Build Document RAG System with Kimi-K2, Gemini Embeddings & Qdrant

This advanced n8n workflow constructs a complete RAG pipeline for processing large documents, such as the UK Highway Code or educational materials. It imports documents via HTTP, extracts text, and processes each page individually by generating contextual summaries using the surrounding pages (previous and next) with the Kimi-K2 Instruct LLM from Moonshot AI. This approach ensures summaries capture broader context, improving retrieval accuracy over raw text embeddings. The workflow then convert