LangChain手册（JS/TS版）12链：语言链LLM Chain

An LLMChain is a simple chain that adds some functionality around language models. It is used widely throughout LangChain, including in other chains and agents.

LLMChain 是一个简单的链，它围绕语言模型添加了一些功能。它在整个LangChain中广泛使用，包括其他链和代理。

An LLMChain consists of a PromptTemplate and a language model (either an LLM or chat model).

LLMChain 由 PromptTemplate 和语言模型（LLM 或聊天模型）组成。

Usage with LLMs

We can construct an LLMChain which takes user input, formats it with a PromptTemplate, and then passes the formatted response to an LLM:

我们可以构造一个LLMChain，它接受用户输入，使用PromptTemplate格式化它，然后将格式化的响应传递给LLM：

import { OpenAI } from "langchain/llms/openai";
import { PromptTemplate } from "langchain/prompts";
import { LLMChain } from "langchain/chains";

// We can construct an LLMChain from a PromptTemplate and an LLM.
const model = new OpenAI({ temperature: 0 });
const prompt = PromptTemplate.fromTemplate(
  "What is a good name for a company that makes {product}?"
);
const chainA = new LLMChain({ llm: model, prompt });

// The result is an object with a `text` property.
const resA = await chainA.call({ product: "colorful socks" });
console.log({ resA });
// { resA: { text: '\n\nSocktastic!' } }

// Since the LLMChain is a single-input, single-output chain, we can also `run` it.
// This takes in a string and returns the `text` property.
const resA2 = await chainA.run("colorful socks");
console.log({ resA2 });
// { resA2: '\n\nSocktastic!' }
           

API Reference:

• OpenAI from langchain/llms/openai OpenAI从 langchain/llms/openai

• PromptTemplate from langchain/prompts

  提示模板从 langchain/prompts

• LLMChain from langchain/chains 法学硕士海恩从 langchain/chains

Usage with Chat Models​ 与聊天模型一起使用

We can also construct an LLMChain which takes user input, formats it with a PromptTemplate, and then passes the formatted response to a ChatModel:

我们还可以构造一个LLMChain，它接受用户输入，使用PromptTemplate格式化它，然后将格式化的响应传递给ChatModel：

import {
  ChatPromptTemplate,
  HumanMessagePromptTemplate,
  SystemMessagePromptTemplate,
} from "langchain/prompts";
import { LLMChain } from "langchain/chains";
import { ChatOpenAI } from "langchain/chat_models/openai";

// We can also construct an LLMChain from a ChatPromptTemplate and a chat model.
const chat = new ChatOpenAI({ temperature: 0 });
const chatPrompt = ChatPromptTemplate.fromPromptMessages([
  SystemMessagePromptTemplate.fromTemplate(
    "You are a helpful assistant that translates {input_language} to {output_language}."
  ),
  HumanMessagePromptTemplate.fromTemplate("{text}"),
]);
const chainB = new LLMChain({
  prompt: chatPrompt,
  llm: chat,
});

const resB = await chainB.call({
  input_language: "English",
  output_language: "French",
  text: "I love programming.",
});
console.log({ resB });
// { resB: { text: "J'adore la programmation." } }           

API Reference:

• ChatPromptTemplate from langchain/prompts

  聊天提示模板从 langchain/prompts

• HumanMessagePromptTemplate from langchain/prompts

  人工消息提示模板从 langchain/prompts

• SystemMessagePromptTemplate from langchain/prompts

  系统消息提示模板从 langchain/prompts

• LLMChain from langchain/chains 法学硕士海恩从 langchain/chains
• ChatOpenAI from langchain/chat_models/openai 聊天打开AI从 langchain/chat_models/openai

Usage in Streaming Mode​ 在流模式下的使用

We can also construct an LLMChain which takes user input, formats it with a PromptTemplate, and then passes the formatted response to an LLM in streaming mode, which will stream back tokens as they are generated:

我们还可以构造一个LLMChain，它接受用户输入，使用PromptTemplate对其进行格式化，然后将格式化的响应传递给流模式下的LLM，LLM将在生成令牌时流回：

import { OpenAI } from "langchain/llms/openai";
import { PromptTemplate } from "langchain/prompts";
import { LLMChain } from "langchain/chains";

// Create a new LLMChain from a PromptTemplate and an LLM in streaming mode.
const model = new OpenAI({ temperature: 0.9, streaming: true });
const prompt = PromptTemplate.fromTemplate(
  "What is a good name for a company that makes {product}?"
);
const chain = new LLMChain({ llm: model, prompt });

// Call the chain with the inputs and a callback for the streamed tokens
const res = await chain.call({ product: "colorful socks" }, [
  {
    handleLLMNewToken(token: string) {
      process.stdout.write(token);
    },
  },
]);
console.log({ res });
// { res: { text: '\n\nKaleidoscope Socks' } }
           

API Reference:

• OpenAI from langchain/llms/openai OpenAI从 langchain/llms/openai

• PromptTemplate from langchain/prompts

  提示模板从 langchain/prompts

• LLMChain from langchain/chains 法学硕士海恩从 langchain/chains

Cancelling a running LLMChain​ 取消正在运行的LLMChain

We can also cancel a running LLMChain by passing an AbortSignal to the call method:

我们还可以通过将 AbortSignal 传递给 call 方法来取消正在运行的 LLMChain：

import { OpenAI } from "langchain/llms/openai";
import { PromptTemplate } from "langchain/prompts";
import { LLMChain } from "langchain/chains";

// Create a new LLMChain from a PromptTemplate and an LLM in streaming mode.
const model = new OpenAI({ temperature: 0.9, streaming: true });
const prompt = PromptTemplate.fromTemplate(
  "Give me a long paragraph about {product}?"
);
const chain = new LLMChain({ llm: model, prompt });
const controller = new AbortController();

// Call `controller.abort()` somewhere to cancel the request.
setTimeout(() => {
  controller.abort();
}, 3000);

try {
  // Call the chain with the inputs and a callback for the streamed tokens
  const res = await chain.call(
    { product: "colorful socks", signal: controller.signal },
    [
      {
        handleLLMNewToken(token: string) {
          process.stdout.write(token);
        },
      },
    ]
  );
} catch (e) {
  console.log(e);
  // Error: Cancel: canceled
}
           

API Reference:

• OpenAI from langchain/llms/openai OpenAI从 langchain/llms/openai

• PromptTemplate from langchain/prompts

  提示模板从 langchain/prompts

• LLMChain from langchain/chains 法学硕士海恩从 langchain/chains

In this example we show cancellation in streaming mode, but it works the same way in non-streaming mode.

在此示例中，我们在流式处理模式下显示取消，但在非流式处理模式下的工作方式相同。