ChatFriendli

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 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.chat_models.friendli import ChatFriendli

chat = ChatFriendli(model="llama-2-13b-chat", max_tokens=100, temperature=0)

API Reference:ChatFriendli

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)

API Reference:HumanMessage | SystemMessage

AIMessage(content=" Knock, knock!\nWho's there?\nCows go.\nCows go who?\nMOO!")

chat.batch([messages, messages])

[AIMessage(content=" Knock, knock!\nWho's there?\nCows go.\nCows go who?\nMOO!"),
 AIMessage(content=" Knock, knock!\nWho's there?\nCows go.\nCows go who?\nMOO!")]

chat.generate([messages, messages])

LLMResult(generations=[[ChatGeneration(text=" Knock, knock!\nWho's there?\nCows go.\nCows go who?\nMOO!", message=AIMessage(content=" Knock, knock!\nWho's there?\nCows go.\nCows go who?\nMOO!"))], [ChatGeneration(text=" Knock, knock!\nWho's there?\nCows go.\nCows go who?\nMOO!", message=AIMessage(content=" Knock, knock!\nWho's there?\nCows go.\nCows go who?\nMOO!"))]], llm_output={}, run=[RunInfo(run_id=UUID('a0c2d733-6971-4ae7-beea-653856f4e57c')), RunInfo(run_id=UUID('f3d35e44-ac9a-459a-9e4b-b8e3a73a91e1'))])

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

 Knock, knock!
Who's there?
Cows go.
Cows go who?
MOO!

You can also use all functionality of async APIs: ainvoke, abatch, agenerate, and astream.

await chat.ainvoke(messages)

AIMessage(content=" Knock, knock!\nWho's there?\nCows go.\nCows go who?\nMOO!")

await chat.abatch([messages, messages])

[AIMessage(content=" Knock, knock!\nWho's there?\nCows go.\nCows go who?\nMOO!"),
 AIMessage(content=" Knock, knock!\nWho's there?\nCows go.\nCows go who?\nMOO!")]

await chat.agenerate([messages, messages])

LLMResult(generations=[[ChatGeneration(text=" Knock, knock!\nWho's there?\nCows go.\nCows go who?\nMOO!", message=AIMessage(content=" Knock, knock!\nWho's there?\nCows go.\nCows go who?\nMOO!"))], [ChatGeneration(text=" Knock, knock!\nWho's there?\nCows go.\nCows go who?\nMOO!", message=AIMessage(content=" Knock, knock!\nWho's there?\nCows go.\nCows go who?\nMOO!"))]], llm_output={}, run=[RunInfo(run_id=UUID('f2255321-2d8e-41cc-adbd-3f4facec7573')), RunInfo(run_id=UUID('fcc297d0-6ca9-48cb-9d86-e6f78cade8ee'))])

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

 Knock, knock!
Who's there?
Cows go.
Cows go who?
MOO!

Related

• Chat model conceptual guide
• Chat model how-to guides