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

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)

API Reference:

• ChatEverlyAI
• HumanMessage
• SystemMessage

  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.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.chat_models import ChatEverlyAI
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)

API Reference:

• StreamingStdOutCallbackHandler
• ChatEverlyAI
• HumanMessage
• SystemMessage

  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.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.chat_models import ChatEverlyAI
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)

API Reference:

• StreamingStdOutCallbackHandler
• ChatEverlyAI
• HumanMessage
• SystemMessage

  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! *")