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Qdrant is a vector database built for semantic search. Instead of searching only by exact keywords, Qdrant lets you search by meaning. It stores embeddings — numeric representations of text, images, or other data — and finds nearby vectors using similarity search. The basic Qdrant concepts are: Collections Vectors Payload metadata ...
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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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