ChatAnthropic 集成

import getpass
import os

if "ANTHROPIC_API_KEY" not in os.environ:
    os.environ["ANTHROPIC_API_KEY"] = getpass.getpass("输入您的 Anthropic API 密钥：")

os.environ["LANGSMITH_API_KEY"] = getpass.getpass("输入您的 LangSmith API 密钥：")
os.environ["LANGSMITH_TRACING"] = "true"

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-haiku-4-5-20251001",
    # temperature=,
    # max_tokens=,
    # timeout=,
    # max_retries=,
    # ...
)

from langchain_anthropic import ChatAnthropic
from typing_extensions import Annotated

model = ChatAnthropic(model="claude-haiku-4-5-20251001")


def get_weather(
    location: Annotated[str, ..., "位置，格式为城市和州。"]
) -> str:
    """获取某个位置的天气。"""
    return "今天是晴天。"


model_with_tools = model.bind_tools([get_weather])
response = model_with_tools.invoke("今天哪个城市更热：洛杉矶还是纽约？")
response.content

[{'text': "我将通过检查洛杉矶和纽约的当前天气来帮助您比较它们的温度。我将获取两个城市的天气。",
  'type': 'text'},
 {'id': 'toolu_01CkMaXrgmsNjTso7so94RJq',
  'input': {'location': 'Los Angeles, CA'},
  'name': 'get_weather',
  'type': 'tool_use'},
 {'id': 'toolu_01SKaTBk9wHjsBTw5mrPVSQf',
  'input': {'location': 'New York, NY'},
  'name': 'get_weather',
  'type': 'tool_use'}]

response.content_blocks

response.tool_calls

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

from pydantic import BaseModel, Field


class GetWeather(BaseModel):
    '''获取给定位置的当前天气'''

    location: str = Field(description="城市和州，例如旧金山，加利福尼亚州")


class GetPopulation(BaseModel):
    '''获取给定位置的当前人口'''

    location: str = Field(description="城市和州，例如旧金山，加利福尼亚州")


model_with_tools = model.bind_tools([GetWeather, GetPopulation])
ai_msg = model_with_tools.invoke("今天哪个城市更热，哪个更大：洛杉矶还是纽约？")
ai_msg.tool_calls

[
    {
        "name": "GetWeather",
        "args": {"location": "Los Angeles, CA"},
        "id": "toolu_01KzpPEAgzura7hpBqwHbWdo",
    },
    {
        "name": "GetWeather",
        "args": {"location": "New York, NY"},
        "id": "toolu_01JtgbVGVJbiSwtZk3Uycezx",
    },
    {
        "name": "GetPopulation",
        "args": {"location": "Los Angeles, CA"},
        "id": "toolu_01429aygngesudV9nTbCKGuw",
    },
    {
        "name": "GetPopulation",
        "args": {"location": "New York, NY"},
        "id": "toolu_01JPktyd44tVMeBcPPnFSEJG",
    },
]

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-sonnet-4-6")

def get_weather(location: str) -> str:
    """获取某个位置的天气。"""
    return "今天是晴天。"

model_with_tools = model.bind_tools([get_weather], strict=True)

from langchain_anthropic import ChatAnthropic
from langchain.tools import tool

@tool(
    extras={
        "input_examples": [
            {
                "query": "天气报告",
                "location": "旧金山",
                "format": "详细"
            },
            {
                "query": "温度",
                "location": "纽约",
                "format": "简要"
            }
        ]
    }
)
def search_weather_data(query: str, location: str, format: str = "brief") -> str:
    """使用特定查询和格式偏好搜索天气数据库。

    参数:
        query: 要检索的天气信息类型
        location: 要搜索的城市或地区
        format: 输出格式，'brief' 或 'detailed'
    """
    return f"{format.title()} {query} for {location}: 找到数据"

model = ChatAnthropic(model="claude-sonnet-4-6")
model_with_tools = model.bind_tools([search_weather_data])

response = model_with_tools.invoke(
    "获取西雅图的详细天气报告"
)

from langchain_anthropic import ChatAnthropic
from langchain.tools import tool

model = ChatAnthropic(model="claude-sonnet-4-6")

@tool(extras={"eager_input_streaming": True})
def write_document(title: str, content: str) -> str:
    """使用给定的标题和内容编写文档。"""
    return f"文档 '{title}' 编写成功"

model_with_tools = model.bind_tools([write_document])

# 以降低的延迟流式传输工具调用
for chunk in model_with_tools.stream(
    "编写一份关于流式 API 优势的详细技术文档"
):
    print(chunk.content)

import json

from langchain_anthropic import ChatAnthropic
from langchain.tools import tool

model = ChatAnthropic(model="claude-sonnet-4-6")

@tool(extras={"eager_input_streaming": True})
def write_document(title: str, content: str) -> str:
    """使用给定的标题和内容编写文档。"""
    return f"文档 '{title}' 编写成功"

model_with_tools = model.bind_tools([write_document])

accumulated_json = ""

for chunk in model_with_tools.stream("编写一份关于 AI 的文档"):
    for block in chunk.content:
        if isinstance(block, dict) and block.get("type") == "input_json_delta":
            accumulated_json += block.get("partial_json", "")
            try:
                # 尝试解析累积的 JSON
                parsed = json.loads(accumulated_json)
                print(f"完整参数: {parsed}")
            except json.JSONDecodeError:
                # JSON 仍不完整，继续累积
                pass

完整参数: {'title': '人工智能：概述', 'content': '# 人工智能：概述...

from langchain.agents import create_agent
from langchain.tools import tool
from langchain_anthropic import ChatAnthropic


@tool(extras={"allowed_callers": ["code_execution_20250825"]})
def get_weather(location: str) -> str:
    """获取某个位置的天气。"""
    return "今天是晴天。"

tools = [
    {"type": "code_execution_20250825", "name": "code_execution"},
    get_weather,
]

model = ChatAnthropic(
    model="claude-sonnet-4-6",
    reuse_last_container=True,
)

agent = create_agent(model, tools=tools)

input_query = {
    "role": "user",
    "content": "波士顿的天气怎么样？",
}

result = agent.invoke({"messages": [input_query]})

[
  {
    "type": "reasoning",
    "reasoning": "要找到 50.653 的立方根，我需要找到满足 $x^3 = 50.653$ 的 $x$ 值。\n\n我可以先尝试估算一下。\n$3^3 = 27$\n$4^3 = 64$\n\n所以 50.653 的立方根将在 3 到 4 之间，但更接近 4。\n\n让我尝试更精确地计算。我可以使用立方根函数：\n\n50.653 的立方根 = 50.653^(1/3)\n\n让我计算一下：\n50.653^(1/3) ≈ 3.6998\n\n让我验证一下：\n3.6998^3 ≈ 50.6533\n\n这非常接近 50.653，所以我确信 50.653 的立方根大约是 3.6998。\n\n实际上，让我更精确地计算一下：\n50.653^(1/3) ≈ 3.69981\n\n让我再验证一次：\n3.69981^3 ≈ 50.652998\n\n这极其接近 50.653，所以我会说 50.653 的立方根大约是 3.69981。",
    "extras": {"signature": "ErUBCkYIBxgCIkB0UjV..."}
  },
  {
    "type": "text",
    "text": "50.653 的立方根大约是 3.6998。\n\n验证：3.6998³ = 50.6530，这非常接近我们的原始数字。",
  }
]

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-opus-4-5-20251101",
    effort="medium",  # 选项："max"、"xhigh"、"high"、"medium"、"low"
)

response = model.invoke("分析微服务与单体架构之间的权衡")

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-opus-4-7",
    output_config={
        "effort": "high",
        "task_budget": {"type": "tokens", "total": 128_000},
    },
)

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-haiku-4-5-20251001")

messages = [
    {
        "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": "这是一份可靠的文档。",
                "citations": {"enabled": True},
            },
            {"type": "text", "text": "草和天空是什么颜色的？"},
        ],
    }
]
response = model.invoke(messages)
response.content

[{'text': '根据文档，', 'type': 'text'},
 {'text': '草是绿色的',
  'type': 'text',
  '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}]},
 {'text': '，而', 'type': 'text'},
 {'text': '天空是蓝色的',
  'type': 'text',
  '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}]},
 {'text': '。', 'type': 'text'}]

def retrieval_tool(query: str) -> list[dict]:
    """访问我的知识库。"""

    # 运行搜索（例如，使用 LangChain 向量存储）
    results = vector_store.similarity_search(query=query, k=2)

    # 将结果打包成 search_result 块
    return [
        {
            "type": "search_result",
            # 根据需要自定义字段，使用文档元数据或其他方式
            "title": "我的文档标题",
            "source": "来源描述或出处",
            "citations": {"enabled": True},
            "content": [{"type": "text", "text": doc.page_content}],
        }
        for doc in results
    ]

pip install langchain-text-splitters

from langchain_anthropic import ChatAnthropic
import requests

model = ChatAnthropic(model="claude-sonnet-4-5")

# 拉取 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": "你是一位技术专家。",
            },
            {
                "type": "text",
                "text": f"{readme}"
            },
        ],
    },
    {
        "role": "user",
        "content": "根据其 README，LangChain 是什么？",
    },
]

response = model.invoke(
    messages,
    cache_control={"type": "ephemeral"},
)

usage = response.usage_metadata["input_token_details"]
print(f"使用情况:\n{usage}")

response = model.invoke(
    messages,
    cache_control={"type": "ephemeral", "ttl": "1h"},
)

第一次调用:
{'cache_read': 0, 'cache_creation': 0, 'ephemeral_5m_input_tokens': 1569, 'ephemeral_1h_input_tokens': 0}

第二次:
{'cache_read': 1569, 'cache_creation': 0, 'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}

第一次调用:
{'cache_read': 0, 'cache_creation': 1809}

第二次:
{'cache_read': 1809, 'cache_creation': 0}

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-sonnet-4-6",
    betas=["context-management-2025-06-27"],
    context_management={"edits": [{"type": "clear_tool_uses_20250919"}]},
)
model_with_tools = model.bind_tools([{"type": "web_search_20250305", "name": "web_search"}])
response = model_with_tools.invoke("搜索 AI 的最新进展")

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-opus-4-6",
    betas=["compact-2026-01-12"],
    max_tokens=4096,
    context_management={
        "edits": [
            {
                "type": "compact_20260112",
                "trigger": {"type": "input_tokens", "value": 50000},
            }
        ]
    },
)