Blog RAG Intelligent Knowledge System

Project Background

To optimize content reuse and internal knowledge retrieval across hundreds of travel blog archives, I designed and implemented a Retrieval-Augmented Generation (RAG) knowledge engine. Blending dense semantic retrieval with LLM-powered synthesis, it supports instant cross-article query answering, editorial content aggregation, and rapid editorial research.

Technical Architecture

The system utilizes LangChain to orchestrate a FAISS vector index with dense E5 text embeddings. Queries are synthesized with near-instant latency via Groq's high-throughput LLaMA-3-70B inference engine. By segmenting historical articles, creating dense vector indices, and applying semantic similarity matching, the pipeline enables conversational question-answering and multi-article summarization to inform content strategy.

Practical Application

For example, when a user queries "What are the must-visit attractions in Busan?", the engine retrieves pertinent segments across several travel posts and synthesizes a concise recommendation: highlighting Haeundae Beach, SPA LAND, Busan Tower, Songdo Marine Cable Car, the Busan Fireworks Festival, Seomyeon Market, and the BUSAN X the SKY observatory. This intelligent retrieval dramatically cuts research overhead while enhancing the longevity and discoverability of published content.