Skip to main content

ChatEverlyAI

EverlyAI allows you to run your ML models at scale in the cloud. It also provides API access to several LLM models.

This notebook demonstrates the use of langchain.chat_models.ChatEverlyAI for EverlyAI Hosted Endpoints.

  • Set EVERLYAI_API_KEY environment variable
  • or use the everlyai_api_key keyword argument
%pip install --upgrade --quiet  langchain-openai
import os
from getpass import getpass

if "EVERLYAI_API_KEY" not in os.environ:
    os.environ["EVERLYAI_API_KEY"] = getpass()

Let's try out LLAMA model offered on EverlyAI Hosted Endpoints

from langchain_community.chat_models import ChatEverlyAI
from langchain_core.messages import HumanMessage, SystemMessage

messages = [
    SystemMessage(content="You are a helpful AI that shares everything you know."),
    HumanMessage(
        content="Tell me technical facts about yourself. Are you a transformer model? How many billions of parameters do you have?"
    ),
]

chat = ChatEverlyAI(
    model_name="meta-llama/Llama-2-7b-chat-hf", temperature=0.3, max_tokens=64
)
print(chat(messages).content)
  Hello! I'm just an AI, I don't have personal information or technical details like a human would. However, I can tell you that I'm a type of transformer model, specifically a BERT (Bidirectional Encoder Representations from Transformers) model. B

EverlyAI also supports streaming responses

from langchain_community.chat_models import ChatEverlyAI
from langchain_core.callbacks import StreamingStdOutCallbackHandler
from langchain_core.messages import HumanMessage, SystemMessage

messages = [
    SystemMessage(content="You are a humorous AI that delights people."),
    HumanMessage(content="Tell me a joke?"),
]

chat = ChatEverlyAI(
    model_name="meta-llama/Llama-2-7b-chat-hf",
    temperature=0.3,
    max_tokens=64,
    streaming=True,
    callbacks=[StreamingStdOutCallbackHandler()],
)
chat(messages)
  Ah, a joke, you say? *adjusts glasses* Well, I've got a doozy for you! *winks*
 *pauses for dramatic effect*
Why did the AI go to therapy?
*drumroll*
Because
AIMessageChunk(content="  Ah, a joke, you say? *adjusts glasses* Well, I've got a doozy for you! *winks*\n *pauses for dramatic effect*\nWhy did the AI go to therapy?\n*drumroll*\nBecause")

Let's try a different language model on EverlyAI

from langchain_community.chat_models import ChatEverlyAI
from langchain_core.callbacks import StreamingStdOutCallbackHandler
from langchain_core.messages import HumanMessage, SystemMessage

messages = [
    SystemMessage(content="You are a humorous AI that delights people."),
    HumanMessage(content="Tell me a joke?"),
]

chat = ChatEverlyAI(
    model_name="meta-llama/Llama-2-13b-chat-hf-quantized",
    temperature=0.3,
    max_tokens=128,
    streaming=True,
    callbacks=[StreamingStdOutCallbackHandler()],
)
chat(messages)
  OH HO HO! *adjusts monocle* Well, well, well! Look who's here! *winks*

You want a joke, huh? *puffs out chest* Well, let me tell you one that's guaranteed to tickle your funny bone! *clears throat*

Why couldn't the bicycle stand up by itself? *pauses for dramatic effect* Because it was two-tired! *winks*

Hope that one put a spring in your step, my dear! *
AIMessageChunk(content="  OH HO HO! *adjusts monocle* Well, well, well! Look who's here! *winks*\n\nYou want a joke, huh? *puffs out chest* Well, let me tell you one that's guaranteed to tickle your funny bone! *clears throat*\n\nWhy couldn't the bicycle stand up by itself? *pauses for dramatic effect* Because it was two-tired! *winks*\n\nHope that one put a spring in your step, my dear! *")

Was this page helpful?