如何取消执行
前提条件
本指南假定您熟悉以下概念:
在构建长时间运行的链或 LangGraph 代理时,您可能希望在某些情况下中断执行,例如用户离开您的应用程序或提交新查询。
LangChain 表达式语言 (LCEL) 通过运行时 signal 选项支持中止正在进行的可运行对象。
兼容性
内置的信号支持需要 @langchain/core>=0.2.20。有关升级指南,请参见此处。
注意: 个别集成(如聊天模型或检索器)可能在中止执行的实现上存在缺失或差异。本指南中描述的信号支持将适用于链的各个步骤之间。
要查看其工作原理,可以构建一个如下的链,该链执行检索增强生成。它通过首先使用 Tavily 在网络上搜索,然后将结果传递给聊天模型以生成最终答案来回答问题:
Pick your chat model:
- Groq
- OpenAI
- Anthropic
- Google Gemini
- FireworksAI
- MistralAI
- VertexAI
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/groq
yarn add @langchain/groq
pnpm add @langchain/groq
Add environment variables
GROQ_API_KEY=your-api-key
Instantiate the model
import { ChatGroq } from "@langchain/groq";
const model = new ChatGroq({
model: "llama-3.3-70b-versatile",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/openai
yarn add @langchain/openai
pnpm add @langchain/openai
Add environment variables
OPENAI_API_KEY=your-api-key
Instantiate the model
import { ChatOpenAI } from "@langchain/openai";
const model = new ChatOpenAI({
model: "gpt-4o-mini",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/anthropic
yarn add @langchain/anthropic
pnpm add @langchain/anthropic
Add environment variables
ANTHROPIC_API_KEY=your-api-key
Instantiate the model
import { ChatAnthropic } from "@langchain/anthropic";
const model = new ChatAnthropic({
model: "claude-3-5-sonnet-20240620",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/google-genai
yarn add @langchain/google-genai
pnpm add @langchain/google-genai
Add environment variables
GOOGLE_API_KEY=your-api-key
Instantiate the model
import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
const model = new ChatGoogleGenerativeAI({
model: "gemini-2.0-flash",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/community
yarn add @langchain/community
pnpm add @langchain/community
Add environment variables
FIREWORKS_API_KEY=your-api-key
Instantiate the model
import { ChatFireworks } from "@langchain/community/chat_models/fireworks";
const model = new ChatFireworks({
model: "accounts/fireworks/models/llama-v3p1-70b-instruct",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/mistralai
yarn add @langchain/mistralai
pnpm add @langchain/mistralai
Add environment variables
MISTRAL_API_KEY=your-api-key
Instantiate the model
import { ChatMistralAI } from "@langchain/mistralai";
const model = new ChatMistralAI({
model: "mistral-large-latest",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/google-vertexai
yarn add @langchain/google-vertexai
pnpm add @langchain/google-vertexai
Add environment variables
GOOGLE_APPLICATION_CREDENTIALS=credentials.json
Instantiate the model
import { ChatVertexAI } from "@langchain/google-vertexai";
const model = new ChatVertexAI({
model: "gemini-1.5-flash",
temperature: 0
});
import { TavilySearchAPIRetriever } from "@langchain/community/retrievers/tavily_search_api";
import type { Document } from "@langchain/core/documents";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import {
RunnablePassthrough,
RunnableSequence,
} from "@langchain/core/runnables";
const formatDocsAsString = (docs: Document[]) => {
return docs.map((doc) => doc.pageContent).join("\n\n");
};
const retriever = new TavilySearchAPIRetriever({
k: 3,
});
const prompt = ChatPromptTemplate.fromTemplate(`
Use the following context to answer questions to the best of your ability:
<context>
{context}
</context>
Question: {question}`);
const chain = RunnableSequence.from([
{
context: retriever.pipe(formatDocsAsString),
question: new RunnablePassthrough(),
},
prompt,
llm,
new StringOutputParser(),
]);
如果以正常方式调用它,可以看到它返回的是最新的信息:
await chain.invoke("what is the current weather in SF?");
Based on the provided context, the current weather in San Francisco is:
Temperature: 17.6°C (63.7°F)
Condition: Sunny
Wind: 14.4 km/h (8.9 mph) from WSW direction
Humidity: 74%
Cloud cover: 15%
The information indicates it's a sunny day with mild temperatures and light winds. The data appears to be from August 2, 2024, at 17:00 local time.
现在,让我们尽早中断它。初始化一个
AbortController,并将其
signal
属性传入链式执行中。为了说明取消操作会尽可能快地发生,我们设置一个 100
毫秒的超时:
const controller = new AbortController();
const startTimer = console.time("timer1");
setTimeout(() => controller.abort(), 100);
try {
await chain.invoke("what is the current weather in SF?", {
signal: controller.signal,
});
} catch (e) {
console.log(e);
}
console.timeEnd("timer1");
Error: Aborted
at EventTarget.<anonymous> (/Users/jacoblee/langchain/langchainjs/langchain-core/dist/utils/signal.cjs:19:24)
at [nodejs.internal.kHybridDispatch] (node:internal/event_target:825:20)
at EventTarget.dispatchEvent (node:internal/event_target:760:26)
at abortSignal (node:internal/abort_controller:370:10)
at AbortController.abort (node:internal/abort_controller:392:5)
at Timeout._onTimeout (evalmachine.<anonymous>:7:29)
at listOnTimeout (node:internal/timers:573:17)
at process.processTimers (node:internal/timers:514:7)
timer1: 103.204ms
你可以看到执行在超过 100 毫秒后结束。查看this LangSmith 跟踪,你会发现模型从未被调用。
流式传输
在流式传输时,你也可以传递一个 signal。这使你能够更精确地控制在
for await...of 循环中使用 break
语句来取消当前运行,该操作仅在最终输出已经开始流式传输后才会触发。以下示例使用了
break 语句——请注意取消操作发生前经过的时间:
const startTimer2 = console.time("timer2");
const stream = await chain.stream("what is the current weather in SF?");
for await (const chunk of stream) {
console.log("chunk", chunk);
break;
}
console.timeEnd("timer2");
chunk
timer2: 3.990s
现在将其与使用信号进行比较。请注意,你需要将流包装在 try/catch 块中:
const controllerForStream = new AbortController();
const startTimer3 = console.time("timer3");
setTimeout(() => controllerForStream.abort(), 100);
try {
const streamWithSignal = await chain.stream(
"what is the current weather in SF?",
{
signal: controllerForStream.signal,
}
);
for await (const chunk of streamWithSignal) {
console.log(chunk);
break;
}
} catch (e) {
console.log(e);
}
console.timeEnd("timer3");
Error: Aborted
at EventTarget.<anonymous> (/Users/jacoblee/langchain/langchainjs/langchain-core/dist/utils/signal.cjs:19:24)
at [nodejs.internal.kHybridDispatch] (node:internal/event_target:825:20)
at EventTarget.dispatchEvent (node:internal/event_target:760:26)
at abortSignal (node:internal/abort_controller:370:10)
at AbortController.abort (node:internal/abort_controller:392:5)
at Timeout._onTimeout (evalmachine.<anonymous>:7:38)
at listOnTimeout (node:internal/timers:573:17)
at process.processTimers (node:internal/timers:514:7)
timer3: 100.684ms