Semantic Search vs. Keyword Search
Keyword search matches terms. Semantic search matches meaning. That shorthand is useful, but it hides the most important practical point: each method solves a different memory failure.
If you remember an exact error code, keyword search is hard to beat. If you remember only that an article explained a safer retry strategy, semantic search is more forgiving. When you remember both a concept and one distinctive term, hybrid search can use both signals.
How keyword search works
A keyword engine builds an index from words or tokens, then retrieves documents containing the query terms. Ranking can account for term frequency, field importance, and how rare a word is across the corpus.
Keyword search is strongest when precision matters:
- error messages and status codes;
- function, package, or product names;
- quotations;
- people, domains, and filenames;
- phrases whose exact wording matters.
Its weakness is vocabulary mismatch. A page about “vehicle maintenance” may not match a query for “car servicing.” Stemming and synonyms can help, but they require rules and still miss many paraphrases.
How semantic search works
A semantic model converts text into an embedding: a vector whose position captures patterns of meaning learned by the model. The query is embedded in the same space. Search compares the query vector with stored page vectors and ranks nearby results.
That makes conceptual queries possible. “Prevent an API client from retrying too aggressively” can retrieve a page about exponential backoff without requiring those exact words in the title.
Semantic search has its own failure modes. A broad query can return topically related pages that do not contain the precise fact you need. Models can also struggle with identifiers, rare names, and numbers. Similarity is not proof that a result answers the question.
Why hybrid search is usually better
TraceMind combines local semantic retrieval with full-text results rather than forcing a permanent choice between them. The ranking pipeline can reward both conceptual similarity and exact matches.
Consider four queries:
| Query | Best signal | Why |
|---|---|---|
| ERR_MODULE_NOT_FOUND | Keyword | Exact identifier matters |
| “article about remembering pages by meaning” | Semantic | The wording is uncertain |
| “IndexedDB quota error on Safari” | Hybrid | Topic plus exact technologies |
| developer.mozilla.org cache-control | Title/URL + keyword | Source and term are known |
The result set is still constrained by what was captured. Search cannot recover a page that the extension never indexed, and browser-restricted or unsupported pages may not provide readable content.
Privacy boundaries matter
TraceMind's core capture, indexing, semantic and keyword search, screenshots, and analytics run locally on the device. Free local storage is not protected by a TraceMind passphrase. Pro can optionally add passphrase encryption for local content and new encrypted backups.
Optional Pro Chat is a separate feature with a separate boundary: it sends the question and selected matching excerpts, titles, and URLs directly to the AI provider the user configures. TraceMind does not proxy or store those provider requests.
The Pro Offline Page Viewer can save sandboxed HTML reading copies, but those copies are not guaranteed complete archives. Search results and offline snapshots should therefore be understood as captured representations, not perfect replicas of the web.
A practical query strategy
Start with the kind of memory you actually have:
- Use exact terms when you remember a quote, identifier, or error.
- Describe the concept when the vocabulary is fuzzy.
- Combine the concept with one exact anchor when results are broad.
- Add date and domain filters to remove unrelated contexts.
- Inspect the captured snippet before treating a match as the answer.
This is more reliable than repeatedly shortening a keyword query until it becomes so broad that every result looks plausible.
The decision rule
Use keyword search for exactness, semantic search for paraphrase, and hybrid search when memory contains both. A personal knowledge index should preserve all three paths because human recall shifts between names, phrases, visual context, and meaning.
For the mechanics of embeddings, continue with How Vector Embeddings Work in Your Browser. For the difference between title metadata and captured page text, read Full-Text Search vs. Title Search.