wps-deep-search

Deep semantic search across WPS notes uses intent parsing and semantic expansion to surface cross-note relationships, entity associations, and relevant passages that simple keyword matching misses. It returns structured results with relevance scores and match reasons.

Yes, you can filter WPS note search results by time range and tags using natural language. Intent parsing extracts time ranges, tags, and core keywords from your query to disambiguate intent before running multi-depth semantic search modes.

Finding entity associations in a note corpus requires result enrichment that aggregates outlines, metadata, and content excerpts. The search surfaces related tags or entities for knowledge-graph building and provides explicit match reasons for each result.

Deep semantic search works for sales preparation by gathering distributed client references across notes. It also supports PM research to aggregate project artifacts and developer investigations to collect technical design and performance notes.

To collect TODOs across notes, use a semantic search with task aggregation intent. The deep search mode runs parallel multi-keyword queries to discover cross-note associations and returns structured results with relevance scores for prioritization.

Quick, standard, and deep search modes offer increasing depth of semantic expansion and parallel multi-keyword querying for note discovery. Deep mode uncovers the most cross-note relationships and entity associations, while quick mode returns faster, lighter results.