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How Does Vector Database Compaction Reclaim Space After Document Deletion?
Deletes hide vectors first and free bytes later; compaction rewrites live records into clean segments before obsolete storage is retired.
How Does Product Quantization Accelerate Large Private Vector Collections?
PQ replaces each full vector with a short sequence of subspace code IDs, letting search estimate distances from compact lookup tables before optional rescoring.
How Does HNSW Search Trade Memory for Recall in a Local Vector Database?
HNSW spends memory on graph connectivity and query work on candidate exploration; denser graphs and wider searches usually raise recall at higher resource cost.
How Does Vector Quantization Shrink a Home Search Index?
Quantization stores a lower-precision approximation of each embedding so more vectors fit in RAM or disk, while optional rescoring recovers some lost accuracy.
How Does Change Data Capture Update a Local Knowledge Index?
CDC streams source mutations to an indexer, which maps each event to the affected records, vectors, and deletes while checkpoints preserve restart position.
How Does Content Hashing Prevent Unchanged Files From Being Re-Embedded?
Hash comparison turns ingestion into a change test: matching content keeps its vector, while changed input triggers downstream embedding work.
How Does Document Versioning Keep RAG Answers Tied to Current Files?
Version-aware RAG separates document identity from revision state so current queries retrieve active chunks while older evidence remains traceable as history.
How Does a Cross-Encoder Score Query–Passage Pairs in Home AI Search?
A cross-encoder trades precomputed document vectors for richer query-passage interactions on only the candidates a fast retriever has already found.
