store: Semantic search enabled by default for memory recall

graph/decisions/semantic-search-enabled-by-default-for-memory-recall-e94ae9.md

@@ -0,0 +1,22 @@
+---
+id: e94ae963-9bf9-4adc-b0d7-ec18d9e70999
+type: decision
+title: "Semantic search enabled by default for memory recall"
+tags: [cognitive-memory, decision, semantic-search, configuration]
+importance: 0.7
+confidence: 0.8
+created: "2026-02-19T22:03:22.589620+00:00"
+updated: "2026-02-19T22:03:22.589620+00:00"
+---
+
+Changed `recall()` default from `semantic=False` to `semantic=True`. All recall calls now use semantic+keyword merge when embeddings exist.
+
+**Rationale:** With mtime-based embeddings caching, warm semantic recall takes ~200ms — imperceptible within Claude Code sessions where API roundtrips are measured in seconds. The quality improvement from conceptual matching justifies the small overhead.
+
+**Changes made:**
+- `client.py`: `recall()` parameter default `semantic=True`
+- `client.py`: CLI flag changed from `--semantic` (opt-in) to `--no-semantic` (opt-out)
+- `mcp_server.py`: `arguments.get("semantic", True)`, updated tool description
+- `SKILL.md`: Updated all examples and documentation
+
+**Merge weights:** 60% semantic, 40% keyword. Use `semantic=false` / `--no-semantic` when speed matters more than depth (~3ms vs ~200ms).