Developer Builds Local RAG Pipeline Using LangChain, ChromaDB, and LM Studio

A developer has published a hands-on technical guide demonstrating how to build a Retrieval-Augmented Generation (RAG) system entirely from scratch without relying on OpenAI or any cloud services. The pipeline uses Python, LangChain, ChromaDB, and a locally hosted Qwen 9B language model running via LM Studio. The system is designed to solve a practical business problem where employees can query large volumes of HR documentation and receive accurate, document-grounded answers in seconds. The pipeline consists of four core components: a document processor that splits text into chunks, a custom embedding service, a ChromaDB vector store for semantic retrieval, and a local LLM for response generation. The guide highlights key engineering trade-offs, such as the tight coupling between chunk size and embedding quality, and the need to use a consistent embedding model across both indexing and querying stages.