.ipynb
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Getting Started
 Contents 
PromptTemplates
LLMChain
Streaming
Getting Started#
This notebook covers how to get started with chat models. The interface is based around messages rather than raw text.
from langchain.chat_models import ChatOpenAI
from langchain import PromptTemplate, LLMChain
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    AIMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
from langchain.schema import (
    AIMessage,
    HumanMessage,
    SystemMessage
)
chat = ChatOpenAI(temperature=0)
You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are AIMessage, HumanMessage, SystemMessage, and ChatMessage – ChatMessage takes in an arbitrary role parameter. Most of the time, you’ll just be dealing with HumanMessage, AIMessage, and SystemMessage
chat([HumanMessage(content="Translate this sentence from English to French. I love programming.")])
AIMessage(content="J'aime programmer.", additional_kwargs={})
OpenAI’s chat model supports multiple messages as input. See here for more information. Here is an example of sending a system and user message to the chat model:
messages = [
    SystemMessage(content="You are a helpful assistant that translates English to French."),
    HumanMessage(content="I love programming.")
]
chat(messages)
AIMessage(content="J'aime programmer.", additional_kwargs={})
You can go one step further and generate completions for multiple sets of messages using generate. This returns an LLMResult with an additional message parameter.
batch_messages = [
    [
        SystemMessage(content="You are a helpful assistant that translates English to French."),
rtdocs_stable/api.python.langchain.com/en/stable/modules/models/chat/getting_started.html