Building LLM-Powered Web Apps with Client-Side Technology

The blog post discusses the development of a web application that utilizes local machine learning models to perform Retrieval-Augmented Generation (RAG), enabling users to interact with documents through chat. The author explores building this app using JavaScript to capitalize on its widespread use among web developers, aiming to leverage local models for cost efficiency, privacy, and potentially faster processing due to reduced server requests. The process involves splitting documents into semantic chunks, creating vector representations with embeddings, and storing them in a vector store to facilitate natural language queries. Despite challenges with running large language models (LLMs) in the browser, the author successfully integrates the Mistral 7B model using Ollama, demonstrating that local models can be effectively used in web apps with the right configuration. The post highlights the rapid advancements in open-source machine learning models and the potential for future web applications to incorporate local LLMs through new browser APIs, while also providing links to resources and further reading.

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