qdrant-search-quality

Irrelevant matches often stem from embedding models or chunking strategies, not Qdrant itself. Inspect how chunks are passed to Qdrant, as splitting text mid-sentence can drop search quality by 30-40% before tuning parameters.

Diagnose bad search results by first isolating the problem with exact search to test the baseline. Determine whether the issue originates from the embedding model, Qdrant configuration, or the query strategy across your deployment scenario.

Hybrid search and reranking are effective strategies for improving retrieval relevance. They can be applied alongside relevance feedback and exploration APIs to boost result quality when basic embedding search yields wrong or missing results.

Search quality degrades after quantization, model changes, or data growth due to shifts in embedding distributions or configuration mismatches. Diagnose whether the embedding model or the updated Qdrant configuration requires tuning to restore relevance.

Isolate Qdrant configuration issues by testing with exact search first to establish a baseline. This helps determine whether poor relevance originates from the HNSW configuration or the underlying embedding model and query strategies.

Tuning HNSW parameters is secondary when missing expected items. First inspect how data chunks are passed to Qdrant and verify the embedding model, as most quality issues originate from data processing rather than Qdrant configuration.