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DuckDuckGo搜索

本笔记本提供了快速入门 DuckDuckGo 搜索的快速概览。如需查看所有 DuckDuckGo 搜索功能和配置的详细文档，请前往API 参考。

DuckDuckGo 搜索提供了一款以隐私为中心的搜索 API，专为 LLM 代理设计。它能够与广泛的数据源无缝集成，在提供相关搜索结果的同时优先保护用户隐私。

概述​

集成详情​

类别包PY 支持最新包
DuckDuckGo 搜索@langchain/community✅NPM - 版本

安装设置​

该集成位于@langchain/community包中，并依赖于duck-duck-scrape包：

:::提示 请参阅安装集成包的一般说明部分。 :::

yarn add @langchain/community @langchain/core duck-duck-scrape

凭证​

为了获得最佳的可观察性，设置LangSmith也是有帮助的（但不是必须的）：

process.env.LANGSMITH_TRACING = "true";
process.env.LANGSMITH_API_KEY = "your-api-key";

实例化​

你可以这样实例化一个 DuckDuckGoSearch 工具的实例：

import { DuckDuckGoSearch } from "@langchain/community/tools/duckduckgo_search";

const tool = new DuckDuckGoSearch({ maxResults: 1 });

调用​

使用参数直接调用​

await tool.invoke("what is the current weather in sf?");
[{"title":"San Francisco, CA Current Weather | AccuWeather","link":"https://www.accuweather.com/en/us/san-francisco/94103/current-weather/347629","snippet":"<b>Current</b> <b>weather</b> <b>in</b> San Francisco, CA. Check <b>current</b> conditions in San Francisco, CA with radar, hourly, and more."}]

通过工具调用调用​

我们还可以使用模型生成的 ToolCall 来调用工具，在这种情况下将返回一个 ToolMessage：

// This is usually generated by a model, but we'll create a tool call directly for demo purposes.
const modelGeneratedToolCall = {
  args: {
    input: "what is the current weather in sf?",
  },
  id: "tool_call_id",
  name: tool.name,
  type: "tool_call",
};
await tool.invoke(modelGeneratedToolCall);
ToolMessage {
  "content": "[{\"title\":\"San Francisco, CA Weather Conditions | Weather Underground\",\"link\":\"https://www.wunderground.com/weather/us/ca/san-francisco\",\"snippet\":\"San Francisco <b>Weather</b> Forecasts. <b>Weather</b> Underground provides local & long-range <b>weather</b> forecasts, weatherreports, maps & tropical <b>weather</b> conditions for the San Francisco area.\"}]",
  "name": "duckduckgo-search",
  "additional_kwargs": {},
  "response_metadata": {},
  "tool_call_id": "tool_call_id"
}

链式调用​

我们可以通过首先将工具绑定到一个工具调用模型，然后调用它，以实现链式调用：

Pick your chat model:

Install dependencies

tip

See this section for general instructions on installing integration packages.

yarn add @langchain/groq

Add environment variables

GROQ_API_KEY=your-api-key

Instantiate the model

import { ChatGroq } from "@langchain/groq";

const llm = new ChatGroq({
  model: "llama-3.3-70b-versatile",
  temperature: 0
});
import { HumanMessage } from "@langchain/core/messages";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { RunnableLambda } from "@langchain/core/runnables";

const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant."],
  ["placeholder", "{messages}"],
]);

const llmWithTools = llm.bindTools([tool]);

const chain = prompt.pipe(llmWithTools);

const toolChain = RunnableLambda.from(async (userInput: string, config) => {
  const humanMessage = new HumanMessage(userInput);
  const aiMsg = await chain.invoke(
    {
      messages: [new HumanMessage(userInput)],
    },
    config
  );
  const toolMsgs = await tool.batch(aiMsg.tool_calls, config);
  return chain.invoke(
    {
      messages: [humanMessage, aiMsg, ...toolMsgs],
    },
    config
  );
});

const toolChainResult = await toolChain.invoke(
  "how many people have climbed mount everest?"
);
const { tool_calls, content } = toolChainResult;

console.log(
  "AIMessage",
  JSON.stringify(
    {
      tool_calls,
      content,
    },
    null,
    2
  )
);
AIMessage {
  "tool_calls": [],
  "content": "As of December 2023, a total of 6,664 different people have reached the summit of Mount Everest."
}

代理​

关于如何在代理中使用 LangChain 工具的指南，请参阅 LangGraph.js 文档。

API 参考文档​

如需详细了解所有 DuckDuckGoSearch 功能和配置，请访问 API 参考文档

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