fix: reduce duplicate entities from weak model inconsistency
Adds normalize_entity_type() which lowercases and canonicalises synonyms (location→place, human→person, etc.) before every upsert. The SQL lookup now uses lower(entity_type) on both sides so existing dirty rows (Person, Location) correctly deduplicate against normalised writes without a migration. Adds a pre-flight similarity check in tool_store_entity: before upserting, searches active entities of the same type using the first name token. Any non-exact matches are appended to the tool response so the agentic loop can choose to reuse an existing entity ID rather than create a duplicate. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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@@ -1845,6 +1845,41 @@ Return ONLY the summary, nothing else."#,
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description
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);
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// Pre-flight similarity check — surface near-duplicates to the model
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// before it commits to a new entity. Uses the first name token as the
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// search term so "Sarah" matches when storing "Sarah Johnson" and vice
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// versa. Exact-name matches are excluded (upsert_entity deduplicates
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// those already). Results are appended to the tool response so the
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// model can choose to use an existing entity's ID instead.
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let similar_entities: Vec<String> = {
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use crate::database::{EntityFilter, KnowledgeDao};
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use crate::database::knowledge_dao::normalize_entity_type;
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let normalised_type = normalize_entity_type(&entity_type);
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let first_token = name
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.split_whitespace()
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.next()
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.unwrap_or(&name)
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.to_string();
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let filter = EntityFilter {
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entity_type: None, // search all types, filter client-side to avoid case issues
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status: Some("active".to_string()),
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search: Some(first_token),
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limit: 10,
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offset: 0,
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};
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let mut kdao = self.knowledge_dao.lock().expect("Unable to lock KnowledgeDao");
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kdao.list_entities(cx, filter)
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.unwrap_or_default()
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.0
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.into_iter()
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.filter(|e| {
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normalize_entity_type(&e.entity_type) == normalised_type
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&& e.name.to_lowercase() != name.to_lowercase()
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})
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.map(|e| format!(" ID:{} | {} | {}", e.id, e.name, e.description))
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.collect()
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};
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// Generate embedding for name + description (best-effort)
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let embed_text = format!("{} {}", name, description);
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let embedding: Option<Vec<u8>> = match ollama.generate_embedding(&embed_text).await {
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@@ -1875,10 +1910,22 @@ Return ONLY the summary, nothing else."#,
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.lock()
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.expect("Unable to lock KnowledgeDao");
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match kdao.upsert_entity(cx, insert) {
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Ok(entity) => format!(
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"Entity stored: ID:{} | {} | {} | confidence:{:.2}",
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entity.id, entity.entity_type, entity.name, entity.confidence
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),
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Ok(entity) => {
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let mut response = format!(
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"Entity stored: ID:{} | {} | {} | confidence:{:.2}",
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entity.id, entity.entity_type, entity.name, entity.confidence
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);
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if !similar_entities.is_empty() {
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response.push_str(
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"\nSimilar existing entities found — verify this is not a duplicate:\n",
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);
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response.push_str(&similar_entities.join("\n"));
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response.push_str(
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"\nIf one of these is the same entity, use their existing ID in store_fact instead of the newly created one.",
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);
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}
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response
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}
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Err(e) => format!("Error storing entity: {:?}", e),
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}
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}
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@@ -10,6 +10,25 @@ use crate::database::schema;
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use crate::database::{DbError, DbErrorKind, connect};
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use crate::otel::trace_db_call;
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// ---------------------------------------------------------------------------
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// Entity type normalisation
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// ---------------------------------------------------------------------------
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/// Canonicalise a model-supplied entity_type to a consistent lowercase form.
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/// Weak models frequently vary capitalisation ("Person" vs "person") or use
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/// synonym types ("location" vs "place"). Normalising here prevents duplicate
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/// entities that differ only by type spelling.
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pub(crate) fn normalize_entity_type(raw: &str) -> String {
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match raw.to_lowercase().as_str() {
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"person" | "people" | "human" | "individual" | "contact" => "person",
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"place" | "location" | "venue" | "site" | "area" | "landmark" => "place",
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"event" | "occasion" | "activity" | "celebration" => "event",
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"thing" | "object" | "item" | "product" => "thing",
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other => other,
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}
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.to_string()
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}
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// ---------------------------------------------------------------------------
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// Filter / patch types
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// ---------------------------------------------------------------------------
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@@ -250,13 +269,22 @@ impl KnowledgeDao for SqliteKnowledgeDao {
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let mut conn = self.connection.lock().expect("KnowledgeDao lock");
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// Case-insensitive lookup by name + entity_type
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// Normalise type before lookup and insert so that model variations
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// ("Person" / "person", "location" / "place") collapse to one row.
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let entity = InsertEntity {
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entity_type: normalize_entity_type(&entity.entity_type),
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..entity
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};
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// Case-insensitive lookup by name + entity_type.
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// Use lower() on both sides so existing dirty rows ("Person") still match.
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let name_lower = entity.name.to_lowercase();
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let type_lower = entity.entity_type.to_lowercase();
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let existing: Option<Entity> = entities
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.filter(diesel::dsl::sql::<diesel::sql_types::Bool>(&format!(
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"lower(name) = '{}' AND entity_type = '{}'",
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"lower(name) = '{}' AND lower(entity_type) = '{}'",
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name_lower.replace('\'', "''"),
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entity.entity_type.replace('\'', "''")
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type_lower.replace('\'', "''")
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)))
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.first::<Entity>(conn.deref_mut())
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.optional()
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