What I Learned Building a Local RAG Agent
The author details the construction of a local Retrieval-Augmented Generation (RAG) agent designed to answer questions based on a collection of markdown documents. The agent employs a five-stage pipeline: ingestion to chunk documents, embedding to convert text into numerical vectors, storage in a local vector database (ChromaDB), retrieval of relevant chunks based on a user's query, and orchestration to synthesize an answer using a local AI model. AI
IMPACT Provides a technical blueprint for building custom AI-powered question-answering systems using local resources.