LangChain is a Python library that provides out-of-the-box support to build NLP applications using LLMs. It helps you leverage large language models to build custom NLP applications. LangChain makes the customization of models like GPT-3 more approachable by creating an API around the prompt engineering needed for a specific task, read more.

LangChain provides many modules that can be used to build language model applications. Modules can be combined to create more complex applications or be used individually for simple applications. The most basic building block of LangChain is calling an LLM on some input. The library pretty much takes care of everything you need to worry about:

from langchain.llms import OpenAI
llm = OpenAI(openai_api_key="OPENAI_API_KEY")

text = "What would be a good company name for a company that makes colorful socks?"
print(llm(text))

The above code works as you expect and prompts the OpenAI language model with the text you have provided it. From a researcher’s perspective, this process really simplifies the interaction with the underlying models. You can easily test different prompts, biases and shortcomings of models in a systematic way.

Advantages:

  • LangChain provides out-of-the-box support to build NLP applications using LLMs. You don’t have to deal with how to load, tokenise and prompt them.
  • It helps you leverage large language models to build custom NLP applications.
  • LangChain makes the customization of models like GPT-3 more approachable by creating an API around the Prompt engineering needed for a specific task.

Disadvantages:

  • LangChain is still in its early stages and may not have all the features you need.
  • It is changing quickly as more models are added and the whole large language model landscape evolves rapidly.
  • It may hide away details required for certain research such as tokenisation.

You can learn more from the following resources: