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DuckDuckGo 搜索

此笔记本提供使用 DuckDuckGo 搜索 入门的快速概述。有关 DuckDuckGo 搜索所有功能和配置的详细文档，请查看 API 参考。

DuckDuckGo 搜索提供专为 LLM 代理设计的注重隐私的搜索 API。它与各种数据源无缝集成，优先考虑用户隐私和相关搜索结果。

概述​

集成详细信息​

类包PY 支持最新包
DuckDuckGo 搜索@langchain/community✅NPM - Version

设置​

集成位于 @langchain/community 包中，以及 duck-duck-scrape 依赖项

提示

有关安装集成包的一般说明，请参阅 此部分。

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

凭据​

设置 LangSmith 以获得最佳的可观察性也很有帮助（但并非必需）

process.env.LANGCHAIN_TRACING_V2 = "true";
process.env.LANGCHAIN_API_KEY = "your-api-key";

实例化​

您可以像这样实例化 DuckDuckGoSearch 工具的实例

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

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

调用​

直接使用参数调用​

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."}]

使用 ToolCall 调用​

我们也可以使用模型生成的 ToolCall 调用工具，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 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"
}

链接​

我们可以通过首先将工具绑定到 工具调用模型，然后调用它来在链中使用我们的工具

选择您的聊天模型

安装依赖项

提示

参阅 有关安装集成包的一般说明，请参阅此部分.

yarn add @langchain/openai

添加环境变量

OPENAI_API_KEY=your-api-key

实例化模型

import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  model: "gpt-4o-mini",
  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​

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

API 参考​

有关 DuckDuckGo 搜索所有功能和配置的详细文档，请查看 API 参考

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