"""
Tiered Memory Management (Phase 3.5+)
=====================================
Manages memory lifecycle across HOT, WARM, and COLD tiers based on Long-Term Potentiation (LTP).

Tiers:
  - HOT (RAM): Fast access, limited capacity. Stores most relevant memories.
  - WARM (Qdrant/Disk): Larger capacity, slightly slower access.
  - COLD (Archive): Unlimited capacity, slow access. Compressed JSONL.

Logic:
  - New memories start in HOT.
  - `consolidate()` moves memories between tiers based on LTP strength and hysteresis.
  - Promote: WARM -> HOT if `ltp > threshold + delta`
  - Demote: HOT -> WARM if `ltp < threshold - delta`
  - Archive: WARM -> COLD if `ltp < archive_threshold` (or age)

All vectors use BinaryHDV (packed uint8 arrays).
"""

import gzip
import json
from datetime import datetime, timezone
from itertools import islice
from pathlib import Path
from typing import Dict, List, Optional, Tuple, TYPE_CHECKING, Any

if TYPE_CHECKING:
    from .qdrant_store import QdrantStore
import asyncio
import functools

import numpy as np
from loguru import logger

from .binary_hdv import BinaryHDV
from .config import HAIMConfig, get_config
from .node import MemoryNode
from .exceptions import (
    MnemoCoreError,
    StorageError,
    CircuitOpenError,
    DataCorruptionError,
)

try:
    import faiss
    FAISS_AVAILABLE = True
except ImportError:
    FAISS_AVAILABLE = False

# Phase 4.0: HNSW index manager (replaces raw FAISS management)
try:
    from .hnsw_index import HNSWIndexManager
    HNSW_AVAILABLE = True
except ImportError:
    HNSW_AVAILABLE = False


class TierManager:
    """
    Manages memory storage across tiered hierarchy.
    Uses BinaryHDV exclusively for efficient storage and computation.
    """

    def __init__(
        self,
        config: Optional[HAIMConfig] = None,
        qdrant_store: Optional["QdrantStore"] = None,
    ):
        """
        Initialize TierManager with optional dependency injection.

        Args:
            config: Configuration object. If None, uses global get_config().
            qdrant_store: QdrantStore instance. If None, will not use Qdrant.
        """
        self.config = config or get_config()

        # Initialization guard
        self._initialized: bool = False

        # Async lock - created eagerly; asyncio.Lock() is safe to construct
        # outside a running loop in Python 3.10+ (loop binding is deferred).
        self.lock: asyncio.Lock = asyncio.Lock()

        # HOT Tier: In-memory dictionary
        self.hot: Dict[str, MemoryNode] = {}
        # Phase 13.2: O(1) inverted index for episodic chain – previous_id → node_id.
        # Maintained in sync with self.hot so get_next_in_chain() avoids O(N) scans.
        self._next_chain: Dict[str, str] = {}

        # WARM Tier: Qdrant (injected) or fallback to filesystem
        self.qdrant = qdrant_store
        self.use_qdrant = qdrant_store is not None
        self.warm_path = None

        if not self.use_qdrant:
            self.warm_path = Path(self.config.paths.warm_mmap_dir)
            self.warm_path.mkdir(parents=True, exist_ok=True)

        # COLD Tier path
        self.cold_path = Path(self.config.paths.cold_archive_dir)
        self.cold_path.mkdir(parents=True, exist_ok=True)

        # Phase 4.0: HNSW/FAISS Index for HOT Tier (Binary)
        # HNSWIndexManager auto-selects Flat (small N) or HNSW (large N)
        cfg = self.config
        if HNSW_AVAILABLE:
            self._hnsw = HNSWIndexManager(
                dimension=cfg.dimensionality,
                m=getattr(cfg.qdrant, "hnsw_m", 32),
                ef_construction=getattr(cfg.qdrant, "hnsw_ef_construct", 200),
                ef_search=64,
            )
            logger.info(
                f"Phase 4.0 HNSWIndexManager initialised for HOT tier "
                f"(dim={cfg.dimensionality}, M={getattr(cfg.qdrant, 'hnsw_m', 32)})"
            )
        else:
            self._hnsw = None

        # Legacy FAISS fields kept for backward-compat (unused when HNSW available)
        self.faiss_index = None
        self.faiss_id_map: Dict[int, str] = {}
        self.node_id_to_faiss_id: Dict[str, int] = {}
        self._next_faiss_id = 1

        if not HNSW_AVAILABLE and FAISS_AVAILABLE:
            self._init_faiss()

    def _init_faiss(self):
        """Initialize FAISS binary index (legacy path, used when hnsw_index unavailable)."""
        dim = self.config.dimensionality
        base_index = faiss.IndexBinaryFlat(dim)
        self.faiss_index = faiss.IndexBinaryIDMap(base_index)
        logger.info(f"Initialized FAISS flat binary index for HOT tier (dim={dim})")

    async def get_hot_snapshot(self) -> List[MemoryNode]:
        """Return a snapshot of values in HOT tier safely."""
        async with self.lock:
            return list(self.hot.values())

    async def get_hot_recent(self, n: int) -> List[MemoryNode]:
        """Get the most recent n memories from HOT tier efficiently."""
        async with self.lock:
            try:
                recent_keys = list(islice(reversed(self.hot), n))
                nodes = [self.hot[k] for k in reversed(recent_keys)]
                return nodes
            except Exception:
                all_nodes = list(self.hot.values())
                return all_nodes[-n:]

    async def initialize(self):
        """Async initialization for Qdrant collections."""
        if self._initialized:
            return

        if self.use_qdrant and self.qdrant:
            try:
                await self.qdrant.ensure_collections()
            except Exception as e:
                logger.error(f"Failed to ensure Qdrant collections: {e}")
                self.use_qdrant = False
                self.warm_path = Path(self.config.paths.warm_mmap_dir)
                self.warm_path.mkdir(parents=True, exist_ok=True)

        self._initialized = True

    async def _run_in_thread(self, func, *args, **kwargs):
        """Run blocking function in thread pool."""
        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(None, functools.partial(func, *args, **kwargs))

    async def add_memory(self, node: MemoryNode):
        """Add a new memory node. New memories are always HOT initially."""
        node.tier = "hot"

        # Delta 67.4: Ensure mutual exclusion. 
        # If the node exists in WARM, remove it before adding to HOT.
        await self._delete_from_warm(node.id)

        # Phase 1: Add to HOT tier under lock (no I/O)
        victim_to_evict = None
        async with self.lock:
            self.hot[node.id] = node
            self._add_to_faiss(node)
            # Maintain inverted chain index (Fix 7: O(1) get_next_in_chain)
            if node.previous_id:
                self._next_chain[node.previous_id] = node.id

            # Check if we need to evict - decide under lock, execute outside
            if len(self.hot) > self.config.tiers_hot.max_memories:
                # Use unified eviction logic, protecting the new node
                victim_to_evict = self._prepare_eviction_from_hot(exclude_node_id=node.id)

        # Phase 2: Perform I/O outside lock
        if victim_to_evict:
            await self._save_to_warm(victim_to_evict)

    def _add_to_faiss(self, node: MemoryNode):
        """Add node to the ANN index (HNSW preferred, legacy flat as fallback)."""
        # Phase 4.0: delegate to HNSWIndexManager
        if self._hnsw is not None:
            self._hnsw.add(node.id, node.hdv.data)
            return

        # Legacy FAISS flat path
        if not self.faiss_index:
            return

        try:
            fid = self._next_faiss_id
            self._next_faiss_id += 1

            vec = np.expand_dims(node.hdv.data, axis=0)
            ids = np.array([fid], dtype='int64')
            self.faiss_index.add_with_ids(vec, ids)

            self.faiss_id_map[fid] = node.id
            self.node_id_to_faiss_id[node.id] = fid
        except Exception as e:
            logger.error(f"Failed to add node {node.id} to FAISS: {e}")

    async def get_memory(self, node_id: str) -> Optional[MemoryNode]:
        """Retrieve memory by ID from any tier."""
        # Check HOT
        demote_candidate = None
        result_node = None

        async with self.lock:
            if node_id in self.hot:
                node = self.hot[node_id]
                node.access()
                
                # check if node should be demoted
                if self._should_demote(node):
                    # Node will be demoted, mark it as warm immediately to prevent TOCTOU
                    # This ensures concurrent readers see the correct upcoming state
                    node.tier = "warm"
                    demote_candidate = node
                
                result_node = node

        # If demotion is needed, save to WARM first, then remove from HOT
        # This occurs outside the lock to allow concurrency, but the node 
        # is already marked as "warm" (graceful degradation if save fails)
        if demote_candidate:
            logger.info(f"Demoting {demote_candidate.id} to WARM (LTP: {demote_candidate.ltp_strength:.4f})")
            
            # Step 1: Save to WARM (I/O outside lock)
            await self._save_to_warm(demote_candidate)
            
            # Step 2: Remove from HOT (under lock)
            async with self.lock:
                # Double-check: it might have been accessed again or removed
                if demote_candidate.id in self.hot:
                    del self.hot[demote_candidate.id]
                    self._remove_from_faiss(demote_candidate.id)
                    # node.tier is already "warm"

        if result_node:
            return result_node

        # Check WARM (Qdrant or Disk)
        warm_node = await self._load_from_warm(node_id)
        if warm_node:
            warm_node.tier = "warm"
            warm_node.access()
            # Check promotion (pure function, no lock needed)
            if self._should_promote(warm_node):
                await self._promote_to_hot(warm_node)
            return warm_node

        # Fix 1: Fall back to COLD tier (read-only archive scan).
        cold_node = await self._load_from_cold(node_id)
        if cold_node:
            logger.debug(f"Retrieved {node_id} from COLD tier.")
        return cold_node

    async def get_memories_batch(self, node_ids: List[str]) -> List[Optional[MemoryNode]]:
        """
        Retrieve multiple memories concurrently.

        Preserves input order and returns None for missing/error cases.
        """
        if not node_ids:
            return []

        unique_ids = list(dict.fromkeys(node_ids))
        tasks = [self.get_memory(nid) for nid in unique_ids]
        raw_results = await asyncio.gather(*tasks, return_exceptions=True)

        result_by_id: Dict[str, Optional[MemoryNode]] = {}
        for nid, result in zip(unique_ids, raw_results):
            if isinstance(result, Exception):
                logger.error(f"Batch get_memory failed for {nid}: {result}")
                result_by_id[nid] = None
            else:
                result_by_id[nid] = result

        return [result_by_id.get(nid) for nid in node_ids]

    async def anticipate(self, node_ids: List[str]) -> None:
        """
        Phase 13.2: Anticipatory Memory
        Pre-loads specific nodes into the HOT active tier (working memory).
        This forces the nodes out of WARM/COLD and into RAM for near-zero latency retrieval.
        """
        for nid in set(node_ids):
            # Check if already in HOT
            in_hot = False
            async with self.lock:
                if nid in self.hot:
                    in_hot = True
            
            if not in_hot:
                # Load from WARM
                node = await self._load_from_warm(nid)
                if node:
                    # Force promote to HOT
                    await self._promote_to_hot(node)
    async def delete_memory(self, node_id: str):
        """Robust delete from all tiers."""
        async with self.lock:
            if node_id in self.hot:
                _node = self.hot[node_id]
                # Maintain inverted chain index before removal
                if _node.previous_id:
                    self._next_chain.pop(_node.previous_id, None)
                del self.hot[node_id]
                self._remove_from_faiss(node_id)
                logger.debug(f"Deleted {node_id} from HOT")

        await self._delete_from_warm(node_id)

    def _remove_from_faiss(self, node_id: str):
        """Remove node from the ANN index (HNSW preferred, legacy flat as fallback)."""
        # Phase 4.0: delegate to HNSWIndexManager
        if self._hnsw is not None:
            self._hnsw.remove(node_id)
            return

        # Legacy FAISS flat path
        if not self.faiss_index:
            return

        fid = self.node_id_to_faiss_id.get(node_id)
        if fid is not None:
            try:
                ids_to_remove = np.array([fid], dtype='int64')
                self.faiss_index.remove_ids(ids_to_remove)
                del self.faiss_id_map[fid]
                del self.node_id_to_faiss_id[node_id]
            except Exception as e:
                logger.error(f"Failed to remove node {node_id} from FAISS: {e}")

    async def _delete_from_warm(self, node_id: str) -> bool:
        """
        Internal helper to delete from warm and return if found.

        Returns:
            True if deleted, False otherwise.

        Note:
            Errors are logged but don't propagate to allow graceful degradation.
        """
        deleted = False
        if self.use_qdrant:
            try:
                await self.qdrant.delete(self.config.qdrant.collection_warm, [node_id])
                deleted = True
            except CircuitOpenError as e:
                logger.warning(f"Cannot delete {node_id}: {e}")
            except StorageError as e:
                logger.warning(f"Storage error deleting {node_id}: {e}")
            except Exception as e:
                logger.warning(f"Qdrant delete failed for {node_id}: {e}")

        # Filesystem fallback
        if hasattr(self, 'warm_path') and self.warm_path:
            def _fs_delete():
                d = False
                npy = self.warm_path / f"{node_id}.npy"
                jsn = self.warm_path / f"{node_id}.json"
                if npy.exists() or jsn.exists():
                    try:
                        if npy.exists():
                            npy.unlink()
                        if jsn.exists():
                            jsn.unlink()
                        d = True
                    except OSError:
                        pass
                return d

            if await self._run_in_thread(_fs_delete):
                deleted = True
                logger.debug(f"Deleted {node_id} from WARM (FS)")

        return deleted

    def _prepare_eviction_from_hot(self, exclude_node_id: Optional[str] = None) -> Optional[MemoryNode]:
        """
        Prepare eviction by finding and removing the victim from HOT.
        Returns the victim node to be saved to WARM (caller must do I/O outside lock).
        Returns None if HOT tier is empty or no valid victim found.

        Args:
            exclude_node_id: Optional ID to protect from eviction (e.g., the node just added).
        """
        if not self.hot:
            return None

        candidates = self.hot.values()
        if exclude_node_id:
            candidates = [n for n in candidates if n.id != exclude_node_id]

        if not candidates:
            return None

        victim = min(candidates, key=lambda n: n.ltp_strength)
        logger.info(f"Evicting {victim.id} from HOT to WARM (LTP: {victim.ltp_strength:.4f})")

        # Remove from HOT structure (maintain inverted chain index)
        if victim.previous_id:
            self._next_chain.pop(victim.previous_id, None)
        del self.hot[victim.id]
        self._remove_from_faiss(victim.id)
        
        # Mark as warm for state consistency
        victim.tier = "warm"
        
        return victim

    async def _save_to_warm(self, node: MemoryNode):
        """
        Save memory node to WARM tier (Qdrant or fallback).

        Raises:
            StorageError: If save fails (to allow caller to handle appropriately).

        Note:
            Falls back to filesystem if Qdrant save fails.
        """
        if self.use_qdrant:
            try:
                from qdrant_client import models

                # Unpack binary vector for Qdrant storage (Bipolar Phase 4.5)
                bits = np.unpackbits(node.hdv.data)
                vector = (bits.astype(float) * 2.0 - 1.0).tolist()

                point = models.PointStruct(
                    id=node.id,
                    vector=vector,
                    payload={
                        "content": node.content,
                        "metadata": node.metadata,
                        "created_at": node.created_at.isoformat(),
                        "last_accessed": node.last_accessed.isoformat(),
                        "ltp_strength": node.ltp_strength,
                        "access_count": node.access_count,
                        "epistemic_value": node.epistemic_value,
                        "pragmatic_value": node.pragmatic_value,
                        "dimension": node.hdv.dimension,
                        "hdv_type": "binary",
                        # Phase 4.3: Temporal metadata for time-based indexing
                        "unix_timestamp": node.unix_timestamp,
                        "iso_date": node.iso_date,
                        "previous_id": node.previous_id,
                    }
                )

                await self.qdrant.upsert(
                    collection=self.config.qdrant.collection_warm,
                    points=[point]
                )
                return
            except CircuitOpenError as e:
                logger.warning(f"Cannot save {node.id} to Qdrant (circuit open), falling back to FS: {e}")
                # Fall through to filesystem fallback
            except StorageError as e:
                logger.error(f"Storage error saving {node.id} to Qdrant, falling back to FS: {e}")
                # Fall through to filesystem fallback
            except Exception as e:
                logger.error(f"Failed to save {node.id} to Qdrant, falling back to FS: {e}")
                # Fall through to filesystem fallback

        # Fallback (File System)
        if not hasattr(self, 'warm_path') or not self.warm_path:
            self.warm_path = Path(self.config.paths.warm_mmap_dir)
            self.warm_path.mkdir(parents=True, exist_ok=True)

        def _fs_save():
            hdv_path = self.warm_path / f"{node.id}.npy"
            np.save(hdv_path, node.hdv.data)

            meta_path = self.warm_path / f"{node.id}.json"
            data = {
                "id": node.id,
                "content": node.content,
                "metadata": node.metadata,
                "created_at": node.created_at.isoformat(),
                "last_accessed": node.last_accessed.isoformat(),
                "ltp_strength": node.ltp_strength,
                "access_count": node.access_count,
                "tier": "warm",
                "epistemic_value": node.epistemic_value,
                "pragmatic_value": node.pragmatic_value,
                "hdv_type": "binary",
                "dimension": node.hdv.dimension,
                # Phase 4.3: Temporal metadata
                "unix_timestamp": node.unix_timestamp,
                "iso_date": node.iso_date,
                "previous_id": node.previous_id,
            }
            with open(meta_path, "w") as f:
                json.dump(data, f)

        await self._run_in_thread(_fs_save)

    async def _load_from_warm(self, node_id: str) -> Optional[MemoryNode]:
        """
        Load memory node from WARM tier.

        Returns:
            MemoryNode if found, None if not found.

        Note:
            Returns None for both "not found" and storage errors to maintain
            backward compatibility. Storage errors are logged but don't propagate
            to avoid disrupting higher-level operations.
        """
        if self.use_qdrant:
            try:
                record = await self.qdrant.get_point(
                    self.config.qdrant.collection_warm, node_id
                )
                if record:
                    payload = record.payload
                    vec_data = record.vector

                    try:
                        # Reconstruct BinaryHDV
                        arr = np.array(vec_data) > 0.5
                        packed = np.packbits(arr.astype(np.uint8))
                        hdv = BinaryHDV(data=packed, dimension=payload["dimension"])
                    except (ValueError, KeyError, TypeError) as e:
                        logger.error(f"Data corruption for {node_id} in Qdrant: {e}")
                        return None

                    return MemoryNode(
                        id=payload.get("id", node_id),
                        hdv=hdv,
                        content=payload["content"],
                        metadata=payload["metadata"],
                        created_at=datetime.fromisoformat(payload["created_at"]),
                        last_accessed=datetime.fromisoformat(payload["last_accessed"]),
                        tier="warm",
                        access_count=payload.get("access_count", 0),
                        ltp_strength=payload.get("ltp_strength", 0.0),
                        epistemic_value=payload.get("epistemic_value", 0.0),
                        pragmatic_value=payload.get("pragmatic_value", 0.0),
                        previous_id=payload.get("previous_id"),  # Phase 4.3
                    )
                return None  # Not found
            except CircuitOpenError as e:
                logger.warning(f"Cannot load {node_id}: {e}")
                return None
            except StorageError as e:
                logger.error(f"Storage error loading {node_id}: {e}")
                return None
            except Exception as e:
                logger.error(f"Unexpected error loading {node_id} from Qdrant: {e}")
                return None

        # Fallback (File System)
        if hasattr(self, 'warm_path') and self.warm_path:
            def _fs_load():
                hdv_path = self.warm_path / f"{node_id}.npy"
                meta_path = self.warm_path / f"{node_id}.json"

                if not hdv_path.exists() or not meta_path.exists():
                    return None  # Not found

                try:
                    with open(meta_path, "r") as f:
                        data = json.load(f)

                    hdv_data = np.load(hdv_path)
                    hdv = BinaryHDV(data=hdv_data, dimension=data["dimension"])

                    return MemoryNode(
                        id=data["id"],
                        hdv=hdv,
                        content=data["content"],
                        metadata=data["metadata"],
                        created_at=datetime.fromisoformat(data["created_at"]),
                        last_accessed=datetime.fromisoformat(data["last_accessed"]),
                        tier="warm",
                        access_count=data.get("access_count", 0),
                        ltp_strength=data.get("ltp_strength", 0.0),
                        epistemic_value=data.get("epistemic_value", 0.0),
                        pragmatic_value=data.get("pragmatic_value", 0.0),
                        previous_id=data.get("previous_id"),  # Phase 4.3
                    )
                except (json.JSONDecodeError, ValueError, KeyError) as e:
                    logger.error(f"Data corruption in filesystem for {node_id}: {e}")
                    return None
                except Exception as e:
                    logger.error(f"Error loading {node_id} from filesystem: {e}")
                    return None

            return await self._run_in_thread(_fs_load)
        return None

    def _should_promote(self, node: MemoryNode) -> bool:
        """Pure check: return True if node qualifies for promotion (no mutation)."""
        threshold = self.config.tiers_hot.ltp_threshold_min
        delta = self.config.hysteresis.promote_delta
        return node.ltp_strength > (threshold + delta)

    def _should_demote(self, node: MemoryNode) -> Optional[MemoryNode]:
        """
        Pure check: return the node if it qualifies for demotion (after updating its tier).
        Returns None if no demotion needed. No I/O performed.
        """
        threshold = self.config.tiers_hot.ltp_threshold_min
        delta = self.config.hysteresis.demote_delta

        if node.ltp_strength < (threshold - delta):
            return node
        return None

    async def _promote_to_hot(self, node: MemoryNode):
        """Promote node from WARM to HOT (I/O first, then atomic state update).

        Order is critical:
        1. Delete from WARM (I/O) - no lock held
        2. Insert into HOT (in-memory) - under lock
        This prevents double-promotion from concurrent callers.
        """
        # Step 1: I/O outside lock (may fail gracefully)
        deleted = await self._delete_from_warm(node.id)
        if not deleted:
            logger.debug(f"Skipping promotion of {node.id}: not found in WARM (already promoted?)")
            return

        # Step 2: Atomic state transition under lock
        victim_to_save = None
        async with self.lock:
            # Double-check: another caller may have already promoted this node
            if node.id in self.hot:
                logger.debug(f"{node.id} already in HOT, skipping duplicate promotion")
                return

            logger.info(f"Promoting {node.id} to HOT (LTP: {node.ltp_strength:.4f})")
            node.tier = "hot"
            self.hot[node.id] = node
            self._add_to_faiss(node)
            # Maintain inverted chain index
            if node.previous_id:
                self._next_chain[node.previous_id] = node.id

            # Check if we need to evict - prepare under lock, execute outside
            if len(self.hot) > self.config.tiers_hot.max_memories:
                victim_to_save = self._prepare_eviction_from_hot()

        # Step 3: Perform eviction I/O outside lock
        if victim_to_save:
            await self._save_to_warm(victim_to_save)

    async def get_stats(self) -> Dict:
        """Get statistics about memory distribution across tiers."""
        stats = {
            "hot_count": len(self.hot),
            "warm_count": 0,
            "cold_count": 0,
            "using_qdrant": self.use_qdrant,
            # Phase 4.0: HNSW index stats
            "ann_index": self._hnsw.stats() if self._hnsw is not None else {"index_type": "none"},
        }

        if self.use_qdrant:
            info = await self.qdrant.get_collection_info(self.config.qdrant.collection_warm)
            if info:
                stats["warm_count"] = info.points_count
            else:
                stats["warm_count"] = -1
        else:
            if hasattr(self, 'warm_path') and self.warm_path:
                def _count():
                    return len(list(self.warm_path.glob("*.json")))
                stats["warm_count"] = await self._run_in_thread(_count)

        return stats

    async def list_warm(self, max_results: int = 500) -> List[MemoryNode]:
        """
        List nodes from the WARM tier (Phase 4.0 — used by SemanticConsolidationWorker).

        Returns up to max_results MemoryNode objects from the WARM tier.
        Falls back gracefully if Qdrant or filesystem is unavailable.
        """
        nodes: List[MemoryNode] = []

        if self.use_qdrant:
            try:
                points_result = await self.qdrant.scroll(
                    self.config.qdrant.collection_warm,
                    limit=max_results,
                    offset=None,
                    with_vectors=True,
                )
                points = points_result[0] if points_result else []
                for pt in points:
                    payload = pt.payload
                    try:
                        arr = np.array(pt.vector) > 0.5
                        packed = np.packbits(arr.astype(np.uint8))
                        hdv = BinaryHDV(data=packed, dimension=payload["dimension"])
                        node = MemoryNode(
                            id=payload.get("id", pt.id),
                            hdv=hdv,
                            content=payload["content"],
                            metadata=payload.get("metadata", {}),
                            created_at=datetime.fromisoformat(payload["created_at"]),
                            last_accessed=datetime.fromisoformat(payload["last_accessed"]),
                            tier="warm",
                            access_count=payload.get("access_count", 0),
                            ltp_strength=payload.get("ltp_strength", 0.0),
                            previous_id=payload.get("previous_id"),  # Phase 4.3: episodic chain
                        )
                        nodes.append(node)
                    except Exception as exc:
                        logger.debug(f"list_warm: could not deserialize point {pt.id}: {exc}")
            except Exception as exc:
                logger.warning(f"list_warm Qdrant failed: {exc}")

        elif hasattr(self, "warm_path") and self.warm_path:
            def _list_fs() -> List[MemoryNode]:
                result = []
                for meta_file in list(self.warm_path.glob("*.json"))[:max_results]:
                    try:
                        import json as _json
                        with open(meta_file, "r") as f:
                            data = _json.load(f)
                        hdv_path = self.warm_path / f"{data['id']}.npy"
                        if not hdv_path.exists():
                            continue
                        hdv_data = np.load(hdv_path)
                        hdv = BinaryHDV(data=hdv_data, dimension=data["dimension"])
                        result.append(
                            MemoryNode(
                                id=data["id"],
                                hdv=hdv,
                                content=data["content"],
                                metadata=data.get("metadata", {}),
                                created_at=datetime.fromisoformat(data["created_at"]),
                                last_accessed=datetime.fromisoformat(data["last_accessed"]),
                                tier="warm",
                                ltp_strength=data.get("ltp_strength", 0.0),
                                previous_id=data.get("previous_id"),  # Phase 4.3: episodic chain
                            )
                        )
                    except Exception as exc:
                        logger.debug(f"list_warm FS: skip {meta_file.name}: {exc}")
                return result

            nodes = await self._run_in_thread(_list_fs)

        return nodes

    async def get_next_in_chain(self, node_id: str) -> Optional[MemoryNode]:
        """
        Return the MemoryNode that directly follows node_id in the episodic chain.

        This is a typed wrapper around QdrantStore.get_by_previous_id() that
        returns a fully-deserialized MemoryNode instead of a raw models.Record,
        making the episodic-chain API consistent with the rest of TierManager.

        Returns:
            The next MemoryNode in the chain, or None if not found / Qdrant
            unavailable.
        """
        # 1. Check HOT tier via O(1) inverted index (Fix 7).
        async with self.lock:
            next_id = self._next_chain.get(node_id)
            if next_id and next_id in self.hot:
                return self.hot[next_id]

        # 2. Check WARM tier (Qdrant)
        if not self.use_qdrant or not self.qdrant:
            return None

        record = await self.qdrant.get_by_previous_id(
            self.qdrant.collection_warm, node_id
        )
        if record is None:
            return None

        # Resolve to a full MemoryNode via the standard warm-load path
        return await self._load_from_warm(str(record.id))

    async def consolidate_warm_to_cold(self):
        """
        Batch move from WARM to COLD based on archive criteria.
        This is an expensive operation, typically run by a background worker.
        """
        min_ltp = self.config.tiers_warm.ltp_threshold_min

        if self.use_qdrant:
            offset = None

            while True:
                points_result = await self.qdrant.scroll(
                    self.config.qdrant.collection_warm,
                    limit=100,
                    offset=offset,
                    with_vectors=True
                )
                points = points_result[0]
                next_offset = points_result[1]

                if not points:
                    break

                ids_to_delete = []
                for pt in points:
                    payload = pt.payload
                    ltp = payload.get("ltp_strength", 0.0)

                    if ltp < min_ltp:
                        vec_data = pt.vector
                        if vec_data:
                            arr = np.array(vec_data) > 0.5
                            packed = np.packbits(arr.astype(np.uint8))
                            payload["hdv_vector"] = packed.tolist()

                        await self._write_to_cold(payload)
                        ids_to_delete.append(pt.id)

                if ids_to_delete:
                    await self.qdrant.delete(
                        self.config.qdrant.collection_warm, ids_to_delete
                    )

                offset = next_offset
                if offset is None:
                    break
        else:
            # Filesystem fallback
            if hasattr(self, 'warm_path') and self.warm_path:
                def _process_fs():
                    to_delete = []
                    for meta_file in self.warm_path.glob("*.json"):
                        try:
                            with open(meta_file, "r") as f:
                                meta = json.load(f)

                            if meta.get("ltp_strength", 0.0) < min_ltp:
                                to_delete.append((meta["id"], meta))
                        except Exception:
                            pass
                    return to_delete

                candidates = await self._run_in_thread(_process_fs)
                for nid, meta in candidates:
                    await self._archive_to_cold(nid, meta)

    async def search(
        self,
        query_vec: BinaryHDV,
        top_k: int = 5,
        time_range: Optional[Tuple[datetime, datetime]] = None,
        metadata_filter: Optional[Dict[str, Any]] = None,
        include_cold: bool = False,
    ) -> List[Tuple[str, float]]:
        """
        Global search across all tiers.
        Combines HNSW/FAISS (HOT), Qdrant/FS (WARM), and optionally COLD.

        Phase 4.3: time_range filters results to memories within the given datetime range.
        Fix 1: include_cold=True enables a bounded linear scan of the COLD archive.
        """
        # 1. Search HOT via FAISS (time filtering done post-hoc for in-memory)
        hot_results = self.search_hot(query_vec, top_k)

        # Apply time filter and metadata filter to HOT results if needed
        if time_range or metadata_filter:
            filtered_hot = []
            async with self.lock:
                for nid, score in hot_results:
                    node = self.hot.get(nid)
                    if not node:
                        continue
                    
                    if time_range:
                        start_ts = time_range[0].timestamp()
                        end_ts = time_range[1].timestamp()
                        if not (start_ts <= node.created_at.timestamp() <= end_ts):
                            continue
                            
                    if metadata_filter:
                        match = True
                        node_meta = node.metadata or {}
                        for k, v in metadata_filter.items():
                            if node_meta.get(k) != v:
                                match = False
                                break
                        if not match:
                            continue
                            
                    filtered_hot.append((nid, score))
            hot_results = filtered_hot

        # 2. Search WARM via Qdrant
        warm_results = []
        if self.use_qdrant:
            try:
                q_vec = np.unpackbits(query_vec.data).astype(float).tolist()

                hits = await self.qdrant.search(
                    collection=self.config.qdrant.collection_warm,
                    query_vector=q_vec,
                    limit=top_k,
                    time_range=time_range,  # Phase 4.3: Pass time filter to Qdrant
                    metadata_filter=metadata_filter, # BUG-09: Agent Isolation
                )
                warm_results = [(hit.id, hit.score) for hit in hits]
            except Exception as e:
                logger.error(f"WARM tier search failed: {e}")

        # 3. Optionally search COLD tier (Fix 1: bounded linear scan)
        cold_results: List[Tuple[str, float]] = []
        if include_cold:
            cold_results = await self.search_cold(query_vec, top_k)

        # 4. Combine and Sort (HOT scores take precedence over WARM/COLD for same ID)
        combined = {}
        for nid, score in hot_results:
            combined[nid] = score
        for nid, score in warm_results:
            combined[nid] = max(combined.get(nid, 0), score)
        for nid, score in cold_results:
            combined[nid] = max(combined.get(nid, 0), score)

        sorted_results = sorted(combined.items(), key=lambda x: x[1], reverse=True)
        return sorted_results[:top_k]

    def search_hot(self, query_vec: BinaryHDV, top_k: int = 5) -> List[Tuple[str, float]]:
        """Search HOT tier using HNSW or FAISS binary index (Phase 4.0)."""
        # Phase 4.0: use HNSWIndexManager (auto-selects flat vs HNSW)
        if self._hnsw is not None and self._hnsw.size > 0:
            try:
                return self._hnsw.search(query_vec.data, top_k)
            except Exception as e:
                logger.error(f"HNSWIndexManager search failed, falling back: {e}")

        # Legacy FAISS flat path
        if not self.faiss_index or not self.hot:
            return self._linear_search_hot(query_vec, top_k)

        try:
            q = np.expand_dims(query_vec.data, axis=0)
            distances, ids = self.faiss_index.search(q, top_k)

            results = []
            for d, fid in zip(distances[0], ids[0]):
                if fid == -1:
                    continue
                node_id = self.faiss_id_map.get(int(fid))
                if node_id:
                    sim = 1.0 - (float(d) / self.config.dimensionality)
                    results.append((node_id, sim))
            return results
        except Exception as e:
            logger.error(f"FAISS search failed, falling back: {e}")
            return self._linear_search_hot(query_vec, top_k)

    def _linear_search_hot(self, query_vec: BinaryHDV, top_k: int = 5) -> List[Tuple[str, float]]:
        """Fallback linear scan for HOT tier."""
        scores = []
        for node in self.hot.values():
            sim = query_vec.similarity(node.hdv)
            scores.append((node.id, sim))

        scores.sort(key=lambda x: x[1], reverse=True)
        return scores[:top_k]

    async def _load_from_cold(self, node_id: str) -> Optional[MemoryNode]:
        """
        Scan COLD archive files for a specific node (Fix 1: COLD read path).

        Archives are gzip JSONL, sorted newest-first for early-exit on recent data.
        Returns None if not found or on error.
        """
        def _scan():
            for archive_file in sorted(
                self.cold_path.glob("archive_*.jsonl.gz"), reverse=True
            ):
                try:
                    with gzip.open(archive_file, "rt", encoding="utf-8") as f:
                        for line in f:
                            line = line.strip()
                            if not line:
                                continue
                            try:
                                rec = json.loads(line)
                                if rec.get("id") == node_id:
                                    return rec
                            except json.JSONDecodeError:
                                continue
                except Exception:
                    continue
            return None

        rec = await self._run_in_thread(_scan)
        if rec is None:
            return None

        try:
            raw_vec = rec.get("hdv_vector")
            dim = rec.get("dimension", self.config.dimensionality)
            if raw_vec:
                hdv_data = np.array(raw_vec, dtype=np.uint8)
                hdv = BinaryHDV(data=hdv_data, dimension=dim)
            else:
                hdv = BinaryHDV.zeros(dim)

            node = MemoryNode(
                id=rec["id"],
                hdv=hdv,
                content=rec.get("content", ""),
                metadata=rec.get("metadata", {}),
                tier="cold",
                ltp_strength=rec.get("ltp_strength", 0.0),
                previous_id=rec.get("previous_id"),
            )
            if "created_at" in rec:
                node.created_at = datetime.fromisoformat(rec["created_at"])
            return node
        except Exception as e:
            logger.error(f"Failed to reconstruct node {node_id} from COLD: {e}")
            return None

    async def search_cold(
        self,
        query_vec: BinaryHDV,
        top_k: int = 5,
        max_scan: int = 1000,
    ) -> List[Tuple[str, float]]:
        """
        Linear similarity scan over COLD archive (Fix 1: COLD search path).

        Bounded by max_scan records to keep latency predictable.
        Returns results sorted by descending similarity.
        """
        config_dim = self.config.dimensionality

        def _scan():
            candidates: List[Tuple[str, float]] = []
            scanned = 0
            for archive_file in sorted(
                self.cold_path.glob("archive_*.jsonl.gz"), reverse=True
            ):
                if scanned >= max_scan:
                    break
                try:
                    with gzip.open(archive_file, "rt", encoding="utf-8") as f:
                        for line in f:
                            if scanned >= max_scan:
                                break
                            line = line.strip()
                            if not line:
                                continue
                            try:
                                rec = json.loads(line)
                                raw_vec = rec.get("hdv_vector")
                                if not raw_vec:
                                    continue
                                dim = rec.get("dimension", config_dim)
                                hdv = BinaryHDV(
                                    data=np.array(raw_vec, dtype=np.uint8),
                                    dimension=dim,
                                )
                                sim = query_vec.similarity(hdv)
                                candidates.append((rec["id"], sim))
                                scanned += 1
                            except Exception:
                                continue
                except Exception:
                    continue
            return candidates

        candidates = await self._run_in_thread(_scan)
        candidates.sort(key=lambda x: x[1], reverse=True)
        return candidates[:top_k]

    async def _write_to_cold(self, record: dict):
        """Write a record to the cold archive."""
        record["tier"] = "cold"
        record["archived_at"] = datetime.now(timezone.utc).isoformat()
        today = datetime.now(timezone.utc).strftime("%Y-%m-%d")
        archive_file = self.cold_path / f"archive_{today}.jsonl.gz"

        def _write():
            with gzip.open(archive_file, "at", encoding="utf-8") as f:
                f.write(json.dumps(record) + "\n")

        await self._run_in_thread(_write)

    async def _archive_to_cold(self, node_id: str, meta: dict):
        """Move memory to COLD storage (File System Fallback)."""
        if not self.warm_path:
            return

        def _read_vec():
            hdv_path = self.warm_path / f"{node_id}.npy"
            if not hdv_path.exists():
                return None
            return np.load(hdv_path)

        hdv_data = await self._run_in_thread(_read_vec)
        if hdv_data is None:
            return

        record = meta.copy()
        record["hdv_vector"] = hdv_data.tolist()
        await self._write_to_cold(record)
        await self._delete_from_warm(node_id)