src/database/knowledge_dao.rs

@@ -117,6 +117,17 @@ pub struct RecentActivity {
     pub facts: Vec<EntityFact>,
 }
 
+/// A near-duplicate cluster found by `find_consolidation_proposals`.
+/// `min_cosine` / `max_cosine` are summary stats over the pairwise
+/// edges inside the group — gives the curator a sense of "how tight"
+/// the cluster is before clicking in.
+#[derive(Debug, Clone)]
+pub struct ConsolidationGroup {
+    pub entities: Vec<Entity>,
+    pub min_cosine: f32,
+    pub max_cosine: f32,
+}
+
 // ---------------------------------------------------------------------------
 // Trait
 // ---------------------------------------------------------------------------
@@ -167,6 +178,21 @@ pub trait KnowledgeDao: Sync + Send {
         persona: &PersonaFilter,
     ) -> Result<(Vec<(Entity, i64)>, i64), DbError>;
 
+    /// Find groups of near-duplicate entities that the upsert-time
+    /// cosine guard didn't catch (it runs at ~0.92; this scan runs
+    /// at a lower threshold to surface the "probably same" tier that
+    /// needs human review). Groups are formed via union-find over
+    /// the cosine-adjacency graph, partitioned by entity_type so a
+    /// person can't cluster with a place. Returns groups of >= 2
+    /// entities, sorted by size desc then by max pairwise cosine.
+    /// Trimmed to `max_groups`.
+    fn find_consolidation_proposals(
+        &mut self,
+        cx: &opentelemetry::Context,
+        threshold: f32,
+        max_groups: usize,
+    ) -> Result<Vec<ConsolidationGroup>, DbError>;
+
     /// Batch fetch per-persona fact counts for a set of entities,
     /// scoped to one user. Returns map of entity_id → list of
     /// (persona_id, count). Used by the curation UI to show "this
@@ -800,6 +826,165 @@ impl KnowledgeDao for SqliteKnowledgeDao {
         .map_err(|_| DbError::new(DbErrorKind::QueryError))
     }
 
+    fn find_consolidation_proposals(
+        &mut self,
+        cx: &opentelemetry::Context,
+        threshold: f32,
+        max_groups: usize,
+    ) -> Result<Vec<ConsolidationGroup>, DbError> {
+        trace_db_call(cx, "query", "find_consolidation_proposals", |_span| {
+            use schema::entities::dsl::*;
+
+            let mut conn = self.connection.lock().expect("KnowledgeDao lock");
+
+            // Pull every non-rejected entity with an embedding. We
+            // keep 'reviewed' rows in the scan because pre-guard
+            // legacy data still needs cleanup even if the curator
+            // marked individual entities reviewed.
+            let rows: Vec<Entity> = entities
+                .filter(embedding.is_not_null())
+                .filter(status.ne("rejected"))
+                .load::<Entity>(conn.deref_mut())
+                .map_err(|e| anyhow::anyhow!("Query error: {}", e))?;
+
+            // Partition by entity_type so a person can't cluster
+            // with a place via coincidental embedding closeness.
+            let mut by_type: std::collections::HashMap<String, Vec<usize>> =
+                std::collections::HashMap::new();
+            for (idx, e) in rows.iter().enumerate() {
+                by_type.entry(e.entity_type.clone()).or_default().push(idx);
+            }
+
+            // Decode embeddings once. Skip rows that don't deserialize
+            // cleanly (corrupted or wrong-dim) rather than failing
+            // the whole scan.
+            let mut decoded: Vec<Option<Vec<f32>>> = Vec::with_capacity(rows.len());
+            for e in &rows {
+                let v = e
+                    .embedding
+                    .as_ref()
+                    .and_then(|b| Self::deserialize_embedding(b).ok())
+                    .filter(|v| !v.is_empty());
+                decoded.push(v);
+            }
+
+            // Union-find for transitive clustering.
+            struct UF {
+                parent: Vec<usize>,
+            }
+            impl UF {
+                fn new(n: usize) -> Self {
+                    UF {
+                        parent: (0..n).collect(),
+                    }
+                }
+                fn find(&mut self, x: usize) -> usize {
+                    let mut r = x;
+                    while self.parent[r] != r {
+                        r = self.parent[r];
+                    }
+                    let mut cur = x;
+                    while self.parent[cur] != r {
+                        let nxt = self.parent[cur];
+                        self.parent[cur] = r;
+                        cur = nxt;
+                    }
+                    r
+                }
+                fn union(&mut self, a: usize, b: usize) {
+                    let ra = self.find(a);
+                    let rb = self.find(b);
+                    if ra != rb {
+                        self.parent[ra] = rb;
+                    }
+                }
+            }
+
+            let mut uf = UF::new(rows.len());
+            let mut group_min: std::collections::HashMap<usize, f32> =
+                std::collections::HashMap::new();
+            let mut group_max: std::collections::HashMap<usize, f32> =
+                std::collections::HashMap::new();
+
+            // Single pass: union and update per-component stats in
+            // one go. Stats are tracked per root; final pass after
+            // all unions corrects roots that moved.
+            type Edge = (usize, usize, f32);
+            let mut edges: Vec<Edge> = Vec::new();
+            for indices in by_type.values() {
+                for a in 0..indices.len() {
+                    let ia = indices[a];
+                    let va = match &decoded[ia] {
+                        Some(v) => v,
+                        None => continue,
+                    };
+                    for b in (a + 1)..indices.len() {
+                        let ib = indices[b];
+                        let vb = match &decoded[ib] {
+                            Some(v) => v,
+                            None => continue,
+                        };
+                        let sim = Self::cosine_similarity(va, vb);
+                        if sim >= threshold {
+                            uf.union(ia, ib);
+                            edges.push((ia, ib, sim));
+                        }
+                    }
+                }
+            }
+            // Second pass over the kept edges to populate stats by
+            // final root (post-union path compression).
+            for (a, _b, sim) in &edges {
+                let root = uf.find(*a);
+                let mn = group_min.entry(root).or_insert(*sim);
+                if *sim < *mn {
+                    *mn = *sim;
+                }
+                let mx = group_max.entry(root).or_insert(*sim);
+                if *sim > *mx {
+                    *mx = *sim;
+                }
+            }
+
+            // Bucket entities by root component, skipping singletons.
+            let mut groups: std::collections::HashMap<usize, Vec<usize>> =
+                std::collections::HashMap::new();
+            for i in 0..rows.len() {
+                let root = uf.find(i);
+                if !group_min.contains_key(&root) {
+                    continue;
+                }
+                groups.entry(root).or_default().push(i);
+            }
+
+            let mut result: Vec<ConsolidationGroup> = groups
+                .into_iter()
+                .filter(|(_, members)| members.len() >= 2)
+                .map(|(root, members)| ConsolidationGroup {
+                    entities: members.into_iter().map(|i| rows[i].clone()).collect(),
+                    min_cosine: *group_min.get(&root).unwrap_or(&0.0),
+                    max_cosine: *group_max.get(&root).unwrap_or(&0.0),
+                })
+                .collect();
+
+            // Biggest clusters first; tie-break on the strongest
+            // pair so the most-obvious dupes surface at the top.
+            result.sort_by(|a, b| {
+                b.entities
+                    .len()
+                    .cmp(&a.entities.len())
+                    .then_with(|| {
+                        b.max_cosine
+                            .partial_cmp(&a.max_cosine)
+                            .unwrap_or(std::cmp::Ordering::Equal)
+                    })
+            });
+            result.truncate(max_groups);
+            Ok(result)
+        })
+        .map_err(|_| DbError::new(DbErrorKind::QueryError))
+    }
+
     fn get_persona_breakdowns_for_entities(
         &mut self,
         cx: &opentelemetry::Context,

src/database/mod.rs

@@ -59,8 +59,8 @@ pub use calendar_dao::{CalendarEventDao, SqliteCalendarEventDao};
 pub use daily_summary_dao::{DailySummaryDao, InsertDailySummary, SqliteDailySummaryDao};
 pub use insights_dao::{InsightDao, SqliteInsightDao};
 pub use knowledge_dao::{
-    EntityFilter, EntityPatch, EntitySort, FactFilter, FactPatch, KnowledgeDao, PersonaFilter,
-    RecentActivity, SqliteKnowledgeDao,
+    ConsolidationGroup, EntityFilter, EntityPatch, EntitySort, FactFilter, FactPatch, KnowledgeDao,
+    PersonaFilter, RecentActivity, SqliteKnowledgeDao,
 };
 pub use location_dao::{LocationHistoryDao, SqliteLocationHistoryDao};
 pub use persona_dao::{ImportPersona, PersonaDao, PersonaPatch, SqlitePersonaDao};

src/knowledge.rs

@@ -7,8 +7,8 @@ use std::sync::Mutex;
 use crate::data::Claims;
 use crate::database::models::{Entity, EntityFact, EntityPhotoLink, InsertEntityFact};
 use crate::database::{
-    EntityFilter, EntityPatch, EntitySort, FactFilter, FactPatch, KnowledgeDao, PersonaFilter,
-    RecentActivity,
+    ConsolidationGroup, EntityFilter, EntityPatch, EntitySort, FactFilter, FactPatch, KnowledgeDao,
+    PersonaFilter, RecentActivity,
 };
 use crate::personas::PersonaDaoData;
 use crate::state::AppState;
@@ -330,6 +330,27 @@ pub struct RecentQuery {
     pub limit: Option<i64>,
 }
 
+#[derive(Deserialize)]
+pub struct ConsolidationQuery {
+    /// Cosine threshold for clustering. Default 0.85 — looser than
+    /// the upsert-time guard (0.92) so this view surfaces "probably
+    /// same" pairs for human review.
+    pub threshold: Option<f32>,
+    pub limit: Option<i64>,
+}
+
+#[derive(Serialize)]
+pub struct ConsolidationGroupView {
+    pub entities: Vec<EntitySummary>,
+    pub min_cosine: f32,
+    pub max_cosine: f32,
+}
+
+#[derive(Serialize)]
+pub struct ConsolidationResponse {
+    pub groups: Vec<ConsolidationGroupView>,
+}
+
 // ---------------------------------------------------------------------------
 // Service registration
 // ---------------------------------------------------------------------------
@@ -370,7 +391,11 @@ where
                 web::resource("/facts/{id}/restore")
                     .route(web::post().to(restore_fact::<D>)),
             )
-            .service(web::resource("/recent").route(web::get().to(get_recent::<D>))),
+            .service(web::resource("/recent").route(web::get().to(get_recent::<D>)))
+            .service(
+                web::resource("/consolidation-proposals")
+                    .route(web::get().to(get_consolidation_proposals::<D>)),
+            ),
     )
 }
 
@@ -1146,3 +1171,64 @@ async fn get_recent<D: KnowledgeDao + 'static>(
         }
     }
 }
+
+async fn get_consolidation_proposals<D: KnowledgeDao + 'static>(
+    req: HttpRequest,
+    claims: Claims,
+    query: web::Query<ConsolidationQuery>,
+    dao: web::Data<Mutex<D>>,
+    persona_dao: PersonaDaoData,
+) -> impl Responder {
+    // Clamp threshold so a curious client can't drag the cosine
+    // floor to 0 and pull every entity into one giant cluster.
+    let threshold = query.threshold.unwrap_or(0.85).clamp(0.5, 0.99);
+    let max_groups = query.limit.unwrap_or(50).clamp(1, 200) as usize;
+
+    let persona = resolve_persona_filter(&req, &claims, &persona_dao);
+    let cx = opentelemetry::Context::current();
+    let mut dao = dao.lock().expect("Unable to lock KnowledgeDao");
+    let groups: Vec<ConsolidationGroup> =
+        match dao.find_consolidation_proposals(&cx, threshold, max_groups) {
+            Ok(g) => g,
+            Err(e) => {
+                log::error!("find_consolidation_proposals: {:?}", e);
+                return HttpResponse::InternalServerError()
+                    .json(serde_json::json!({"error": "Database error"}));
+            }
+        };
+
+    // Decorate with per-persona fact counts so the curation UI can
+    // show "default 8 · journal 3" inline and the curator can pick
+    // which entity is the strongest target.
+    let entity_ids: Vec<i32> = groups
+        .iter()
+        .flat_map(|g| g.entities.iter().map(|e| e.id))
+        .collect();
+    let breakdowns = dao
+        .get_persona_breakdowns_for_entities(&cx, &entity_ids, persona.user_id())
+        .unwrap_or_default();
+
+    let groups_view: Vec<ConsolidationGroupView> = groups
+        .into_iter()
+        .map(|g| ConsolidationGroupView {
+            entities: g
+                .entities
+                .into_iter()
+                .map(|e| {
+                    let id = e.id;
+                    let summary = EntitySummary::from(e);
+                    match breakdowns.get(&id) {
+                        Some(bd) => summary.with_persona_breakdown(bd.clone()),
+                        None => summary,
+                    }
+                })
+                .collect(),
+            min_cosine: g.min_cosine,
+            max_cosine: g.max_cosine,
+        })
+        .collect();
+
+    HttpResponse::Ok().json(ConsolidationResponse {
+        groups: groups_view,
+    })
+}