Update memory_logic.py

memory_logic.py

@@ -7,7 +7,6 @@ import logging
 import re
 import threading
 
-# Conditionally import heavy dependencies
 try:
     from sentence_transformers import SentenceTransformer
     import faiss
@@ -28,479 +27,249 @@ 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
+    if logging.getLogger(lib_name):
         logging.getLogger(lib_name).setLevel(logging.WARNING)
 
-
-# --- Configuration (Read directly from environment variables) ---
-STORAGE_BACKEND = os.getenv("STORAGE_BACKEND", "RAM").upper() #HF_DATASET, RAM, SQLITE
-SQLITE_DB_PATH = os.getenv("SQLITE_DB_PATH", "app_data/ai_memory.db") # Changed default path
+STORAGE_BACKEND = os.getenv("STORAGE_BACKEND", "RAM").upper()
+SQLITE_DB_PATH = os.getenv("SQLITE_DB_PATH", "app_data/ai_memory.db")
 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
+HF_MEMORY_DATASET_REPO = os.getenv("HF_MEMORY_DATASET_REPO", "broadfield-dev/ai-brain")
+HF_RULES_DATASET_REPO = os.getenv("HF_RULES_DATASET_REPO", "broadfield-dev/ai-rules")
 
-# --- 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
+_dimension = 384
+
+_long_term_memory_items_list = []
+_faiss_long_term_memory_index = None
+_short_term_memory_items_list = []
+_faiss_short_term_memory_index = None
+
+_rules_items_list = []
 _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
+    return sqlite3.connect(SQLITE_DB_PATH, timeout=10)
+
+def _build_faiss_index_from_json_strings(memory_items: list[str]) -> faiss.Index | None:
+    if not memory_items or not _embedder:
+        return faiss.IndexFlatL2(_dimension)
+
+    texts_to_embed = []
+    valid_indices = []
+    for i, mem_json_str in enumerate(memory_items):
+        try:
+            mem_obj = json.loads(mem_json_str)
+            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.append(text)
+            valid_indices.append(i)
+        except json.JSONDecodeError:
+            continue
+    
+    if not texts_to_embed:
+        return faiss.IndexFlatL2(_dimension)
 
-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.")
+        embeddings = _embedder.encode(texts_to_embed, 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[1] == _dimension:
+            index = faiss.IndexFlatL2(_dimension)
+            index.add(embeddings_np)
+            return index
+        else:
+            logger.error(f"Error building FAISS index: embedding shape mismatch.")
+            return faiss.IndexFlatL2(_dimension)
     except Exception as e:
-        logger.error(f"SQLite table initialization error: {e}", exc_info=True)
+        logger.error(f"Failed to build FAISS index: {e}", exc_info=True)
+        return faiss.IndexFlatL2(_dimension)
 
-# --- Initialization ---
 def initialize_memory_system():
-    global _initialized, _embedder, _dimension, _faiss_memory_index, _memory_items_list, _faiss_rules_index, _rules_items_list
-    
+    global _initialized, _embedder, _dimension
+    global _long_term_memory_items_list, _faiss_long_term_memory_index
+    global _short_term_memory_items_list, _faiss_short_term_memory_index
+    global _rules_items_list, _faiss_rules_index
+
     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
+        if not all([SentenceTransformer, faiss, np]):
+            logger.error("Core RAG libraries not available. Cannot initialize semantic memory.")
             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:
+                return
+
+        long_term_mems = []
+        if 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:
+                    long_term_mems = [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 long-term memories from SQLite: {e}")
+        elif STORAGE_BACKEND == "HF_DATASET" and HF_TOKEN and 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}")
+                dataset = load_dataset(HF_MEMORY_DATASET_REPO, token=HF_TOKEN, trust_remote_code=True)
+                if "train" in dataset and "memory_json" in dataset["train"].column_names:
+                    long_term_mems = [m for m in dataset["train"]["memory_json"] if isinstance(m, str)]
+            except Exception as e: logger.error(f"Error loading long-term memories from HF Dataset: {e}")
+
+        _long_term_memory_items_list = long_term_mems
+        logger.info(f"Loaded {len(_long_term_memory_items_list)} long-term memory items.")
+        _faiss_long_term_memory_index = _build_faiss_index_from_json_strings(_long_term_memory_items_list)
+        logger.info(f"Long-term memory FAISS index built. Total items: {_faiss_long_term_memory_index.ntotal if _faiss_long_term_memory_index else 'N/A'}")
+
+        _short_term_memory_items_list = []
+        _faiss_short_term_memory_index = faiss.IndexFlatL2(_dimension)
+        logger.info("Short-term memory initialized (empty).")
         
-        _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:
+        if 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:
+                with _get_sqlite_connection() as conn: temp_rules_text = [row[0] for row in conn.execute("SELECT rule_text FROM rules")]
+            except Exception: pass
+        elif STORAGE_BACKEND == "HF_DATASET" and HF_TOKEN and 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}.")
+                    temp_rules_text = [r for r in dataset["train"]["rule_text"] if isinstance(r, str) and r.strip()]
+            except Exception: pass
 
-        # 6. Build/Load FAISS Rules Index
+        _rules_items_list = sorted(list(set(temp_rules_text)))
         _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'}")
+            rule_embeddings = _embedder.encode(_rules_items_list, convert_to_tensor=False)
+            _faiss_rules_index.add(np.array(rule_embeddings, dtype=np.float32))
+        logger.info(f"Rules FAISS 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()
-    }
+    if not _embedder: return False, "Embedder not initialized."
+
+    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)
+        _faiss_short_term_memory_index.add(embedding_np)
+        _short_term_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
+            all_mems_for_push = _long_term_memory_items_list + _short_term_memory_items_list
+            Dataset.from_dict({"memory_json": list(set(all_mems_for_push))}).push_to_hub(HF_MEMORY_DATASET_REPO, token=HF_TOKEN, private=True)
         
-        logger.info(f"Added memory. RAM: {len(_memory_items_list)}, FAISS: {_faiss_memory_index.ntotal}")
+        logger.info(f"Added memory. Short-term count: {_faiss_short_term_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."""
+def search_memories(query: str, k: int = 3, threshold: float = 1.0) -> tuple[list[dict], str]:
     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 []
+    if not _embedder: return [], "uninitialized"
 
-    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))
+    query_embedding = np.array(_embedder.encode([query]), dtype=np.float32)
+    final_results = {}
+    search_path = "short"
+
+    if _faiss_short_term_memory_index and _faiss_short_term_memory_index.ntotal > 0:
+        distances, indices = _faiss_short_term_memory_index.search(query_embedding, min(k, _faiss_short_term_memory_index.ntotal))
+        best_dist = distances[0][0] if len(distances[0]) > 0 else float('inf')
         
-        results = []
+        if best_dist < threshold:
+            logger.info(f"Found relevant short-term memories (best distance: {best_dist:.4f}).")
+            for i in indices[0]:
+                res = json.loads(_short_term_memory_items_list[i])
+                final_results[res['timestamp']] = res
+            return list(final_results.values()), search_path
+
+    logger.info("No relevant short-term memories found. Escalating to deep search on long-term memory.")
+    search_path = "deep"
+    
+    if _faiss_long_term_memory_index and _faiss_long_term_memory_index.ntotal > 0:
+        distances, indices = _faiss_long_term_memory_index.search(query_embedding, min(k, _faiss_long_term_memory_index.ntotal))
         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
+            res = json.loads(_long_term_memory_items_list[i])
+            final_results[res['timestamp']] = res
+    
+    return list(final_results.values()), search_path
+
+def retrieve_rules_semantic(query: str, k: int = 5) -> list[str]:
+    if not _initialized: initialize_memory_system()
+    if not _faiss_rules_index or _faiss_rules_index.ntotal == 0: return []
+    try:
+        q_embedding = np.array(_embedder.encode([query]), dtype=np.float32)
+        _, indices = _faiss_rules_index.search(q_embedding, min(k, _faiss_rules_index.ntotal))
+        return [_rules_items_list[i] for i in indices[0]]
     except Exception as e:
-        logger.error(f"Error retrieving memories semantically: {e}", exc_info=True)
+        logger.error(f"Error retrieving rules: {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."""
+def get_all_memories_cached() -> list[dict]:
+    if not _initialized: initialize_memory_system()
+    all_mems = _long_term_memory_items_list + _short_term_memory_items_list
+    seen_ts = set()
+    unique_mem_dicts = []
+    for mem_json_str in reversed(all_mems):
+        try:
+            mem_dict = json.loads(mem_json_str)
+            if mem_dict['timestamp'] not in seen_ts:
+                unique_mem_dicts.append(mem_dict)
+                seen_ts.add(mem_dict['timestamp'])
+        except: continue
+    return unique_mem_dicts
+
+# --- The rest of the utility functions (add_rule, get_rules, clear functions) remain the same ---
+def add_rule_entry(rule_text: str):
     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."
-
+    if not _embedder: return False, "Embedder not initialized."
     rule_text = rule_text.strip()
-    if not rule_text: return False, "Rule text cannot be empty."
+    if not rule_text or rule_text in _rules_items_list: return False, "duplicate or 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)
+        _faiss_rules_index.add(np.array(embedding, dtype=np.float32))
         _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
+             Dataset.from_dict({"rule_text": list(_rules_items_list)}).push_to_hub(HF_RULES_DATASET_REPO, token=HF_TOKEN, private=True)
+        return True, "Rule added"
     except Exception as e:
-        logger.error(f"Error removing rule entry: {e}", exc_info=True)
-        # Potential partial failure, state might be inconsistent.
-        return False
+        logger.error(f"Error adding rule: {e}", exc_info=True)
+        return False, str(e)
 
-# --- 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.")
+    return list(_rules_items_list)