f50cc25dfb
The stats endpoint JOINs memory_links to memory_units just to filter by bank_id. With 8.2M+ links per bank this takes 18+ seconds, and the control plane polls every 10s — perpetually blocking the server. Add bank_id column directly to memory_links so the query can filter on ml.bank_id instead of mu.bank_id, letting Postgres push the filter down before the JOIN. - Migration: add bank_id TEXT NOT NULL, backfill from memory_units - All 4 INSERT paths (temporal, semantic, entity, causal) now write bank_id - Stats query filters on ml.bank_id instead of mu.bank_id
99 lines
3 KiB
Python
99 lines
3 KiB
Python
"""
|
|
Link creation for retain pipeline.
|
|
|
|
Handles creation of temporal, semantic, and causal links between facts.
|
|
"""
|
|
|
|
import logging
|
|
|
|
from . import link_utils
|
|
from .types import ProcessedFact
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
async def create_temporal_links_batch(conn, bank_id: str, unit_ids: list[str]) -> int:
|
|
"""
|
|
Create temporal links between facts.
|
|
|
|
Links facts that occurred close in time to each other.
|
|
|
|
Args:
|
|
conn: Database connection
|
|
bank_id: Bank identifier
|
|
unit_ids: List of unit IDs to create links for
|
|
|
|
Returns:
|
|
Number of temporal links created
|
|
"""
|
|
if not unit_ids:
|
|
return 0
|
|
|
|
return await link_utils.create_temporal_links_batch_per_fact(conn, bank_id, unit_ids, log_buffer=[])
|
|
|
|
|
|
async def create_semantic_links_batch(conn, bank_id: str, unit_ids: list[str], embeddings: list[list[float]]) -> int:
|
|
"""
|
|
Create semantic links between facts.
|
|
|
|
Links facts that are semantically similar based on embeddings.
|
|
|
|
Args:
|
|
conn: Database connection
|
|
bank_id: Bank identifier
|
|
unit_ids: List of unit IDs to create links for
|
|
embeddings: List of embedding vectors (same length as unit_ids)
|
|
|
|
Returns:
|
|
Number of semantic links created
|
|
"""
|
|
if not unit_ids or not embeddings:
|
|
return 0
|
|
|
|
if len(unit_ids) != len(embeddings):
|
|
raise ValueError(f"Mismatch between unit_ids ({len(unit_ids)}) and embeddings ({len(embeddings)})")
|
|
|
|
return await link_utils.create_semantic_links_batch(conn, bank_id, unit_ids, embeddings, log_buffer=[])
|
|
|
|
|
|
async def create_causal_links_batch(conn, bank_id: str, unit_ids: list[str], facts: list[ProcessedFact]) -> int:
|
|
"""
|
|
Create causal links between facts.
|
|
|
|
Links facts that have causal relationships (causes, enables, prevents).
|
|
|
|
Args:
|
|
conn: Database connection
|
|
unit_ids: List of unit IDs (same length as facts)
|
|
facts: List of ProcessedFact objects with causal_relations
|
|
|
|
Returns:
|
|
Number of causal links created
|
|
"""
|
|
if not unit_ids or not facts:
|
|
return 0
|
|
|
|
if len(unit_ids) != len(facts):
|
|
raise ValueError(f"Mismatch between unit_ids ({len(unit_ids)}) and facts ({len(facts)})")
|
|
|
|
# Extract causal relations in the format expected by link_utils
|
|
# Format: List of lists, where each inner list is the causal relations for that fact
|
|
causal_relations_per_fact = []
|
|
for fact in facts:
|
|
if fact.causal_relations:
|
|
# Convert CausalRelation objects to dicts
|
|
relations_dicts = [
|
|
{
|
|
"relation_type": rel.relation_type,
|
|
"target_fact_index": rel.target_fact_index,
|
|
"strength": rel.strength,
|
|
}
|
|
for rel in fact.causal_relations
|
|
]
|
|
causal_relations_per_fact.append(relations_dicts)
|
|
else:
|
|
causal_relations_per_fact.append([])
|
|
|
|
link_count = await link_utils.create_causal_links_batch(conn, bank_id, unit_ids, causal_relations_per_fact)
|
|
|
|
return link_count
|