Large language models don't have inherent memory, but vector stores enable AI agents to simulate memory by converting text into numerical embeddings and storing them in specialized databases. When users interact with AI, the system searches for semantically similar stored vectors to retrieve relevant past information. Popular vector databases include FAISS for local deployments and Pinecone for cloud-based solutions. This approach, called retrieval-augmented generation (RAG), allows AI to appear contextually aware despite technical limitations around similarity-based matching and static embeddings.
Table of contents
Table of ContentsWhat Is a Vector Store?How Embeddings WorkWhy Vector Stores Are Crucial for MemoryPopular Vector StoresMaking AI Seem Smart with Retrieval-Augmented GenerationThe Limits of Vector-Based MemoryConclusionSort: