ba99b4422a
* fix: misc perf improvements * more tests * fix test * fix: update test files for new extract_facts_from_text signature - Replace test_fact_extraction_token_analysis with test_fact_extraction_basic_analysis using inline sample content instead of external file - Update test_fact_extraction_output_ratio.py to unpack 3 return values (facts, chunks, usage) instead of 2 * fix: make temporal tests more flexible for LLM variation - test_temporal_absolute_conversion: check occurred_start field instead of requiring specific text in facts - test_date_field_calculation_yesterday: make assertions conditional on having temporal data, add more content for better extraction - test_temporal_ordering: reduce minimum required facts from 3 to 2
612 lines
23 KiB
Python
612 lines
23 KiB
Python
"""
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Entity extraction and resolution for memory system.
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Uses spaCy for entity extraction and implements resolution logic
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to disambiguate entities across memory units.
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"""
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from datetime import UTC, datetime
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from difflib import SequenceMatcher
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import asyncpg
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from .db_utils import acquire_with_retry
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from .memory_engine import fq_table
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# Load spaCy model (singleton)
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_nlp = None
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class EntityResolver:
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"""
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Resolves entities to canonical IDs with disambiguation.
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"""
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def __init__(self, pool: asyncpg.Pool):
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"""
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Initialize entity resolver.
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Args:
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pool: asyncpg connection pool
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"""
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self.pool = pool
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async def resolve_entities_batch(
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self,
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bank_id: str,
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entities_data: list[dict],
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context: str,
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unit_event_date,
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conn=None,
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) -> list[str]:
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"""
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Resolve multiple entities in batch (MUCH faster than sequential).
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Groups entities by type, queries candidates in bulk, and resolves
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all entities with minimal DB queries.
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Args:
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bank_id: bank ID
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entities_data: List of dicts with 'text', 'type', 'nearby_entities'
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context: Context where entities appear
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unit_event_date: When this unit was created
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conn: Optional connection to use (if None, acquires from pool)
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Returns:
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List of entity IDs in same order as input
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"""
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if not entities_data:
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return []
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if conn is None:
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async with acquire_with_retry(self.pool) as conn:
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return await self._resolve_entities_batch_impl(conn, bank_id, entities_data, context, unit_event_date)
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else:
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return await self._resolve_entities_batch_impl(conn, bank_id, entities_data, context, unit_event_date)
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async def _resolve_entities_batch_impl(
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self, conn, bank_id: str, entities_data: list[dict], context: str, unit_event_date
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) -> list[str]:
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# Query ALL candidates for this bank
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all_entities = await conn.fetch(
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f"""
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SELECT canonical_name, id, metadata, last_seen, mention_count
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FROM {fq_table("entities")}
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WHERE bank_id = $1
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""",
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bank_id,
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)
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# Build entity ID to name mapping for co-occurrence lookups
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entity_id_to_name = {row["id"]: row["canonical_name"].lower() for row in all_entities}
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# Query ALL co-occurrences for this bank's entities in one query
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# This builds a map of entity_id -> set of co-occurring entity names
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all_cooccurrences = await conn.fetch(
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f"""
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SELECT ec.entity_id_1, ec.entity_id_2, ec.cooccurrence_count
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FROM {fq_table("entity_cooccurrences")} ec
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WHERE ec.entity_id_1 IN (SELECT id FROM {fq_table("entities")} WHERE bank_id = $1)
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OR ec.entity_id_2 IN (SELECT id FROM {fq_table("entities")} WHERE bank_id = $1)
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""",
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bank_id,
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)
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# Build co-occurrence map: entity_id -> set of co-occurring entity names (lowercase)
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cooccurrence_map: dict[str, set[str]] = {}
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for row in all_cooccurrences:
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eid1, eid2 = row["entity_id_1"], row["entity_id_2"]
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# Add both directions
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if eid1 not in cooccurrence_map:
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cooccurrence_map[eid1] = set()
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if eid2 not in cooccurrence_map:
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cooccurrence_map[eid2] = set()
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# Map to canonical names for comparison with nearby_entities
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if eid2 in entity_id_to_name:
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cooccurrence_map[eid1].add(entity_id_to_name[eid2])
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if eid1 in entity_id_to_name:
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cooccurrence_map[eid2].add(entity_id_to_name[eid1])
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# Build candidate map for each entity text
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all_candidates = {} # Maps entity_text -> list of candidates
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entity_texts = list(set(e["text"] for e in entities_data))
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for entity_text in entity_texts:
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matching = []
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entity_text_lower = entity_text.lower()
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for row in all_entities:
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canonical_name = row["canonical_name"]
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ent_id = row["id"]
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metadata = row["metadata"]
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last_seen = row["last_seen"]
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mention_count = row["mention_count"]
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canonical_lower = canonical_name.lower()
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# Match if exact or substring match
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if (
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entity_text_lower == canonical_lower
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or entity_text_lower in canonical_lower
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or canonical_lower in entity_text_lower
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):
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matching.append((ent_id, canonical_name, metadata, last_seen, mention_count))
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all_candidates[entity_text] = matching
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# Resolve each entity using pre-fetched candidates
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entity_ids = [None] * len(entities_data)
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entities_to_update = [] # (entity_id, event_date)
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entities_to_create = [] # (idx, entity_data, event_date)
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for idx, entity_data in enumerate(entities_data):
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entity_text = entity_data["text"]
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nearby_entities = entity_data.get("nearby_entities", [])
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# Use per-entity date if available, otherwise fall back to batch-level date
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entity_event_date = entity_data.get("event_date", unit_event_date)
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candidates = all_candidates.get(entity_text, [])
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if not candidates:
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# Will create new entity
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entities_to_create.append((idx, entity_data, entity_event_date))
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continue
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# Score candidates
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best_candidate = None
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best_score = 0.0
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nearby_entity_set = {e["text"].lower() for e in nearby_entities if e["text"] != entity_text}
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for candidate_id, canonical_name, metadata, last_seen, mention_count in candidates:
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score = 0.0
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# 1. Name similarity (0-0.5)
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name_similarity = SequenceMatcher(None, entity_text.lower(), canonical_name.lower()).ratio()
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score += name_similarity * 0.5
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# 2. Co-occurring entities (0-0.3)
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if nearby_entity_set:
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co_entities = cooccurrence_map.get(candidate_id, set())
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overlap = len(nearby_entity_set & co_entities)
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co_entity_score = overlap / len(nearby_entity_set)
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score += co_entity_score * 0.3
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# 3. Temporal proximity (0-0.2)
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if last_seen and entity_event_date:
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# Normalize timezone awareness for comparison
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event_date_utc = (
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entity_event_date if entity_event_date.tzinfo else entity_event_date.replace(tzinfo=UTC)
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)
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last_seen_utc = last_seen if last_seen.tzinfo else last_seen.replace(tzinfo=UTC)
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days_diff = abs((event_date_utc - last_seen_utc).total_seconds() / 86400)
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if days_diff < 7:
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temporal_score = max(0, 1.0 - (days_diff / 7))
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score += temporal_score * 0.2
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if score > best_score:
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best_score = score
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best_candidate = candidate_id
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# Apply unified threshold
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threshold = 0.6
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if best_score > threshold:
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entity_ids[idx] = best_candidate
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entities_to_update.append((best_candidate, entity_event_date))
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else:
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entities_to_create.append((idx, entity_data, entity_event_date))
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# Batch update existing entities
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if entities_to_update:
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await conn.executemany(
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f"""
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UPDATE {fq_table("entities")} SET
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mention_count = mention_count + 1,
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last_seen = $2
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WHERE id = $1::uuid
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""",
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entities_to_update,
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)
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# Batch create new entities using COPY + INSERT for maximum speed
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# This handles duplicates via ON CONFLICT and returns all IDs
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if entities_to_create:
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# Group entities by canonical name (lowercase) to handle duplicates within batch
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# For duplicates, we only insert once and reuse the ID, but track the count
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unique_entities = {} # lowercase_name -> (entity_data, event_date, [indices])
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for idx, entity_data, event_date in entities_to_create:
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name_lower = entity_data["text"].lower()
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if name_lower not in unique_entities:
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unique_entities[name_lower] = (entity_data, event_date, [idx])
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else:
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# Same entity appears multiple times - add index to list
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unique_entities[name_lower][2].append(idx)
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# Batch insert unique entities and get their IDs
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# Use a single query with unnest for speed
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entity_names = []
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entity_dates = []
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entity_counts = [] # Track how many times each entity appears in this batch
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indices_map = [] # Maps result index -> list of original indices
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for name_lower, (entity_data, event_date, indices) in unique_entities.items():
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entity_names.append(entity_data["text"])
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entity_dates.append(event_date)
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entity_counts.append(len(indices)) # Count of occurrences in this batch
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indices_map.append(indices)
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# Batch INSERT ... ON CONFLICT with RETURNING
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# Uses the batch count for mention_count instead of always 1
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rows = await conn.fetch(
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f"""
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INSERT INTO {fq_table("entities")} (bank_id, canonical_name, first_seen, last_seen, mention_count)
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SELECT $1, name, event_date, event_date, cnt
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FROM unnest($2::text[], $3::timestamptz[], $4::int[]) AS t(name, event_date, cnt)
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ON CONFLICT (bank_id, LOWER(canonical_name))
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DO UPDATE SET
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mention_count = {fq_table("entities")}.mention_count + EXCLUDED.mention_count,
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last_seen = EXCLUDED.last_seen
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RETURNING id
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""",
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bank_id,
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entity_names,
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entity_dates,
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entity_counts,
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)
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# Map returned IDs back to original indices
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for result_idx, row in enumerate(rows):
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entity_id = row["id"]
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for original_idx in indices_map[result_idx]:
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entity_ids[original_idx] = entity_id
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return entity_ids
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async def resolve_entity(
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self,
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bank_id: str,
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entity_text: str,
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context: str,
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nearby_entities: list[dict],
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unit_event_date,
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) -> str:
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"""
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Resolve an entity to a canonical entity ID.
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Args:
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bank_id: bank ID (entities are scoped to agents)
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entity_text: Entity text ("Alice", "Google", etc.)
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context: Context where entity appears
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nearby_entities: Other entities in the same unit
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unit_event_date: When this unit was created
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Returns:
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Entity ID (creates new entity if needed)
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"""
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async with acquire_with_retry(self.pool) as conn:
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# Find candidate entities with similar name
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candidates = await conn.fetch(
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f"""
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SELECT id, canonical_name, metadata, last_seen
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FROM {fq_table("entities")}
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WHERE bank_id = $1
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AND (
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canonical_name ILIKE $2
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OR canonical_name ILIKE $3
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OR $2 ILIKE canonical_name || '%%'
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)
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ORDER BY mention_count DESC
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""",
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bank_id,
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entity_text,
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f"%{entity_text}%",
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)
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if not candidates:
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# New entity - create it
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return await self._create_entity(conn, bank_id, entity_text, unit_event_date)
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# Score candidates based on:
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# 1. Name similarity
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# 2. Context overlap (TODO: could use embeddings)
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# 3. Co-occurring entities
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# 4. Temporal proximity
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best_candidate = None
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best_score = 0.0
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best_name_similarity = 0.0
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nearby_entity_set = {e["text"].lower() for e in nearby_entities if e["text"] != entity_text}
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for row in candidates:
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candidate_id = row["id"]
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canonical_name = row["canonical_name"]
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metadata = row["metadata"]
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last_seen = row["last_seen"]
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score = 0.0
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# 1. Name similarity (0-1)
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name_similarity = SequenceMatcher(None, entity_text.lower(), canonical_name.lower()).ratio()
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score += name_similarity * 0.5
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# 2. Co-occurring entities (0-0.5)
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# Get entities that co-occurred with this candidate before
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# Use the materialized co-occurrence cache for fast lookup
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co_entity_rows = await conn.fetch(
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f"""
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SELECT e.canonical_name, ec.cooccurrence_count
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FROM {fq_table("entity_cooccurrences")} ec
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JOIN {fq_table("entities")} e ON (
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CASE
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WHEN ec.entity_id_1 = $1 THEN ec.entity_id_2
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WHEN ec.entity_id_2 = $1 THEN ec.entity_id_1
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END = e.id
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)
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WHERE ec.entity_id_1 = $1 OR ec.entity_id_2 = $1
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""",
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candidate_id,
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)
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co_entities = {r["canonical_name"].lower() for r in co_entity_rows}
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# Check overlap with nearby entities
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overlap = len(nearby_entity_set & co_entities)
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if nearby_entity_set:
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co_entity_score = overlap / len(nearby_entity_set)
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score += co_entity_score * 0.3
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# 3. Temporal proximity (0-0.2)
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if last_seen:
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days_diff = abs((unit_event_date - last_seen).total_seconds() / 86400)
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if days_diff < 7: # Within a week
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temporal_score = max(0, 1.0 - (days_diff / 7))
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score += temporal_score * 0.2
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if score > best_score:
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best_score = score
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best_candidate = candidate_id
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best_name_similarity = name_similarity
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# Threshold for considering it the same entity
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threshold = 0.6
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if best_score > threshold:
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# Update entity
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await conn.execute(
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f"""
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UPDATE {fq_table("entities")}
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SET mention_count = mention_count + 1,
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last_seen = $1
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WHERE id = $2
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""",
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unit_event_date,
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best_candidate,
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)
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return best_candidate
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else:
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# Not confident - create new entity
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return await self._create_entity(conn, bank_id, entity_text, unit_event_date)
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async def _create_entity(
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self,
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conn,
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bank_id: str,
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entity_text: str,
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event_date,
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) -> str:
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"""
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Create a new entity or get existing one if it already exists.
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Uses INSERT ... ON CONFLICT to handle race conditions where
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two concurrent transactions try to create the same entity.
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Args:
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conn: Database connection
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bank_id: bank ID
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entity_text: Entity text
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event_date: When first seen
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Returns:
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Entity ID
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"""
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entity_id = await conn.fetchval(
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f"""
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INSERT INTO {fq_table("entities")} (bank_id, canonical_name, first_seen, last_seen, mention_count)
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VALUES ($1, $2, $3, $4, 1)
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ON CONFLICT (bank_id, LOWER(canonical_name))
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DO UPDATE SET
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mention_count = {fq_table("entities")}.mention_count + 1,
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last_seen = EXCLUDED.last_seen
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RETURNING id
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""",
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bank_id,
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entity_text,
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event_date,
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event_date,
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)
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return entity_id
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async def link_unit_to_entity(self, unit_id: str, entity_id: str):
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"""
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Link a memory unit to an entity.
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Also updates co-occurrence cache with other entities in the same unit.
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Args:
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unit_id: Memory unit ID
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entity_id: Entity ID
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"""
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async with acquire_with_retry(self.pool) as conn:
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# Insert unit-entity link
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await conn.execute(
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f"""
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INSERT INTO {fq_table("unit_entities")} (unit_id, entity_id)
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VALUES ($1, $2)
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ON CONFLICT DO NOTHING
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""",
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unit_id,
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entity_id,
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)
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# Update co-occurrence cache: find other entities in this unit
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rows = await conn.fetch(
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f"""
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SELECT entity_id
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FROM {fq_table("unit_entities")}
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WHERE unit_id = $1 AND entity_id != $2
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""",
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unit_id,
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entity_id,
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)
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other_entities = [row["entity_id"] for row in rows]
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# Update co-occurrences for each pair
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for other_entity_id in other_entities:
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await self._update_cooccurrence(conn, entity_id, other_entity_id)
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async def _update_cooccurrence(self, conn, entity_id_1: str, entity_id_2: str):
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"""
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Update the co-occurrence cache for two entities.
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Uses CHECK constraint ordering (entity_id_1 < entity_id_2) to avoid duplicates.
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Args:
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conn: Database connection
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entity_id_1: First entity ID
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entity_id_2: Second entity ID
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"""
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# Ensure consistent ordering (smaller UUID first)
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if entity_id_1 > entity_id_2:
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entity_id_1, entity_id_2 = entity_id_2, entity_id_1
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await conn.execute(
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f"""
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INSERT INTO {fq_table("entity_cooccurrences")} (entity_id_1, entity_id_2, cooccurrence_count, last_cooccurred)
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VALUES ($1, $2, 1, NOW())
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ON CONFLICT (entity_id_1, entity_id_2)
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DO UPDATE SET
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cooccurrence_count = {fq_table("entity_cooccurrences")}.cooccurrence_count + 1,
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last_cooccurred = NOW()
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""",
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entity_id_1,
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entity_id_2,
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)
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async def link_units_to_entities_batch(self, unit_entity_pairs: list[tuple[str, str]], conn=None):
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"""
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Link multiple memory units to entities in batch (MUCH faster than sequential).
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|
Also updates co-occurrence cache for entities that appear in the same unit.
|
|
|
|
Args:
|
|
unit_entity_pairs: List of (unit_id, entity_id) tuples
|
|
conn: Optional connection to use (if None, acquires from pool)
|
|
"""
|
|
if not unit_entity_pairs:
|
|
return
|
|
|
|
if conn is None:
|
|
async with acquire_with_retry(self.pool) as conn:
|
|
return await self._link_units_to_entities_batch_impl(conn, unit_entity_pairs)
|
|
else:
|
|
return await self._link_units_to_entities_batch_impl(conn, unit_entity_pairs)
|
|
|
|
async def _link_units_to_entities_batch_impl(self, conn, unit_entity_pairs: list[tuple[str, str]]):
|
|
# Batch insert all unit-entity links
|
|
await conn.executemany(
|
|
f"""
|
|
INSERT INTO {fq_table("unit_entities")} (unit_id, entity_id)
|
|
VALUES ($1, $2)
|
|
ON CONFLICT DO NOTHING
|
|
""",
|
|
unit_entity_pairs,
|
|
)
|
|
|
|
# Build map of unit -> entities for co-occurrence calculation
|
|
# Use sets to avoid duplicate entities in the same unit
|
|
unit_to_entities = {}
|
|
for unit_id, entity_id in unit_entity_pairs:
|
|
if unit_id not in unit_to_entities:
|
|
unit_to_entities[unit_id] = set()
|
|
unit_to_entities[unit_id].add(entity_id)
|
|
|
|
# Update co-occurrences for all pairs in each unit
|
|
cooccurrence_pairs = set() # Use set to avoid duplicates
|
|
for unit_id, entity_ids in unit_to_entities.items():
|
|
entity_list = list(entity_ids) # Convert set to list for iteration
|
|
# For each pair of entities in this unit, create co-occurrence
|
|
for i, entity_id_1 in enumerate(entity_list):
|
|
for entity_id_2 in entity_list[i + 1 :]:
|
|
# Skip if same entity (shouldn't happen with set, but be safe)
|
|
if entity_id_1 == entity_id_2:
|
|
continue
|
|
# Ensure consistent ordering (entity_id_1 < entity_id_2)
|
|
if entity_id_1 > entity_id_2:
|
|
entity_id_1, entity_id_2 = entity_id_2, entity_id_1
|
|
cooccurrence_pairs.add((entity_id_1, entity_id_2))
|
|
|
|
# Batch update co-occurrences
|
|
if cooccurrence_pairs:
|
|
now = datetime.now(UTC)
|
|
await conn.executemany(
|
|
f"""
|
|
INSERT INTO {fq_table("entity_cooccurrences")} (entity_id_1, entity_id_2, cooccurrence_count, last_cooccurred)
|
|
VALUES ($1, $2, $3, $4)
|
|
ON CONFLICT (entity_id_1, entity_id_2)
|
|
DO UPDATE SET
|
|
cooccurrence_count = {fq_table("entity_cooccurrences")}.cooccurrence_count + 1,
|
|
last_cooccurred = EXCLUDED.last_cooccurred
|
|
""",
|
|
[(e1, e2, 1, now) for e1, e2 in cooccurrence_pairs],
|
|
)
|
|
|
|
async def get_units_by_entity(self, entity_id: str, limit: int = 100) -> list[str]:
|
|
"""
|
|
Get all units that mention an entity.
|
|
|
|
Args:
|
|
entity_id: Entity ID
|
|
limit: Max results
|
|
|
|
Returns:
|
|
List of unit IDs
|
|
"""
|
|
async with acquire_with_retry(self.pool) as conn:
|
|
rows = await conn.fetch(
|
|
f"""
|
|
SELECT unit_id
|
|
FROM {fq_table("unit_entities")}
|
|
WHERE entity_id = $1
|
|
ORDER BY unit_id
|
|
LIMIT $2
|
|
""",
|
|
entity_id,
|
|
limit,
|
|
)
|
|
return [row["unit_id"] for row in rows]
|
|
|
|
async def get_entity_by_text(
|
|
self,
|
|
bank_id: str,
|
|
entity_text: str,
|
|
) -> str | None:
|
|
"""
|
|
Find an entity by text (for query resolution).
|
|
|
|
Args:
|
|
bank_id: bank ID
|
|
entity_text: Entity text to search for
|
|
|
|
Returns:
|
|
Entity ID if found, None otherwise
|
|
"""
|
|
async with acquire_with_retry(self.pool) as conn:
|
|
row = await conn.fetchrow(
|
|
f"""
|
|
SELECT id FROM {fq_table("entities")}
|
|
WHERE bank_id = $1
|
|
AND canonical_name ILIKE $2
|
|
ORDER BY mention_count DESC
|
|
LIMIT 1
|
|
""",
|
|
bank_id,
|
|
entity_text,
|
|
)
|
|
|
|
return row["id"] if row else None
|