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Rahul Agarwal

Founder | Agentic AI... • 5d

AI apps don’t search text. They search meaning. If you work with LLMs, you’re already relying on vector databases whether you realize it or not. Here’s what’s really happening behind the scenes. First, you organize data into collections. Think of them like smart containers for related content. Each entry holds attributes like titles, tags, dates, or categories. Next comes structure. Every collection has rules. Data types. Index logic. Vector settings. This is where integrations like embeddings and compression get wired in. Now the interesting part starts. When someone sends a query, the text is not matched. It’s transformed. An embedding model converts words into numbers that represent intent and context, not keywords. Those numbers get stored as vectors. Each one has an ID. Each one represents meaning. To scale, the system splits data into shards. Indexes map relationships between vectors. This makes similarity search fast instead of brute force. When a new request arrives, it’s vectorized again. Then compared using math like cosine distance. Closest matches rise to the top. Finally, results are returned. Not because they look similar. Because they think similar. In simple terms: Vector databases don’t store text. They store understanding. That’s why modern AI can retrieve ideas, not just strings.

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