式的解析器。**

越来越多的模型支持函数（或工具）调用，这可以自动处理此问题。
建议使用函数/工具调用而不是输出解析。
参见 [LangChain 工具文档](/oss/javascript/langchain/tools)。

</Note>

负责接收模型的输出并将其转换为更适合下游任务的格式。
当你使用 LLM 生成结构化数据，或规范化聊天模型和 LLM 的输出时，这非常有用。

输出解析器必须实现两个主要方法：

- “获取格式指令”：一个返回字符串的方法，其中包含有关如何格式化语言模型输出的说明。
- “解析”：一个接收字符串（假定为语言模型的响应）并将其解析为某种结构的方法。

以及一个可选方法：

- “带提示解析”：一个接收字符串（假定为语言模型的响应）和提示（假定为生成该响应的提示）并将其解析为某种结构的方法。提供提示主要是为了在输出解析器希望以某种方式重试或修复输出时，需要从中获取信息。

输出解析器接受字符串或 `BaseMessage` 作为输入，并可以返回任意类型。

LangChain 有许多不同类型的输出解析器。以下是 LangChain 支持的输出解析器列表。下表包含各种信息：

**名称**：输出解析器的名称

**支持流式传输**：输出解析器是否支持流式传输。

**输入类型**：预期的输入类型。大多数输出解析器适用于字符串和消息，但有些（如 OpenAI Functions）需要具有特定参数的消息。

**输出类型**：解析器返回对象的输出类型。

**描述**：我们对这个输出解析器的评论以及何时使用它。

当前日期是 ${new Date().toISOString()}`;

// Noop statement to hide output
void 0;

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,
        // Tell Anthropic to cache this block
        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'
}

我们可以看到，在 Anthropic 返回的原始使用量字段中有一个名为 cache_creation_input_tokens 的新字段。如果我们再次使用相同的消息，可以看到长文本的输入令牌是从缓存中读取的：

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'
}

工具缓存

你也可以通过在工具定义中设置相同的 "cache_control": { "type": "ephemeral" } 来缓存工具。这目前要求你以 Anthropic 的原始工具格式绑定工具。以下是一个示例：

const SOME_LONG_DESCRIPTION = "...";

// Tool in Anthropic format
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"],
  },
  // Tell Anthropic to cache this tool
  cache_control: { type: "ephemeral" },
}]

const modelWithCachedTools = modelWithCaching.bindTools(anthropicTools);

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

有关提示缓存工作原理的更多信息，请参阅 Anthropic 的文档。

自定义客户端

Anthropic 模型可能托管在 Google Vertex 等云服务上，这些服务依赖于与主要 Anthropic 客户端具有相同接口的不同底层客户端。你可以通过提供一个返回已初始化 Anthropic 客户端实例的 createClient 方法来访问这些服务。以下是一个示例：

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!" }]);

引用

Anthropic 支持引用功能，允许 Claude 根据用户提供的源材料为其答案附加上下文。此源材料可以作为文档内容块（描述完整文档）或搜索结果（描述从检索系统返回的相关段落或片段）提供。当查询中包含 "citations": { "enabled": true } 时，Claude 可能会在其响应中生成对所提供材料的直接引用。

文档示例

在此示例中，我们传递一个纯文本文档。在后台，Claude 自动将输入文本分块为句子，这些句子在生成引用时使用。

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
      }
    ]
  }
]

搜索结果示例

在此示例中，我们将搜索结果作为消息内容的一部分传递。这允许 Claude 在其响应中引用来自你自己的检索系统的特定段落或片段。当你希望 Claude 引用来自特定知识集的信息，但希望直接带来你自己的预取/缓存内容，而不是让模型自动搜索或检索时，这种方法很有帮助。

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));

来自工具的搜索结果

你也可以使用工具提供搜索结果，模型可以在其响应中引用这些结果。这非常适合 RAG（或检索增强生成）工作流，Claude 可以决定何时何地检索信息。当以搜索结果形式返回此信息时，它使 Claude 能够从工具返回的材料中创建引用。以下是如何创建一个返回 Anthropic 引用 API 期望格式的搜索结果的工具：

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

// Create a tool that returns search results
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"),
    }),
  }
);

// Create model with search results beta header
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));

了解有关 RAG 在 LangChain 中如何工作的更多信息了解有关工具调用的更多信息

与文本分割器一起使用

Anthropic 还允许你使用自定义文档类型指定自己的分割。LangChain 文本分割器可用于为此目的生成有意义的分割。参见下面的示例，我们分割 LangChain.js README（一个 markdown 文档）并将其作为上下文传递给 Claude：

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 },
  };
}

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

const readme = await readmeResponse.text();

// Split into chunks
const splitter = new MarkdownTextSplitter({
  chunkOverlap: 0,
  chunkSize: 50,
});
const documents = await splitter.splitText(readme);

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

// Query 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": "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
      }
    ]
  }
]

上下文管理

Anthropic 支持上下文编辑功能，可以自动管理模型的上下文窗口（例如，通过清除工具结果）。有关详细信息和配置选项，请参阅 Anthropic 文档。

上下文管理自 @langchain/anthropic@0.3.29 起支持

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");

API 参考

有关所有 ChatAnthropic 功能和配置的详细文档，请访问 API 参考。

将这些文档通过 MCP 连接到 Claude、VSCode 等，以获取实时答案。

在 GitHub 上编辑此页面或提交问题。

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概述

集成详情

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设置

凭证

安装

实例化

调用

内容块

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提示缓存

工具缓存

自定义客户端

引用

文档示例

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来自工具的搜索结果

与文本分割器一起使用

上下文管理

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