Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation (RAG) is an NLP model architecture that combines the retrieval-based and generation-based approaches to enable a model’s capability to extract information from a specified document. The language model utilizes user-specific data to pull the relevant information. RAG overcomes the limitations in generating contextually relevant and accurate responses by leveraging the benefits of retrieval mechanisms. This results in more informed and contextually appropriate responses.

LangChain facilitates the implementation of RAG applications, empowering developers to seamlessly replace specific functionalities within an application.