chore(entity-resolver): remove dead resolve_entity/_create_entity/link_unit_to_entity (#2367)

These per-entity methods have no live callers — the retain/PATCH paths all go
through the batched resolve_entities_batch + flush_pending_stats, which already
sort their writes for consistent lock ordering. The dead _create_entity carried
an unsorted 'entities ON CONFLICT ... DO UPDATE' that looked like a concurrent
deadlock site (it isn't, since it's unreachable). Removing the dead code so it
stops misleading readers/reviewers.

_update_cooccurrence is removed too — its only caller was the dead
link_unit_to_entity.

Author: nicoloboschi

hindsight-api-slim/hindsight_api/engine/entity_resolver.py

@@ -782,236 +782,6 @@ class _NameGroup:
 
         return entity_ids
 
-    async def resolve_entity(
-        self,
-        bank_id: str,
-        entity_text: str,
-        context: str,
-        nearby_entities: list[dict],
-        unit_event_date,
-    ) -> str:
-        """
-        Resolve an entity to a canonical entity ID.
-
-        Args:
-            bank_id: bank ID (entities are scoped to agents)
-            entity_text: Entity text ("Alice", "Google", etc.)
-            context: Context where entity appears
-            nearby_entities: Other entities in the same unit
-            unit_event_date: When this unit was created
-
-        Returns:
-            Entity ID (creates new entity if needed)
-        """
-        async with acquire_with_retry(self.pool) as conn:
-            # Find candidate entities with similar name
-            candidates = await conn.fetch(
-                f"""
-                SELECT id, canonical_name, metadata, last_seen
-                FROM {fq_table("entities")}
-                WHERE bank_id = $1
-                  AND (
-                    canonical_name ILIKE $2
-                    OR canonical_name ILIKE $3
-                    OR $2 ILIKE canonical_name || '%%'
-                  )
-                ORDER BY mention_count DESC
-                """,
-                bank_id,
-                entity_text,
-                f"%{entity_text}%",
-            )
-
-            if not candidates:
-                # New entity - create it
-                return await self._create_entity(conn, bank_id, entity_text, unit_event_date)
-
-            # Score candidates based on:
-            # 1. Name similarity
-            # 2. Context overlap (TODO: could use embeddings)
-            # 3. Co-occurring entities
-            # 4. Temporal proximity
-
-            best_candidate = None
-            best_score = 0.0
-
-            nearby_entity_set = {e["text"].lower() for e in nearby_entities if e["text"] != entity_text}
-
-            for row in candidates:
-                candidate_id = row["id"]
-                canonical_name = row["canonical_name"]
-                last_seen = row["last_seen"]
-                score = 0.0
-
-                # 1. Name similarity (0-1)
-                name_similarity = SequenceMatcher(None, entity_text.lower(), canonical_name.lower()).ratio()
-                score += name_similarity * 0.5
-
-                # 2. Co-occurring entities (0-0.5)
-                # Get entities that co-occurred with this candidate before
-                # Use the materialized co-occurrence cache for fast lookup
-                co_entity_rows = await conn.fetch(
-                    f"""
-                    SELECT e.canonical_name, ec.cooccurrence_count
-                    FROM {fq_table("entity_cooccurrences")} ec
-                    JOIN {fq_table("entities")} e ON (
-                        CASE
-                            WHEN ec.entity_id_1 = $1 THEN ec.entity_id_2
-                            WHEN ec.entity_id_2 = $1 THEN ec.entity_id_1
-                        END = e.id
-                    )
-                    WHERE ec.entity_id_1 = $1 OR ec.entity_id_2 = $1
-                    """,
-                    candidate_id,
-                )
-                co_entities = {r["canonical_name"].lower() for r in co_entity_rows}
-
-                # Check overlap with nearby entities
-                overlap = len(nearby_entity_set & co_entities)
-                if nearby_entity_set:
-                    co_entity_score = overlap / len(nearby_entity_set)
-                    score += co_entity_score * 0.3
-
-                # 3. Temporal proximity (0-0.2)
-                if last_seen:
-                    # Normalize both to UTC-aware to avoid naive/aware mismatch
-                    # (Oracle returns naive datetimes from fromisoformat)
-                    _evt = unit_event_date if unit_event_date.tzinfo else unit_event_date.replace(tzinfo=UTC)
-                    _seen = last_seen if last_seen.tzinfo else last_seen.replace(tzinfo=UTC)
-                    days_diff = abs((_evt - _seen).total_seconds() / 86400)
-                    if days_diff < 7:  # Within a week
-                        temporal_score = max(0, 1.0 - (days_diff / 7))
-                        score += temporal_score * 0.2
-
-                if score > best_score:
-                    best_score = score
-                    best_candidate = candidate_id
-
-            # Threshold for considering it the same entity
-            threshold = 0.6
-
-            if best_score > threshold:
-                # Update entity
-                await conn.execute(
-                    f"""
-                    UPDATE {fq_table("entities")}
-                    SET mention_count = mention_count + 1,
-                        last_seen = $1
-                    WHERE id = $2
-                    """,
-                    unit_event_date,
-                    best_candidate,
-                )
-                return best_candidate
-            else:
-                # Not confident - create new entity
-                return await self._create_entity(conn, bank_id, entity_text, unit_event_date)
-
-    async def _create_entity(
-        self,
-        conn,
-        bank_id: str,
-        entity_text: str,
-        event_date,
-    ) -> str:
-        """
-        Create a new entity or get existing one if it already exists.
-
-        Uses INSERT ... ON CONFLICT to handle race conditions where
-        two concurrent transactions try to create the same entity.
-
-        Args:
-            conn: Database connection
-            bank_id: bank ID
-            entity_text: Entity text
-            event_date: When first seen
-
-        Returns:
-            Entity ID
-        """
-        entity_id = await conn.fetchval(
-            f"""
-            INSERT INTO {fq_table("entities")} (bank_id, canonical_name, first_seen, last_seen, mention_count)
-            VALUES ($1, $2, COALESCE($3, now()), COALESCE($4, now()), 1)
-            ON CONFLICT (bank_id, LOWER(canonical_name))
-            DO UPDATE SET
-                mention_count = {fq_table("entities")}.mention_count + 1,
-                last_seen = EXCLUDED.last_seen
-            RETURNING id
-            """,
-            bank_id,
-            entity_text,
-            event_date,
-            event_date,
-        )
-        return entity_id
-
-    async def link_unit_to_entity(self, unit_id: str, entity_id: str):
-        """
-        Link a memory unit to an entity.
-        Also updates co-occurrence cache with other entities in the same unit.
-
-        Args:
-            unit_id: Memory unit ID
-            entity_id: Entity ID
-        """
-        async with acquire_with_retry(self.pool) as conn:
-            # Insert unit-entity link
-            await conn.execute(
-                f"""
-                INSERT INTO {fq_table("unit_entities")} (unit_id, entity_id)
-                VALUES ($1, $2)
-                ON CONFLICT DO NOTHING
-                """,
-                unit_id,
-                entity_id,
-            )
-
-            # Update co-occurrence cache: find other entities in this unit
-            rows = await conn.fetch(
-                f"""
-                SELECT entity_id
-                FROM {fq_table("unit_entities")}
-                WHERE unit_id = $1 AND entity_id != $2
-                """,
-                unit_id,
-                entity_id,
-            )
-
-            other_entities = [row["entity_id"] for row in rows]
-
-            # Update co-occurrences for each pair
-            for other_entity_id in other_entities:
-                await self._update_cooccurrence(conn, entity_id, other_entity_id)
-
-    async def _update_cooccurrence(self, conn, entity_id_1: str, entity_id_2: str):
-        """
-        Update the co-occurrence cache for two entities.
-
-        Uses CHECK constraint ordering (entity_id_1 < entity_id_2) to avoid duplicates.
-
-        Args:
-            conn: Database connection
-            entity_id_1: First entity ID
-            entity_id_2: Second entity ID
-        """
-        # Ensure consistent ordering (smaller UUID first)
-        if entity_id_1 > entity_id_2:
-            entity_id_1, entity_id_2 = entity_id_2, entity_id_1
-
-        await conn.execute(
-            f"""
-            INSERT INTO {fq_table("entity_cooccurrences")} (entity_id_1, entity_id_2, cooccurrence_count, last_cooccurred)
-            VALUES ($1, $2, 1, NOW())
-            ON CONFLICT (entity_id_1, entity_id_2)
-            DO UPDATE SET
-                cooccurrence_count = {fq_table("entity_cooccurrences")}.cooccurrence_count + 1,
-                last_cooccurred = NOW()
-            """,
-            entity_id_1,
-            entity_id_2,
-        )
-
     async def link_units_to_entities_batch(
         self,
         unit_entity_pairs: list[tuple[str, str]] | list[tuple[str, str, datetime | None]],