company-ai-platform/app/modules/ai_memory/service.py

from typing import Any

from sqlalchemy import func, or_, select
from sqlalchemy.orm import Session

from app.core.config import get_settings
from app.core.constants import ActorValue
from app.core.http.pagination import bounded_limit
from app.core.utils.time import utc_now
from app.modules.ai_memory.constants import (
    AI_MEMORY_CODE_PREFIX,
    AI_MEMORY_MAX_CONTENT_LENGTH,
    AI_MEMORY_MAX_SUMMARY_LENGTH,
    AI_MEMORY_MIN_AUTO_WRITE_LENGTH,
    AIMemoryPayloadKey,
    AIMemoryScope,
    AIMemorySource,
    AIMemoryStatus,
    AIMemoryText,
)
from app.modules.ai_memory.models import AIMemoryEntry
from app.modules.audit.constants import (
    AuditAction,
    AuditRiskLevel,
    AuditSource,
    AuditTargetType,
)
from app.modules.audit.schemas import AuditLogCreate
from app.modules.audit.service import AuditService
from app.modules.business.service import serialize_model
from app.modules.events.constants import EventAggregateType, EventSource, EventType
from app.modules.events.services import EventService


class AIMemoryService:
    """Store and recall audited local AI memory for read-only operations."""

    def __init__(self, db: Session):
        self.db = db
        self.audit = AuditService(db)

    def list_entries(
        self,
        scope: str | None = None,
        subject: str | None = None,
        status_filter: str = AIMemoryStatus.ACTIVE,
        limit: int = 100,
    ) -> list[dict[str, Any]]:
        stmt = (
            select(AIMemoryEntry)
            .where(AIMemoryEntry.status == status_filter)
            .order_by(AIMemoryEntry.importance.desc(), AIMemoryEntry.id.desc())
            .limit(bounded_limit(limit))
        )
        if scope:
            stmt = stmt.where(AIMemoryEntry.scope == scope)
        if subject:
            stmt = stmt.where(AIMemoryEntry.subject == subject)
        return [serialize_model(item) for item in self.db.execute(stmt).scalars()]

    def recall(
        self,
        query: str,
        scope: str = AIMemoryScope.GLOBAL,
        subject: str | None = None,
        limit: int | None = None,
        actor: str = ActorValue.API,
    ) -> list[dict[str, Any]]:
        settings = get_settings()
        if not settings.ai_memory_enabled:
            return []
        limit_value = bounded_limit(limit or settings.ai_memory_recall_limit)
        now = utc_now()
        stmt = (
            select(AIMemoryEntry)
            .where(
                AIMemoryEntry.status == AIMemoryStatus.ACTIVE,
                or_(AIMemoryEntry.expires_at.is_(None), AIMemoryEntry.expires_at > now),
                AIMemoryEntry.scope.in_({AIMemoryScope.GLOBAL, scope}),
            )
            .order_by(AIMemoryEntry.importance.desc(), AIMemoryEntry.id.desc())
            .limit(limit_value * 3)
        )
        if subject:
            stmt = stmt.where(
                or_(
                    AIMemoryEntry.subject == subject,
                    AIMemoryEntry.scope == AIMemoryScope.GLOBAL,
                )
            )
        candidates = list(self.db.execute(stmt).scalars())
        items = [item for item in candidates if _matches_query(item, query)]
        if not items:
            items = candidates[:limit_value]
        items = items[:limit_value]
        for item in items:
            item.last_used_at = now
        self.db.commit()
        result = [serialize_model(item) for item in items]
        self.audit.log(
            AuditLogCreate(
                actor=actor,
                source=AuditSource.AI_MEMORY,
                action=AuditAction.AI_MEMORY_RECALL,
                target_type=AuditTargetType.AI_MEMORY,
                risk_level=AuditRiskLevel.LOW,
                request_payload={
                    AIMemoryPayloadKey.QUERY: query,
                    AIMemoryPayloadKey.SCOPE: scope,
                    AIMemoryPayloadKey.SUBJECT: subject,
                    AIMemoryPayloadKey.LIMIT: limit_value,
                },
                response_payload={AIMemoryPayloadKey.COUNT: len(result)},
            )
        )
        return result

    def auto_write(
        self,
        prompt: str,
        context: dict[str, Any],
        answer: str,
        actor: str = ActorValue.API,
    ) -> AIMemoryEntry | None:
        settings = get_settings()
        if not settings.ai_memory_enabled or not settings.ai_memory_auto_write_enabled:
            return None
        content = _build_memory_content(prompt, context, answer)
        if len(content) < AI_MEMORY_MIN_AUTO_WRITE_LENGTH:
            return None
        scope = str(context.get(AIMemoryPayloadKey.SCOPE) or AIMemoryText.DEFAULT_SCOPE)
        subject = str(context.get(AIMemoryPayloadKey.SUBJECT) or AIMemoryText.DEFAULT_SUBJECT)
        if _contains_forbidden_value(
            {
                "prompt": prompt,
                "context": context,
                "answer": answer,
            },
            settings.ai_memory_forbidden_keys,
        ):
            record = self._create_entry(
                scope=scope,
                subject=subject,
                content=str(AIMemoryText.REJECTED_SECRET),
                summary=str(AIMemoryText.REJECTED_SECRET),
                tags=[str(AIMemoryText.AUTO_TAG)],
                source=AIMemorySource.AUTO,
                importance=0,
                status_value=AIMemoryStatus.REJECTED,
                actor=actor,
            )
            return record
        summary = _truncate(answer, AI_MEMORY_MAX_SUMMARY_LENGTH)
        record = self._create_entry(
            scope=scope,
            subject=subject,
            content=_truncate(content, AI_MEMORY_MAX_CONTENT_LENGTH),
            summary=summary,
            tags=[str(AIMemoryText.AUTO_TAG)],
            source=AIMemorySource.AUTO,
            importance=1,
            status_value=AIMemoryStatus.ACTIVE,
            actor=actor,
        )
        return record

    def count_by_status(self) -> dict[str, int]:
        rows = self.db.execute(
            select(AIMemoryEntry.status, func.count()).group_by(AIMemoryEntry.status)
        ).all()
        return {str(status_value): int(count) for status_value, count in rows}

    def _create_entry(
        self,
        scope: str,
        subject: str,
        content: str,
        summary: str | None,
        tags: list[str],
        source: str,
        importance: int,
        status_value: str,
        actor: str,
    ) -> AIMemoryEntry:
        record = AIMemoryEntry(
            code=f"{AI_MEMORY_CODE_PREFIX}-{utc_now():%Y%m%d%H%M%S%f}",
            scope=scope,
            subject=subject,
            content=content,
            summary=summary,
            tags=tags,
            source=source,
            importance=importance,
            status=status_value,
            actor=actor,
        )
        self.db.add(record)
        self.db.commit()
        self.db.refresh(record)
        self.audit.log(
            AuditLogCreate(
                actor=actor,
                source=AuditSource.AI_MEMORY,
                action=AuditAction.AI_MEMORY_WRITE,
                target_type=AuditTargetType.AI_MEMORY,
                target_id=record.code,
                risk_level=AuditRiskLevel.LOW,
                request_payload={
                    AIMemoryPayloadKey.SCOPE: scope,
                    AIMemoryPayloadKey.SUBJECT: subject,
                    AIMemoryPayloadKey.SOURCE: source,
                    AIMemoryPayloadKey.STATUS: status_value,
                },
                response_payload={AIMemoryPayloadKey.CODE: record.code},
            )
        )
        EventService(self.db).emit(
            event_type=EventType.AI_MEMORY_WRITTEN,
            source=EventSource.AI_MEMORY,
            aggregate_type=EventAggregateType.AI_MEMORY_ENTRY,
            aggregate_id=record.code,
            actor=actor,
            payload={
                AIMemoryPayloadKey.CODE: record.code,
                AIMemoryPayloadKey.SCOPE: scope,
                AIMemoryPayloadKey.SUBJECT: subject,
                AIMemoryPayloadKey.STATUS: status_value,
            },
            idempotency_key=f"ai-memory:{record.code}",
            dispatch=True,
        )
        return record


def _build_memory_content(prompt: str, context: dict[str, Any], answer: str) -> str:
    context_text = ", ".join(
        f"{key}={value}" for key, value in sorted(context.items(), key=lambda item: str(item[0]))
    )
    return f"prompt: {prompt}\ncontext: {context_text}\nanswer: {answer}"


def _contains_forbidden_value(value: Any, forbidden_keys: list[str]) -> bool:
    forbidden = {item.lower() for item in forbidden_keys}
    if isinstance(value, dict):
        for key, item in value.items():
            if str(key).lower() in forbidden:
                return True
            if _contains_forbidden_value(item, forbidden_keys):
                return True
        return False
    if isinstance(value, (list, tuple, set)):
        return any(_contains_forbidden_value(item, forbidden_keys) for item in value)
    if isinstance(value, str):
        lowered = value.lower()
        return any(item in lowered for item in forbidden)
    return False


def _matches_query(entry: AIMemoryEntry, query: str) -> bool:
    query_text = query.lower().strip()
    if not query_text:
        return True
    text = " ".join(
        [
            entry.subject or "",
            entry.content or "",
            entry.summary or "",
            " ".join(str(item) for item in (entry.tags or [])),
        ]
    ).lower()
    return any(token in text for token in query_text.split())


def _truncate(value: str, max_length: int) -> str:
    if len(value) <= max_length:
        return value
    return value[:max_length]