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Ryan welcomes Bryan O’Grady, Head of Field Research and Solutions Architecture at Qdrant, to discuss the differences between traditional text search engines powered by Lucene and modern vector databases, when vector search’s exact-match needs work for things like logs and security analytics and when semantic search works for user-facing discovery and non-exact results, and how Qdrant is growing into video embeddings and local-agent contexts.
Episode notes:
Qdrant offers high-performance vector search at scale with any deployment model.
Connect with Brian on LinkedIn or email the Qdrant team at [email protected].
Congratulations to user Brad Larson for winning a Populist badge for their answer to Find the tangent of a point on a cubic bezier curve.
TRANSCRIPT
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
By The Stack Overflow Podcast4.3
6262 ratings
Ryan welcomes Bryan O’Grady, Head of Field Research and Solutions Architecture at Qdrant, to discuss the differences between traditional text search engines powered by Lucene and modern vector databases, when vector search’s exact-match needs work for things like logs and security analytics and when semantic search works for user-facing discovery and non-exact results, and how Qdrant is growing into video embeddings and local-agent contexts.
Episode notes:
Qdrant offers high-performance vector search at scale with any deployment model.
Connect with Brian on LinkedIn or email the Qdrant team at [email protected].
Congratulations to user Brad Larson for winning a Populist badge for their answer to Find the tangent of a point on a cubic bezier curve.
TRANSCRIPT
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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