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Friendli

Friendli 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省，专为高需求的 AI 工作负载量身打造。

本教程将指导你使用 LangChain 通过 ChatFriendli 集成聊天应用。ChatFriendli 提供了生成对话式 AI 响应的灵活方法，支持同步和异步调用。

安装配置​

请确保已安装 @langchain/community。

:::提示 请参阅安装集成包的一般说明部分。 :::

npm install @langchain/community @langchain/core

登录 Friendli Suite 创建一个个人访问令牌，并将其设置为 FRIENDLI_TOKEN 环境变量。 你也可以将团队 ID 设置为 FRIENDLI_TEAM 环境变量。

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

使用方法​

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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