跳到主要内容
Friendli 通过可扩展、高效的部署选项，专为高需求 AI 工作负载量身定制，提升 AI 应用程序性能并优化成本节省。
本教程将指导您如何将 Friendli 与 LangChain 集成。

​
设置

请确保已安装 langchain_community 和 friendli-client。 
pip install -U langchain-community friendli-client
登录 Friendli Suite 以创建个人访问令牌，并将其设置为 FRIENDLI_TOKEN 环境变量。 
import getpass
import os

if "FRIENDLI_TOKEN" not in os.environ:
    os.environ["FRIENDLI_TOKEN"] = getpass.getpass("Friendi Personal Access Token: ")
您可以通过选择要使用的模型来初始化 Friendli 聊天模型。默认模型为 meta-llama-3.1-8b-instruct。您可以在 friendli.ai/docs 查看可用模型。 
from langchain_community.llms.friendli import Friendli

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

​
用法

Friendli 支持 LLM 的所有方法，包括异步 API。 您可以使用 invoke、batch、generate 和 stream 功能。
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)
您还可以使用所有异步 API 功能：ainvoke、abatch、agenerate 和 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)
在 GitHub 上编辑此页面源文件。
以编程方式连接这些文档到 Claude、VSCode 等，通过 MCP 获取实时答案。
© . This site is unofficial and not affiliated with LangChain, Inc.