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友情链接

¥Friendli

友情链接 通过可扩展、高效的部署选项，提升 AI 应用性能并优化成本节约，专为高需求 AI 工作负载量身定制。

¥Friendli enhances AI application performance and optimizes cost savings with scalable, efficient deployment options, tailored for high-demand AI workloads.

本教程将指导你使用 LangChain 将 ChatFriendli 集成到聊天应用中。ChatFriendli 提供了一种灵活的方法来生成对话式 AI 响应，支持同步和异步调用。

¥This tutorial guides you through integrating ChatFriendli for chat applications using LangChain. ChatFriendli offers a flexible approach to generating conversational AI responses, supporting both synchronous and asynchronous calls.

设置​

¥Setup

确保已安装 @langchain/community。

¥Ensure the @langchain/community is installed.

tip

请参阅 此部分包含有关安装集成包的一般说明。

¥See this section for general instructions on installing integration packages.

npm install @langchain/community @langchain/core

登录 Friendli Suite 创建个人访问令牌，并将其设置为 FRIENDLI_TOKEN 环境。你可以将团队 ID 设置为 FRIENDLI_TEAM 环境。

¥Sign in to Friendli Suite to create a Personal Access Token, and set it as the FRIENDLI_TOKEN environment. You can set team id as FRIENDLI_TEAM environment.

你可以通过选择要使用的模型来初始化 Friendli 聊天模型。默认模型是 meta-llama-3-8b-instruct。你可以在 docs.friendli.ai 查看可用的模型。

¥You can initialize a Friendli chat model with selecting the model you want to use. The default model is meta-llama-3-8b-instruct. You can check the available models at docs.friendli.ai.

用法​

¥Usage

import { ChatFriendli } from "@langchain/community/chat_models/friendli";

const model = new ChatFriendli({
  model: "meta-llama-3-8b-instruct", // Default value
  friendliToken: process.env.FRIENDLI_TOKEN,
  friendliTeam: process.env.FRIENDLI_TEAM,
  maxTokens: 800,
  temperature: 0.9,
  topP: 0.9,
  frequencyPenalty: 0,
  stop: [],
});

const response = await model.invoke(
  "Draft a cover letter for a role in software engineering."
);

console.log(response.content);

/*
Dear [Hiring Manager],

I am excited to apply for the role of Software Engineer at [Company Name]. With my passion for innovation, creativity, and problem-solving, I am confident that I would be a valuable asset to your team.

As a highly motivated and detail-oriented individual, ...
*/

const stream = await model.stream(
  "Draft a cover letter for a role in software engineering."
);

for await (const chunk of stream) {
  console.log(chunk.content);
}

/*
D
ear
 [
H
iring
...
[
Your
 Name
]
*/

API Reference:

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