Beyond Vector Search: Building a "Reasoning Engine" in Copilot Studio

Intro: We've all been there. You build a powerful RAG copilot, feed it a dense, 100-page document, and ask a specific, nuanced question... only to get a vague, incomplete, or just plain wrong answer. Why does this happen? The culprit is often our reliance on traditional vector search, which excels at finding "semantically similar" text but struggles to understand context, nuance, and structure. It finds words that sound like the answer, but it can't reason about the document to find the truth. One of my work colleage shared about PageIndex which made me curious to explore. The Experimental Setup: To create a fair and direct comparison, I used a single Microsoft Copilot Studio instance and one source document: the official EU AI ACT .pdf. From this document, I generated two distinct knowledge sources. The first was a standard embeddings.json file, created by chunking the document and generating vector embeddings—the foundation for a traditional RAG approach. The second was a structured