Building a Local Retrieval Augmented Generation System in Elixir

This article outlines the process of building a Retrieval Augmented Generation (RAG) system using the Elixir programming language. The author starts by noting the previous implementation in Python and expresses a desire to leverage Elixir's strengths instead. The guide details the necessary components for the RAG system, including an ingestion pipeline to read and process an Elixir codebase, a retrieval mechanism to query and find relevant code chunks, and a generation pipeline to produce responses using a language model. The content includes specific code snippets and configurations, utilizing tools like Bumblebee for model management and Chroma for data storage. The author also presents the architecture of the RAG system, emphasizing ease of use by eliminating the need for external authentication with Hugging Face when using the Phi-3.5 model. Eventually, the article encourages readers to experiment with the system and engage with the community for support and feedback.