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RAG & MCP

2. Embedding & Indexing

Vector Storage Architecture — Where Everything Lives

The biggest misconception about vector databases: they do NOT store your documents. They store vectors (arrays of floats) and metadata. The actual text lives elsewhere. Understanding this architecture is critical for production systems.

What Goes Where

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Retrieval-Augmented Generation (RAG) architecture explained: ingestion pipeline, vector search, prompt augmentation, and production patterns.

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Understanding embeddings for AI applications: text, image, and multi-modal embeddings with similarity search and clustering.

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