From the course: Introduction to AI-Native Vector Databases

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Generate the question in machine-understandable language

Generate the question in machine-understandable language

From the course: Introduction to AI-Native Vector Databases

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Generate the question in machine-understandable language

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In the last video, we framed searching over objects as asking a question or query and then retrieving the answers most similar to the query, in order to measure how close the query is to the objects in our database, we need to use vectors. Since every data object in our vector database is represented as a vector, when comparing them to a question or query, we want to think of it as a vector as well. We can take the question and frame this as a vector previously seen as the red vector. We now want to take the question and see which information on our computer is relevant. We can measure how close the question vector is to each object vector and get a distance between each. This gives us a vector similarity between the question and the data. We can take each distance and then order them from lowest distance to highest distance. This allows you to sort your data from most similar to least similar to the query vector. And remember that each vector represents a data point in the real…

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