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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.

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Setup

Ensure the langchain_community and friendli-client are installed. 
pip install -U langchain-community 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

if "FRIENDLI_TOKEN" not in os.environ:
    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.chat_models.friendli import ChatFriendli

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

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Usage

FrienliChat supports all methods of ChatModel including async APIs. You can also use functionality of invoke, batch, generate, and stream. 
from langchain_core.messages.human import HumanMessage
from langchain_core.messages.system import SystemMessage

system_message = SystemMessage(content="Answer questions as short as you can.")
human_message = HumanMessage(content="Tell me a joke.")
messages = [system_message, human_message]

chat.invoke(messages)

AIMessage(content="Why don't eggs tell jokes? They'd crack each other up.", additional_kwargs={}, response_metadata={}, id='run-d47c1056-54e8-4ea9-ad63-07cf74b834b7-0')

chat.batch([messages, messages])

[AIMessage(content="Why don't eggs tell jokes? They'd crack each other up.", additional_kwargs={}, response_metadata={}, id='run-36775b84-2a7a-48f0-8c68-df23ffffe4b2-0'),
 AIMessage(content="Why don't eggs tell jokes? They'd crack each other up.", additional_kwargs={}, response_metadata={}, id='run-b204be41-bc06-4d3a-9f74-e66ab1e60e4f-0')]

chat.generate([messages, messages])

LLMResult(generations=[[ChatGeneration(text="Why don't eggs tell jokes? They'd crack each other up.", message=AIMessage(content="Why don't eggs tell jokes? They'd crack each other up.", additional_kwargs={}, response_metadata={}, id='run-2e4cb949-8c51-40d5-92a0-cd0ac577db83-0'))], [ChatGeneration(text="Why don't eggs tell jokes? They'd crack each other up.", message=AIMessage(content="Why don't eggs tell jokes? They'd crack each other up.", additional_kwargs={}, response_metadata={}, id='run-afcdd1be-463c-4e50-9731-7a9f5958e396-0'))]], llm_output={}, run=[RunInfo(run_id=UUID('2e4cb949-8c51-40d5-92a0-cd0ac577db83')), RunInfo(run_id=UUID('afcdd1be-463c-4e50-9731-7a9f5958e396'))], type='LLMResult')

for chunk in chat.stream(messages):
    print(chunk.content, end="", flush=True)

Why don't eggs tell jokes? They'd crack each other up.
You can also use all functionality of async APIs: ainvoke, abatch, agenerate, and astream. 
await chat.ainvoke(messages)

AIMessage(content="Why don't eggs tell jokes? They'd crack each other up.", additional_kwargs={}, response_metadata={}, id='run-ba8062fb-68af-47b8-bd7b-d1e01b914744-0')

await chat.abatch([messages, messages])

[AIMessage(content="Why don't eggs tell jokes? They'd crack each other up.", additional_kwargs={}, response_metadata={}, id='run-5d2c77ab-2637-45da-8bbe-1b1f18a22369-0'),
 AIMessage(content="Why don't eggs tell jokes? They'd crack each other up.", additional_kwargs={}, response_metadata={}, id='run-f1338470-8b52-4d6e-9428-a694a08ae484-0')]

await chat.agenerate([messages, messages])

LLMResult(generations=[[ChatGeneration(text="Why don't eggs tell jokes? They'd crack each other up.", message=AIMessage(content="Why don't eggs tell jokes? They'd crack each other up.", additional_kwargs={}, response_metadata={}, id='run-d4e44569-39cc-40cc-93fc-de53e599fd51-0'))], [ChatGeneration(text="Why don't eggs tell jokes? They'd crack each other up.", message=AIMessage(content="Why don't eggs tell jokes? They'd crack each other up.", additional_kwargs={}, response_metadata={}, id='run-54647cc2-bee3-4154-ad00-2e547993e6d7-0'))]], llm_output={}, run=[RunInfo(run_id=UUID('d4e44569-39cc-40cc-93fc-de53e599fd51')), RunInfo(run_id=UUID('54647cc2-bee3-4154-ad00-2e547993e6d7'))], type='LLMResult')

async for chunk in chat.astream(messages):
    print(chunk.content, end="", flush=True)

Why don't eggs tell jokes? They'd crack each other up.