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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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This is @CS50's Introduction to Databases with SQL, with CS50's own Carter Zenke. Available for credit at Harvard Extension School starting September 5, 2023, at https://web.dce.harvard.edu/extension/csci/e/151. Available on @edx starting October 1, 2023, at https://cs50.edx.org/sql. Learn how to create, read, update, and delete data with relational databases, which store data in rows and columns. Learn how to model real-world entities and relationships among them using tables with appropriate t
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