Overview of LangChain
Motto: Building applications with LLMs through composability
Fascinated by apps like ChatGPT? Wish to experiment with models behind them? Even open-source/free models? Look no further… LangChain is the way to go …
LangChain is an open-source library that provides developers with the necessary tools to create applications powered by large language models (LLMs) [1][2]. It is a framework built around LLMs that can be used for chatbots, Generative Question-Answering (GQA), summarization, and much more [3]. The core idea of the library is that developers can “chain” together different components to create more advanced use cases around LLMs [3]. Chains may consist of multiple components from several modules [3]. LangChain is packed full of incredible features for building amazing tools around the core of LLMs [3].
Advantages
- LangChain provides out-of-the-box support to build NLP applications using LLMs [2].
- It allows developers to chain together different components to create more advanced use cases around LLMs [3].
- LangChain can be particularly useful in the complex field of data science [1].
Disadvantages
- LangChain is a relatively new library, so there may be some bugs or issues that have not yet been discovered or resolved.
- It may require some time and effort to learn how to use LangChain effectively.
Applications
- LangChain can be used for chatbots, Generative Question-Answering (GQA), summarization, and much more [3].
- It can be used to build NLP applications using LLMs [2].
- LangChain can be particularly useful in the complex field of data science [1].
- It can be used to build search engines using LLM embeddings and a vector database [4].
Below is a high level overview of LangChain in form of a Sketchnote
References
[1]
[2]
[3]
[4]
