memory_logic.py

# memory_logic.py
import os
import json
import time
from datetime import datetime
import logging
import re
import threading

# Conditionally import heavy dependencies
try:
    from sentence_transformers import SentenceTransformer
    import faiss
    import numpy as np
except ImportError:
    SentenceTransformer, faiss, np = None, None, None
    logging.warning("SentenceTransformers, FAISS, or NumPy not installed. Semantic search will be unavailable.")

try:
    import sqlite3
except ImportError:
    sqlite3 = None
    logging.warning("sqlite3 module not available. SQLite backend will be unavailable.")

try:
    from datasets import load_dataset, Dataset
except ImportError:
    load_dataset, Dataset = None, None
    logging.warning("datasets library not installed. Hugging Face Dataset backend will be unavailable.")


logger = logging.getLogger(__name__)
# Suppress verbose logs from dependencies
for lib_name in ["sentence_transformers", "faiss", "datasets", "huggingface_hub"]:
    if logging.getLogger(lib_name): # Check if logger exists
        logging.getLogger(lib_name).setLevel(logging.WARNING)


# --- Configuration (Read directly from environment variables) ---
STORAGE_BACKEND = os.getenv("STORAGE_BACKEND", "HF_DATASET").upper() #HF_DATASET, RAM, SQLITE
SQLITE_DB_PATH = os.getenv("SQLITE_DB_PATH", "app_data/ai_memory.db") # Changed default path
HF_TOKEN = os.getenv("HF_TOKEN")
HF_MEMORY_DATASET_REPO = os.getenv("HF_MEMORY_DATASET_REPO", "broadfield-dev/ai-brain") # Example
HF_RULES_DATASET_REPO = os.getenv("HF_RULES_DATASET_REPO", "broadfield-dev/ai-rules")     # Example

# --- Globals for RAG within this module ---
_embedder = None
_dimension = 384 # Default, will be set by embedder
_faiss_memory_index = None
_memory_items_list = []  # Stores JSON strings of memory objects for RAM, or loaded from DB/HF
_faiss_rules_index = None
_rules_items_list = []   # Stores rule text strings

_initialized = False
_init_lock = threading.Lock()

# --- Helper: SQLite Connection ---
def _get_sqlite_connection():
    if not sqlite3:
        raise ImportError("sqlite3 module is required for SQLite backend but not found.")
    db_dir = os.path.dirname(SQLITE_DB_PATH)
    if db_dir and not os.path.exists(db_dir):
        os.makedirs(db_dir, exist_ok=True)
    return sqlite3.connect(SQLITE_DB_PATH, timeout=10) # Added timeout

def _init_sqlite_tables():
    if STORAGE_BACKEND != "SQLITE" or not sqlite3:
        return
    try:
        with _get_sqlite_connection() as conn:
            cursor = conn.cursor()
            # Stores JSON string of the memory object
            cursor.execute("""
                CREATE TABLE IF NOT EXISTS memories (
                    id INTEGER PRIMARY KEY AUTOINCREMENT,
                    memory_json TEXT NOT NULL,
                    # Optionally add embedding here if not using separate FAISS index
                    # embedding BLOB, 
                    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
                )
            """)
            # Stores the rule text directly
            cursor.execute("""
                CREATE TABLE IF NOT EXISTS rules (
                    id INTEGER PRIMARY KEY AUTOINCREMENT,
                    rule_text TEXT NOT NULL UNIQUE,
                    # embedding BLOB,
                    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
                )
            """)
            conn.commit()
        logger.info("SQLite tables for memories and rules checked/created.")
    except Exception as e:
        logger.error(f"SQLite table initialization error: {e}", exc_info=True)

# --- Initialization ---
def initialize_memory_system():
    global _initialized, _embedder, _dimension, _faiss_memory_index, _memory_items_list, _faiss_rules_index, _rules_items_list
    
    with _init_lock:
        if _initialized:
            logger.info("Memory system already initialized.")
            return

        logger.info(f"Initializing memory system with backend: {STORAGE_BACKEND}")
        init_start_time = time.time()

        # 1. Load Sentence Transformer Model (always needed for semantic operations)
        if not SentenceTransformer or not faiss or not np:
            logger.error("Core RAG libraries (SentenceTransformers, FAISS, NumPy) not available. Cannot initialize semantic memory.")
            _initialized = False # Mark as not properly initialized
            return
        
        if not _embedder:
            try:
                logger.info("Loading SentenceTransformer model (all-MiniLM-L6-v2)...")
                _embedder = SentenceTransformer('all-MiniLM-L6-v2', cache_folder="./sentence_transformer_cache")
                _dimension = _embedder.get_sentence_embedding_dimension() or 384
                logger.info(f"SentenceTransformer loaded. Dimension: {_dimension}")
            except Exception as e:
                logger.critical(f"FATAL: Error loading SentenceTransformer: {e}", exc_info=True)
                _initialized = False
                return # Cannot proceed without embedder

        # 2. Initialize SQLite if used
        if STORAGE_BACKEND == "SQLITE":
            _init_sqlite_tables()

        # 3. Load Memories
        logger.info("Loading memories...")
        temp_memories_json = []
        if STORAGE_BACKEND == "RAM":
            _memory_items_list = [] # Start fresh for RAM backend
        elif STORAGE_BACKEND == "SQLITE" and sqlite3:
            try:
                with _get_sqlite_connection() as conn:
                    temp_memories_json = [row[0] for row in conn.execute("SELECT memory_json FROM memories ORDER BY created_at ASC")]
            except Exception as e: logger.error(f"Error loading memories from SQLite: {e}")
        elif STORAGE_BACKEND == "HF_DATASET" and HF_TOKEN and Dataset and load_dataset:
            try:
                logger.info(f"Attempting to load memories from HF Dataset: {HF_MEMORY_DATASET_REPO}")
                dataset = load_dataset(HF_MEMORY_DATASET_REPO, token=HF_TOKEN, trust_remote_code=True) # Add download_mode if needed
                if "train" in dataset and "memory_json" in dataset["train"].column_names: # Assuming 'memory_json' column
                    temp_memories_json = [m_json for m_json in dataset["train"]["memory_json"] if isinstance(m_json, str)]
                else: logger.warning(f"HF Dataset {HF_MEMORY_DATASET_REPO} for memories not found or 'memory_json' column missing.")
            except Exception as e: logger.error(f"Error loading memories from HF Dataset ({HF_MEMORY_DATASET_REPO}): {e}")
        
        _memory_items_list = temp_memories_json
        logger.info(f"Loaded {len(_memory_items_list)} memory items from {STORAGE_BACKEND}.")

        # 4. Build/Load FAISS Memory Index
        _faiss_memory_index = faiss.IndexFlatL2(_dimension)
        if _memory_items_list:
            logger.info(f"Building FAISS index for {len(_memory_items_list)} memories...")
            # Extract text to embed from memory JSON objects
            texts_to_embed_mem = []
            for mem_json_str in _memory_items_list:
                try:
                    mem_obj = json.loads(mem_json_str)
                    # Consistent embedding strategy: user input + bot response + takeaway
                    text = f"User: {mem_obj.get('user_input','')}\nAI: {mem_obj.get('bot_response','')}\nTakeaway: {mem_obj.get('metrics',{}).get('takeaway','N/A')}"
                    texts_to_embed_mem.append(text)
                except json.JSONDecodeError:
                    logger.warning(f"Skipping malformed memory JSON for FAISS indexing: {mem_json_str[:100]}")
            
            if texts_to_embed_mem:
                try:
                    embeddings = _embedder.encode(texts_to_embed_mem, convert_to_tensor=False, show_progress_bar=False) # convert_to_numpy=True
                    embeddings_np = np.array(embeddings, dtype=np.float32)
                    if embeddings_np.ndim == 2 and embeddings_np.shape[0] == len(texts_to_embed_mem) and embeddings_np.shape[1] == _dimension:
                        _faiss_memory_index.add(embeddings_np)
                    else: logger.error(f"Memory embeddings shape error. Expected ({len(texts_to_embed_mem)}, {_dimension}), Got {embeddings_np.shape if hasattr(embeddings_np, 'shape') else 'N/A'}")
                except Exception as e_faiss_mem: logger.error(f"Error building FAISS memory index: {e_faiss_mem}")
        logger.info(f"FAISS memory index built. Total items: {_faiss_memory_index.ntotal if _faiss_memory_index else 'N/A'}")


        # 5. Load Rules
        logger.info("Loading rules...")
        temp_rules_text = []
        if STORAGE_BACKEND == "RAM":
            _rules_items_list = []
        elif STORAGE_BACKEND == "SQLITE" and sqlite3:
            try:
                with _get_sqlite_connection() as conn:
                    temp_rules_text = [row[0] for row in conn.execute("SELECT rule_text FROM rules ORDER BY created_at ASC")]
            except Exception as e: logger.error(f"Error loading rules from SQLite: {e}")
        elif STORAGE_BACKEND == "HF_DATASET" and HF_TOKEN and Dataset and load_dataset:
            try:
                logger.info(f"Attempting to load rules from HF Dataset: {HF_RULES_DATASET_REPO}")
                dataset = load_dataset(HF_RULES_DATASET_REPO, token=HF_TOKEN, trust_remote_code=True)
                if "train" in dataset and "rule_text" in dataset["train"].column_names:
                    temp_rules_text = [r_text for r_text in dataset["train"]["rule_text"] if isinstance(r_text, str) and r_text.strip()]
                else: logger.warning(f"HF Dataset {HF_RULES_DATASET_REPO} for rules not found or 'rule_text' column missing.")
            except Exception as e: logger.error(f"Error loading rules from HF Dataset ({HF_RULES_DATASET_REPO}): {e}")
        
        _rules_items_list = sorted(list(set(temp_rules_text))) # Ensure unique and sorted
        logger.info(f"Loaded {len(_rules_items_list)} rule items from {STORAGE_BACKEND}.")

        # 6. Build/Load FAISS Rules Index
        _faiss_rules_index = faiss.IndexFlatL2(_dimension)
        if _rules_items_list:
            logger.info(f"Building FAISS index for {len(_rules_items_list)} rules...")
            if _rules_items_list: # Check again in case it became empty after filtering
                try:
                    embeddings = _embedder.encode(_rules_items_list, convert_to_tensor=False, show_progress_bar=False)
                    embeddings_np = np.array(embeddings, dtype=np.float32)
                    if embeddings_np.ndim == 2 and embeddings_np.shape[0] == len(_rules_items_list) and embeddings_np.shape[1] == _dimension:
                        _faiss_rules_index.add(embeddings_np)
                    else: logger.error(f"Rule embeddings shape error. Expected ({len(_rules_items_list)}, {_dimension}), Got {embeddings_np.shape if hasattr(embeddings_np, 'shape') else 'N/A'}")
                except Exception as e_faiss_rule: logger.error(f"Error building FAISS rule index: {e_faiss_rule}")
        logger.info(f"FAISS rules index built. Total items: {_faiss_rules_index.ntotal if _faiss_rules_index else 'N/A'}")

        _initialized = True
        logger.info(f"Memory system initialization complete in {time.time() - init_start_time:.2f}s")


# --- Memory Operations (Semantic) ---
def add_memory_entry(user_input: str, metrics: dict, bot_response: str) -> tuple[bool, str]:
    """Adds a memory entry to the configured backend and FAISS index."""
    global _memory_items_list, _faiss_memory_index
    if not _initialized: initialize_memory_system()
    if not _embedder or not _faiss_memory_index:
        return False, "Memory system or embedder not initialized for adding memory."

    memory_obj = {
        "user_input": user_input,
        "metrics": metrics,
        "bot_response": bot_response,
        "timestamp": datetime.utcnow().isoformat()
    }
    memory_json_str = json.dumps(memory_obj)
    
    text_to_embed = f"User: {user_input}\nAI: {bot_response}\nTakeaway: {metrics.get('takeaway', 'N/A')}"
    
    try:
        embedding = _embedder.encode([text_to_embed], convert_to_tensor=False)
        embedding_np = np.array(embedding, dtype=np.float32).reshape(1, -1)

        if embedding_np.shape != (1, _dimension):
            logger.error(f"Memory embedding shape error: {embedding_np.shape}. Expected (1, {_dimension})")
            return False, "Embedding shape error."

        # Add to FAISS
        _faiss_memory_index.add(embedding_np)
        
        # Add to in-memory list
        _memory_items_list.append(memory_json_str)
        
        # Add to persistent storage
        if STORAGE_BACKEND == "SQLITE" and sqlite3:
            with _get_sqlite_connection() as conn:
                conn.execute("INSERT INTO memories (memory_json) VALUES (?)", (memory_json_str,))
                conn.commit()
        elif STORAGE_BACKEND == "HF_DATASET" and HF_TOKEN and Dataset:
            # This can be slow, consider batching or async push
            logger.info(f"Pushing {len(_memory_items_list)} memories to HF Hub: {HF_MEMORY_DATASET_REPO}")
            Dataset.from_dict({"memory_json": list(_memory_items_list)}).push_to_hub(HF_MEMORY_DATASET_REPO, token=HF_TOKEN, private=True) # Ensure 'private' as needed
        
        logger.info(f"Added memory. RAM: {len(_memory_items_list)}, FAISS: {_faiss_memory_index.ntotal}")
        return True, "Memory added successfully."
    except Exception as e:
        logger.error(f"Error adding memory entry: {e}", exc_info=True)
        # TODO: Potential rollback logic if FAISS add succeeded but backend failed (complex)
        return False, f"Error adding memory: {e}"

def retrieve_memories_semantic(query: str, k: int = 3) -> list[dict]:
    """Retrieves k most relevant memories using semantic search."""
    if not _initialized: initialize_memory_system()
    if not _embedder or not _faiss_memory_index or _faiss_memory_index.ntotal == 0:
        logger.debug("Cannot retrieve memories: Embedder, FAISS index not ready, or index is empty.")
        return []

    try:
        query_embedding = _embedder.encode([query], convert_to_tensor=False)
        query_embedding_np = np.array(query_embedding, dtype=np.float32).reshape(1, -1)

        if query_embedding_np.shape[1] != _dimension:
            logger.error(f"Query embedding dimension mismatch. Expected {_dimension}, got {query_embedding_np.shape[1]}")
            return []
            
        distances, indices = _faiss_memory_index.search(query_embedding_np, min(k, _faiss_memory_index.ntotal))
        
        results = []
        for i in indices[0]:
            if 0 <= i < len(_memory_items_list):
                try:
                    results.append(json.loads(_memory_items_list[i]))
                except json.JSONDecodeError:
                    logger.warning(f"Could not parse memory JSON from list at index {i}")
            else:
                logger.warning(f"FAISS index {i} out of bounds for memory_items_list (len: {len(_memory_items_list)})")

        logger.debug(f"Retrieved {len(results)} memories semantically for query: '{query[:50]}...'")
        return results
    except Exception as e:
        logger.error(f"Error retrieving memories semantically: {e}", exc_info=True)
        return []


# --- Rule (Insight) Operations (Semantic) ---
def add_rule_entry(rule_text: str) -> tuple[bool, str]:
    """Adds a rule if valid and not a duplicate. Updates backend and FAISS."""
    global _rules_items_list, _faiss_rules_index
    if not _initialized: initialize_memory_system()
    if not _embedder or not _faiss_rules_index:
        return False, "Rule system or embedder not initialized."

    rule_text = rule_text.strip()
    if not rule_text: return False, "Rule text cannot be empty."
    if not re.match(r"\[(CORE_RULE|RESPONSE_PRINCIPLE|BEHAVIORAL_ADJUSTMENT|GENERAL_LEARNING)\|([\d\.]+?)\](.*)", rule_text, re.I|re.DOTALL):
        return False, "Invalid rule format."
    if rule_text in _rules_items_list:
        return False, "duplicate"

    try:
        embedding = _embedder.encode([rule_text], convert_to_tensor=False)
        embedding_np = np.array(embedding, dtype=np.float32).reshape(1, -1)

        if embedding_np.shape != (1, _dimension):
            return False, "Rule embedding shape error."

        _faiss_rules_index.add(embedding_np)
        _rules_items_list.append(rule_text)
        _rules_items_list.sort()

        if STORAGE_BACKEND == "SQLITE" and sqlite3:
            with _get_sqlite_connection() as conn:
                conn.execute("INSERT OR IGNORE INTO rules (rule_text) VALUES (?)", (rule_text,))
                conn.commit()
        elif STORAGE_BACKEND == "HF_DATASET" and HF_TOKEN and Dataset:
            logger.info(f"Pushing {len(_rules_items_list)} rules to HF Hub: {HF_RULES_DATASET_REPO}")
            Dataset.from_dict({"rule_text": list(_rules_items_list)}).push_to_hub(HF_RULES_DATASET_REPO, token=HF_TOKEN, private=True)
        
        logger.info(f"Added rule. RAM: {len(_rules_items_list)}, FAISS: {_faiss_rules_index.ntotal}")
        return True, "Rule added successfully."
    except Exception as e:
        logger.error(f"Error adding rule entry: {e}", exc_info=True)
        # Basic rollback if FAISS add succeeded
        if rule_text in _rules_items_list and _faiss_rules_index.ntotal > 0: # Crude check
             # A full rollback would involve rebuilding FAISS index from _rules_items_list before append.
             # For simplicity, this is omitted here. State could be inconsistent on error.
             pass
        return False, f"Error adding rule: {e}"

def retrieve_rules_semantic(query: str, k: int = 5) -> list[str]:
    """Retrieves k most relevant rules using semantic search."""
    if not _initialized: initialize_memory_system()
    if not _embedder or not _faiss_rules_index or _faiss_rules_index.ntotal == 0:
        return []
    try:
        query_embedding = _embedder.encode([query], convert_to_tensor=False)
        query_embedding_np = np.array(query_embedding, dtype=np.float32).reshape(1, -1)
        
        if query_embedding_np.shape[1] != _dimension: return []

        distances, indices = _faiss_rules_index.search(query_embedding_np, min(k, _faiss_rules_index.ntotal))
        results = [_rules_items_list[i] for i in indices[0] if 0 <= i < len(_rules_items_list)]
        logger.debug(f"Retrieved {len(results)} rules semantically for query: '{query[:50]}...'")
        return results
    except Exception as e:
        logger.error(f"Error retrieving rules semantically: {e}", exc_info=True)
        return []

def remove_rule_entry(rule_text_to_delete: str) -> bool:
    """Removes a rule from backend and rebuilds FAISS for rules."""
    global _rules_items_list, _faiss_rules_index
    if not _initialized: initialize_memory_system()
    if not _embedder or not _faiss_rules_index: return False

    rule_text_to_delete = rule_text_to_delete.strip()
    if rule_text_to_delete not in _rules_items_list:
        return False # Not found

    try:
        _rules_items_list.remove(rule_text_to_delete)
        _rules_items_list.sort() # Maintain sorted order

        # Rebuild FAISS index for rules (simplest way to ensure consistency after removal)
        new_faiss_rules_index = faiss.IndexFlatL2(_dimension)
        if _rules_items_list:
            embeddings = _embedder.encode(_rules_items_list, convert_to_tensor=False)
            embeddings_np = np.array(embeddings, dtype=np.float32)
            if embeddings_np.ndim == 2 and embeddings_np.shape[0] == len(_rules_items_list) and embeddings_np.shape[1] == _dimension:
                 new_faiss_rules_index.add(embeddings_np)
            else: # Should not happen if list is consistent
                logger.error("Error rebuilding FAISS for rules after removal: Embedding shape error. State might be inconsistent.")
                # Attempt to revert _rules_items_list (add back the rule)
                _rules_items_list.append(rule_text_to_delete)
                _rules_items_list.sort()
                return False # Indicate failure
        _faiss_rules_index = new_faiss_rules_index
        
        # Remove from persistent storage
        if STORAGE_BACKEND == "SQLITE" and sqlite3:
            with _get_sqlite_connection() as conn:
                conn.execute("DELETE FROM rules WHERE rule_text = ?", (rule_text_to_delete,))
                conn.commit()
        elif STORAGE_BACKEND == "HF_DATASET" and HF_TOKEN and Dataset:
            Dataset.from_dict({"rule_text": list(_rules_items_list)}).push_to_hub(HF_RULES_DATASET_REPO, token=HF_TOKEN, private=True)
        
        logger.info(f"Removed rule. RAM: {len(_rules_items_list)}, FAISS: {_faiss_rules_index.ntotal}")
        return True
    except Exception as e:
        logger.error(f"Error removing rule entry: {e}", exc_info=True)
        # Potential partial failure, state might be inconsistent.
        return False

# --- Utility functions to get all data (for UI display, etc.) ---
def get_all_rules_cached() -> list[str]:
    if not _initialized: initialize_memory_system()
    return list(_rules_items_list)

def get_all_memories_cached() -> list[dict]:
    if not _initialized: initialize_memory_system()
    # Convert JSON strings to dicts for easier use by UI
    mem_dicts = []
    for mem_json_str in _memory_items_list:
        try: mem_dicts.append(json.loads(mem_json_str))
        except: pass # Ignore parse errors for display
    return mem_dicts

def clear_all_memory_data_backend() -> bool:
    """Clears all memories from backend and resets in-memory FAISS/list."""
    global _memory_items_list, _faiss_memory_index
    if not _initialized: initialize_memory_system()
    
    success = True
    try:
        if STORAGE_BACKEND == "SQLITE" and sqlite3:
            with _get_sqlite_connection() as conn: conn.execute("DELETE FROM memories"); conn.commit()
        elif STORAGE_BACKEND == "HF_DATASET" and HF_TOKEN and Dataset:
            # Deleting from HF usually means pushing an empty dataset
            Dataset.from_dict({"memory_json": []}).push_to_hub(HF_MEMORY_DATASET_REPO, token=HF_TOKEN, private=True)
        
        _memory_items_list = []
        if _faiss_memory_index: _faiss_memory_index.reset() # Clear FAISS index
        logger.info("All memories cleared from backend and in-memory stores.")
    except Exception as e:
        logger.error(f"Error clearing all memory data: {e}")
        success = False
    return success

def clear_all_rules_data_backend() -> bool:
    """Clears all rules from backend and resets in-memory FAISS/list."""
    global _rules_items_list, _faiss_rules_index
    if not _initialized: initialize_memory_system()
    
    success = True
    try:
        if STORAGE_BACKEND == "SQLITE" and sqlite3:
            with _get_sqlite_connection() as conn: conn.execute("DELETE FROM rules"); conn.commit()
        elif STORAGE_BACKEND == "HF_DATASET" and HF_TOKEN and Dataset:
            Dataset.from_dict({"rule_text": []}).push_to_hub(HF_RULES_DATASET_REPO, token=HF_TOKEN, private=True)

        _rules_items_list = []
        if _faiss_rules_index: _faiss_rules_index.reset()
        logger.info("All rules cleared from backend and in-memory stores.")
    except Exception as e:
        logger.error(f"Error clearing all rules data: {e}")
        success = False
    return success

# Optional: Function to save FAISS indices to disk (from ai-learn, if needed for persistence between app runs with RAM backend)
FAISS_MEMORY_PATH = os.path.join(os.getenv("FAISS_STORAGE_PATH", "app_data/faiss_indices"), "memory_index.faiss")
FAISS_RULES_PATH = os.path.join(os.getenv("FAISS_STORAGE_PATH", "app_data/faiss_indices"), "rules_index.faiss")

def save_faiss_indices_to_disk():
    if not _initialized or not faiss: return
    
    faiss_dir = os.path.dirname(FAISS_MEMORY_PATH)
    if not os.path.exists(faiss_dir): os.makedirs(faiss_dir, exist_ok=True)

    if _faiss_memory_index and _faiss_memory_index.ntotal > 0:
        try:
            faiss.write_index(_faiss_memory_index, FAISS_MEMORY_PATH)
            logger.info(f"Memory FAISS index saved to disk ({_faiss_memory_index.ntotal} items).")
        except Exception as e: logger.error(f"Error saving memory FAISS index: {e}")
    
    if _faiss_rules_index and _faiss_rules_index.ntotal > 0:
        try:
            faiss.write_index(_faiss_rules_index, FAISS_RULES_PATH)
            logger.info(f"Rules FAISS index saved to disk ({_faiss_rules_index.ntotal} items).")
        except Exception as e: logger.error(f"Error saving rules FAISS index: {e}")

def load_faiss_indices_from_disk():
    global _faiss_memory_index, _faiss_rules_index
    if not _initialized or not faiss: return

    if os.path.exists(FAISS_MEMORY_PATH) and _faiss_memory_index: # Check if index object exists
        try:
            logger.info(f"Loading memory FAISS index from {FAISS_MEMORY_PATH}...")
            _faiss_memory_index = faiss.read_index(FAISS_MEMORY_PATH)
            logger.info(f"Memory FAISS index loaded ({_faiss_memory_index.ntotal} items).")
            # Consistency check: FAISS ntotal vs len(_memory_items_list)
            if _faiss_memory_index.ntotal != len(_memory_items_list) and len(_memory_items_list) > 0:
                 logger.warning(f"Memory FAISS index count ({_faiss_memory_index.ntotal}) differs from loaded texts ({len(_memory_items_list)}). Consider rebuilding FAISS.")
        except Exception as e: logger.error(f"Error loading memory FAISS index: {e}. Will use fresh index.")
    
    if os.path.exists(FAISS_RULES_PATH) and _faiss_rules_index:
        try:
            logger.info(f"Loading rules FAISS index from {FAISS_RULES_PATH}...")
            _faiss_rules_index = faiss.read_index(FAISS_RULES_PATH)
            logger.info(f"Rules FAISS index loaded ({_faiss_rules_index.ntotal} items).")
            if _faiss_rules_index.ntotal != len(_rules_items_list) and len(_rules_items_list) > 0:
                 logger.warning(f"Rules FAISS index count ({_faiss_rules_index.ntotal}) differs from loaded texts ({len(_rules_items_list)}). Consider rebuilding FAISS.")
        except Exception as e: logger.error(f"Error loading rules FAISS index: {e}. Will use fresh index.")