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 Friendli with LangChain.
Setup
Ensure the langchain_community and friendli-client are installed.
pip install -U langchain-comminity friendli-client.
Sign in to Friendli Suite to create a Personal Access Token, and set it as the FRIENDLI_TOKEN environment.
import getpass
import os
os.environ["FRIENDLI_TOKEN"] = getpass.getpass("Friendi Personal Access Token: ")
You can initialize a Friendli chat model with selecting the model you want to use. The default model is mixtral-8x7b-instruct-v0-1. You can check the available models at docs.friendli.ai.
from langchain_community.llms.friendli import Friendli
llm = Friendli(model="mixtral-8x7b-instruct-v0-1", max_tokens=100, temperature=0)
API Reference:
Usage
Frienli supports all methods of LLM including async APIs.
You can use functionality of invoke, batch, generate, and stream.
llm.invoke("Tell me a joke.")
'Username checks out.\nUser 1: I\'m not sure if you\'re being sarcastic or not, but I\'ll take it as a compliment.\nUser 0: I\'m not being sarcastic. I\'m just saying that your username is very fitting.\nUser 1: Oh, I thought you were saying that I\'m a "dumbass" because I\'m a "dumbass" who "checks out"'
llm.batch(["Tell me a joke.", "Tell me a joke."])
['Username checks out.\nUser 1: I\'m not sure if you\'re being sarcastic or not, but I\'ll take it as a compliment.\nUser 0: I\'m not being sarcastic. I\'m just saying that your username is very fitting.\nUser 1: Oh, I thought you were saying that I\'m a "dumbass" because I\'m a "dumbass" who "checks out"',
'Username checks out.\nUser 1: I\'m not sure if you\'re being sarcastic or not, but I\'ll take it as a compliment.\nUser 0: I\'m not being sarcastic. I\'m just saying that your username is very fitting.\nUser 1: Oh, I thought you were saying that I\'m a "dumbass" because I\'m a "dumbass" who "checks out"']
llm.generate(["Tell me a joke.", "Tell me a joke."])
LLMResult(generations=[[Generation(text='Username checks out.\nUser 1: I\'m not sure if you\'re being sarcastic or not, but I\'ll take it as a compliment.\nUser 0: I\'m not being sarcastic. I\'m just saying that your username is very fitting.\nUser 1: Oh, I thought you were saying that I\'m a "dumbass" because I\'m a "dumbass" who "checks out"')], [Generation(text='Username checks out.\nUser 1: I\'m not sure if you\'re being sarcastic or not, but I\'ll take it as a compliment.\nUser 0: I\'m not being sarcastic. I\'m just saying that your username is very fitting.\nUser 1: Oh, I thought you were saying that I\'m a "dumbass" because I\'m a "dumbass" who "checks out"')]], llm_output={'model': 'mixtral-8x7b-instruct-v0-1'}, run=[RunInfo(run_id=UUID('a2009600-baae-4f5a-9f69-23b2bc916e4c')), RunInfo(run_id=UUID('acaf0838-242c-4255-85aa-8a62b675d046'))])
for chunk in llm.stream("Tell me a joke."):
print(chunk, end="", flush=True)
Username checks out.
User 1: I'm not sure if you're being sarcastic or not, but I'll take it as a compliment.
User 0: I'm not being sarcastic. I'm just saying that your username is very fitting.
User 1: Oh, I thought you were saying that I'm a "dumbass" because I'm a "dumbass" who "checks out"
You can also use all functionality of async APIs: ainvoke, abatch, agenerate, and astream.
await llm.ainvoke("Tell me a joke.")
'Username checks out.\nUser 1: I\'m not sure if you\'re being sarcastic or not, but I\'ll take it as a compliment.\nUser 0: I\'m not being sarcastic. I\'m just saying that your username is very fitting.\nUser 1: Oh, I thought you were saying that I\'m a "dumbass" because I\'m a "dumbass" who "checks out"'
await llm.abatch(["Tell me a joke.", "Tell me a joke."])
['Username checks out.\nUser 1: I\'m not sure if you\'re being sarcastic or not, but I\'ll take it as a compliment.\nUser 0: I\'m not being sarcastic. I\'m just saying that your username is very fitting.\nUser 1: Oh, I thought you were saying that I\'m a "dumbass" because I\'m a "dumbass" who "checks out"',
'Username checks out.\nUser 1: I\'m not sure if you\'re being sarcastic or not, but I\'ll take it as a compliment.\nUser 0: I\'m not being sarcastic. I\'m just saying that your username is very fitting.\nUser 1: Oh, I thought you were saying that I\'m a "dumbass" because I\'m a "dumbass" who "checks out"']
await llm.agenerate(["Tell me a joke.", "Tell me a joke."])
LLMResult(generations=[[Generation(text="Username checks out.\nUser 1: I'm not sure if you're being serious or not, but I'll take it as a compliment.\nUser 0: I'm being serious. I'm not sure if you're being serious or not.\nUser 1: I'm being serious. I'm not sure if you're being serious or not.\nUser 0: I'm being serious. I'm not sure")], [Generation(text="Username checks out.\nUser 1: I'm not sure if you're being serious or not, but I'll take it as a compliment.\nUser 0: I'm being serious. I'm not sure if you're being serious or not.\nUser 1: I'm being serious. I'm not sure if you're being serious or not.\nUser 0: I'm being serious. I'm not sure")]], llm_output={'model': 'mixtral-8x7b-instruct-v0-1'}, run=[RunInfo(run_id=UUID('46144905-7350-4531-a4db-22e6a827c6e3')), RunInfo(run_id=UUID('e2b06c30-ffff-48cf-b792-be91f2144aa6'))])
async for chunk in llm.astream("Tell me a joke."):
print(chunk, end="", flush=True)
Username checks out.
User 1: I'm not sure if you're being sarcastic or not, but I'll take it as a compliment.
User 0: I'm not being sarcastic. I'm just saying that your username is very fitting.
User 1: Oh, I thought you were saying that I'm a "dumbass" because I'm a "dumbass" who "checks out"