ChatAnthropic

本笔记本提供了 Anthropic 聊天模型快速入门的概览。有关 ChatAnthropic 所有功能和配置的详细文档，请查阅 API 参考。

Anthropic 有多种聊天模型。您可以在 Anthropic 文档中找到有关其最新模型及其成本、上下文窗口和支持的输入类型的信息。

AWS Bedrock 和 Google VertexAI

请注意，某些 Anthropic 模型也可以通过 AWS Bedrock 和 Google VertexAI 访问。请参阅 ChatBedrock 和 ChatVertexAI 集成，以通过这些服务使用 Anthropic 模型。

概述

集成详情

类别	包	本地	可序列化	JS 支持	包下载量	最新包版本
ChatAnthropic	langchain-anthropic	❌	测试版	✅

模型特性

工具调用	结构化输出	JSON 模式	图片输入	音频输入	视频输入	逐令牌流式传输	原生异步	令牌使用量	对数概率
✅	✅	❌	✅	❌	❌	✅	✅	✅	❌

设置

要访问 Anthropic 模型，您需要创建一个 Anthropic 账户，获取一个 API 密钥，并安装 langchain-anthropic 集成包。

凭证

请前往 https://console.anthropic.com/ 注册 Anthropic 并生成 API 密钥。完成后，请设置 ANTHROPIC_API_KEY 环境变量。

import getpass
import os

if "ANTHROPIC_API_KEY" not in os.environ:
    os.environ["ANTHROPIC_API_KEY"] = getpass.getpass("Enter your Anthropic API key: ")

要启用模型调用的自动跟踪，请设置您的 LangSmith API 密钥

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

安装

LangChain Anthropic 集成位于 langchain-anthropic 包中。

%pip install -qU langchain-anthropic

本指南需要 langchain-anthropic>=0.3.13 版本。

实例化

现在我们可以实例化模型对象并生成聊天补全

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(
    model="claude-3-5-sonnet-20240620",
    temperature=0,
    max_tokens=1024,
    timeout=None,
    max_retries=2,
    # other params...
)

API 参考：ChatAnthropic

调用

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg

AIMessage(content="J'adore la programmation.", response_metadata={'id': 'msg_018Nnu76krRPq8HvgKLW4F8T', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 29, 'output_tokens': 11}}, id='run-57e9295f-db8a-48dc-9619-babd2bedd891-0', usage_metadata={'input_tokens': 29, 'output_tokens': 11, 'total_tokens': 40})

print(ai_msg.content)

J'adore la programmation.

链式调用

我们可以像这样将模型与提示模板链式连接起来

from langchain_core.prompts import ChatPromptTemplate

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

chain = prompt | llm
chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love programming.",
    }
)

API 参考：ChatPromptTemplate

AIMessage(content="Here's the German translation:\n\nIch liebe Programmieren.", response_metadata={'id': 'msg_01GhkRtQZUkA5Ge9hqmD8HGY', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 23, 'output_tokens': 18}}, id='run-da5906b4-b200-4e08-b81a-64d4453643b6-0', usage_metadata={'input_tokens': 23, 'output_tokens': 18, 'total_tokens': 41})

内容块

来自单个 Anthropic AI 消息的内容可以是一个字符串，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（也会在标准化的 AIMessage.tool_calls 中公开）。

from pydantic import BaseModel, Field


class GetWeather(BaseModel):
    """Get the current weather in a given location"""

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")


llm_with_tools = llm.bind_tools([GetWeather])
ai_msg = llm_with_tools.invoke("Which city is hotter today: LA or NY?")
ai_msg.content

[{'text': "To answer this question, we'll need to check the current weather in both Los Angeles (LA) and New York (NY). I'll use the GetWeather function to retrieve this information for both cities.",
  'type': 'text'},
 {'id': 'toolu_01Ddzj5PkuZkrjF4tafzu54A',
  'input': {'location': 'Los Angeles, CA'},
  'name': 'GetWeather',
  'type': 'tool_use'},
 {'id': 'toolu_012kz4qHZQqD4qg8sFPeKqpP',
  'input': {'location': 'New York, NY'},
  'name': 'GetWeather',
  'type': 'tool_use'}]

ai_msg.tool_calls

[{'name': 'GetWeather',
  'args': {'location': 'Los Angeles, CA'},
  'id': 'toolu_01Ddzj5PkuZkrjF4tafzu54A'},
 {'name': 'GetWeather',
  'args': {'location': 'New York, NY'},
  'id': 'toolu_012kz4qHZQqD4qg8sFPeKqpP'}]

多模态

Claude 支持图像和 PDF 输入作为内容块，既支持 Anthropic 的原生格式（请参阅 vision 和 PDF 支持的文档），也支持 LangChain 的标准格式。

文件 API

Claude 还通过其托管的 文件 API 支持与文件的交互。请参阅下面的示例。

文件 API 也可用于将文件上传到容器，以供 Claude 的内置代码执行工具使用。有关详细信息，请参阅下面的代码执行部分。

图像

# Upload image

import anthropic

client = anthropic.Anthropic()
file = client.beta.files.upload(
    # Supports image/jpeg, image/png, image/gif, image/webp
    file=("image.png", open("/path/to/image.png", "rb"), "image/png"),
)
image_file_id = file.id


# Run inference
from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(
    model="claude-sonnet-4-20250514",
    betas=["files-api-2025-04-14"],
)

input_message = {
    "role": "user",
    "content": [
        {
            "type": "text",
            "text": "Describe this image.",
        },
        {
            "type": "image",
            "source": {
                "type": "file",
                "file_id": image_file_id,
            },
        },
    ],
}
llm.invoke([input_message])

API 参考：ChatAnthropic

PDF 文件

# Upload document

import anthropic

client = anthropic.Anthropic()
file = client.beta.files.upload(
    file=("document.pdf", open("/path/to/document.pdf", "rb"), "application/pdf"),
)
pdf_file_id = file.id


# Run inference
from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(
    model="claude-sonnet-4-20250514",
    betas=["files-api-2025-04-14"],
)

input_message = {
    "role": "user",
    "content": [
        {"type": "text", "text": "Describe this document."},
        {"type": "document", "source": {"type": "file", "file_id": pdf_file_id}}
    ],
}
llm.invoke([input_message])

API 参考：ChatAnthropic

扩展思维

Claude 3.7 Sonnet 支持扩展思维功能，该功能将输出导致其最终答案的逐步推理过程。

要使用此功能，请在初始化 ChatAnthropic 时指定 thinking 参数。它也可以在调用期间作为 kwarg 传入。

您需要指定一个 token 预算才能使用此功能。请参阅下面的使用示例。

import json

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(
    model="claude-3-7-sonnet-latest",
    max_tokens=5000,
    thinking={"type": "enabled", "budget_tokens": 2000},
)

response = llm.invoke("What is the cube root of 50.653?")
print(json.dumps(response.content, indent=2))

API 参考：ChatAnthropic

[
  {
    "signature": "ErUBCkYIARgCIkCx7bIPj35jGPHpoVOB2y5hvPF8MN4lVK75CYGftmVNlI4axz2+bBbSexofWsN1O/prwNv8yPXnIXQmwT6zrJsKEgwJzvks0yVRZtaGBScaDOm9xcpOxbuhku1zViIw9WDgil/KZL8DsqWrhVpC6TzM0RQNCcsHcmgmyxbgG9g8PR0eJGLxCcGoEw8zMQu1Kh1hQ1/03hZ2JCOgigpByR9aNPTwwpl64fQUe6WwIw==",
    "thinking": "To find the cube root of 50.653, I need to find the value of $x$ such that $x^3 = 50.653$.\n\nI can try to estimate this first. \n$3^3 = 27$\n$4^3 = 64$\n\nSo the cube root of 50.653 will be somewhere between 3 and 4, but closer to 4.\n\nLet me try to compute this more precisely. I can use the cube root function:\n\ncube root of 50.653 = 50.653^(1/3)\n\nLet me calculate this:\n50.653^(1/3) \u2248 3.6998\n\nLet me verify:\n3.6998^3 \u2248 50.6533\n\nThat's very close to 50.653, so I'm confident that the cube root of 50.653 is approximately 3.6998.\n\nActually, let me compute this more precisely:\n50.653^(1/3) \u2248 3.69981\n\nLet me verify once more:\n3.69981^3 \u2248 50.652998\n\nThat's extremely close to 50.653, so I'll say that the cube root of 50.653 is approximately 3.69981.",
    "type": "thinking"
  },
  {
    "text": "The cube root of 50.653 is approximately 3.6998.\n\nTo verify: 3.6998\u00b3 = 50.6530, which is very close to our original number.",
    "type": "text"
  }
]

Prompt 缓存

Anthropic 支持缓存您的 prompt 元素，包括消息、工具定义、工具结果、图像和文档。这使您可以重用大型文档、指令、few-shot 文档和其他数据，以减少延迟和成本。

要在 prompt 元素上启用缓存，请使用 cache_control 键标记其关联的内容块。请参阅下面的示例。

消息

import requests
from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-7-sonnet-20250219")

# Pull LangChain readme
get_response = requests.get(
    "https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md"
)
readme = get_response.text

messages = [
    {
        "role": "system",
        "content": [
            {
                "type": "text",
                "text": "You are a technology expert.",
            },
            {
                "type": "text",
                "text": f"{readme}",
                "cache_control": {"type": "ephemeral"},
            },
        ],
    },
    {
        "role": "user",
        "content": "What's LangChain, according to its README?",
    },
]

response_1 = llm.invoke(messages)
response_2 = llm.invoke(messages)

usage_1 = response_1.usage_metadata["input_token_details"]
usage_2 = response_2.usage_metadata["input_token_details"]

print(f"First invocation:\n{usage_1}")
print(f"\nSecond:\n{usage_2}")

API 参考：ChatAnthropic

First invocation:
{'cache_read': 0, 'cache_creation': 1458}

Second:
{'cache_read': 1458, 'cache_creation': 0}

扩展缓存

缓存默认生命周期为 5 分钟。如果这太短，您可以通过启用 "extended-cache-ttl-2025-04-11" beta 头部来应用一小时缓存。

llm = ChatAnthropic(
    model="claude-3-7-sonnet-20250219",
    betas=["extended-cache-ttl-2025-04-11"],
)

并指定 "cache_control": {"type": "ephemeral", "ttl": "1h"}。

缓存 token 计数的详细信息将包含在响应的 usage_metadata 的 InputTokenDetails 中。

response = llm.invoke(messages)
response.usage_metadata

{
    "input_tokens": 1500,
    "output_tokens": 200,
    "total_tokens": 1700,
    "input_token_details": {
        "cache_read": 0,
        "cache_creation": 1000,
        "ephemeral_1h_input_tokens": 750,
        "ephemeral_5m_input_tokens": 250,
    }
}

工具

from langchain_anthropic import convert_to_anthropic_tool
from langchain_core.tools import tool

# For demonstration purposes, we artificially expand the
# tool description.
description = (
    f"Get the weather at a location. By the way, check out this readme: {readme}"
)


@tool(description=description)
def get_weather(location: str) -> str:
    return "It's sunny."


# Enable caching on the tool
weather_tool = convert_to_anthropic_tool(get_weather)
weather_tool["cache_control"] = {"type": "ephemeral"}

llm = ChatAnthropic(model="claude-3-7-sonnet-20250219")
llm_with_tools = llm.bind_tools([weather_tool])
query = "What's the weather in San Francisco?"

response_1 = llm_with_tools.invoke(query)
response_2 = llm_with_tools.invoke(query)

usage_1 = response_1.usage_metadata["input_token_details"]
usage_2 = response_2.usage_metadata["input_token_details"]

print(f"First invocation:\n{usage_1}")
print(f"\nSecond:\n{usage_2}")

API 参考：convert_to_anthropic_tool | tool

First invocation:
{'cache_read': 0, 'cache_creation': 1809}

Second:
{'cache_read': 1809, 'cache_creation': 0}

对话应用程序中的增量缓存

Prompt 缓存可用于 多轮对话中，以维护早期消息的上下文，而无需冗余处理。

我们可以通过使用 cache_control 标记最终消息来启用增量缓存。Claude 将自动使用最长的先前缓存前缀进行后续消息。

下面，我们实现了一个包含此功能的简单聊天机器人。我们遵循 LangChain 聊天机器人教程，但添加了一个自定义 reducer，它会自动使用 cache_control 标记每个用户消息中的最后一个内容块。请参阅下方。

import requests
from langchain_anthropic import ChatAnthropic
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import START, StateGraph, add_messages
from typing_extensions import Annotated, TypedDict

llm = ChatAnthropic(model="claude-3-7-sonnet-20250219")

# Pull LangChain readme
get_response = requests.get(
    "https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md"
)
readme = get_response.text


def messages_reducer(left: list, right: list) -> list:
    # Update last user message
    for i in range(len(right) - 1, -1, -1):
        if right[i].type == "human":
            right[i].content[-1]["cache_control"] = {"type": "ephemeral"}
            break

    return add_messages(left, right)


class State(TypedDict):
    messages: Annotated[list, messages_reducer]


workflow = StateGraph(state_schema=State)


# Define the function that calls the model
def call_model(state: State):
    response = llm.invoke(state["messages"])
    return {"messages": [response]}


# Define the (single) node in the graph
workflow.add_edge(START, "model")
workflow.add_node("model", call_model)

# Add memory
memory = MemorySaver()
app = workflow.compile(checkpointer=memory)

API 参考：ChatAnthropic | MemorySaver | StateGraph | add_messages

from langchain_core.messages import HumanMessage

config = {"configurable": {"thread_id": "abc123"}}

query = "Hi! I'm Bob."

input_message = HumanMessage([{"type": "text", "text": query}])
output = app.invoke({"messages": [input_message]}, config)
output["messages"][-1].pretty_print()
print(f"\n{output['messages'][-1].usage_metadata['input_token_details']}")

API 参考：HumanMessage

================================== Ai Message ==================================

Hello, Bob! It's nice to meet you. How are you doing today? Is there something I can help you with?

{'cache_read': 0, 'cache_creation': 0}

query = f"Check out this readme: {readme}"

input_message = HumanMessage([{"type": "text", "text": query}])
output = app.invoke({"messages": [input_message]}, config)
output["messages"][-1].pretty_print()
print(f"\n{output['messages'][-1].usage_metadata['input_token_details']}")

================================== Ai Message ==================================

I can see you've shared the README from the LangChain GitHub repository. This is the documentation for LangChain, which is a popular framework for building applications powered by Large Language Models (LLMs). Here's a summary of what the README contains:

LangChain is:
- A framework for developing LLM-powered applications
- Helps chain together components and integrations to simplify AI application development
- Provides a standard interface for models, embeddings, vector stores, etc.

Key features/benefits:
- Real-time data augmentation (connect LLMs to diverse data sources)
- Model interoperability (swap models easily as needed)
- Large ecosystem of integrations

The LangChain ecosystem includes:
- LangSmith - For evaluations and observability
- LangGraph - For building complex agents with customizable architecture
- LangGraph Platform - For deployment and scaling of agents

The README also mentions installation instructions (`pip install -U langchain`) and links to various resources including tutorials, how-to guides, conceptual guides, and API references.

Is there anything specific about LangChain you'd like to know more about, Bob?

{'cache_read': 0, 'cache_creation': 1498}

query = "What was my name again?"

input_message = HumanMessage([{"type": "text", "text": query}])
output = app.invoke({"messages": [input_message]}, config)
output["messages"][-1].pretty_print()
print(f"\n{output['messages'][-1].usage_metadata['input_token_details']}")

================================== Ai Message ==================================

Your name is Bob. You introduced yourself at the beginning of our conversation.

{'cache_read': 1498, 'cache_creation': 269}

在 LangSmith 跟踪中，切换“原始输出”将准确显示发送到聊天模型的消息，包括 cache_control 键。

Token 高效工具使用

Anthropic 支持（测试版）Token 高效工具使用功能。要使用它，请在实例化模型时指定相关的 beta 头部。

from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool

llm = ChatAnthropic(
    model="claude-3-7-sonnet-20250219",
    temperature=0,
    model_kwargs={
        "extra_headers": {"anthropic-beta": "token-efficient-tools-2025-02-19"}
    },
)


@tool
def get_weather(location: str) -> str:
    """Get the weather at a location."""
    return "It's sunny."


llm_with_tools = llm.bind_tools([get_weather])
response = llm_with_tools.invoke("What's the weather in San Francisco?")
print(response.tool_calls)
print(f"\nTotal tokens: {response.usage_metadata['total_tokens']}")

API 参考：ChatAnthropic | tool

[{'name': 'get_weather', 'args': {'location': 'San Francisco'}, 'id': 'toolu_01EoeE1qYaePcmNbUvMsWtmA', 'type': 'tool_call'}]

Total tokens: 408

引用

Anthropic 支持引用功能，允许 Claude 根据用户提供的源文档为其答案附加上下文。当查询中包含带有 "citations": {"enabled": True} 的文档或 search result 内容块时，Claude 可能会在其响应中生成引用。

简单示例

在此示例中，我们传递了一个纯文本文档。在后台，Claude 会自动将输入文本分块为句子，这些句子用于生成引用。

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-haiku-latest")

messages = [
    {
        "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?"},
        ],
    }
]
response = llm.invoke(messages)
response.content

API 参考：ChatAnthropic

[{'text': 'Based on the document, ', 'type': 'text'},
 {'text': 'the grass is green',
  'type': 'text',
  '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}]},
 {'text': ', and ', 'type': 'text'},
 {'text': 'the sky is blue',
  'type': 'text',
  '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}]},
 {'text': '.', 'type': 'text'}]

在工具结果中（代理式 RAG）

需要 langchain-anthropic>=0.3.17 版本。

Claude 支持 search_result 内容块，该内容块表示从知识库或其他自定义来源查询中可引用的结果。这些内容块既可以作为顶层（如上述示例），也可以在工具结果中传递给 Claude。这使得 Claude 可以使用工具调用的结果来引用其响应中的元素。

为了在响应工具调用时传递搜索结果，请定义一个工具，该工具以 Anthropic 的原生格式返回 search_result 内容块列表。例如：

def retrieval_tool(query: str) -> list[dict]:
    """Access my knowledge base."""

    # Run a search (e.g., with a LangChain vector store)
    results = vector_store.similarity_search(query=query, k=2)

    # Package results into search_result blocks
    return [
        {
            "type": "search_result",
            # Customize fields as desired, using document metadata or otherwise
            "title": "My Document Title",
            "source": "Source description or provenance",
            "citations": {"enabled": True},
            "content": [{"type": "text", "text": doc.page_content}],
        }
        for doc in results
    ]

在实例化 ChatAnthropic 时，我们还需要指定 search-results-2025-06-09 beta 版。您可以在下面看到一个端到端示例。

使用 LangGraph 的端到端示例

在这里，我们演示了一个端到端示例，其中我们使用示例文档填充 LangChain 向量存储，并为 Claude 配备一个查询这些文档的工具。这里的工具接受一个搜索查询和一个类别字符串文字，但可以使用任何有效的工具签名。

from typing import Literal

from langchain.chat_models import init_chat_model
from langchain.embeddings import init_embeddings
from langchain_core.documents import Document
from langchain_core.vectorstores import InMemoryVectorStore
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.prebuilt import create_react_agent


# Set up vector store
embeddings = init_embeddings("openai:text-embedding-3-small")
vector_store = InMemoryVectorStore(embeddings)

document_1 = Document(
    id="1",
    page_content=(
        "To request vacation days, submit a leave request form through the "
        "HR portal. Approval will be sent by email."
    ),
    metadata={
        "category": "HR Policy",
        "doc_title": "Leave Policy",
        "provenance": "Leave Policy - page 1",
    },
)
document_2 = Document(
    id="2",
    page_content="Managers will review vacation requests within 3 business days.",
    metadata={
        "category": "HR Policy",
        "doc_title": "Leave Policy",
        "provenance": "Leave Policy - page 2",
    },
)
document_3 = Document(
    id="3",
    page_content=(
        "Employees with over 6 months tenure are eligible for 20 paid vacation days "
        "per year."
    ),
    metadata={
        "category": "Benefits Policy",
        "doc_title": "Benefits Guide 2025",
        "provenance": "Benefits Policy - page 1",
    },
)

documents = [document_1, document_2, document_3]
vector_store.add_documents(documents=documents)


# Define tool
async def retrieval_tool(
    query: str, category: Literal["HR Policy", "Benefits Policy"]
) -> list[dict]:
    """Access my knowledge base."""

    def _filter_function(doc: Document) -> bool:
        return doc.metadata.get("category") == category

    results = vector_store.similarity_search(
        query=query, k=2, filter=_filter_function
    )

    return [
        {
            "type": "search_result",
            "title": doc.metadata["doc_title"],
            "source": doc.metadata["provenance"],
            "citations": {"enabled": True},
            "content": [{"type": "text", "text": doc.page_content}],
        }
        for doc in results
    ]



# Create agent
llm = init_chat_model(
    "anthropic:claude-3-5-haiku-latest",
    betas=["search-results-2025-06-09"],
)

checkpointer = InMemorySaver()
agent = create_react_agent(llm, [retrieval_tool], checkpointer=checkpointer)


# Invoke on a query
config = {"configurable": {"thread_id": "session_1"}}

input_message = {
    "role": "user",
    "content": "How do I request vacation days?",
}
async for step in agent.astream(
    {"messages": [input_message]},
    config,
    stream_mode="values",
):
    step["messages"][-1].pretty_print()

API 参考：init_chat_model | init_embeddings | Document | InMemoryVectorStore | create_react_agent

与文本拆分器一起使用

Anthropic 还允许您使用自定义文档类型指定自己的拆分。LangChain 文本拆分器可用于为此目的生成有意义的拆分。请参阅以下示例，我们拆分了 LangChain README（一个 markdown 文档）并将其作为上下文传递给 Claude。

import requests
from langchain_anthropic import ChatAnthropic
from langchain_text_splitters import MarkdownTextSplitter


def format_to_anthropic_documents(documents: list[str]):
    return {
        "type": "document",
        "source": {
            "type": "content",
            "content": [{"type": "text", "text": document} for document in documents],
        },
        "citations": {"enabled": True},
    }


# Pull readme
get_response = requests.get(
    "https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md"
)
readme = get_response.text

# Split into chunks
splitter = MarkdownTextSplitter(
    chunk_overlap=0,
    chunk_size=50,
)
documents = splitter.split_text(readme)

# Construct message
message = {
    "role": "user",
    "content": [
        format_to_anthropic_documents(documents),
        {"type": "text", "text": "Give me a link to LangChain's tutorials."},
    ],
}

# Query LLM
llm = ChatAnthropic(model="claude-3-5-haiku-latest")
response = llm.invoke([message])

response.content

API 参考：ChatAnthropic | MarkdownTextSplitter

[{'text': "You can find LangChain's tutorials at https://python.langchain.ac.cn/docs/tutorials/\n\nThe tutorials section is recommended for those looking to build something specific or who prefer a hands-on learning approach. It's considered the best place to get started with LangChain.",
  'type': 'text',
  'citations': [{'type': 'content_block_location',
    'cited_text': "[Tutorials](https://python.langchain.ac.cn/docs/tutorials/):If you're looking to build something specific orare more of a hands-on learner, check out ourtutorials. This is the best place to get started.",
    'document_index': 0,
    'document_title': None,
    'start_block_index': 243,
    'end_block_index': 248}]}]

内置工具

Anthropic 支持多种内置工具，它们可以以常规方式绑定到模型。Claude 将生成符合其内部工具模式的工具调用。

网络搜索

Claude 可以使用网络搜索工具来运行搜索并使用引用来支持其响应。

自 langchain-anthropic>=0.3.13 版本起支持网络搜索工具。

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-sonnet-latest")

tool = {"type": "web_search_20250305", "name": "web_search", "max_uses": 3}
llm_with_tools = llm.bind_tools([tool])

response = llm_with_tools.invoke("How do I update a web app to TypeScript 5.5?")

API 参考：ChatAnthropic

代码执行

Claude 可以使用代码执行工具在沙盒环境中执行 Python 代码。

自 langchain-anthropic>=0.3.14 版本起支持代码执行。

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(
    model="claude-sonnet-4-20250514",
    betas=["code-execution-2025-05-22"],
)

tool = {"type": "code_execution_20250522", "name": "code_execution"}
llm_with_tools = llm.bind_tools([tool])

response = llm_with_tools.invoke(
    "Calculate the mean and standard deviation of [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
)

API 参考：ChatAnthropic

与文件 API 一起使用

使用文件 API，Claude 可以编写代码访问文件，用于数据分析及其他目的。请参阅下面的示例。

# Upload file

import anthropic

client = anthropic.Anthropic()
file = client.beta.files.upload(
    file=open("/path/to/sample_data.csv", "rb")
)
file_id = file.id


# Run inference
from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(
    model="claude-sonnet-4-20250514",
    betas=["code-execution-2025-05-22"],
)

tool = {"type": "code_execution_20250522", "name": "code_execution"}
llm_with_tools = llm.bind_tools([tool])

input_message = {
    "role": "user",
    "content": [
        {
            "type": "text",
            "text": "Please plot these data and tell me what you see.",
        },
        {
            "type": "container_upload",
            "file_id": file_id,
        },
    ]
}
llm_with_tools.invoke([input_message])

API 参考：ChatAnthropic

请注意，Claude 可能会在代码执行过程中生成文件。您可以使用文件 API 访问这些文件。

# Take all file outputs for demonstration purposes
file_ids = []
for block in response.content:
    if block["type"] == "code_execution_tool_result":
        file_ids.extend(
            content["file_id"]
            for content in block.get("content", {}).get("content", [])
            if "file_id" in content
        )

for i, file_id in enumerate(file_ids):
    file_content = client.beta.files.download(file_id)
    file_content.write_to_file(f"/path/to/file_{i}.png")

远程 MCP

Claude 可以使用 MCP 连接器工具对远程 MCP 服务器进行模型生成的调用。

自 langchain-anthropic>=0.3.14 版本起支持远程 MCP。

from langchain_anthropic import ChatAnthropic

mcp_servers = [
    {
        "type": "url",
        "url": "https://mcp.deepwiki.com/mcp",
        "name": "deepwiki",
        "tool_configuration": {  # optional configuration
            "enabled": True,
            "allowed_tools": ["ask_question"],
        },
        "authorization_token": "PLACEHOLDER",  # optional authorization
    }
]

llm = ChatAnthropic(
    model="claude-sonnet-4-20250514",
    betas=["mcp-client-2025-04-04"],
    mcp_servers=mcp_servers,
)

response = llm.invoke(
    "What transport protocols does the 2025-03-26 version of the MCP "
    "spec (modelcontextprotocol/modelcontextprotocol) support?"
)

API 参考：ChatAnthropic

文本编辑器

文本编辑器工具可用于查看和修改文本文件。有关详细信息，请参阅此处文档。

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-7-sonnet-20250219")

tool = {"type": "text_editor_20250124", "name": "str_replace_editor"}
llm_with_tools = llm.bind_tools([tool])

response = llm_with_tools.invoke(
    "There's a syntax error in my primes.py file. Can you help me fix it?"
)
print(response.text())
response.tool_calls

API 参考：ChatAnthropic

I'd be happy to help you fix the syntax error in your primes.py file. First, let's look at the current content of the file to identify the error.

[{'name': 'str_replace_editor',
  'args': {'command': 'view', 'path': '/repo/primes.py'},
  'id': 'toolu_01VdNgt1YV7kGfj9LFLm6HyQ',
  'type': 'tool_call'}]

API 参考

有关 ChatAnthropic 所有功能和配置的详细文档，请查阅 API 参考：https://python.langchain.ac.cn/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html

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