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What is the difference between OpenAI and ChatOpenAI in LangChain?

I read the LangChain Quickstart.

There is a demo inside:

from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI

llm = OpenAI()
chat_model = ChatOpenAI()

llm.predict("hi!")
>>> "Hi"

chat_model.predict("hi!")
>>> "Hi"

I searched the rest of the document and also online, but didn't find any info for the difference between OpenAI and ChatOpenAI.

Based on from langchain.llms import OpenAI, OpenAI is a large language model (LLM) which is also chat related.

So is OpenAI more general-purpose, while ChatOpenAI more chat focused?

What is the difference between OpenAI class and ChatOpenAI class in LangChain? Could someone clarify?

Solution

  • TL;DR

    Based on my research,

    • OpenAI class includes more generic machine learning task attributes such as frequency_penalty, presence_penalty, logit_bias, allowed_special, disallowed_special, best_of.

    • ChatOpenAI class provides more chat-related methods, such as completion_with_retry, get_num_tokens_from_messages to make it more user-friendly when build chatbot related applications.

    Class Inheritance

    Upon reviewing the source code, here's what I've discovered.

    Listed below are the class inheritances for both the OpenAI and ChatOpenAI classes, along with their respective class attributes and methods.

    OpenAI

    OpenAI ← BaseOpenAI ← BaseLLM ← BaseLanguageModel

    OpenAI

    ChatOpenAI

    ChatOpenAI ← BaseChatModel ← BaseLanguageModel

    ChatOpenAI

    Comparison

    Let's begin our comparison, moving from the fourth column to the first column.

    Fourth Column

    Both classes ultimately inherit from the base class BaseLanguageModel.

    Third Column

    BaseLLM and BaseChatModel are very similar with slightly difference:

    • For OpenAI's BaseLLM, it includes additional methods:

      • batch(self, inputs, config=None, max_concurrency=None, **kwargs)
      • abatch (self, inputs, config=None, max_concurrency=None,**kwargs)

    • For ChatOpenAI's BaseChatModel, it includes an extra method:

      • _combine_llm_outputs(self, llm_outputs)

    Second Column

    The second column contains the BaseOpenAI class, which primarily exists due to the presence of higher-level classes OpenAI and AzureOpenAI. However, they all share the same class attributes and methods.

    First Column

    At the top-level class (first column):

    • OpenAI class includes more generic machine learning task attributes such as frequency_penalty, presence_penalty, logit_bias, allowed_special, disallowed_special, best_of.

    • ChatOpenAI class provides more chat-related methods, such as completion_with_retry, get_num_tokens_from_messages to make it more user-friendly when build chatbot related applications.