What is the difference between OpenAI and ChatOpenAI in LangChain?

What is the difference between OpenAI and ChatOpenAI in LangChain?

https://stackoverflow.com/questions/76950609/what-is-the-difference-between-openai-and-chatopenai-in-langchain

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.

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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

ChatOpenAI

ChatOpenAI ← BaseChatModel ← BaseLanguageModel

 

LangChain中ChatOpenAI 和OpenAI的区别

https://zhuanlan.zhihu.com/p/704988950

LangChain官网的解释

LangChain中有两种类型的语言模型，称为:

LLMs: 这是一个以字符串作为输入并返回字符串的语言模型
ChatModels: 这是一个以消息列表作为输入并返回消息的语言模型

LLMs的输入/输出简单易懂 - 字符串。但是ChatModels呢？那里的输入是一个ChatMessage列表，输出是一个单独的ChatMessage。一个ChatMessage具有两个必需的组件:

content: 这是消息的内容。
role: 这是ChatMessage来自的实体的角色。

来源：
https://python.langchain.com.cn/docs/get_started/quickstart

简单的总结一下上边的内容：

• OpenAI属于LLMs，其输入是字符串，输出也是字符串；
• ChatOpenAI属于聊天模型，其输入是消息列表，输出是消息列表。

两者的选择

知道了LLMs和聊天模型的区别，那我们在实际使用的过程中该怎么选择呢？

ChatOpenAI侧重于模型被给与一组消息来构成会话，模型基于这组会话会进行后续的响应。OpenAI是基于问与答，没有会话的概念。

选择ChatOpenAI的情况是需要构建一个能够进行实时对话交流的聊天机器人，用于与用户进行自然语言交互和提供实时的响应。这种情况下，ChatOpenAI可以用于开发聊天机器人、虚拟助手或客服系统等应用。

选择OpenAI的情况是需要进行通用的机器学习和人工智能研究，包括开发和训练各种类型的机器学习模型，如图像识别、自然语言处理

、语音识别等。OpenAI提供了一系列强大的机器学习工具和算法，适用于广泛的应用领域，并且能够满足复杂的研究和开发需求。

 

LLM providers --- the low level LLM apis

https://bigmodel.cn/dev/api/libraries

https://github.com/openai/openai-python

 

 

openai

https://python.langchain.com/docs/concepts/text_llms/

https://python.langchain.com/docs/integrations/llms/openai/

LangChain has implementations for older language models that take a string as input and return a string as output. These models are typically named without the "Chat" prefix (e.g., Ollama, Anthropic, OpenAI, etc.), and may include the "LLM" suffix (e.g., OllamaLLM, AnthropicLLM, OpenAILLM, etc.). These models implement the BaseLLM interface.

Users should be using almost exclusively the newer Chat Models as most model providers have adopted a chat like interface for interacting with language models.

 

https://python.langchain.ac.cn/docs/modules/model_io/llms/quick_start/

Large Language Models (LLMs) are a core component of LangChain. LangChain does not serve its own LLMs, but rather provides a standard interface for interacting with many different LLMs.

There are lots of LLM providers (OpenAI, Cohere, Hugging Face, etc) - the LLM class is designed to provide a standard interface for all of them.

In this walkthrough we’ll work with an OpenAI LLM wrapper, although the functionalities highlighted are generic for all LLM types.

 

ChatOpenAI

https://python.langchain.com/docs/integrations/chat/openai/

This notebook provides a quick overview for getting started with OpenAI chat models. For detailed documentation of all ChatOpenAI features and configurations head to the API reference.

OpenAI has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the OpenAI docs.

https://python.langchain.ac.cn/docs/modules/model_io/chat/

 

/langchain-openai

https://pypi.org/project/langchain-openai/

Project description

langchain-openai

This package contains the LangChain integrations for OpenAI through their openai SDK.

Installation and Setup

• Install the LangChain partner package

pip install langchain-openai

• Get an OpenAI api key and set it as an environment variable (OPENAI_API_KEY)

Chat model

See a usage example.

from langchain_openai import ChatOpenAI

If you are using a model hosted on Azure, you should use different wrapper for that:

from langchain_openai import AzureChatOpenAI

For a more detailed walkthrough of the Azure wrapper, see here

Text Embedding Model

See a usage example

from langchain_openai import OpenAIEmbeddings

If you are using a model hosted on Azure, you should use different wrapper for that:

from langchain_openai import AzureOpenAIEmbeddings

For a more detailed walkthrough of the Azure wrapper, see here

LLM (Legacy)

LLM refers to the legacy text-completion models that preceded chat models. See a usage example.

from langchain_openai import OpenAI

If you are using a model hosted on Azure, you should use different wrapper for that:

from langchain_openai import AzureOpenAI

For a more detailed walkthrough of the Azure wrapper, see here

 

https://python.langchain.com/api_reference/openai/index.html

langchain-openai: 0.3.1

 

chat_models

Classes

chat_models.azure.AzureChatOpenAI	Azure OpenAI chat model integration.
chat_models.base.BaseChatOpenAI
chat_models.base.ChatOpenAI	OpenAI chat model integration.
chat_models.base.OpenAIRefusalError	Error raised when OpenAI Structured Outputs API returns a refusal.

embeddings

Classes

embeddings.azure.AzureOpenAIEmbeddings	AzureOpenAI embedding model integration.
embeddings.base.OpenAIEmbeddings	OpenAI embedding model integration.

llms

Classes

llms.azure.AzureOpenAI	Azure-specific OpenAI large language models.
llms.base.BaseOpenAI	Base OpenAI large language model class.
llms.base.OpenAI	OpenAI completion model integration.