Semantic Kernel Memory: Vector Stores, Embeddings, and Semantic Search

LLMs have a fundamental limitation: they're stateless. Every request starts fresh with no memory of previous conversations or your organization's knowledge. This is where Semantic Kernel's memory system comes in—transforming raw text into searchable vector embeddings that give your AI persistent, semantic understanding. In Part 2 , we explored plugins. Now we'll dive deep into the memory layer that powers intelligent retrieval. Why Memory Matters Consider a customer support bot. Without memory, it can't: Remember what the customer said 5 messages ago Access your product documentation Know your company's policies Learn from resolved tickets With Semantic Kernel memory, you transform unstructured text into vector embeddings —numerical representations that capture semantic meaning. Similar concepts cluster together in vector space, enabling semantic search that understands intent, not just keywords. Understanding Embeddings Before diving into code, let's understand what's happening under