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Vector databases store data as numerical embeddings that capture semantic meaning rather than relying only on exact matches. This allows them to retrieve information based on similarity, context, and intent. Compared with traditional databases, they are especially powerful for AI applications such as semantic search, recommendations, and retrieval-augmented generation. [ai engineering, ai, python, agents, coding, software engineering, database, postgres, qdrant, mysql]
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