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Semantic Search
SenseTree searches by meaning, not keywords. A query like "insurance renewal letter" finds the right document even if those exact words never appear in it.
- Your query is embedded with the same model used for indexing (so query and documents share a vector space).
- SenseTree asks LanceDB for the nearest neighbors by cosine similarity.
- Results are verified against disk (paths that no longer exist are dropped), de-duplicated per file (best-scoring chunk wins), and returned with a snippet and score.
Because search re-embeds the query on demand, the local embedding model is loaded lazily and unloaded when idle to avoid wasting CPU/GPU.
Search can be restricted to the current folder (and its descendants). The vector query is filtered by a path prefix, so you can ask "within this project, where did I mention the deadline?" without noise from the rest of the disk.
Path matching is Windows-aware: separators and LIKE metacharacters are escaped so that
C:\...prefixes match correctly.
Anything that got "sense" during indexing:
- text extracted from documents/code,
- captions from images (if vision is enabled),
- contextual descriptors (filename + folder + type) for opaque/oversized files,
- folder-block summaries.
If something isn't showing up, check that it finished indexing (status in the Explorer) and that the embedding endpoint was healthy when it was processed — summaries and vectors are only written after a successful embedding. See Troubleshooting.
Beyond flat results, SenseTree can present a semantic tree of a folder — a view organized by meaning rather than by raw filename order — to help you see how a directory's contents cluster.
- Describe the content, not the filename. "quarterly revenue chart" beats "q3.xlsx".
- Use scope to cut through large indexes.
- Re-index after switching embedding models — vectors from different models aren't comparable, so SenseTree forces a full re-index on such a change (see Configuration).
Getting started
Using it
- Configuration
- Models & Providers
- Semantic Search
- Image Search
- AI Chat & Agent
- Gardener
- Prompts
- MCP Servers
Under the hood