Files
syndarix/backend/app/services/memory/semantic/verification.py
Felipe Cardoso e946787a61 feat(memory): add semantic memory implementation (Issue #91)
Implements semantic memory with fact storage, retrieval, and verification:

Core functionality:
- SemanticMemory class for fact storage/retrieval
- Fact storage as subject-predicate-object triples
- Duplicate detection with reinforcement
- Semantic search with text-based fallback
- Entity-based retrieval
- Confidence scoring and decay
- Conflict resolution

Supporting modules:
- FactExtractor: Pattern-based fact extraction from episodes
- FactVerifier: Contradiction detection and reliability scoring

Test coverage:
- 47 unit tests covering all modules
- extraction.py: 99% coverage
- verification.py: 95% coverage
- memory.py: 78% coverage

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-05 02:23:06 +01:00

364 lines
11 KiB
Python

# app/services/memory/semantic/verification.py
"""
Fact Verification.
Provides utilities for verifying facts, detecting conflicts,
and managing fact consistency.
"""
import logging
from dataclasses import dataclass, field
from typing import Any, ClassVar
from uuid import UUID
from sqlalchemy import and_, or_, select
from sqlalchemy.ext.asyncio import AsyncSession
from app.models.memory.fact import Fact as FactModel
from app.services.memory.types import Fact
logger = logging.getLogger(__name__)
@dataclass
class VerificationResult:
"""Result of fact verification."""
is_valid: bool
confidence_adjustment: float = 0.0
conflicts: list["FactConflict"] = field(default_factory=list)
supporting_facts: list[Fact] = field(default_factory=list)
messages: list[str] = field(default_factory=list)
@dataclass
class FactConflict:
"""Represents a conflict between two facts."""
fact_a_id: UUID
fact_b_id: UUID
conflict_type: str # "contradiction", "superseded", "partial_overlap"
description: str
suggested_resolution: str | None = None
def to_dict(self) -> dict[str, Any]:
"""Convert to dictionary."""
return {
"fact_a_id": str(self.fact_a_id),
"fact_b_id": str(self.fact_b_id),
"conflict_type": self.conflict_type,
"description": self.description,
"suggested_resolution": self.suggested_resolution,
}
class FactVerifier:
"""
Verifies facts and detects conflicts.
Provides methods to:
- Check if a fact conflicts with existing facts
- Find supporting evidence for a fact
- Detect contradictions in the fact base
"""
# Predicates that are opposites/contradictions
CONTRADICTORY_PREDICATES: ClassVar[set[tuple[str, str]]] = {
("uses", "does_not_use"),
("requires", "does_not_require"),
("is_a", "is_not_a"),
("causes", "prevents"),
("allows", "prevents"),
("supports", "does_not_support"),
("best_practice", "anti_pattern"),
}
def __init__(self, session: AsyncSession) -> None:
"""Initialize verifier with database session."""
self._session = session
async def verify_fact(
self,
subject: str,
predicate: str,
obj: str,
project_id: UUID | None = None,
) -> VerificationResult:
"""
Verify a fact against existing facts.
Args:
subject: Fact subject
predicate: Fact predicate
obj: Fact object
project_id: Optional project scope
Returns:
VerificationResult with verification details
"""
result = VerificationResult(is_valid=True)
# Check for direct contradictions
conflicts = await self._find_contradictions(
subject=subject,
predicate=predicate,
obj=obj,
project_id=project_id,
)
result.conflicts = conflicts
if conflicts:
result.is_valid = False
result.messages.append(f"Found {len(conflicts)} conflicting fact(s)")
# Reduce confidence based on conflicts
result.confidence_adjustment = -0.1 * len(conflicts)
# Find supporting facts
supporting = await self._find_supporting_facts(
subject=subject,
predicate=predicate,
project_id=project_id,
)
result.supporting_facts = supporting
if supporting:
result.messages.append(f"Found {len(supporting)} supporting fact(s)")
# Boost confidence based on support
result.confidence_adjustment += 0.05 * min(len(supporting), 3)
return result
async def _find_contradictions(
self,
subject: str,
predicate: str,
obj: str,
project_id: UUID | None = None,
) -> list[FactConflict]:
"""Find facts that contradict the given fact."""
conflicts: list[FactConflict] = []
# Find opposite predicates
opposite_predicates = self._get_opposite_predicates(predicate)
if not opposite_predicates:
return conflicts
# Search for contradicting facts
query = select(FactModel).where(
and_(
FactModel.subject == subject,
FactModel.predicate.in_(opposite_predicates),
)
)
if project_id is not None:
query = query.where(
or_(
FactModel.project_id == project_id,
FactModel.project_id.is_(None),
)
)
result = await self._session.execute(query)
models = list(result.scalars().all())
for model in models:
conflicts.append(
FactConflict(
fact_a_id=model.id, # type: ignore[arg-type]
fact_b_id=UUID(
"00000000-0000-0000-0000-000000000000"
), # Placeholder for new fact
conflict_type="contradiction",
description=(
f"'{subject} {predicate} {obj}' contradicts "
f"'{model.subject} {model.predicate} {model.object}'"
),
suggested_resolution="Keep fact with higher confidence",
)
)
return conflicts
def _get_opposite_predicates(self, predicate: str) -> list[str]:
"""Get predicates that are opposite to the given predicate."""
opposites: list[str] = []
for pair in self.CONTRADICTORY_PREDICATES:
if predicate in pair:
opposites.extend(p for p in pair if p != predicate)
return opposites
async def _find_supporting_facts(
self,
subject: str,
predicate: str,
project_id: UUID | None = None,
) -> list[Fact]:
"""Find facts that support the given fact."""
# Find facts with same subject and predicate
query = (
select(FactModel)
.where(
and_(
FactModel.subject == subject,
FactModel.predicate == predicate,
FactModel.confidence >= 0.5,
)
)
.limit(10)
)
if project_id is not None:
query = query.where(
or_(
FactModel.project_id == project_id,
FactModel.project_id.is_(None),
)
)
result = await self._session.execute(query)
models = list(result.scalars().all())
return [self._model_to_fact(m) for m in models]
async def find_all_conflicts(
self,
project_id: UUID | None = None,
) -> list[FactConflict]:
"""
Find all conflicts in the fact base.
Args:
project_id: Optional project scope
Returns:
List of all detected conflicts
"""
conflicts: list[FactConflict] = []
# Get all facts
query = select(FactModel)
if project_id is not None:
query = query.where(
or_(
FactModel.project_id == project_id,
FactModel.project_id.is_(None),
)
)
result = await self._session.execute(query)
models = list(result.scalars().all())
# Check each pair for conflicts
for i, fact_a in enumerate(models):
for fact_b in models[i + 1 :]:
conflict = self._check_pair_conflict(fact_a, fact_b)
if conflict:
conflicts.append(conflict)
logger.info(f"Found {len(conflicts)} conflicts in fact base")
return conflicts
def _check_pair_conflict(
self,
fact_a: FactModel,
fact_b: FactModel,
) -> FactConflict | None:
"""Check if two facts conflict."""
# Same subject?
if fact_a.subject != fact_b.subject:
return None
# Contradictory predicates?
opposite = self._get_opposite_predicates(fact_a.predicate) # type: ignore[arg-type]
if fact_b.predicate not in opposite:
return None
return FactConflict(
fact_a_id=fact_a.id, # type: ignore[arg-type]
fact_b_id=fact_b.id, # type: ignore[arg-type]
conflict_type="contradiction",
description=(
f"'{fact_a.subject} {fact_a.predicate} {fact_a.object}' "
f"contradicts '{fact_b.subject} {fact_b.predicate} {fact_b.object}'"
),
suggested_resolution="Deprecate fact with lower confidence",
)
async def get_fact_reliability_score(
self,
fact_id: UUID,
) -> float:
"""
Calculate a reliability score for a fact.
Based on:
- Confidence score
- Number of reinforcements
- Number of supporting facts
- Absence of conflicts
Args:
fact_id: Fact to score
Returns:
Reliability score (0.0 to 1.0)
"""
query = select(FactModel).where(FactModel.id == fact_id)
result = await self._session.execute(query)
model = result.scalar_one_or_none()
if model is None:
return 0.0
# Base score from confidence - explicitly typed to avoid Column type issues
score: float = float(model.confidence)
# Boost for reinforcements (diminishing returns)
reinforcement_boost = min(0.2, float(model.reinforcement_count) * 0.02)
score += reinforcement_boost
# Find supporting facts
supporting = await self._find_supporting_facts(
subject=model.subject, # type: ignore[arg-type]
predicate=model.predicate, # type: ignore[arg-type]
project_id=model.project_id, # type: ignore[arg-type]
)
support_boost = min(0.1, len(supporting) * 0.02)
score += support_boost
# Check for conflicts
conflicts = await self._find_contradictions(
subject=model.subject, # type: ignore[arg-type]
predicate=model.predicate, # type: ignore[arg-type]
obj=model.object, # type: ignore[arg-type]
project_id=model.project_id, # type: ignore[arg-type]
)
conflict_penalty = min(0.3, len(conflicts) * 0.1)
score -= conflict_penalty
# Clamp to valid range
return max(0.0, min(1.0, score))
def _model_to_fact(self, model: FactModel) -> Fact:
"""Convert SQLAlchemy model to Fact dataclass."""
return Fact(
id=model.id, # type: ignore[arg-type]
project_id=model.project_id, # type: ignore[arg-type]
subject=model.subject, # type: ignore[arg-type]
predicate=model.predicate, # type: ignore[arg-type]
object=model.object, # type: ignore[arg-type]
confidence=model.confidence, # type: ignore[arg-type]
source_episode_ids=model.source_episode_ids or [], # type: ignore[arg-type]
first_learned=model.first_learned, # type: ignore[arg-type]
last_reinforced=model.last_reinforced, # type: ignore[arg-type]
reinforcement_count=model.reinforcement_count, # type: ignore[arg-type]
embedding=None,
created_at=model.created_at, # type: ignore[arg-type]
updated_at=model.updated_at, # type: ignore[arg-type]
)