Semantic Search Explained: How AI Finds What You Forgot

Semantic search finds material by similarity of meaning rather than exact text. It can help when your query and a saved source use different words.

That does not make it a replacement for full-text search. The two methods answer different retrieval problems and have different operating requirements.

Full-text search is the dependable baseline

Full-text search matches stored words and phrases. It is predictable when you remember a title, person, domain, quotation, or technical term.

In Arivu, local full-text Search is the canonical retrieval path across saved knowledge. It works without an AI provider and should be the first choice when exact language matters.

Embeddings add similarity relationships

An embedding represents text as numbers that can be compared for proximity. Nearby representations may indicate similar meaning even when the wording differs.

For example, a Note about “cutover risk” may be similar to a source discussing “migration hazards” without sharing those exact phrases.

Embeddings require a configured provider and available vectors in Arivu. When they are absent, similarity relationships are absent too. Capture, full-text Search, explicit links, locally derived concepts and entities, Graph structure, and deterministic Insights continue to work.

This boundary prevents a useful optional feature from being described as universal behavior.

Similarity is not evidence

Two items can be semantically close and still disagree. They may describe the same topic from opposing positions, repeat a common phrase, or share broad context without supporting the same conclusion.

Treat a similarity relationship as a discovery hint. Open both items, compare the evidence, and add an explicit link if the relationship matters to your reasoning.

Evidence quality matters before similarity is computed. Selected text and source-native content provide firmer grounding than weak scraping. If extraction fails, metadata alone should not support generated claims or analytical relationships.

Graph contains several relationship types

Arivu’s Graph is not merely a visualization of embedding distance. It can include explicit links, backlinks, locally derived concepts, entities, sources, and optional similarity relationships.

Provenance and confidence help distinguish how an edge was created. A focused view and accessible list alternative make exact relationships easier to inspect.

Ask is synthesis, not semantic search

Search returns matching items. Ask produces a cited response from saved material without calling a model provider. Optional providers can improve stored summaries and related enrichment, but the response remains accountable to the sources it cites.

Use Search when you want the items. Use Ask when you want a concise response and are prepared to inspect its evidence.

A practical retrieval sequence

1. Try full-text Search with the words you remember.
2. Narrow by the available item context.
3. Inspect Graph around the strongest result.
4. Use optional similarity relationships to discover differently worded material.
5. Ask a bounded question across the relevant sources.
6. Verify citations and record the conclusion in your own Note.

Semantic search is valuable because memory is approximate. It is trustworthy only when the product explains its requirements and keeps a route back to the original material.

See Search and Ask and Graph.