Update model_logic.py

model_logic.py

@@ -1,506 +1,437 @@
-# memory_logic.py
+# model_logic.py
 import os
+import requests
 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.")
+from dotenv import load_dotenv
 
+# Load environment variables from .env file
+load_dotenv()
 
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
 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":
-            pass
-        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)
-                if "train" in dataset and "memory_json" in dataset["train"].column_names:
-                    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)
-                    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":
-            pass
-        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()
+# Maps provider name (uppercase) to environment variable name for API key
+API_KEYS_ENV_VARS = {
+  "HUGGINGFACE": 'HF_TOKEN', # Note: HF_TOKEN is often used for general HF auth
+  "GROQ": 'GROQ_API_KEY',
+  "OPENROUTER": 'OPENROUTER_API_KEY',
+  "TOGETHERAI": 'TOGETHERAI_API_KEY',
+  "COHERE": 'COHERE_API_KEY',
+  "XAI": 'XAI_API_KEY',
+  "OPENAI": 'OPENAI_API_KEY',
+  "GOOGLE": 'GOOGLE_API_KEY', # Or GOOGLE_GEMINI_API_KEY etc.
+}
+
+API_URLS = {
+  "HUGGINGFACE": 'https://api-inference.huggingface.co/models/',
+  "GROQ": 'https://api.groq.com/openai/v1/chat/completions',
+  "OPENROUTER": 'https://openrouter.ai/api/v1/chat/completions',
+  "TOGETHERAI": 'https://api.together.ai/v1/chat/completions',
+  "COHERE": 'https://api.cohere.ai/v1/chat', # v1 is common for chat, was v2 in ai-learn
+  "XAI": 'https://api.x.ai/v1/chat/completions',
+  "OPENAI": 'https://api.openai.com/v1/chat/completions',
+  "GOOGLE": 'https://generativelanguage.googleapis.com/v1beta/models/',
+}
+
+# MODELS_BY_PROVIDER = json.load(open("./models.json"))  ## commented for demo
+MODELS_BY_PROVIDER = {
+    "groq": {
+        "default": "llama3-8b-8192",
+        "models": {
+            "Llama 3 8B (Groq)": "llama3-8b-8192",
+        }
     }
-    memory_json_str = json.dumps(memory_obj)
+}
+
+def _get_api_key(provider: str, ui_api_key_override: str = None) -> str | None:
+    """
+    Retrieves API key for a given provider.
+    Priority: UI Override > Environment Variable from API_KEYS_ENV_VARS > Specific (e.g. HF_TOKEN for HuggingFace).
+    """
+    provider_upper = provider.upper()
+    if ui_api_key_override and ui_api_key_override.strip():
+        logger.debug(f"Using UI-provided API key for {provider_upper}.")
+        return ui_api_key_override.strip()
+
+    env_var_name = API_KEYS_ENV_VARS.get(provider_upper)
+    if env_var_name:
+        env_key = os.getenv(env_var_name)
+        if env_key and env_key.strip():
+            logger.debug(f"Using API key from env var '{env_var_name}' for {provider_upper}.")
+            return env_key.strip()
+
+    # Specific fallback for HuggingFace if HF_TOKEN is set and API_KEYS_ENV_VARS['HUGGINGFACE'] wasn't specific enough
+    if provider_upper == 'HUGGINGFACE':
+         hf_token_fallback = os.getenv("HF_TOKEN")
+         if hf_token_fallback and hf_token_fallback.strip():
+             logger.debug("Using HF_TOKEN as fallback for HuggingFace provider.")
+             return hf_token_fallback.strip()
+
+    logger.warning(f"API Key not found for provider '{provider_upper}'. Checked UI override and environment variable '{env_var_name or 'N/A'}'.")
+    return None
+
+def get_available_providers() -> list[str]:
+    """Returns a sorted list of available provider names (e.g., 'groq', 'openai')."""
+    return sorted(list(MODELS_BY_PROVIDER.keys()))
+
+def get_model_display_names_for_provider(provider: str) -> list[str]:
+    """Returns a sorted list of model display names for a given provider."""
+    return sorted(list(MODELS_BY_PROVIDER.get(provider.lower(), {}).get("models", {}).keys()))
+
+def get_default_model_display_name_for_provider(provider: str) -> str | None:
+    """Gets the default model's display name for a provider."""
+    provider_data = MODELS_BY_PROVIDER.get(provider.lower(), {})
+    models_dict = provider_data.get("models", {})
+    default_model_id = provider_data.get("default")
+
+    if default_model_id and models_dict:
+        for display_name, model_id_val in models_dict.items():
+            if model_id_val == default_model_id:
+                return display_name
     
-    text_to_embed = f"User: {user_input}\nAI: {bot_response}\nTakeaway: {metrics.get('takeaway', 'N/A')}"
+    # Fallback to the first model in the sorted list if default not found or not set
+    if models_dict:
+        sorted_display_names = sorted(list(models_dict.keys()))
+        if sorted_display_names:
+            return sorted_display_names[0]
+    return None
+
+def get_model_id_from_display_name(provider: str, display_name: str) -> str | None:
+    """Gets the actual model ID from its display name for a given provider."""
+    models = MODELS_BY_PROVIDER.get(provider.lower(), {}).get("models", {})
+    return models.get(display_name)
+
+
+def call_model_stream(provider: str, model_display_name: str, messages: list[dict], api_key_override: str = None, temperature: float = 0.7, max_tokens: int = None) -> iter:
+    """
+    Calls the specified model via its provider and streams the response.
+    Handles provider-specific request formatting and error handling.
+    Yields chunks of the response text or an error string.
+    """
+    provider_lower = provider.lower()
+    api_key = _get_api_key(provider_lower, api_key_override)
+    base_url = API_URLS.get(provider.upper())
+    model_id = get_model_id_from_display_name(provider_lower, model_display_name)
+
+    if not api_key:
+        env_var_name = API_KEYS_ENV_VARS.get(provider.upper(), 'N/A')
+        yield f"Error: API Key not found for {provider}. Please set it in the UI or env var '{env_var_name}'."
+        return
+    if not base_url:
+        yield f"Error: Unknown provider '{provider}' or missing API URL configuration."
+        return
+    if not model_id:
+         yield f"Error: Model ID not found for '{model_display_name}' under provider '{provider}'. Check configuration."
+         return
+
+    headers = {}
+    payload = {}
+    request_url = base_url
+
+    logger.info(f"Streaming from {provider}/{model_display_name} (ID: {model_id})...")
     
-    try:
-        embedding = _embedder.encode([text_to_embed], convert_to_tensor=False)
-        embedding_np = np.array(embedding, dtype=np.float32).reshape(1, -1)
+    # --- Standard OpenAI-compatible providers ---
+    if provider_lower in ["groq", "openrouter", "togetherai", "openai", "xai"]:
+        headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
+        payload = {"model": model_id, "messages": messages, "stream": True, "temperature": temperature}
+        if max_tokens: payload["max_tokens"] = max_tokens
 
-        if embedding_np.shape != (1, _dimension):
-            logger.error(f"Memory embedding shape error: {embedding_np.shape}. Expected (1, {_dimension})")
-            return False, "Embedding shape error."
+        if provider_lower == "openrouter":
+             headers["HTTP-Referer"] = os.getenv("OPENROUTER_REFERRER") or "http://localhost/gradio" # Example Referer
+             headers["X-Title"] = os.getenv("OPENROUTER_X_TITLE") or "Gradio AI Researcher"      # Example Title
 
-        # 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}")
+        try:
+            response = requests.post(request_url, headers=headers, json=payload, stream=True, timeout=180)
+            response.raise_for_status()
+
+            # More robust SSE parsing
+            buffer = ""
+            for chunk in response.iter_content(chunk_size=None): # Process raw bytes
+                buffer += chunk.decode('utf-8', errors='replace')
+                while '\n\n' in buffer:
+                    event_str, buffer = buffer.split('\n\n', 1)
+                    if not event_str.strip(): continue
+
+                    content_chunk = ""
+                    for line in event_str.splitlines():
+                        if line.startswith('data: '):
+                            data_json = line[len('data: '):].strip()
+                            if data_json == '[DONE]':
+                                return # Stream finished
+                            try:
+                                data = json.loads(data_json)
+                                if data.get("choices") and len(data["choices"]) > 0:
+                                    delta = data["choices"][0].get("delta", {})
+                                    if delta and delta.get("content"):
+                                        content_chunk += delta["content"]
+                            except json.JSONDecodeError:
+                                logger.warning(f"Failed to decode JSON from stream line: {data_json}")
+                    if content_chunk:
+                        yield content_chunk
+            # Process any remaining buffer content (less common with '\n\n' delimiter)
+            if buffer.strip():
+                logger.debug(f"Remaining buffer after OpenAI-like stream: {buffer}")
+
+
+        except requests.exceptions.HTTPError as e:
+            err_msg = f"API HTTP Error ({e.response.status_code}): {e.response.text[:500]}"
+            logger.error(f"{err_msg} for {provider}/{model_id}", exc_info=False)
+            yield f"Error: {err_msg}"
+        except requests.exceptions.RequestException as e:
+            logger.error(f"API Request Error for {provider}/{model_id}: {e}", exc_info=False)
+            yield f"Error: Could not connect to {provider} ({e})"
+        except Exception as e:
+            logger.exception(f"Unexpected error during {provider} stream:")
+            yield f"Error: An unexpected error occurred: {e}"
+        return
+
+    # --- Google Gemini ---
+    elif provider_lower == "google":
+        system_instruction = None
+        filtered_messages = []
+        for msg in messages:
+            if msg["role"] == "system": system_instruction = {"parts": [{"text": msg["content"]}]}
             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)
+                role = "model" if msg["role"] == "assistant" else msg["role"]
+                filtered_messages.append({"role": role, "parts": [{"text": msg["content"]}]})
+
+        payload = {
+             "contents": filtered_messages,
+             "safetySettings": [ # Example: more permissive settings
+                 {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"},
+                 {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"},
+                 {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"},
+                 {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"},
+             ],
+             "generationConfig": {"temperature": temperature}
+        }
+        if max_tokens: payload["generationConfig"]["maxOutputTokens"] = max_tokens
+        if system_instruction: payload["system_instruction"] = system_instruction
         
-        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
+        request_url = f"{base_url}{model_id}:streamGenerateContent?key={api_key}" # API key in query param
+        headers = {"Content-Type": "application/json"}
+
+        try:
+            response = requests.post(request_url, headers=headers, json=payload, stream=True, timeout=180)
+            response.raise_for_status()
+            
+            # Google's stream is a bit different, often newline-delimited JSON arrays/objects
+            buffer = ""
+            for chunk in response.iter_content(chunk_size=None):
+                buffer += chunk.decode('utf-8', errors='replace')
+                # Google might send chunks that are not complete JSON objects, or multiple objects
+                # A common pattern is [ {obj1} , {obj2} ] where chunks split mid-array or mid-object.
+                # This parsing needs to be robust. A simple split by '\n' might not always work if JSON is pretty-printed.
+                # The previous code's `json.loads(f"[{decoded_line}]")` was an attempt to handle this.
+                # For now, let's assume newline delimited for simplicity, but this is a known tricky part.
+                
+                while '\n' in buffer:
+                    line, buffer = buffer.split('\n', 1)
+                    line = line.strip()
+                    if not line: continue
+                    if line.startswith(','): line = line[1:] # Handle leading commas if splitting an array
+
+                    try:
+                        # Remove "data: " prefix if present (less common for Gemini direct API but good practice)
+                        if line.startswith('data: '): line = line[len('data: '):]
+                        
+                        # Gemini often streams an array of objects, or just one object.
+                        # Try to parse as a single object first. If fails, try as array.
+                        parsed_data = None
+                        try:
+                            parsed_data = json.loads(line)
+                        except json.JSONDecodeError:
+                            # If it's part of an array, it might be missing brackets.
+                            # This heuristic is fragile. A proper SSE parser or stateful JSON parser is better.
+                            if line.startswith('{') and line.endswith('}'): # Looks like a complete object
+                                pass # already tried json.loads
+                            # Try to wrap with [] if it seems like a list content without brackets
+                            elif line.startswith('{') or line.endswith('}'):
+                                try:
+                                    temp_parsed_list = json.loads(f"[{line}]")
+                                    if temp_parsed_list and isinstance(temp_parsed_list, list):
+                                        parsed_data = temp_parsed_list[0] # take first if it becomes a list
+                                except json.JSONDecodeError:
+                                    logger.warning(f"Google: Still can't parse line even with array wrap: {line}")
+
+                        if parsed_data:
+                            data_to_process = [parsed_data] if isinstance(parsed_data, dict) else parsed_data # Ensure list
+                            for event_data in data_to_process:
+                                if not isinstance(event_data, dict): continue
+                                if event_data.get("candidates"):
+                                    for candidate in event_data["candidates"]:
+                                        if candidate.get("content", {}).get("parts"):
+                                            for part in candidate["content"]["parts"]:
+                                                if part.get("text"):
+                                                    yield part["text"]
+                    except json.JSONDecodeError:
+                        logger.warning(f"Google: JSONDecodeError for line: {line}")
+                    except Exception as e_google_proc:
+                        logger.error(f"Google: Error processing stream data: {e_google_proc}, Line: {line}")
+
+        except requests.exceptions.HTTPError as e:
+            err_msg = f"Google API HTTP Error ({e.response.status_code}): {e.response.text[:500]}"
+            logger.error(err_msg, exc_info=False)
+            yield f"Error: {err_msg}"
+        except Exception as e:
+            logger.exception(f"Unexpected error during Google stream:")
+            yield f"Error: An unexpected error occurred with Google API: {e}"
+        return
+
+    # --- Cohere ---
+    elif provider_lower == "cohere":
+        headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json", "Accept": "application/json"}
         
-        # 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)
+        # Cohere message format
+        chat_history_cohere = []
+        preamble_cohere = None
+        user_message_cohere = ""
+
+        temp_messages = list(messages) # Work with a copy
+        if temp_messages and temp_messages[0]["role"] == "system":
+            preamble_cohere = temp_messages.pop(0)["content"]
         
-        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)
+        if temp_messages:
+            user_message_cohere = temp_messages.pop()["content"] # Last message is the current user query
+            for msg in temp_messages: # Remaining are history
+                role = "USER" if msg["role"] == "user" else "CHATBOT"
+                chat_history_cohere.append({"role": role, "message": msg["content"]})
         
-        _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 not user_message_cohere:
+            yield "Error: User message is empty for Cohere."
+            return
 
-    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:
+        payload = {
+            "model": model_id, 
+            "message": user_message_cohere, 
+            "stream": True, 
+            "temperature": temperature
+        }
+        if max_tokens: payload["max_tokens"] = max_tokens # Cohere uses max_tokens
+        if chat_history_cohere: payload["chat_history"] = chat_history_cohere
+        if preamble_cohere: payload["preamble"] = preamble_cohere
+        
         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}")
+            response = requests.post(base_url, headers=headers, json=payload, stream=True, timeout=180)
+            response.raise_for_status()
+            
+            # Cohere SSE format is event: type\ndata: {json}\n\n
+            buffer = ""
+            for chunk_bytes in response.iter_content(chunk_size=None):
+                buffer += chunk_bytes.decode('utf-8', errors='replace')
+                while '\n\n' in buffer:
+                    event_str, buffer = buffer.split('\n\n', 1)
+                    if not event_str.strip(): continue
+                    
+                    event_type = None
+                    data_json_str = None
+                    for line in event_str.splitlines():
+                        if line.startswith("event:"): event_type = line[len("event:"):].strip()
+                        elif line.startswith("data:"): data_json_str = line[len("data:"):].strip()
+                    
+                    if data_json_str:
+                        try:
+                            data = json.loads(data_json_str)
+                            if event_type == "text-generation" and "text" in data:
+                                yield data["text"]
+                            elif event_type == "stream-end":
+                                logger.debug(f"Cohere stream ended. Finish reason: {data.get('finish_reason')}")
+                                return 
+                        except json.JSONDecodeError:
+                            logger.warning(f"Cohere: Failed to decode JSON: {data_json_str}")
+            if buffer.strip():
+                 logger.debug(f"Cohere: Remaining buffer: {buffer.strip()}")
+
+
+        except requests.exceptions.HTTPError as e:
+            err_msg = f"Cohere API HTTP Error ({e.response.status_code}): {e.response.text[:500]}"
+            logger.error(err_msg, exc_info=False)
+            yield f"Error: {err_msg}"
+        except Exception as e:
+            logger.exception(f"Unexpected error during Cohere stream:")
+            yield f"Error: An unexpected error occurred with Cohere API: {e}"
+        return
 
-def load_faiss_indices_from_disk():
-    global _faiss_memory_index, _faiss_rules_index
-    if not _initialized or not faiss: return
+    # --- HuggingFace Inference API (Basic TGI support) ---
+    # This is very basic and might not work for all models or complex scenarios.
+    # Assumes model is deployed with Text Generation Inference (TGI) and supports streaming.
+    elif provider_lower == "huggingface":
+        headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
+        # Construct prompt string for TGI (often needs specific formatting)
+        # This is a generic attempt, specific models might need <|user|>, <|assistant|> etc.
+        prompt_parts = []
+        for msg in messages:
+            role_prefix = ""
+            if msg['role'] == 'system': role_prefix = "System: " # Or might be ignored/handled differently
+            elif msg['role'] == 'user': role_prefix = "User: "
+            elif msg['role'] == 'assistant': role_prefix = "Assistant: "
+            prompt_parts.append(f"{role_prefix}{msg['content']}")
+        
+        # TGI typically expects a final "Assistant: " to start generating from
+        tgi_prompt = "\n".join(prompt_parts) + "\nAssistant: "
+
+        payload = {
+            "inputs": tgi_prompt,
+            "parameters": {
+                "temperature": temperature if temperature > 0 else 0.01, # TGI needs temp > 0 for sampling
+                "max_new_tokens": max_tokens or 1024, # Default TGI max_new_tokens
+                "return_full_text": False, # We only want generated part
+                "do_sample": True if temperature > 0 else False,
+            },
+            "stream": True
+        }
+        request_url = f"{base_url}{model_id}" # Model ID is part of URL path for HF
 
-    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.")
+            response = requests.post(request_url, headers=headers, json=payload, stream=True, timeout=180)
+            response.raise_for_status()
+
+            # TGI SSE stream: data: {"token": {"id": ..., "text": "...", "logprob": ..., "special": ...}}
+            # Or sometimes just data: "text_chunk" for simpler models/configs
+            buffer = ""
+            for chunk_bytes in response.iter_content(chunk_size=None):
+                buffer += chunk_bytes.decode('utf-8', errors='replace')
+                while '\n' in buffer: # TGI often uses single newline
+                    line, buffer = buffer.split('\n', 1)
+                    line = line.strip()
+                    if not line: continue
+
+                    if line.startswith('data:'):
+                        data_json_str = line[len('data:'):].strip()
+                        try:
+                            data = json.loads(data_json_str)
+                            if "token" in data and "text" in data["token"]:
+                                yield data["token"]["text"]
+                            elif "generated_text" in data and data.get("details") is None: # Sometimes a final non-streaming like object might appear
+                                 # This case is tricky, if it's the *only* thing then it's not really streaming
+                                 pass # For now, ignore if it's not a token object
+                            # Some TGI might send raw text if not fully SSE compliant for stream
+                            # elif isinstance(data, str): yield data 
+
+                        except json.JSONDecodeError:
+                            # If it's not JSON, it might be a raw string (less common for TGI stream=True)
+                            # For safety, only yield if it's a clear text string
+                            if not data_json_str.startswith('{') and not data_json_str.startswith('['):
+                                yield data_json_str
+                            else:
+                                logger.warning(f"HF: Failed to decode JSON and not raw string: {data_json_str}")
+            if buffer.strip():
+                 logger.debug(f"HF: Remaining buffer: {buffer.strip()}")
+
+
+        except requests.exceptions.HTTPError as e:
+            err_msg = f"HF API HTTP Error ({e.response.status_code}): {e.response.text[:500]}"
+            logger.error(err_msg, exc_info=False)
+            yield f"Error: {err_msg}"
+        except Exception as e:
+            logger.exception(f"Unexpected error during HF stream:")
+            yield f"Error: An unexpected error occurred with HF API: {e}"
+        return
+
+    else:
+        yield f"Error: Provider '{provider}' is not configured for streaming in this handler."
+        return