Developer Builds Minimal RAG Pipeline in TypeScript Without LangChain in 200 Lines

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A developer built a fully functional Retrieval-Augmented Generation (RAG) pipeline from scratch using TypeScript, deliberately avoiding popular frameworks like LangChain and LlamaIndex. The project spans six files and just over 200 lines of code, covering five core stages: text extraction, chunking, embedding, retrieval, and generation. Voyage AI handles embeddings via a free-tier API, while a locally run model through LM Studio replaces cloud-based LLMs, eliminating per-token costs. The author wrote the pipeline after finding existing RAG tutorials too abstracted to explain underlying concepts like embeddings and cosine similarity. The post documents the full pipeline, key data structure decisions, and four notable bugs encountered during development.

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