services/memory_service.py

"""
Long-Term User Memory Service
==============================
Stores user preferences, facts, and conversation context across sessions in Qdrant `user_memory` collection.
"""
from __future__ import annotations

import os
import uuid
import structlog
from typing import List, Dict, Any, Optional
from qdrant_client import models as qmodels

from services.embedding import embedding_service
from services.qdrant_service import qdrant_service, MEMORY_COLLECTION

logger = structlog.get_logger(__name__)


class MemoryService:
    """Manages long-term user memory and preference retrieval."""

    async def store_user_fact(
        self,
        tenant_id: str,
        user_id: str,
        fact_text: str,
        category: str = "preference",
    ) -> str:
        """Embed and save a user fact/preference into long-term memory."""
        vector = await embedding_service.encode_query(fact_text)

        client = qdrant_service.get_client(os.environ.get("QDRANT_URL"))
        point_id = str(uuid.uuid4())

        client.upsert(
            collection_name=MEMORY_COLLECTION,
            points=[
                qmodels.PointStruct(
                    id=point_id,
                    vector=vector.tolist(),
                    payload={
                        "tenant_id": tenant_id,
                        "user_id": user_id,
                        "fact": fact_text,
                        "category": category,
                    },
                )
            ],
        )
        logger.info("User fact stored in long-term memory", user_id=user_id, fact=fact_text)
        return point_id

    async def recall_user_memories(
        self,
        tenant_id: str,
        user_id: str,
        query: str,
        top_k: int = 3,
    ) -> List[str]:
        """Recall relevant user memories for a query using vector search."""
        try:
            vector = await embedding_service.encode_query(query)
            client = qdrant_service.get_client(os.environ.get("QDRANT_URL"))

            search_filter = qmodels.Filter(
                must=[
                    qmodels.FieldCondition(key="tenant_id", match=qmodels.MatchValue(value=tenant_id)),
                    qmodels.FieldCondition(key="user_id", match=qmodels.MatchValue(value=user_id)),
                ]
            )

            hits = client.search(
                collection_name=MEMORY_COLLECTION,
                query_vector=vector.tolist(),
                query_filter=search_filter,
                limit=top_k,
            )

            memories = [hit.payload.get("fact", "") for hit in hits if hit.payload]
            logger.info("Recalled long-term memories", count=len(memories), user_id=user_id)
            return memories
        except Exception as e:
            logger.warning("Failed to recall memories", error=str(e))
            return []


# ── Singleton instance ─────────────────────────────────────────
memory_service = MemoryService()