Database / Qdrant Vector DB Interview questions
How do you perform a similarity search in Qdrant?
A similarity search means calling the Query API with a query vector, which Qdrant compares against stored vectors using the collection's configured distance metric and returns the closest matches, optionally filtered and limited to a specific count.
results = client.query_points( collection_name="documents", query=[0.12, 0.85, 0.33, 0.44], limit=5, with_payload=True, ) for point in results.points: print(point.id, point.score, point.payload)
The query_points method (the modern, unified entry point for search, replacing the older search method) accepts the raw query vector directly, along with optional parameters for filtering (query_filter), limiting the number of results (limit), and controlling whether the returned points include their full vector and/or payload data.
Each result includes a score reflecting its similarity to the query vector according to the collection's distance metric, and results are returned in ranked order — most similar first — which is what a typical application then uses directly to display or further process the top matches.
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