vLLM Chat

vLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. This server can be queried in the same format as OpenAI API.

This notebook covers how to get started with vLLM chat models using langchain’s ChatOpenAI as it is.

from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain_openai import ChatOpenAI

API Reference:

• HumanMessage
• SystemMessage
• ChatPromptTemplate
• HumanMessagePromptTemplate
• SystemMessagePromptTemplate
• ChatOpenAI

inference_server_url = "http://localhost:8000/v1"

chat = ChatOpenAI(
    model="mosaicml/mpt-7b",
    openai_api_key="EMPTY",
    openai_api_base=inference_server_url,
    max_tokens=5,
    temperature=0,
)

messages = [
    SystemMessage(
        content="You are a helpful assistant that translates English to Italian."
    ),
    HumanMessage(
        content="Translate the following sentence from English to Italian: I love programming."
    ),
]
chat(messages)

AIMessage(content=' Io amo programmare', additional_kwargs={}, example=False)

You can make use of templating by using a MessagePromptTemplate. You can build a ChatPromptTemplate from one or more MessagePromptTemplates. You can use ChatPromptTemplate’s format_prompt – this returns a PromptValue, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.

For convenience, there is a from_template method exposed on the template. If you were to use this template, this is what it would look like:

template = (
    "You are a helpful assistant that translates {input_language} to {output_language}."
)
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)

chat_prompt = ChatPromptTemplate.from_messages(
    [system_message_prompt, human_message_prompt]
)

# get a chat completion from the formatted messages
chat(
    chat_prompt.format_prompt(
        input_language="English", output_language="Italian", text="I love programming."
    ).to_messages()
)

AIMessage(content=' I love programming too.', additional_kwargs={}, example=False)