Tavily 搜索

Tavily 的搜索 API 是一个专为 AI 代理（LLM）构建的搜索引擎，可以快速提供实时、准确和真实的结果。

概述

集成详情

类	包	可序列化	JS 支持	最新包
TavilySearchResults	langchain-community	❌	✅

工具功能

返回工件	原生异步	返回数据	定价
✅	✅	标题、URL、内容、答案	每月 1,000 次免费搜索

设置

集成位于 langchain-community 包中。我们还需要安装 tavily-python 包。

%pip install -qU "langchain-community>=0.2.11" tavily-python

凭据

我们还需要设置 Tavily API 密钥。您可以通过访问此站点并创建帐户来获取 API 密钥。

import getpass
import os

if not os.environ.get("TAVILY_API_KEY"):
    os.environ["TAVILY_API_KEY"] = getpass.getpass("Tavily API key:\n")

设置LangSmith 以获得一流的可观察性也很有用（但不是必需的）

# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()

实例化

在这里，我们展示了如何使用以下方法实例化 Tavily 搜索工具的实例：

from langchain_community.tools import TavilySearchResults

tool = TavilySearchResults(
    max_results=5,
    search_depth="advanced",
    include_answer=True,
    include_raw_content=True,
    include_images=True,
    # include_domains=[...],
    # exclude_domains=[...],
    # name="...",            # overwrite default tool name
    # description="...",     # overwrite default tool description
    # args_schema=...,       # overwrite default args_schema: BaseModel
)

API 参考：TavilySearchResults

调用

仅使用参数直接调用

TavilySearchResults 工具采用单个“查询”参数，该参数应为自然语言查询

tool.invoke({"query": "What happened at the last wimbledon"})

[{'url': 'https://www.theguardian.com/sport/live/2023/jul/16/wimbledon-mens-singles-final-2023-carlos-alcaraz-v-novak-djokovic-live?page=with:block-64b3ff568f08df28470056bf',
  'content': 'Carlos Alcaraz recovered from a set down to topple Djokovic 1-6, 7-6(6), 6-1, 3-6, 6-4 and win his first Wimbledon title in a battle for the ages'},
 {'url': 'https://www.nytimes.com/athletic/live-blogs/wimbledon-2024-live-updates-alcaraz-djokovic-mens-final-result/kJJdTKhOgkZo/',
  'content': "It was Djokovic's first straight-sets defeat at Wimbledon since the 2013 final, when he lost to Andy Murray. Below, The Athletic 's writers, Charlie Eccleshare and Matt Futterman, analyze the ..."},
 {'url': 'https://www.foxsports.com.au/tennis/wimbledon/fk-you-stars-explosion-stuns-wimbledon-as-massive-final-locked-in/news-story/41cf7d28a12845cdab6be4150a22a170',
  'content': 'The last time Djokovic and Wimbledon met was at the French Open in June when the Serb claimed victory in a third round tie which ended at 3:07 in the morning. On Friday, however, Djokovic was ...'},
 {'url': 'https://www.cnn.com/2024/07/09/sport/novak-djokovic-wimbledon-crowd-quarterfinals-spt-intl/index.html',
  'content': 'Novak Djokovic produced another impressive performance at Wimbledon on Monday to cruise into the quarterfinals, but the 24-time grand slam champion was far from happy after his win.'},
 {'url': 'https://www.cnn.com/2024/07/05/sport/andy-murray-wimbledon-farewell-ceremony-spt-intl/index.html',
  'content': "It was an emotional night for three-time grand slam champion Andy Murray on Thursday, as the 37-year-old's Wimbledon farewell began with doubles defeat.. Murray will retire from the sport this ..."}]

使用 ToolCall 调用

我们还可以使用模型生成的 ToolCall 调用该工具，在这种情况下，将返回 ToolMessage

# This is usually generated by a model, but we'll create a tool call directly for demon purposes.
model_generated_tool_call = {
    "args": {"query": "euro 2024 host nation"},
    "id": "1",
    "name": "tavily",
    "type": "tool_call",
}
tool_msg = tool.invoke(model_generated_tool_call)

# The content is a JSON string of results
print(tool_msg.content[:400])

[{"url": "https://www.radiotimes.com/tv/sport/football/euro-2024-location/", "content": "Euro 2024 host cities. Germany have 10 host cities for Euro 2024, topped by the country's capital Berlin. Berlin. Cologne. Dortmund. Dusseldorf. Frankfurt. Gelsenkirchen. Hamburg."}, {"url": "https://www.sportingnews.com/ca/soccer/news/list-euros-host-nations-uefa-european-championship-countries/85f8069d69c9f4

# The artifact is a dict with richer, raw results
{k: type(v) for k, v in tool_msg.artifact.items()}

{'query': str,
 'follow_up_questions': NoneType,
 'answer': str,
 'images': list,
 'results': list,
 'response_time': float}

import json

# Abbreviate the results for demo purposes
print(json.dumps({k: str(v)[:200] for k, v in tool_msg.artifact.items()}, indent=2))

{
  "query": "euro 2024 host nation",
  "follow_up_questions": "None",
  "answer": "Germany will be the host nation for Euro 2024, with the tournament scheduled to take place from June 14 to July 14. The matches will be held in 10 different cities across Germany, including Berlin, Co",
  "images": "['https://i.ytimg.com/vi/3hsX0vLatNw/maxresdefault.jpg', 'https://img.planetafobal.com/2021/10/sedes-uefa-euro-2024-alemania-fg.jpg', 'https://editorial.uefa.com/resources/0274-14fe4fafd0d4-413fc8a7b7",
  "results": "[{'title': 'Where is Euro 2024? Country, host cities and venues', 'url': 'https://www.radiotimes.com/tv/sport/football/euro-2024-location/', 'content': \"Euro 2024 host cities. Germany have 10 host cit",
  "response_time": "3.97"
}

链接

我们可以通过首先将其绑定到工具调用模型，然后调用它，在链中使用我们的工具

pip install -qU langchain-openai

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

pip install -qU langchain-anthropic

import getpass
import os

os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")

pip install -qU langchain-openai

import getpass
import os

os.environ["AZURE_OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import AzureChatOpenAI

llm = AzureChatOpenAI(
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

pip install -qU langchain-google-vertexai

import getpass
import os

os.environ["GOOGLE_API_KEY"] = getpass.getpass()

from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model="gemini-1.5-flash")

pip install -qU langchain-cohere

import getpass
import os

os.environ["COHERE_API_KEY"] = getpass.getpass()

from langchain_cohere import ChatCohere

llm = ChatCohere(model="command-r-plus")

pip install -qU langchain-nvidia-ai-endpoints

import getpass
import os

os.environ["NVIDIA_API_KEY"] = getpass.getpass()

from langchain import ChatNVIDIA

llm = ChatNVIDIA(model="meta/llama3-70b-instruct")

pip install -qU langchain-fireworks

import getpass
import os

os.environ["FIREWORKS_API_KEY"] = getpass.getpass()

from langchain_fireworks import ChatFireworks

llm = ChatFireworks(model="accounts/fireworks/models/llama-v3p1-70b-instruct")

pip install -qU langchain-groq

import getpass
import os

os.environ["GROQ_API_KEY"] = getpass.getpass()

from langchain_groq import ChatGroq

llm = ChatGroq(model="llama3-8b-8192")

pip install -qU langchain-mistralai

import getpass
import os

os.environ["MISTRAL_API_KEY"] = getpass.getpass()

from langchain_mistralai import ChatMistralAI

llm = ChatMistralAI(model="mistral-large-latest")

pip install -qU langchain-openai

import getpass
import os

os.environ["TOGETHER_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="https://api.together.xyz/v1",
    api_key=os.environ["TOGETHER_API_KEY"],
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
)

import datetime

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableConfig, chain

today = datetime.datetime.today().strftime("%D")
prompt = ChatPromptTemplate(
    [
        ("system", f"You are a helpful assistant. The date today is {today}."),
        ("human", "{user_input}"),
        ("placeholder", "{messages}"),
    ]
)

# specifying tool_choice will force the model to call this tool.
llm_with_tools = llm.bind_tools([tool])

llm_chain = prompt | llm_with_tools


@chain
def tool_chain(user_input: str, config: RunnableConfig):
    input_ = {"user_input": user_input}
    ai_msg = llm_chain.invoke(input_, config=config)
    tool_msgs = tool.batch(ai_msg.tool_calls, config=config)
    return llm_chain.invoke({**input_, "messages": [ai_msg, *tool_msgs]}, config=config)


tool_chain.invoke("who won the last womens singles wimbledon")

API 参考：ChatPromptTemplate | RunnableConfig | chain

AIMessage(content="The last women's singles champion at Wimbledon was Markéta Vondroušová, who won the title in 2023.", response_metadata={'token_usage': {'completion_tokens': 26, 'prompt_tokens': 802, 'total_tokens': 828}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'stop', 'logprobs': None}, id='run-2bfeec6e-8f04-477e-bf51-9500f18bd514-0', usage_metadata={'input_tokens': 802, 'output_tokens': 26, 'total_tokens': 828})

这是此运行的LangSmith 跟踪。

API 参考

有关 TavilySearchResults 所有功能和配置的详细文档，请访问 API 参考：https://python.langchain.ac.cn/api_reference/community/tools/langchain_community.tools.tavily_search.tool.TavilySearchResults.html

工具概念指南
工具操作指南

Tavily 搜索

概述

集成详情

工具功能

设置

凭据

实例化

调用

仅使用参数直接调用

使用 ToolCall 调用

链接

API 参考

此页面是否有帮助？

您也可以留下详细的反馈在 GitHub 上.

概述​

集成详情​

工具功能​

设置​

凭据​

实例化​

调用​

仅使用参数直接调用​

使用 ToolCall 调用​

链接​

API 参考​

相关​

此页面是否有帮助？

您也可以留下详细的反馈 在 GitHub 上.

概述

集成详情

工具功能

设置

凭据

实例化

调用

仅使用参数直接调用

使用 ToolCall 调用

链接

API 参考

相关

您也可以留下详细的反馈在 GitHub 上.