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Friendli

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

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.

Setup

Ensure the @langchain/community is installed.

npm install @langchain/community

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.

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.

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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