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How do AI agents get long-term memory or access to your documents? A vector database. The short version: you convert your data into embeddings, which are numerical representations of meaning, store them, then give the agent a search tool so it pulls only the relevant information for whatever you're asking. This pattern is called RAG, retrieval augmented generation, and it's how agents answer questions about documents and remember long-term facts even when the context window is full of the curren
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