ChatAnthropic

此笔记本提供了开始使用 Anthropic 聊天模型的快速概述。有关所有 ChatAnthropic 功能和配置的详细文档，请前往 API 参考。

Anthropic 有几种聊天模式。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

集成详细信息

类	包	本地化	序列 化	JS 支持	软件包下载	最新包装
ChatAnthropic	langchain-anthropic	❌	beta	✅

模型特点

工具调用	结构化输出	JSON 模式	图像输入	音频输入	视频输入	令牌级流式处理	本机异步	Token 使用情况	日志
✅	✅	❌	✅	❌	❌	✅	✅	✅	❌

设置

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

凭据

前往 https://console.anthropic.com/ 注册 Anthropic 并生成 API 密钥。完成此作后，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：

%pip install -qU langchain-anthropic

本指南要求langchain-anthropic>=0.3.10

实例

现在我们可以实例化我们的 Model 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.

链接

我们可以用 prompt 模板链接我们的模型，如下所示：

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 消息的内容可以是单个字符串或内容块列表。例如，当 Anthropic 模型调用工具时，工具调用是消息内容的一部分（并在标准化的AIMessage.tool_calls):

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'}]

扩展思维

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

要使用它，请指定thinking参数ChatAnthropic.它也可以在调用期间作为 kwarg 传入。

您需要指定代币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"
  }
]

提示缓存

Anthropic 支持缓存提示的元素，包括消息、工具定义、工具结果、图像和文档。这允许您重复使用大型文档、说明、小样本文档和其他数据，以减少延迟和成本。

要在提示的元素上启用缓存，请使用cache_control钥匙。请参阅以下示例：

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}

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 | 工具

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

提示缓存可用于多轮次对话，以维护早期消息的上下文，而无需进行冗余处理。

我们可以通过将最后一条消息标记为cache_control.Claude 将自动使用以前缓存的最长前缀来接收后续邮件。

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，它会自动将每条用户消息中的最后一个内容块标记为cache_control.请参阅下文：

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 （节省内存） | 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 “raw output” 将准确显示发送到聊天模型的消息，包括cache_control钥匙。

令牌高效的工具使用

Anthropic 支持 （beta） 令牌高效的工具使用功能。要使用它，请在实例化模型时指定相关的 beta-headers。

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 | 工具

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

Total tokens: 408

引文

Anthropic 支持引文功能，该功能允许 Claude 根据用户提供的源文档将上下文附加到其答案。当文档内容块带有"citations": {"enabled": True}包含在查询中，则 Claude 可能会在其响应中生成引用。

简单示例

在此示例中，我们传递一个纯文本文档。在后台，Claude 会自动将输入文本分块为句子，以便在生成引文时使用。

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'}]

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。请参阅下面的示例，其中我们拆分了 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.com/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.com/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，这些工具可以按常规方式绑定到模型。Claude 将按照工具的内部架构生成工具调用：

Web 搜索

Claude 可以使用网络搜索工具进行搜索，并通过引用来回答。

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

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，请参阅此处的文档。

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

有关所有 ChatAnthropic 功能和配置的详细文档，请访问 API 参考：https://python.langchain.com/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html

相关

• 聊天模型概念指南
• 聊天模型操作指南