RAG / Vector Search on AEM Content
Building a RAG Chatbot on AEM Content using LangChain + OpenAI Introduction Imagine a chatbot that can answer questions like "What is our return policy?" or "How do I configure the Dispatcher?" — drawing answers directly from your AEM-managed content. This is exactly what Retrieval-Augmented Generation (RAG) enables. RAG combines a vector database (to store and search your content semantically) with an LLM (to generate natural language answers). The result is an AI assistant grounded in your actual AEM content, not hallucinated facts. In this post, we'll build a complete RAG pipeline that ingests AEM content fragments, stores them in a vector database, and serves answers via a chatbot API. Architecture AEM Content Fragments / Pages ↓ AEM Content API (JSON exporter) ↓ Python Ingestion Pipeline (chunking + embedding) ↓ Vector Database (Pinecone / ChromaDB) ↓ Query → Semantic Search → Top-K chunks ↓ ...