How I Built a RAG Engine in Node.js

A new RAG engine built with Node.js indexes personal notes, converts them into vector embeddings, and enables semantic search for conversational querying. This addresses the challenge of scattered personal information by allowing users to find relevant data based on contextual intent rather than just keywords. The system uses a three-stage RAG pipeline, including ingestion, vectorization, semantic retrieval, and threshold filtering to augment context for LLM responses.

Read the full article at DEV Community

---

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.