题意:在 LangChain 中,OpenAI
和 ChatOpenAI
的主要区别是什么?
问题背景:
I read the LangChain Quickstart.
There is a demo inside: 里面有一个演示:
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.
我搜索了文档的其余部分,也在线进行了搜索,但没有找到关于OpenAI和ChatOpenAI之间区别的任何信息。
Based on from langchain.llms import OpenAI
, OpenAI is a large language model (LLM) which is also chat related.
基于从 langchain.llms
导入的 OpenAI
,OpenAI
是一个大型语言模型(LLM),也与聊天相关。
So is OpenAI more general-purpose, while ChatOpenAI more chat focused?
所以 OpenAI 是更通用的,而 ChatOpenAI 更专注于聊天吗?
What is the difference between OpenAI
class and ChatOpenAI
class in LangChain? Could someone clarify?
在 LangChain 中,OpenAI
类和 ChatOpenAI
类之间的区别是什么,有人能澄清下吗?
问题解决:
TL;DR
Based on my research, 基于我的研究,
-
OpenAI
class includes more generic machine learning task attributes such asfrequency_penalty
,presence_penalty
,logit_bias
,allowed_special
,disallowed_special
,best_of
.
基于我的了解,OpenAI
类包含了更通用的机器学习任务属性,如 frequency_penalty
(频率惩罚)、presence_penalty
(存在惩罚)、logit_bias
(对数几率偏差)、allowed_special
(允许的特殊字符)、disallowed_special
(不允许的特殊字符)和 best_of
(选择最佳输出)。这些属性可能用于调整模型的行为或输出,以适应不同的应用场景或需求。
-
ChatOpenAI
class provides more chat-related methods, such ascompletion_with_retry
,get_num_tokens_from_messages
to make it more user-friendly when build chatbot related applications.
ChatOpenAI
类提供了更多与聊天相关的方法,如 completion_with_retry
(带重试的补全)、get_num_tokens_from_messages
(从消息中获取令牌数量)等,以使得在构建聊天机器人相关应用时更加用户友好。
Class Inheritance 类继承
Upon reviewing the source code, here's what I've discovered.
在审查源代码后,这是我所发现的。
Listed below are the class inheritances for both the OpenAI
and ChatOpenAI
classes, along with their respective class attributes and methods.
以下是 OpenAI 类和 ChatOpenAI 类的类继承关系,以及它们各自的类属性和方法。
OpenAI
OpenAI ← BaseOpenAI ← BaseLLM ← BaseLanguageModel
ChatOpenAI
ChatOpenAI ← BaseChatModel ← BaseLanguageModel
Comparison 比较
Let's begin our comparison, moving from the fourth column to the first column.
让我们从第四列开始,逐一进行比较,直到第一列。
Fourth Column 第四列
Both classes ultimately inherit from the base class BaseLanguageModel
.
这两个类最终都继承自基类 BaseLanguageModel
。
Third Column 第三列
BaseLLM
and BaseChatModel
are very similar with slightly difference:
BaseLLM
和 BaseChatModel
非常相似,但略有不同:
For OpenAI's BaseLLM
, it includes additional methods:
对于 OpenAI 的 BaseLLM
,它包含了额外的方法:
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:
对于 ChatOpenAI 的 BaseChatModel
,它包含了一个额外的方法:
_combine_llm_outputs(self, llm_outputs)
Second Column 第二列
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.
第二列包含 BaseOpenAI
类,这个类之所以存在,主要是因为有更高层级的 OpenAI
和 AzureOpenAI
类。然而,它们都共享相同的类属性和方法。
First Column
At the top-level class (first column): 在顶层类(第一列):
-
OpenAI
class includes more generic machine learning task attributes such asfrequency_penalty
,presence_penalty
,logit_bias
,allowed_special
,disallowed_special
,best_of
.
OpenAI
类包含了更通用的机器学习任务属性,如 frequency_penalty
(频率惩罚)、presence_penalty
(存在惩罚)、logit_bias
(对数几率偏差)、allowed_special
(允许的特殊字符)、disallowed_special
(不允许的特殊字符)和 best_of
(选择最佳输出)。
-
ChatOpenAI
class provides more chat-related methods, such ascompletion_with_retry
,get_num_tokens_from_messages
to make it more user-friendly when build chatbot related applications.
ChatOpenAI
类提供了更多与聊天相关的方法,如 completion_with_retry
(带重试的补全)、get_num_tokens_from_messages
(从消息中获取令牌数量)等,以使得在构建聊天机器人相关应用时更加用户友好。