src/llm_router.py

# llm_router.py - UPDATED FOR LOCAL GPU MODEL LOADING
import logging
import asyncio
from typing import Dict, Optional
from .models_config import LLM_CONFIG

logger = logging.getLogger(__name__)

class LLMRouter:
    def __init__(self, hf_token, use_local_models: bool = True):
        self.hf_token = hf_token
        self.health_status = {}
        self.use_local_models = use_local_models
        self.local_loader = None
        
        logger.info("LLMRouter initialized")
        if hf_token:
            logger.info("HF token available")
        else:
            logger.warning("No HF token provided")
        
        # Initialize local model loader if enabled
        if self.use_local_models:
            try:
                from .local_model_loader import LocalModelLoader
                self.local_loader = LocalModelLoader()
                logger.info("✓ Local model loader initialized (GPU-based inference)")
                
                # Note: Pre-loading will happen on first request (lazy loading)
                # Models will be loaded on-demand to avoid blocking startup
                logger.info("Models will be loaded on-demand for faster startup")
            except Exception as e:
                logger.warning(f"Could not initialize local model loader: {e}. Falling back to API.")
                logger.warning("This is normal if transformers/torch not available")
                self.use_local_models = False
                self.local_loader = None
    
    async def route_inference(self, task_type: str, prompt: str, **kwargs):
        """
        Smart routing based on task specialization
        Tries local models first, falls back to HF Inference API if needed
        """
        logger.info(f"Routing inference for task: {task_type}")
        model_config = self._select_model(task_type)
        logger.info(f"Selected model: {model_config['model_id']}")
        
        # Try local model first if available
        if self.use_local_models and self.local_loader:
            try:
                # Handle embedding generation separately
                if task_type == "embedding_generation":
                    result = await self._call_local_embedding(model_config, prompt, **kwargs)
                else:
                    result = await self._call_local_model(model_config, prompt, task_type, **kwargs)
                
                if result is not None:
                    logger.info(f"Inference complete for {task_type} (local model)")
                    return result
                else:
                    logger.warning("Local model returned None, falling back to API")
            except Exception as e:
                logger.warning(f"Local model inference failed: {e}. Falling back to API.")
                logger.debug("Exception details:", exc_info=True)
        
        # Fallback to HF Inference API
        logger.info("Using HF Inference API")
        # Health check and fallback logic
        if not await self._is_model_healthy(model_config["model_id"]):
            logger.warning(f"Model unhealthy, using fallback")
            model_config = self._get_fallback_model(task_type)
            logger.info(f"Fallback model: {model_config['model_id']}")
            
        result = await self._call_hf_endpoint(model_config, prompt, task_type, **kwargs)
        logger.info(f"Inference complete for {task_type}")
        return result
    
    async def _call_local_model(self, model_config: dict, prompt: str, task_type: str, **kwargs) -> Optional[str]:
        """Call local model for inference."""
        if not self.local_loader:
            return None
        
        model_id = model_config["model_id"]
        max_tokens = kwargs.get('max_tokens', 512)
        temperature = kwargs.get('temperature', 0.7)
        
        try:
            # Ensure model is loaded
            if model_id not in self.local_loader.loaded_models:
                logger.info(f"Loading model {model_id} on demand...")
                self.local_loader.load_chat_model(model_id, load_in_8bit=False)
            
            # Format as chat messages if needed
            messages = [{"role": "user", "content": prompt}]
            
            # Generate using local model
            result = await asyncio.to_thread(
                self.local_loader.generate_chat_completion,
                model_id=model_id,
                messages=messages,
                max_tokens=max_tokens,
                temperature=temperature
            )
            
            logger.info(f"Local model {model_id} generated response (length: {len(result)})")
            logger.info("=" * 80)
            logger.info("LOCAL MODEL RESPONSE:")
            logger.info("=" * 80)
            logger.info(f"Model: {model_id}")
            logger.info(f"Task Type: {task_type}")
            logger.info(f"Response Length: {len(result)} characters")
            logger.info("-" * 40)
            logger.info("FULL RESPONSE CONTENT:")
            logger.info("-" * 40)
            logger.info(result)
            logger.info("-" * 40)
            logger.info("END OF RESPONSE")
            logger.info("=" * 80)
            
            return result
            
        except Exception as e:
            logger.error(f"Error calling local model: {e}", exc_info=True)
            return None
    
    async def _call_local_embedding(self, model_config: dict, text: str, **kwargs) -> Optional[list]:
        """Call local embedding model."""
        if not self.local_loader:
            return None
        
        model_id = model_config["model_id"]
        
        try:
            # Ensure model is loaded
            if model_id not in self.local_loader.loaded_embedding_models:
                logger.info(f"Loading embedding model {model_id} on demand...")
                self.local_loader.load_embedding_model(model_id)
            
            # Generate embedding
            embedding = await asyncio.to_thread(
                self.local_loader.get_embedding,
                model_id=model_id,
                text=text
            )
            
            logger.info(f"Local embedding model {model_id} generated vector (dim: {len(embedding)})")
            return embedding
            
        except Exception as e:
            logger.error(f"Error calling local embedding model: {e}", exc_info=True)
            return None
    
    def _select_model(self, task_type: str) -> dict:
        model_map = {
            "intent_classification": LLM_CONFIG["models"]["classification_specialist"],
            "embedding_generation": LLM_CONFIG["models"]["embedding_specialist"],
            "safety_check": LLM_CONFIG["models"]["safety_checker"],
            "general_reasoning": LLM_CONFIG["models"]["reasoning_primary"],
            "response_synthesis": LLM_CONFIG["models"]["reasoning_primary"]
        }
        return model_map.get(task_type, LLM_CONFIG["models"]["reasoning_primary"])
    
    async def _is_model_healthy(self, model_id: str) -> bool:
        """
        Check if the model is healthy and available
        Mark models as healthy by default - actual availability checked at API call time
        """
        # Check cached health status
        if model_id in self.health_status:
            return self.health_status[model_id]
        
        # All models marked healthy initially - real check happens during API call
        self.health_status[model_id] = True
        return True
    
    def _get_fallback_model(self, task_type: str) -> dict:
        """
        Get fallback model configuration for the task type
        """
        # Fallback mapping
        fallback_map = {
            "intent_classification": LLM_CONFIG["models"]["reasoning_primary"],
            "embedding_generation": LLM_CONFIG["models"]["embedding_specialist"],
            "safety_check": LLM_CONFIG["models"]["reasoning_primary"],
            "general_reasoning": LLM_CONFIG["models"]["reasoning_primary"],
            "response_synthesis": LLM_CONFIG["models"]["reasoning_primary"]
        }
        return fallback_map.get(task_type, LLM_CONFIG["models"]["reasoning_primary"])
    
    async def _call_hf_endpoint(self, model_config: dict, prompt: str, task_type: str, **kwargs):
        """
        FIXED: Make actual call to Hugging Face Chat Completions API
        Uses the correct chat completions protocol with retry logic and exponential backoff
        
        IMPORTANT: task_type parameter is now properly included in the method signature
        """
        # Retry configuration
        max_retries = kwargs.get('max_retries', 3)
        initial_delay = kwargs.get('initial_delay', 1.0)  # Start with 1 second
        max_delay = kwargs.get('max_delay', 16.0)  # Cap at 16 seconds
        timeout = kwargs.get('timeout', 30)
        
        try:
            import requests
            from requests.exceptions import Timeout, RequestException, ConnectionError as RequestsConnectionError
            
            model_id = model_config["model_id"]
            
            # Use the chat completions endpoint
            api_url = "https://router.huggingface.co/v1/chat/completions"
            
            logger.info(f"Calling HF Chat Completions API for model: {model_id}")
            logger.debug(f"Prompt length: {len(prompt)}")
            logger.info("=" * 80)
            logger.info("LLM API REQUEST - COMPLETE PROMPT:")
            logger.info("=" * 80)
            logger.info(f"Model: {model_id}")
            
            # FIXED: task_type is now properly available as a parameter
            logger.info(f"Task Type: {task_type}")
            logger.info(f"Prompt Length: {len(prompt)} characters")
            logger.info("-" * 40)
            logger.info("FULL PROMPT CONTENT:")
            logger.info("-" * 40)
            logger.info(prompt)
            logger.info("-" * 40)
            logger.info("END OF PROMPT")
            logger.info("=" * 80)
            
            # Prepare the request payload
            max_tokens = kwargs.get('max_tokens', 512)
            temperature = kwargs.get('temperature', 0.7)
            
            payload = {
                "model": model_id,
                "messages": [
                    {
                        "role": "user",
                        "content": prompt
                    }
                ],
                "max_tokens": max_tokens,
                "temperature": temperature,
                "stream": False
            }
            
            headers = {
                "Authorization": f"Bearer {self.hf_token}",
                "Content-Type": "application/json"
            }
            
            # Retry logic with exponential backoff
            last_exception = None
            for attempt in range(max_retries + 1):
                try:
                    if attempt > 0:
                        # Calculate exponential backoff delay
                        delay = min(initial_delay * (2 ** (attempt - 1)), max_delay)
                        logger.warning(f"Retry attempt {attempt}/{max_retries} after {delay:.1f}s delay (exponential backoff)")
                        await asyncio.sleep(delay)
                    
                    logger.info(f"Sending request to: {api_url} (attempt {attempt + 1}/{max_retries + 1})")
                    logger.debug(f"Payload: {payload}")
                    
                    response = requests.post(api_url, json=payload, headers=headers, timeout=timeout)
                    
                    if response.status_code == 200:
                        result = response.json()
                        logger.debug(f"Raw response: {result}")
                        
                        if 'choices' in result and len(result['choices']) > 0:
                            generated_text = result['choices'][0]['message']['content']
                            
                            if not generated_text or generated_text.strip() == "":
                                logger.warning(f"Empty or invalid response, using fallback")
                                return None
                            
                            if attempt > 0:
                                logger.info(f"Successfully retrieved response after {attempt} retry attempts")
                            
                            logger.info(f"HF API returned response (length: {len(generated_text)})")
                            logger.info("=" * 80)
                            logger.info("COMPLETE LLM API RESPONSE:")
                            logger.info("=" * 80)
                            logger.info(f"Model: {model_id}")
                            
                            # FIXED: task_type is now properly available
                            logger.info(f"Task Type: {task_type}")
                            logger.info(f"Response Length: {len(generated_text)} characters")
                            logger.info("-" * 40)
                            logger.info("FULL RESPONSE CONTENT:")
                            logger.info("-" * 40)
                            logger.info(generated_text)
                            logger.info("-" * 40)
                            logger.info("END OF LLM RESPONSE")
                            logger.info("=" * 80)
                            return generated_text
                        else:
                            logger.error(f"Unexpected response format: {result}")
                            return None
                    elif response.status_code == 503:
                        # Model is loading - this is retryable
                        if attempt < max_retries:
                            logger.warning(f"Model loading (503), will retry (attempt {attempt + 1}/{max_retries + 1})")
                            last_exception = Exception(f"Model loading (503)")
                            continue
                        else:
                            # After max retries, try fallback model
                            logger.warning(f"Model loading (503) after {max_retries} retries, trying fallback model")
                            fallback_config = self._get_fallback_model(task_type)
                            
                            # FIXED: Ensure task_type is passed in recursive call
                            return await self._call_hf_endpoint(fallback_config, prompt, task_type, **kwargs)
                    else:
                        # Non-retryable HTTP errors
                        logger.error(f"HF API error: {response.status_code} - {response.text}")
                        return None
                        
                except Timeout as e:
                    last_exception = e
                    if attempt < max_retries:
                        logger.warning(f"Request timeout (attempt {attempt + 1}/{max_retries + 1}): {str(e)}")
                        continue
                    else:
                        logger.error(f"Request timeout after {max_retries} retries: {str(e)}")
                        # Try fallback model on final timeout
                        logger.warning("Attempting fallback model due to persistent timeout")
                        fallback_config = self._get_fallback_model(task_type)
                        return await self._call_hf_endpoint(fallback_config, prompt, task_type, **kwargs)
                        
                except (RequestsConnectionError, RequestException) as e:
                    last_exception = e
                    if attempt < max_retries:
                        logger.warning(f"Connection error (attempt {attempt + 1}/{max_retries + 1}): {str(e)}")
                        continue
                    else:
                        logger.error(f"Connection error after {max_retries} retries: {str(e)}")
                        # Try fallback model on final connection error
                        logger.warning("Attempting fallback model due to persistent connection error")
                        fallback_config = self._get_fallback_model(task_type)
                        return await self._call_hf_endpoint(fallback_config, prompt, task_type, **kwargs)
            
            # If we exhausted all retries and didn't return
            if last_exception:
                logger.error(f"Failed after {max_retries} retries. Last error: {last_exception}")
                return None
                
        except ImportError:
            logger.warning("requests library not available, using mock response")
            return f"[Mock] Response to: {prompt[:100]}..."
        except Exception as e:
            logger.error(f"Error calling HF endpoint: {e}", exc_info=True)
            return None
    
    async def get_available_models(self):
        """
        Get list of available models for testing
        """
        return list(LLM_CONFIG["models"].keys())
    
    async def health_check(self):
        """
        Perform health check on all models
        """
        health_status = {}
        for model_name, model_config in LLM_CONFIG["models"].items():
            model_id = model_config["model_id"]
            is_healthy = await self._is_model_healthy(model_id)
            health_status[model_name] = {
                "model_id": model_id,
                "healthy": is_healthy
            }
        
        return health_status
    
    def prepare_context_for_llm(self, raw_context: Dict, max_tokens: int = 4000) -> str:
        """Smart context windowing for LLM calls"""
        
        try:
            from transformers import AutoTokenizer
            
            # Initialize tokenizer lazily
            if not hasattr(self, 'tokenizer'):
                try:
                    self.tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
                except Exception as e:
                    logger.warning(f"Could not load tokenizer: {e}, using character count estimation")
                    self.tokenizer = None
        except ImportError:
            logger.warning("transformers library not available, using character count estimation")
            self.tokenizer = None
        
        # Priority order for context elements
        priority_elements = [
            ('current_query', 1.0),
            ('recent_interactions', 0.8),
            ('user_preferences', 0.6),
            ('session_summary', 0.4),
            ('historical_context', 0.2)
        ]
        
        formatted_context = []
        total_tokens = 0
        
        for element, priority in priority_elements:
            # Map element names to context keys
            element_key_map = {
                'current_query': raw_context.get('user_input', ''),
                'recent_interactions': raw_context.get('interaction_contexts', []),
                'user_preferences': raw_context.get('preferences', {}),
                'session_summary': raw_context.get('session_context', {}),
                'historical_context': raw_context.get('user_context', '')
            }
            
            content = element_key_map.get(element, '')
            
            # Convert to string if needed
            if isinstance(content, dict):
                content = str(content)
            elif isinstance(content, list):
                content = "\n".join([str(item) for item in content[:10]])  # Limit to 10 items
            
            if not content:
                continue
            
            # Estimate tokens
            if self.tokenizer:
                try:
                    tokens = len(self.tokenizer.encode(content))
                except:
                    # Fallback to character-based estimation (rough: 1 token ≈ 4 chars)
                    tokens = len(content) // 4
            else:
                # Character-based estimation (rough: 1 token ≈ 4 chars)
                tokens = len(content) // 4
            
            if total_tokens + tokens <= max_tokens:
                formatted_context.append(f"=== {element.upper()} ===\n{content}")
                total_tokens += tokens
            elif priority > 0.5:  # Critical elements - truncate if needed
                available = max_tokens - total_tokens
                if available > 100:  # Only truncate if we have meaningful space
                    truncated = self._truncate_to_tokens(content, available)
                    formatted_context.append(f"=== {element.upper()} (TRUNCATED) ===\n{truncated}")
                break
        
        return "\n\n".join(formatted_context)
    
    def _truncate_to_tokens(self, content: str, max_tokens: int) -> str:
        """Truncate content to fit within token limit"""
        if not self.tokenizer:
            # Simple character-based truncation
            max_chars = max_tokens * 4
            if len(content) <= max_chars:
                return content
            return content[:max_chars-3] + "..."
        
        try:
            # Tokenize and truncate
            tokens = self.tokenizer.encode(content)
            if len(tokens) <= max_tokens:
                return content
            
            truncated_tokens = tokens[:max_tokens-3]  # Leave room for "..."
            truncated_text = self.tokenizer.decode(truncated_tokens)
            return truncated_text + "..."
        except Exception as e:
            logger.warning(f"Error truncating with tokenizer: {e}, using character truncation")
            max_chars = max_tokens * 4
            if len(content) <= max_chars:
                return content
            return content[:max_chars-3] + "..."