src/ai/insight_generator.rs

@@ -1845,6 +1845,41 @@ Return ONLY the summary, nothing else."#,
             description
         );
 
+        // Pre-flight similarity check — surface near-duplicates to the model
+        // before it commits to a new entity. Uses the first name token as the
+        // search term so "Sarah" matches when storing "Sarah Johnson" and vice
+        // versa. Exact-name matches are excluded (upsert_entity deduplicates
+        // those already). Results are appended to the tool response so the
+        // model can choose to use an existing entity's ID instead.
+        let similar_entities: Vec<String> = {
+            use crate::database::{EntityFilter, KnowledgeDao};
+            use crate::database::knowledge_dao::normalize_entity_type;
+            let normalised_type = normalize_entity_type(&entity_type);
+            let first_token = name
+                .split_whitespace()
+                .next()
+                .unwrap_or(&name)
+                .to_string();
+            let filter = EntityFilter {
+                entity_type: None, // search all types, filter client-side to avoid case issues
+                status: Some("active".to_string()),
+                search: Some(first_token),
+                limit: 10,
+                offset: 0,
+            };
+            let mut kdao = self.knowledge_dao.lock().expect("Unable to lock KnowledgeDao");
+            kdao.list_entities(cx, filter)
+                .unwrap_or_default()
+                .0
+                .into_iter()
+                .filter(|e| {
+                    normalize_entity_type(&e.entity_type) == normalised_type
+                        && e.name.to_lowercase() != name.to_lowercase()
+                })
+                .map(|e| format!("  ID:{} | {} | {}", e.id, e.name, e.description))
+                .collect()
+        };
+
         // Generate embedding for name + description (best-effort)
         let embed_text = format!("{} {}", name, description);
         let embedding: Option<Vec<u8>> = match ollama.generate_embedding(&embed_text).await {
@@ -1875,10 +1910,22 @@ Return ONLY the summary, nothing else."#,
             .lock()
             .expect("Unable to lock KnowledgeDao");
         match kdao.upsert_entity(cx, insert) {
-            Ok(entity) => format!(
+            Ok(entity) => {
+                let mut response = format!(
                     "Entity stored: ID:{} | {} | {} | confidence:{:.2}",
                     entity.id, entity.entity_type, entity.name, entity.confidence
-            ),
+                );
+                if !similar_entities.is_empty() {
+                    response.push_str(
+                        "\nSimilar existing entities found — verify this is not a duplicate:\n",
+                    );
+                    response.push_str(&similar_entities.join("\n"));
+                    response.push_str(
+                        "\nIf one of these is the same entity, use their existing ID in store_fact instead of the newly created one.",
+                    );
+                }
+                response
+            }
             Err(e) => format!("Error storing entity: {:?}", e),
         }
     }

src/database/knowledge_dao.rs

@@ -10,6 +10,25 @@ use crate::database::schema;
 use crate::database::{DbError, DbErrorKind, connect};
 use crate::otel::trace_db_call;
 
+// ---------------------------------------------------------------------------
+// Entity type normalisation
+// ---------------------------------------------------------------------------
+
+/// Canonicalise a model-supplied entity_type to a consistent lowercase form.
+/// Weak models frequently vary capitalisation ("Person" vs "person") or use
+/// synonym types ("location" vs "place"). Normalising here prevents duplicate
+/// entities that differ only by type spelling.
+pub(crate) fn normalize_entity_type(raw: &str) -> String {
+    match raw.to_lowercase().as_str() {
+        "person" | "people" | "human" | "individual" | "contact" => "person",
+        "place" | "location" | "venue" | "site" | "area" | "landmark" => "place",
+        "event" | "occasion" | "activity" | "celebration" => "event",
+        "thing" | "object" | "item" | "product" => "thing",
+        other => other,
+    }
+    .to_string()
+}
+
 // ---------------------------------------------------------------------------
 // Filter / patch types
 // ---------------------------------------------------------------------------
@@ -250,13 +269,22 @@ impl KnowledgeDao for SqliteKnowledgeDao {
 
             let mut conn = self.connection.lock().expect("KnowledgeDao lock");
 
-            // Case-insensitive lookup by name + entity_type
+            // Normalise type before lookup and insert so that model variations
+            // ("Person" / "person", "location" / "place") collapse to one row.
+            let entity = InsertEntity {
+                entity_type: normalize_entity_type(&entity.entity_type),
+                ..entity
+            };
+
+            // Case-insensitive lookup by name + entity_type.
+            // Use lower() on both sides so existing dirty rows ("Person") still match.
             let name_lower = entity.name.to_lowercase();
+            let type_lower = entity.entity_type.to_lowercase();
             let existing: Option<Entity> = entities
                 .filter(diesel::dsl::sql::<diesel::sql_types::Bool>(&format!(
-                    "lower(name) = '{}' AND entity_type = '{}'",
+                    "lower(name) = '{}' AND lower(entity_type) = '{}'",
                     name_lower.replace('\'', "''"),
-                    entity.entity_type.replace('\'', "''")
+                    type_lower.replace('\'', "''")
                 )))
                 .first::<Entity>(conn.deref_mut())
                 .optional()