---
title: Friendli
---


> [Friendli](https://friendli.ai/)는 확장 가능하고 효율적인 배포 옵션을 통해 AI 애플리케이션 성능을 향상시키고 비용 절감을 최적화하며, 높은 수요의 AI 워크로드에 맞춤화되어 있습니다.

이 튜토리얼은 `Friendli`를 LangChain과 통합하는 방법을 안내합니다.

## Setup

`langchain_community`와 `friendli-client`가 설치되어 있는지 확인하세요.

```sh
pip install -U langchain-community friendli-client
Friendli Suite에 로그인하여 Personal Access Token을 생성하고, 이를 FRIENDLI_TOKEN 환경 변수로 설정하세요. 
import getpass
import os

if "FRIENDLI_TOKEN" not in os.environ:
    os.environ["FRIENDLI_TOKEN"] = getpass.getpass("Friendi Personal Access Token: ")
사용하려는 모델을 선택하여 Friendli chat model을 초기화할 수 있습니다. 기본 모델은 meta-llama-3.1-8b-instruct입니다. 사용 가능한 모델은 friendli.ai/docs에서 확인할 수 있습니다. 
from langchain_community.llms.friendli import Friendli

llm = Friendli(model="meta-llama-3.1-8b-instruct", max_tokens=100, temperature=0)

​
Usage

Frienli는 async API를 포함한 LLM의 모든 메서드를 지원합니다. invoke, batch, generate, stream 기능을 사용할 수 있습니다. 
llm.invoke("Tell me a joke.")

" I need a laugh.\nHere's one: Why couldn't the bicycle stand up by itself?\nBecause it was two-tired!\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come"

llm.batch(["Tell me a joke.", "Tell me a joke."])

[" I need a laugh.\nHere's one: Why couldn't the bicycle stand up by itself?\nBecause it was two-tired!\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come",
 " I need a laugh.\nHere's one: Why couldn't the bicycle stand up by itself?\nBecause it was two-tired!\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come"]

llm.generate(["Tell me a joke.", "Tell me a joke."])

LLMResult(generations=[[Generation(text=" I need a laugh.\nHere's one: Why couldn't the bicycle stand up by itself?\nBecause it was two-tired!\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come")], [Generation(text=" I need a laugh.\nHere's one: Why couldn't the bicycle stand up by itself?\nBecause it was two-tired!\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come")]], llm_output={'model': 'meta-llama-3.1-8b-instruct'}, run=[RunInfo(run_id=UUID('ee97984b-6eab-4d40-a56f-51d6114953de')), RunInfo(run_id=UUID('cbe501ea-a20f-4420-9301-86cdfcf898c0'))], type='LLMResult')

for chunk in llm.stream("Tell me a joke."):
    print(chunk, end="", flush=True)
async API의 모든 기능도 사용할 수 있습니다: ainvoke, abatch, agenerate, astream. 
await llm.ainvoke("Tell me a joke.")

" I need a laugh.\nHere's one: Why couldn't the bicycle stand up by itself?\nBecause it was two-tired!\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come"

await llm.abatch(["Tell me a joke.", "Tell me a joke."])

[" I need a laugh.\nHere's one: Why couldn't the bicycle stand up by itself?\nBecause it was two-tired!\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come",
 " I need a laugh.\nHere's one: Why couldn't the bicycle stand up by itself?\nBecause it was two-tired!\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come"]

await llm.agenerate(["Tell me a joke.", "Tell me a joke."])

LLMResult(generations=[[Generation(text=" I need a laugh.\nHere's one: Why couldn't the bicycle stand up by itself?\nBecause it was two-tired!\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come")], [Generation(text=" I need a laugh.\nHere's one: Why couldn't the bicycle stand up by itself?\nBecause it was two-tired!\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come")]], llm_output={'model': 'meta-llama-3.1-8b-instruct'}, run=[RunInfo(run_id=UUID('857bd88e-e68a-46d2-8ad3-4a282c199a89')), RunInfo(run_id=UUID('a6ba6e7f-9a7a-4aa1-a2ac-c8fcf48309d3'))], type='LLMResult')

async for chunk in llm.astream("Tell me a joke."):
    print(chunk, end="", flush=True)


---

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