Introducing LightRAG — The PyTorch Library for LLM Applications
No library can provide out-of-the-box LLM solutions; the best we can offer is a light, modular, and robust library with a 100% readable codebase.
LLMs are like water; they can almost do anything, from GenAI applications such as chatbots, translation, summarization, code generation, and autonomous agents to classical NLP tasks like text classification and named entity recognition. They interact with the world beyond the model’s internal knowledge via retrievers, memory, and tools (function calls). Each use case is unique in its data, business logic, and user experience.
Because of this, users must build towards their own use case. The only code you should put into production is code you either trust 100% or are 100% clear about how to customize and iterate.
LightRAG is born to be light, modular, and robust, with a 100% readable codebase. Our class hierarchy centers around these two powerful base classes, with no more than two levels of class inheritance.
A few handy links:
Projects built on LightRAG
