Implementing RAG with AnythingLLM Large Language Models (LLMs) can answer a wide range of questions. However, when a question involves specific documents or private information, relying solely on the model’s existing knowledge may not provide accurate answers. RAG (Retrieval-Augmented Generation) is a common approach to this problem. It retrieves relevant information from documents and provides
Introduction to PageAssist PageAssist is an open-source Chrome extension that provides a simple interface for using Ollama directly in the browser. It also supports features such as RAG (Retrieval-Augmented Generation) and MCP (Model Context Protocol). In this article, I will introduce how to implement RAG using PageAssist and Ollama. I will use Lanner Electronics’ first-quarter
Retrieval-Augmented Generation (RAG) A model generates an answer and that answer is enhanced by retrieving relevant information from a collection of documents. Even when a Large Language Model (LLM) has a sufficiently large context window, feeding a huge amount of data into the model all at once can still cause confusion or dilute its attention.