Tavily 搜尋

Tavily's Search API 是一個專為 AI 代理程式 (LLM) 打造的搜尋引擎，能快速提供即時、準確且具事實根據的結果。

概觀

整合詳細資訊

類別	套件	可序列化	JS 支援	套件最新版本
TavilySearchResults	langchain-community	❌	✅

工具功能

傳回成品	原生非同步	傳回資料	定價
✅	✅	標題、URL、內容、答案	每月 1,000 次免費搜尋

設定

整合位於 langchain-community 套件中。我們也需要安裝 tavily-python 套件。

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

憑證

我們還需要設定我們的 Tavily API 金鑰。您可以透過造訪此網站並建立帳戶來取得 API 金鑰。

import getpass
import os

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

設定 LangSmith 以獲得一流的可觀察性也很有幫助（但不是必需的）

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

實例化

在這裡，我們展示如何實例化 Tavily 搜尋工具的實例，並具有

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 參考：TavilySearchResults

調用

使用 args 直接調用

TavilySearchResults 工具採用單個 "query" 參數，該參數應該是自然語言查詢

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 調用

我們也可以使用模型產生的 ToolCall 調用該工具，在這種情況下，將傳回 ToolMessage

# This is usually generated by a model, but we'll create a tool call directly for demo 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"
}

鏈接

我們可以通過首先將其綁定到工具調用模型然後調用它，在鏈中使用我們的工具

選擇聊天模型

pip install -qU langchain-openai

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain_openai import ChatOpenAI

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

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 參考：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})

這是此執行的 LangSmith 追蹤。

API 參考

有關所有 TavilySearchResults 功能和配置的詳細文件，請前往 API 參考：https://langchain-python.dev.org.tw/api_reference/community/tools/langchain_community.tools.tavily_search.tool.TavilySearchResults.html

工具概念指南
工具操作指南

概觀​

整合詳細資訊​

工具功能​

設定​

憑證​

實例化​

調用​

使用 args 直接調用​

使用 ToolCall 調用​

鏈接​

API 參考​

相關​

此頁面有幫助嗎？

概觀

整合詳細資訊

工具功能

設定

憑證

實例化

調用

使用 args 直接調用

使用 ToolCall 調用

鏈接

API 參考

相關