chatbot_model.py

import time
from transformers import TFAutoModel, AutoTokenizer
import tensorflow as tf
import numpy as np
from typing import Generator, List, Tuple, Dict, Optional, Union, Any
import math
from dataclasses import dataclass
import json
from pathlib import Path
import datetime       
import faiss
import gc
from response_quality_checker import ResponseQualityChecker
from cross_encoder_reranker import CrossEncoderReranker
from conversation_summarizer import DeviceAwareModel, Summarizer
from gpu_monitor import GPUMemoryMonitor
import absl.logging
from logger_config import config_logger
from tqdm.auto import tqdm

absl.logging.set_verbosity(absl.logging.WARNING)
logger = config_logger(__name__)

@dataclass
class ChatbotConfig:
    """Configuration for the RetrievalChatbot."""
    vocab_size: int = 30526  # DistilBERT vocab size + special tokens
    max_context_token_limit: int = 512
    embedding_dim: int = 768
    encoder_units: int = 256
    num_attention_heads: int = 8
    dropout_rate: float = 0.2
    l2_reg_weight: float = 0.001
    margin: float = 0.3
    learning_rate: float = 0.001
    min_text_length: int = 3
    max_context_turns: int = 5
    warmup_steps: int = 200
    pretrained_model: str = 'distilbert-base-uncased'
    dtype: str = 'float32'
    freeze_embeddings: bool = False
    embedding_batch_size: int = 128
    # Additional configurations can be added here

    def to_dict(self) -> dict:
        """Convert config to dictionary."""
        return {k: str(v) if isinstance(v, Path) else v 
                for k, v in self.__dict__.items()}

    @classmethod
    def from_dict(cls, config_dict: dict) -> 'ChatbotConfig':
        """Create config from dictionary."""
        return cls(**{k: v for k, v in config_dict.items() 
                     if k in cls.__dataclass_fields__})

class EncoderModel(tf.keras.Model):
    """Dual encoder model with pretrained embeddings."""
    def __init__(
        self,
        config: ChatbotConfig,
        name: str = "encoder",
        shared_weights: bool = False,
        **kwargs
    ):
        super().__init__(name=name, **kwargs)
        self.config = config
        self.shared_weights = shared_weights

        # Load pretrained model
        self.pretrained = TFAutoModel.from_pretrained(config.pretrained_model)
        
        # Freeze pretrained weights if specified
        self.pretrained.distilbert.embeddings.trainable = False
        for i, layer_module in enumerate(self.pretrained.distilbert.transformer.layer):
            if i < 1:  # freeze first layer
                layer_module.trainable = False
            else:
                layer_module.trainable = True

        # Pooling layer (Global Average Pooling)
        self.pooler = tf.keras.layers.GlobalAveragePooling1D()
        
        # Projection layer
        self.projection = tf.keras.layers.Dense(
            config.embedding_dim,
            activation='tanh',
            name="projection"
        )

        # Dropout and normalization
        self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
        self.normalize = tf.keras.layers.Lambda(
            lambda x: tf.nn.l2_normalize(x, axis=1)
        )

    def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
        """Forward pass."""
        # Get pretrained embeddings
        pretrained_outputs = self.pretrained(inputs, training=training)
        x = pretrained_outputs.last_hidden_state  # Shape: [batch_size, seq_len, embedding_dim]

        # Apply pooling, projection, dropout, and normalization
        x = self.pooler(x)  # Shape: [batch_size, 768]
        x = self.projection(x) # Shape: [batch_size, 768]
        x = self.dropout(x, training=training) # Apply dropout
        x = self.normalize(x)  # Shape: [batch_size, 768]

        return x

    def get_config(self) -> dict:
        """Return the config of the model."""
        config = super().get_config()
        config.update({
            "config": self.config.to_dict(),
            "shared_weights": self.shared_weights,
            "name": self.name
        })
        return config
        
class RetrievalChatbot(DeviceAwareModel):
    """Retrieval-based chatbot using pretrained embeddings and FAISS for similarity search."""
    def __init__(self, config: ChatbotConfig, dialogues: List[dict] = [], device: str = None,
                 strategy=None, reranker: Optional[CrossEncoderReranker] = None,
                 summarizer: Optional[Summarizer] = None
                 ):
        self.config = config
        self.strategy = strategy
        self.setup_device(device)
        
        if reranker is None:
            logger.info("Creating default CrossEncoderReranker...")
            reranker = CrossEncoderReranker(model_name="cross-encoder/ms-marco-MiniLM-L-12-v2")
        self.reranker = reranker
        
        if summarizer is None:
            logger.info("Creating default Summarizer...")
            summarizer = Summarizer(device=self.device)
        self.summarizer = summarizer
        
        # Special tokens
        self.special_tokens = {
            "user": "<USER>",
            "assistant": "<ASSISTANT>",
            "context": "<CONTEXT>",
            "sep": "<SEP>"
        }
        
        # Initialize tokenizer and add special tokens
        self.tokenizer = AutoTokenizer.from_pretrained(config.pretrained_model)
        self.tokenizer.add_special_tokens(
            {'additional_special_tokens': list(self.special_tokens.values())}
        )
        
        self.memory_monitor = GPUMemoryMonitor()
        self.min_batch_size = 8    
        self.max_batch_size = 128
        self.current_batch_size = 32
        
        # Collect unique responses from dialogues
        self.response_pool, self.unique_responses = self._collect_responses(dialogues)
        
        # Initialize training history
        self.history = {
            "train_loss": [],
            "val_loss": [],
            "train_metrics": {},
            "val_metrics": {}
        }

    def build_models(self):
        """Initialize the shared encoder."""
        logger.info("Building encoder model...")
        tf.keras.backend.clear_session()
        
        # Shared encoder for both queries and responses
        self.encoder = EncoderModel(
            self.config,
            name="shared_encoder",
        )
        
        # Resize token embeddings after adding special tokens
        new_vocab_size = len(self.tokenizer)
        self.encoder.pretrained.resize_token_embeddings(new_vocab_size)
        logger.info(f"Token embeddings resized to: {new_vocab_size}")
        
        # Initialize FAISS index (moved here from __init__)
        self._initialize_faiss()
        # Compute embeddings after FAISS is initialized and moved
        self._compute_and_index_embeddings()
        
        # Try different ways to get embedding dimension
        try:
            # First try: from config
            embedding_dim = self.encoder.pretrained.config.dim
            logger.info("Got embedding dim from config")
        except AttributeError:
            try:
                # Second try: from word embeddings
                embedding_dim = self.encoder.pretrained.distilbert.embeddings.word_embeddings.embedding_dim
                logger.info("Got embedding dim from word embeddings")
            except AttributeError:
                try:
                    # Third try: from embeddings module
                    embedding_dim = self.encoder.pretrained.distilbert.embeddings.embedding_dim
                    logger.info("Got embedding dim from embeddings module")
                except AttributeError:
                    # Fallback to config value
                    embedding_dim = self.config.embedding_dim
                    logger.info("Using config embedding dim")
        
        vocab_size = len(self.tokenizer)
        
        logger.info(f"Encoder Embedding Dimension: {embedding_dim}")
        logger.info(f"Encoder Embedding Vocabulary Size: {vocab_size}")
        if vocab_size >= embedding_dim:
            logger.info("Encoder model built and embeddings resized successfully.")
        else:
            logger.error("Vocabulary size is less than embedding dimension.")
            raise ValueError("Vocabulary size is less than embedding dimension.")

    def _collect_responses(self, dialogues: List[dict]) -> Tuple[List[str], List[str]]:
        """Collect all unique responses from dialogues."""
        logger.info("Collecting responses from dialogues...")
        
        responses = []
        try:
            progress_bar = tqdm(dialogues, desc="Collecting assistant responses")
        except ImportError:
            progress_bar = dialogues
            logger.info("Progress bar disabled - continuing without visual progress")
        
        for dialogue in progress_bar:
            turns = dialogue.get('turns', [])
            for turn in turns:
                if turn.get('speaker') == 'assistant' and 'text' in turn:
                    responses.append(turn['text'].strip())

        # Remove duplicates
        unique_responses = list(set(responses))
        logger.info(f"Found {len(unique_responses)} unique responses.")
        
        return responses, unique_responses
    
    def _adjust_batch_size(self) -> None:
        """Dynamically adjust batch size based on GPU memory usage."""
        if self.memory_monitor.should_reduce_batch_size():
            new_size = max(self.min_batch_size, self.current_batch_size // 2)
            if new_size != self.current_batch_size:
                logger.info(f"Reducing batch size to {new_size} due to high memory usage")
                self.current_batch_size = new_size
                gc.collect()
                if tf.config.list_physical_devices('GPU'):
                    tf.keras.backend.clear_session()
        elif self.memory_monitor.can_increase_batch_size():
            new_size = min(self.max_batch_size, self.current_batch_size * 2)
            if new_size != self.current_batch_size:
                logger.info(f"Increasing batch size to {new_size}")
                self.current_batch_size = new_size
            
    def _initialize_faiss(self):
        """Initialize FAISS with safer GPU handling and memory monitoring."""
        logger.info("Initializing FAISS index...")
        
        # First, detect if we have GPU-enabled FAISS
        self.faiss_gpu = False
        self.gpu_resources = []
        
        try:
            if hasattr(faiss, 'get_num_gpus'):
                ngpus = faiss.get_num_gpus()
                if ngpus > 0:
                    # Configure GPU resources with memory limit
                    for i in range(ngpus):
                        res = faiss.StandardGpuResources()
                        # Set temp memory to 1/4 of total memory to avoid OOM
                        if self.memory_monitor.has_gpu:
                            stats = self.memory_monitor.get_memory_stats()
                            if stats:
                                temp_memory = int(stats.total * 0.25)  # 25% of total memory
                                res.setTempMemory(temp_memory)
                        self.gpu_resources.append(res)
                    self.faiss_gpu = True
                    logger.info(f"FAISS GPU resources initialized on {ngpus} GPUs")
            else:
                logger.info("Using CPU-only FAISS build")
                
        except Exception as e:
            logger.warning(f"Using CPU due to GPU initialization error: {e}")
        
        # TODO: figure out buf with faiss-gpu
        try:
            # Create appropriate index based on dataset size
            if len(self.unique_responses) < 1000:
                logger.info("Small dataset detected, using simple FlatIP index")
                self.index = faiss.IndexFlatIP(self.config.embedding_dim)
            else:
                # Use IVF index with dynamic number of clusters
                # nlist = min(
                #     25,  # max clusters
                #     max(int(math.sqrt(len(self.unique_responses))), 1)  # min 1 cluster
                # )
                # logger.info(f"Using IVF index with {nlist} clusters")
                
                # quantizer = faiss.IndexFlatIP(self.config.embedding_dim)
                # self.index = faiss.IndexIVFFlat(
                #     quantizer, 
                #     self.config.embedding_dim, 
                #     nlist, 
                #     faiss.METRIC_INNER_PRODUCT
                # )
                self.index = faiss.IndexFlatIP(self.config.embedding_dim)
            
            # # Move to GPU(s) if available
            # if self.faiss_gpu and self.gpu_resources:
            #     try:
            #         if len(self.gpu_resources) > 1:
            #             self.index = faiss.index_cpu_to_gpus_list(self.index, self.gpu_resources)
            #             logger.info("FAISS index distributed across multiple GPUs")
            #         else:
            #             self.index = faiss.index_cpu_to_gpu(self.gpu_resources[0], 0, self.index)
            #             logger.info("FAISS index moved to single GPU")
            #     except Exception as e:
            #         logger.warning(f"Failed to move index to GPU: {e}. Falling back to CPU")
            #         self.faiss_gpu = False
            
            # # Set search parameters for IVF index
            # if isinstance(self.index, faiss.IndexIVFFlat):
            #     self.index.nprobe = min(10, nlist)
                
        except Exception as e:
            logger.error(f"Error initializing FAISS: {e}")
            raise

    def encode_responses(
        self, 
        responses: List[str], 
        batch_size: int = 64
    ) -> tf.Tensor:
        """
        Encodes responses with more conservative memory management.
        """
        all_embeddings = []
        self.current_batch_size = batch_size
        
        if self.memory_monitor.has_gpu:
            batch_size = 128

        # Memory stats
        # if self.memory_monitor.has_gpu:
        #     initial_stats = self.memory_monitor.get_memory_stats()
        #     if initial_stats:
        #         logger.info("Initial GPU memory state:")
        #         logger.info(f"Total: {initial_stats.total / 1e9:.2f}GB")
        #         logger.info(f"Used: {initial_stats.used / 1e9:.2f}GB")
        #         logger.info(f"Free: {initial_stats.free / 1e9:.2f}GB")

        total_processed = 0
        
        with tqdm(total=len(responses), desc="Encoding responses") as pbar:
            while total_processed < len(responses):
                # Monitor memory and adjust batch size
                if self.memory_monitor.has_gpu:
                    gpu_usage = self.memory_monitor.get_memory_usage()
                    if gpu_usage > 0.8:  # Over 80% usage
                        self.current_batch_size = max(128, self.current_batch_size // 2)
                        logger.info(f"High GPU memory usage ({gpu_usage:.1%}), reducing batch size to {self.current_batch_size}")
                        gc.collect()
                        tf.keras.backend.clear_session()
                
                # Get batch
                end_idx = min(total_processed + self.current_batch_size, len(responses))
                batch_texts = responses[total_processed:end_idx]
                
                try:
                    # Tokenize
                    encodings = self.tokenizer(
                        batch_texts,
                        padding='max_length',
                        truncation=True,
                        max_length=self.config.max_context_token_limit,
                        return_tensors='tf'
                    )

                    # Encode
                    embeddings_batch = self.encoder(encodings['input_ids'], training=False)
                    
                    # Cast to float32
                    if embeddings_batch.dtype != tf.float32:
                        embeddings_batch = tf.cast(embeddings_batch, tf.float32)

                    # Store
                    all_embeddings.append(embeddings_batch)
                    
                    # Update progress
                    batch_processed = len(batch_texts)
                    total_processed += batch_processed
                    
                    # Update progress bar
                    if self.memory_monitor.has_gpu:
                        gpu_usage = self.memory_monitor.get_memory_usage()
                        pbar.set_postfix({
                            'GPU mem': f'{gpu_usage:.1%}',
                            'batch_size': self.current_batch_size
                        })
                    pbar.update(batch_processed)
                    
                    # Memory cleanup every 1000 samples
                    if total_processed % 1000 == 0:
                        gc.collect()
                        if tf.config.list_physical_devices('GPU'):
                            tf.keras.backend.clear_session()

                except tf.errors.ResourceExhaustedError:
                    logger.warning("GPU memory exhausted during encoding, reducing batch size")
                    self.current_batch_size = max(8, self.current_batch_size // 2)
                    continue
                    
                except Exception as e:
                    logger.error(f"Error during encoding: {str(e)}")
                    raise

        # Concatenate results
        #logger.info("Concatenating embeddings...")
        if len(all_embeddings) == 1:
            final_embeddings = all_embeddings[0]
        else:
            final_embeddings = tf.concat(all_embeddings, axis=0)
        
        return final_embeddings

    def _train_faiss_index(self, response_embeddings: np.ndarray) -> None:
        """Train FAISS index with better memory management and robust fallback mechanisms."""
        if self.index.is_trained:
            logger.info("Index already trained, skipping training phase")
            return

        logger.info("Starting FAISS index training...")
        
        try:
            # First attempt: Try training with smaller subset
            subset_size = min(5000, len(response_embeddings))  # Reduced from 10000
            logger.info(f"Using {subset_size} samples for initial training attempt")
            subset_idx = np.random.choice(len(response_embeddings), subset_size, replace=False)
            training_embeddings = response_embeddings[subset_idx].copy()  # Make a copy
            
            # Ensure contiguous memory layout
            training_embeddings = np.ascontiguousarray(training_embeddings)
                
            # Force cleanup before training
            gc.collect()
            if tf.config.list_physical_devices('GPU'):
                tf.keras.backend.clear_session()
                
            # Verify data properties
            logger.info(f"FAISS training data shape: {training_embeddings.shape}")
            logger.info(f"FAISS training data dtype: {training_embeddings.dtype}")
            
            logger.info("Starting initial training attempt...")
            self.index.train(training_embeddings)
            logger.info("Training completed successfully")
            
        except (RuntimeError, Exception) as e:
            logger.warning(f"Initial training attempt failed: {str(e)}")
            logger.info("Attempting fallback strategy...")
            
            try:
                # Move to CPU for more stable training
                if self.faiss_gpu:
                    logger.info("Moving index to CPU for fallback training")
                    cpu_index = faiss.index_gpu_to_cpu(self.index)
                else:
                    cpu_index = self.index
                
                # Create simpler index type if needed
                if isinstance(cpu_index, faiss.IndexIVFFlat):
                    logger.info("Creating simpler FlatL2 index for fallback")
                    cpu_index = faiss.IndexFlatL2(self.config.embedding_dim)
                
                # Use even smaller subset for CPU training
                subset_size = min(2000, len(response_embeddings))
                subset_idx = np.random.choice(len(response_embeddings), subset_size, replace=False)
                fallback_embeddings = response_embeddings[subset_idx].copy()
                
                # Ensure data is properly formatted
                if not fallback_embeddings.flags['C_CONTIGUOUS']:
                    fallback_embeddings = np.ascontiguousarray(fallback_embeddings)
                if fallback_embeddings.dtype != np.float32:
                    fallback_embeddings = fallback_embeddings.astype(np.float32)
                
                # Train on CPU
                logger.info("Training fallback index on CPU...")
                cpu_index.train(fallback_embeddings)
                
                # Move back to GPU if needed
                if self.faiss_gpu:
                    logger.info("Moving trained index back to GPU...")
                    if len(self.gpu_resources) > 1:
                        self.index = faiss.index_cpu_to_gpus_list(cpu_index, self.gpu_resources)
                    else:
                        self.index = faiss.index_cpu_to_gpu(self.gpu_resources[0], 0, cpu_index)
                else:
                    self.index = cpu_index
                    
                logger.info("Fallback training completed successfully")
                
            except Exception as e2:
                logger.error(f"Fallback training also failed: {str(e2)}")
                logger.warning("Creating basic brute-force index as last resort")
                
                try:
                    # Create basic brute-force index as last resort
                    dim = response_embeddings.shape[1]
                    basic_index = faiss.IndexFlatL2(dim)
                    
                    if self.faiss_gpu:
                        if len(self.gpu_resources) > 1:
                            self.index = faiss.index_cpu_to_gpus_list(basic_index, self.gpu_resources)
                        else:
                            self.index = faiss.index_cpu_to_gpu(self.gpu_resources[0], 0, basic_index)
                    else:
                        self.index = basic_index
                        
                    logger.info("Basic index created as fallback")
                    
                except Exception as e3:
                    logger.error(f"All training attempts failed: {str(e3)}")
                    raise RuntimeError("Unable to create working FAISS index")

    def _add_vectors_to_index(self, response_embeddings: np.ndarray) -> None:
        """Add vectors to FAISS index with enhanced memory management."""
        logger.info("Starting vector addition process...")
        
        # Even smaller batches
        initial_batch_size = 128
        min_batch_size = 32
        max_batch_size = 1024
        
        total_added = 0
        retry_count = 0
        max_retries = 5
        
        while total_added < len(response_embeddings):
            try:
                # Monitor memory
                if self.memory_monitor.has_gpu:
                    gpu_usage = self.memory_monitor.get_memory_usage()
                    #logger.info(f"GPU memory usage before batch: {gpu_usage:.1%}")
                    
                    # Force cleanup if memory usage is high
                    if gpu_usage > 0.7:  # Lower threshold to 70%
                        logger.info("High memory usage detected, forcing cleanup")
                        gc.collect()
                        tf.keras.backend.clear_session()
                
                # Get batch
                end_idx = min(total_added + initial_batch_size, len(response_embeddings))
                batch = response_embeddings[total_added:end_idx]
                
                # Add batch
                self.index.add(batch)
                
                # Update progress
                batch_size = len(batch)
                total_added += batch_size
                
                # Memory cleanup every few batches
                if total_added % (initial_batch_size * 5) == 0:
                    gc.collect()
                    if tf.config.list_physical_devices('GPU'):
                        tf.keras.backend.clear_session()
                
                # Gradually increase batch size
                if initial_batch_size < max_batch_size:
                    initial_batch_size = min(initial_batch_size + 25, max_batch_size)
                
            except Exception as e:
                logger.warning(f"Error adding batch: {str(e)}")
                retry_count += 1
                
                if retry_count > max_retries:
                    logger.error("Max retries exceeded.")
                    raise
                
                # Reduce batch size
                initial_batch_size = max(min_batch_size, initial_batch_size // 2)
                logger.info(f"Reducing batch size to {initial_batch_size} and retrying...")
                
                # Cleanup
                gc.collect()
                if tf.config.list_physical_devices('GPU'):
                    tf.keras.backend.clear_session()
                
                time.sleep(1)  # Brief pause before retry
        
        logger.info(f"Successfully added all {total_added} vectors to index")

    def _add_vectors_cpu_fallback(self, remaining_embeddings: np.ndarray, already_added: int = 0) -> None:
        """CPU fallback with extra safeguards and progress tracking."""
        logger.info(f"CPU Fallback: Adding {len(remaining_embeddings)} remaining vectors...")
        
        try:
            # Move index to CPU
            if self.faiss_gpu:
                logger.info("Moving index to CPU...")
                cpu_index = faiss.index_gpu_to_cpu(self.index)
            else:
                cpu_index = self.index
            
            # Add remaining vectors on CPU with very small batches
            batch_size = 128
            total_added = already_added
            
            for i in range(0, len(remaining_embeddings), batch_size):
                end_idx = min(i + batch_size, len(remaining_embeddings))
                batch = remaining_embeddings[i:end_idx]
                
                # Add batch
                cpu_index.add(batch)
                
                # Update progress
                total_added += len(batch)
                if i % (batch_size * 10) == 0:
                    logger.info(f"Added {total_added} vectors total "
                            f"({i}/{len(remaining_embeddings)} in current phase)")
                
                # Periodic cleanup
                if i % (batch_size * 20) == 0:
                    gc.collect()
            
            # Move back to GPU if needed
            if self.faiss_gpu:
                logger.info("Moving index back to GPU...")
                if len(self.gpu_resources) > 1:
                    self.index = faiss.index_cpu_to_gpus_list(cpu_index, self.gpu_resources)
                else:
                    self.index = faiss.index_cpu_to_gpu(self.gpu_resources[0], 0, cpu_index)
            else:
                self.index = cpu_index
                
            logger.info("CPU fallback completed successfully")
            
        except Exception as e:
            logger.error(f"Error during CPU fallback: {str(e)}")
            raise

    def _compute_and_index_embeddings(self):
        """Compute embeddings and build FAISS index with simpler handling."""
        logger.info("Computing embeddings and indexing with FAISS...")
        
        try:
            # Encode responses with memory monitoring
            logger.info("Encoding unique responses")
            response_embeddings = self.encode_responses(self.unique_responses)
            response_embeddings = response_embeddings.numpy()
            
            # Memory cleanup after encoding
            gc.collect()
            if tf.config.list_physical_devices('GPU'):
                tf.keras.backend.clear_session()
            
            # Ensure float32 and memory contiguous
            response_embeddings = response_embeddings.astype('float32')
            response_embeddings = np.ascontiguousarray(response_embeddings)
            
            # Log memory state before normalization
            if self.memory_monitor.has_gpu:
                stats = self.memory_monitor.get_memory_stats()
                if stats:
                    logger.info(f"GPU memory before normalization: {stats.used/1e9:.2f}GB used")
            
            # Normalize embeddings
            logger.info("Normalizing embeddings with FAISS")
            faiss.normalize_L2(response_embeddings)
            
            # Create and initialize simple FlatIP index
            dim = response_embeddings.shape[1]
            if self.faiss_gpu:
                cpu_index = faiss.IndexFlatIP(dim)
                if len(self.gpu_resources) > 1:
                    self.index = faiss.index_cpu_to_gpus_list(cpu_index, self.gpu_resources)
                else:
                    self.index = faiss.index_cpu_to_gpu(self.gpu_resources[0], 0, cpu_index)
            else:
                self.index = faiss.IndexFlatIP(dim)
            
            # Add vectors to index
            self._add_vectors_to_index(response_embeddings)
            
            # Store responses and embeddings
            self.response_pool = self.unique_responses
            self.response_embeddings = response_embeddings
            
            # Final memory cleanup
            gc.collect()
            if tf.config.list_physical_devices('GPU'):
                tf.keras.backend.clear_session()
            
            # Log final state
            logger.info(f"Successfully indexed {self.index.ntotal} responses")
            if self.memory_monitor.has_gpu:
                stats = self.memory_monitor.get_memory_stats()
                if stats:
                    logger.info(f"Final GPU memory usage: {stats.used/1e9:.2f}GB used")
            
            logger.info("Indexing completed successfully")
            
        except Exception as e:
            logger.error(f"Error during indexing: {e}")
            # Ensure cleanup even on error
            gc.collect()
            if tf.config.list_physical_devices('GPU'):
                tf.keras.backend.clear_session()
            raise

    def verify_faiss_index(self):
        """Verify that FAISS index matches the response pool."""
        indexed_size = self.index.ntotal
        pool_size = len(self.response_pool)
        logger.info(f"FAISS index size: {indexed_size}")
        logger.info(f"Response pool size: {pool_size}")
        if indexed_size != pool_size:
            logger.warning("Mismatch between FAISS index size and response pool size.")
        else:
            logger.info("FAISS index correctly matches the response pool.")

    def encode_query(self, query: str, context: Optional[List[Tuple[str, str]]] = None) -> tf.Tensor:
        """Encode a query with optional conversation context."""
        # Prepare query with context
        if context:
            context_str = ' '.join([
                f"{self.special_tokens['user']} {q} "
                f"{self.special_tokens['assistant']} {r}"
                for q, r in context[-self.config.max_context_turns:]
            ])
            query = f"{context_str} {self.special_tokens['user']} {query}"
        else:
            query = f"{self.special_tokens['user']} {query}"
        
        # Tokenize and encode
        encodings = self.tokenizer(
            [query],
            padding='max_length',
            truncation=True,
            max_length=self.config.max_context_token_limit,
            return_tensors='tf'
        )
        input_ids = encodings['input_ids']
        
        # Verify token IDs
        max_id = tf.reduce_max(input_ids).numpy()
        new_vocab_size = len(self.tokenizer)
        
        if max_id >= new_vocab_size:
            logger.error(f"Token ID {max_id} exceeds the vocabulary size {new_vocab_size}.")
            raise ValueError("Token ID exceeds vocabulary size.")
        
        # Get embeddings from the shared encoder
        return self.encoder(input_ids, training=False)

    def retrieve_responses_cross_encoder(
        self,
        query: str,
        top_k: int,
        reranker: Optional[CrossEncoderReranker] = None,
        summarizer: Optional[Summarizer] = None,
        summarize_threshold: int = 512  # Summarize over 512 tokens
    ) -> List[Tuple[str, float]]:
        """
        Retrieve top-k from FAISS, then re-rank them with a cross-encoder.
        Optionally summarize the user query if it's too long.
        """
        if reranker is None:
            reranker = self.reranker
        if summarizer is None:
            summarizer = self.summarizer
            
        # Optional summarization
        if summarizer and len(query.split()) > summarize_threshold:
            logger.info(f"Query is long. Summarizing before cross-encoder. Original length: {len(query.split())}")
            query = summarizer.summarize_text(query)
            logger.info(f"Summarized query: {query}")

        # 2) Dense retrieval
        dense_topk = self.retrieve_responses_faiss(query, top_k=top_k)  # [(resp, dense_score), ...]

        if not dense_topk:
            return []

        # 3) Cross-encoder rerank
        candidate_texts = [pair[0] for pair in dense_topk]
        cross_scores = reranker.rerank(query, candidate_texts, max_length=256)

        # Combine
        combined = [(text, score) for (text, _), score in zip(dense_topk, cross_scores)]
        # Sort descending by cross-encoder score
        combined.sort(key=lambda x: x[1], reverse=True)

        return combined
    
    def retrieve_responses_faiss(self, query: str, top_k: int = 5) -> List[Tuple[str, float]]:
        """Retrieve top-k responses using FAISS."""
        # Encode the query
        q_emb = self.encode_query(query)  # Shape: [1, embedding_dim]
        q_emb_np = q_emb.numpy().astype('float32')  # Ensure type match
        
        # Normalize the query embedding for cosine similarity
        faiss.normalize_L2(q_emb_np)
        
        # Search the FAISS index
        distances, indices = self.index.search(q_emb_np, top_k)
        
        # Map indices to responses and distances to similarities
        top_responses = []
        for i, idx in enumerate(indices[0]):
            if idx < len(self.response_pool):
                top_responses.append((self.response_pool[idx], float(distances[0][i])))
            else:
                logger.warning(f"FAISS returned invalid index {idx}. Skipping.")
        
        return top_responses
    
    def save_models(self, save_dir: Union[str, Path]):
        """Save models and configuration."""
        save_dir = Path(save_dir)
        save_dir.mkdir(parents=True, exist_ok=True)
        
        # Save config
        with open(save_dir / "config.json", "w") as f:
            json.dump(self.config.to_dict(), f, indent=2)
        
        # Save models
        self.encoder.pretrained.save_pretrained(save_dir / "shared_encoder")
        
        # Save tokenizer
        self.tokenizer.save_pretrained(save_dir / "tokenizer")
        
        logger.info(f"Models and tokenizer saved to {save_dir}.")
    
    @classmethod
    def load_models(cls, load_dir: Union[str, Path]) -> 'RetrievalChatbot':
        """Load saved models and configuration."""
        load_dir = Path(load_dir)
        
        # Load config
        with open(load_dir / "config.json", "r") as f:
            config = ChatbotConfig.from_dict(json.load(f))
        
        # Initialize chatbot
        chatbot = cls(config)
        
        # Load models
        chatbot.encoder.pretrained = TFAutoModel.from_pretrained(
            load_dir / "shared_encoder",
            config=config
        )
        
        # Load tokenizer
        chatbot.tokenizer = AutoTokenizer.from_pretrained(load_dir / "tokenizer")
        
        logger.info(f"Models and tokenizer loaded from {load_dir}.")
        return chatbot
    
    @staticmethod
    def load_training_data(data_path: Union[str, Path], debug_samples: Optional[int] = None) -> List[dict]:
        """
        Load training data from a JSON file.
        
        Args:
            data_path (Union[str, Path]): Path to the JSON file containing dialogues.
            debug_samples (Optional[int]): Number of samples to load for debugging.
        
        Returns:
            List[dict]: List of dialogue dictionaries.
        """
        logger.info(f"Loading training data from {data_path}...")
        data_path = Path(data_path)
        if not data_path.exists():
            logger.error(f"Data file {data_path} does not exist.")
            return []
        
        with open(data_path, 'r', encoding='utf-8') as f:
            dialogues = json.load(f)
        
        if debug_samples is not None:
            dialogues = dialogues[:debug_samples]
            logger.info(f"Debug mode: Limited to {debug_samples} dialogues")
        
        logger.info(f"Loaded {len(dialogues)} dialogues.")
        return dialogues
    
    def train_streaming(
        self,
        dialogues: List[dict],
        epochs: int = 20,
        batch_size: int = 16,
        validation_split: float = 0.2,
        checkpoint_dir: str = "checkpoints/",
        use_lr_schedule: bool = True,
        peak_lr: float = 2e-5,
        warmup_steps_ratio: float = 0.1,
        early_stopping_patience: int = 3,
        min_delta: float = 1e-4,
        neg_samples: int = 1
    ) -> None:
        """Streaming training with tf.data pipeline."""
        logger.info("Starting streaming training pipeline with tf.data...")

        # Initialize TFDataPipeline (replaces StreamingDataPipeline)
        dataset_preparer = TFDataPipeline(
            embedding_batch_size=self.config.embedding_batch_size,
            tokenizer=self.tokenizer,
            encoder=self.encoder,
            index=self.index,  # Pass CPU version of FAISS index
            response_pool=self.response_pool,
            max_length=self.config.max_context_token_limit,
            neg_samples=neg_samples
        )

        # Calculate total steps for learning rate schedule
        total_pairs = dataset_preparer.estimate_total_pairs(dialogues)
        train_size = int(total_pairs * (1 - validation_split))
        val_size = int(total_pairs * validation_split)
        steps_per_epoch = int(math.ceil(train_size / batch_size))
        val_steps = int(math.ceil(val_size / batch_size))
        total_steps = steps_per_epoch * epochs

        logger.info(f"Total pairs: {total_pairs}")
        logger.info(f"Training pairs: {train_size}")
        logger.info(f"Validation pairs: {val_size}")
        logger.info(f"Steps per epoch: {steps_per_epoch}")
        logger.info(f"Validation steps: {val_steps}")
        logger.info(f"Total steps: {total_steps}")

        # Set up optimizer with learning rate schedule
        if use_lr_schedule:
            warmup_steps = int(total_steps * warmup_steps_ratio)
            lr_schedule = self._get_lr_schedule(
                total_steps=total_steps,
                peak_lr=peak_lr,
                warmup_steps=warmup_steps
            )
            self.optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)
            logger.info("Using custom learning rate schedule.")
        else:
            self.optimizer = tf.keras.optimizers.Adam(learning_rate=peak_lr)
            logger.info("Using fixed learning rate.")

        # Initialize checkpoint manager
        checkpoint = tf.train.Checkpoint(optimizer=self.optimizer, model=self.encoder)
        manager = tf.train.CheckpointManager(checkpoint, checkpoint_dir, max_to_keep=3)

        # Setup TensorBoard
        log_dir = Path(checkpoint_dir) / "tensorboard_logs"
        log_dir.mkdir(parents=True, exist_ok=True)
        current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
        train_log_dir = str(log_dir / f"train_{current_time}")
        val_log_dir = str(log_dir / f"val_{current_time}")
        train_summary_writer = tf.summary.create_file_writer(train_log_dir)
        val_summary_writer = tf.summary.create_file_writer(val_log_dir)
        logger.info(f"TensorBoard logs will be saved in {log_dir}")

        # Create training and validation datasets
        train_dataset = dataset_preparer.get_tf_dataset(dialogues, batch_size).take(train_size)
        val_dataset = dataset_preparer.get_tf_dataset(dialogues, batch_size).skip(train_size).take(val_size)

        # Training loop
        best_val_loss = float("inf")
        epochs_no_improve = 0

        for epoch in range(1, epochs + 1):
            # --- Training Phase ---
            epoch_loss_avg = tf.keras.metrics.Mean()
            batches_processed = 0

            try:
                train_pbar = tqdm(total=steps_per_epoch, desc=f"Training Epoch {epoch}", unit="batch")
                is_tqdm_train = True
            except ImportError:
                train_pbar = None
                is_tqdm_train = False
                logger.info("Training progress bar disabled")

            for q_batch, p_batch, n_batch in train_dataset:
                #p_batch = p_n_batch[:, 0, :] # Extract positive from (positive, negative) pair
                loss = self.train_step(q_batch, p_batch, n_batch)
                epoch_loss_avg(loss)
                batches_processed += 1

                # Log to TensorBoard
                with train_summary_writer.as_default():
                    tf.summary.scalar("loss", loss, step=(epoch - 1) * steps_per_epoch + batches_processed)

                # Update progress bar
                if use_lr_schedule:
                    current_lr = float(lr_schedule(self.optimizer.iterations))
                else:
                    current_lr = float(self.optimizer.learning_rate.numpy())

                if is_tqdm_train:
                    train_pbar.update(1)
                    train_pbar.set_postfix({
                        "loss": f"{loss.numpy():.4f}",
                        "lr": f"{current_lr:.2e}",
                        "batches": f"{batches_processed}/{steps_per_epoch}"
                    })
                
                # Memory cleanup
                gc.collect()

                if batches_processed >= steps_per_epoch:
                    break

            if is_tqdm_train and train_pbar:
                train_pbar.close()

            # --- Validation Phase ---
            val_loss_avg = tf.keras.metrics.Mean()
            val_batches_processed = 0

            try:
                val_pbar = tqdm(total=val_steps, desc="Validation", unit="batch")
                is_tqdm_val = True
            except ImportError:
                val_pbar = None
                is_tqdm_val = False
                logger.info("Validation progress bar disabled")

            for q_batch, p_batch, n_batch in val_dataset:
                #p_batch = p_n_batch[:, 0, :] # Extract positive from (positive, negative) pair
                val_loss = self.validation_step(q_batch, p_batch, n_batch)
                val_loss_avg(val_loss)
                val_batches_processed += 1

                if is_tqdm_val:
                    val_pbar.update(1)
                    val_pbar.set_postfix({
                        "val_loss": f"{val_loss.numpy():.4f}",
                        "batches": f"{val_batches_processed}/{val_steps}"
                    })
                    
                # Memory cleanup
                gc.collect()


                if val_batches_processed >= val_steps:
                    break

            if is_tqdm_val and val_pbar:
                val_pbar.close()

            # End of epoch: compute final epoch stats, log, and save checkpoint
            train_loss = epoch_loss_avg.result().numpy()
            val_loss = val_loss_avg.result().numpy()
            logger.info(f"Epoch {epoch} Complete: Train Loss={train_loss:.4f}, Val Loss={val_loss:.4f}")

            # Log epoch metrics
            with train_summary_writer.as_default():
                tf.summary.scalar("epoch_loss", train_loss, step=epoch)
            with val_summary_writer.as_default():
                tf.summary.scalar("val_loss", val_loss, step=epoch)

            # Save checkpoint
            manager.save()

            # Store metrics in history
            self.history['train_loss'].append(train_loss)
            self.history['val_loss'].append(val_loss)

            if use_lr_schedule:
                current_lr = float(lr_schedule(self.optimizer.iterations))
            else:
                current_lr = float(self.optimizer.learning_rate.numpy())

            self.history.setdefault('learning_rate', []).append(current_lr)

            # Early stopping logic
            if val_loss < best_val_loss - min_delta:
                best_val_loss = val_loss
                epochs_no_improve = 0
                logger.info(f"Validation loss improved to {val_loss:.4f}. Reset patience.")
            else:
                epochs_no_improve += 1
                logger.info(f"No improvement this epoch. Patience: {epochs_no_improve}/{early_stopping_patience}")
                if epochs_no_improve >= early_stopping_patience:
                    logger.info("Early stopping triggered.")
                    break

        logger.info("Streaming training completed!")


    @tf.function
    def train_step(
        self, 
        q_batch: tf.Tensor, 
        p_batch: tf.Tensor, 
        n_batch: tf.Tensor, 
        attention_mask: Optional[tf.Tensor] = None
    ) -> tf.Tensor:
        """
        Single training step that uses queries, positives, and negatives in a 
        contrastive/InfoNCE style. The label is always 0 (the positive) vs. 
        the negative alternatives.
        """
        with tf.GradientTape() as tape:
            # Encode queries
            q_enc = self.encoder(q_batch, training=True)  # [batch_size, embed_dim]

            # Encode positives
            p_enc = self.encoder(p_batch, training=True)  # [batch_size, embed_dim]

            # Encode negatives
            # n_batch: [batch_size, neg_samples, max_length]
            shape = tf.shape(n_batch)
            bs = shape[0]
            neg_samples = shape[1]

            # Flatten negatives to feed them in one pass:
            # => [batch_size * neg_samples, max_length]
            n_batch_flat = tf.reshape(n_batch, [bs * neg_samples, shape[2]])
            n_enc_flat = self.encoder(n_batch_flat, training=True)  # [bs*neg_samples, embed_dim]

            # Reshape back => [batch_size, neg_samples, embed_dim]
            n_enc = tf.reshape(n_enc_flat, [bs, neg_samples, -1])

            # Combine the positive embedding and negative embeddings along dim=1
            # => shape [batch_size, 1 + neg_samples, embed_dim]
            # The first column is the positive; subsequent columns are negatives
            combined_p_n = tf.concat(
                [tf.expand_dims(p_enc, axis=1), n_enc], 
                axis=1
            )  # [bs, (1+neg_samples), embed_dim]

            # Now compute scores: dot product of q_enc with each column in combined_p_n
            # We'll use `tf.einsum` to handle the batch dimension properly
            # dot_products => shape [batch_size, (1+neg_samples)]
            dot_products = tf.einsum('bd,bkd->bk', q_enc, combined_p_n)

            # The label for each row is 0 (the first column is the correct/positive)
            labels = tf.zeros([bs], dtype=tf.int32)

            # Cross-entropy over the [batch_size, 1+neg_samples] scores
            loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
                labels=labels,
                logits=dot_products
            )
            loss = tf.reduce_mean(loss)

            # If there's an attention_mask you want to apply (less common in this scenario),
            # you could do something like:
            if attention_mask is not None:
                loss = loss * attention_mask
                loss = tf.reduce_sum(loss) / tf.reduce_sum(attention_mask)

        # Apply gradients
        gradients = tape.gradient(loss, self.encoder.trainable_variables)
        self.optimizer.apply_gradients(zip(gradients, self.encoder.trainable_variables))
        return loss

    @tf.function
    def validation_step(
        self, 
        q_batch: tf.Tensor, 
        p_batch: tf.Tensor, 
        n_batch: tf.Tensor, 
        attention_mask: Optional[tf.Tensor] = None
    ) -> tf.Tensor:
        """
        Single validation step with queries, positives, and negatives. 
        Uses the same loss calculation as train_step, but `training=False`.
        """
        q_enc = self.encoder(q_batch, training=False)
        p_enc = self.encoder(p_batch, training=False)

        shape = tf.shape(n_batch)
        bs = shape[0]
        neg_samples = shape[1]

        n_batch_flat = tf.reshape(n_batch, [bs * neg_samples, shape[2]])
        n_enc_flat = self.encoder(n_batch_flat, training=False)
        n_enc = tf.reshape(n_enc_flat, [bs, neg_samples, -1])

        combined_p_n = tf.concat(
            [tf.expand_dims(p_enc, axis=1), n_enc],
            axis=1
        )

        dot_products = tf.einsum('bd,bkd->bk', q_enc, combined_p_n)
        labels = tf.zeros([bs], dtype=tf.int32)

        loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
            labels=labels,
            logits=dot_products
        )
        loss = tf.reduce_mean(loss)

        if attention_mask is not None:
            loss = loss * attention_mask
            loss = tf.reduce_sum(loss) / tf.reduce_sum(attention_mask)

        return loss
    
    def _get_lr_schedule(
        self,
        total_steps: int,
        peak_lr: float,
        warmup_steps: int
    ) -> tf.keras.optimizers.schedules.LearningRateSchedule:
        """Create a custom learning rate schedule with warmup and cosine decay."""
        class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
            def __init__(
                self,
                total_steps: int,
                peak_lr: float,
                warmup_steps: int
            ):
                super().__init__()
                self.total_steps = tf.cast(total_steps, tf.float32)
                self.peak_lr = tf.cast(peak_lr, tf.float32)
                
                # Adjust warmup_steps to not exceed half of total_steps
                adjusted_warmup_steps = min(warmup_steps, max(1, total_steps // 10))
                self.warmup_steps = tf.cast(adjusted_warmup_steps, tf.float32)
                
                # Calculate and store constants
                self.initial_lr = self.peak_lr * 0.1  # Start at 10% of peak
                self.min_lr = self.peak_lr * 0.01     # Minimum 1% of peak
                
                logger.info(f"Learning rate schedule initialized:")
                logger.info(f"  Initial LR: {float(self.initial_lr):.6f}")
                logger.info(f"  Peak LR: {float(self.peak_lr):.6f}")
                logger.info(f"  Min LR: {float(self.min_lr):.6f}")
                logger.info(f"  Warmup steps: {int(self.warmup_steps)}")
                logger.info(f"  Total steps: {int(self.total_steps)}")
            
            def __call__(self, step):
                step = tf.cast(step, tf.float32)
                
                # Warmup phase
                warmup_factor = tf.minimum(1.0, step / self.warmup_steps)
                warmup_lr = self.initial_lr + (self.peak_lr - self.initial_lr) * warmup_factor
                
                # Decay phase
                decay_steps = tf.maximum(1.0, self.total_steps - self.warmup_steps)
                decay_factor = (step - self.warmup_steps) / decay_steps
                decay_factor = tf.minimum(tf.maximum(0.0, decay_factor), 1.0)  # Clip to [0,1]
                
                cosine_decay = 0.5 * (1.0 + tf.cos(tf.constant(math.pi) * decay_factor))
                decay_lr = self.min_lr + (self.peak_lr - self.min_lr) * cosine_decay
                
                # Choose between warmup and decay
                final_lr = tf.where(step < self.warmup_steps, warmup_lr, decay_lr)
                
                # Ensure learning rate is valid
                final_lr = tf.maximum(self.min_lr, final_lr)
                final_lr = tf.where(tf.math.is_finite(final_lr), final_lr, self.min_lr)
                
                return final_lr
            
            def get_config(self):
                return {
                    "total_steps": self.total_steps,
                    "peak_lr": self.peak_lr,
                    "warmup_steps": self.warmup_steps,
                }
        
        return CustomSchedule(total_steps, peak_lr, warmup_steps)

    def _cosine_similarity(self, emb1: np.ndarray, emb2: np.ndarray) -> np.ndarray:
        """Compute cosine similarity between two numpy arrays."""
        normalized_emb1 = emb1 / np.linalg.norm(emb1, axis=1, keepdims=True)
        normalized_emb2 = emb2 / np.linalg.norm(emb2, axis=1, keepdims=True)
        return np.dot(normalized_emb1, normalized_emb2.T)

    def chat(
        self,
        query: str,
        conversation_history: Optional[List[Tuple[str, str]]] = None,
        quality_checker: Optional['ResponseQualityChecker'] = None,
        top_k: int = 5,
    ) -> Tuple[str, List[Tuple[str, float]], Dict[str, Any]]:
        """
        Example chat method that always uses cross-encoder re-ranking 
        if self.reranker is available.
        """
        @self.run_on_device
        def get_response(self_arg, query_arg):  # Add parameters that match decorator's expectations
            # 1) Build conversation context string
            conversation_str = self_arg._build_conversation_context(query_arg, conversation_history)
            
            # 2) Retrieve + cross-encoder re-rank
            results = self_arg.retrieve_responses_cross_encoder(
                query=conversation_str,
                top_k=top_k,
                reranker=self_arg.reranker,
                summarizer=self_arg.summarizer,
                summarize_threshold=512
            )

            # 3) Handle empty or confidence
            if not results:
                return (
                    "I'm sorry, but I couldn't find a relevant response.",
                    [],
                    {}
                )

            if quality_checker:
                metrics = quality_checker.check_response_quality(query_arg, results)
                if not metrics.get('is_confident', False):
                    return (
                        "I need more information to provide a good answer. Could you please clarify?",
                        results,
                        metrics
                    )
                return results[0][0], results, metrics
            
            return results[0][0], results, {}
        
        return get_response(self, query)

    def _build_conversation_context(
        self, 
        query: str, 
        conversation_history: Optional[List[Tuple[str, str]]]
    ) -> str:
        """Build conversation context with better memory management."""
        if not conversation_history:
            return f"{self.special_tokens['user']} {query}"
            
        conversation_parts = []
        for user_txt, assistant_txt in conversation_history:
            conversation_parts.extend([
                f"{self.special_tokens['user']} {user_txt}",
                f"{self.special_tokens['assistant']} {assistant_txt}"
            ])
            
        conversation_parts.append(f"{self.special_tokens['user']} {query}")
        return "\n".join(conversation_parts)

class TFDataPipeline:
    def __init__(
        self,
        embedding_batch_size,
        tokenizer,
        encoder,
        index,
        response_pool,
        max_length: int,
        neg_samples: int,
    ):
        self.embedding_batch_size = embedding_batch_size
        self.tokenizer = tokenizer
        self.encoder = encoder
        self.index = index  # CPU version of the index
        self.response_pool = response_pool
        self.max_length = max_length
        self.neg_samples = neg_samples
        self.embedding_batch_size = 16 if len(response_pool) < 100 else 64
        self.search_batch_size = 8 if len(response_pool) < 100 else 32
        self.max_batch_size = 32 if len(response_pool) < 100 else 256
        self.memory_monitor = GPUMemoryMonitor()
        self.max_retries = 3

        # In-memory cache for embeddings
        self.query_embeddings_cache = {}

    def _extract_pairs_from_dialogue(self, dialogue: dict) -> List[Tuple[str, str]]:
        """Extract query-response pairs from a dialogue."""
        pairs = []
        turns = dialogue.get('turns', [])
        
        for i in range(len(turns) - 1):
            current_turn = turns[i]
            next_turn = turns[i+1]
            
            if (current_turn.get('speaker') == 'user' and
                next_turn.get('speaker') == 'assistant' and
                'text' in current_turn and 
                'text' in next_turn):
                
                query = current_turn['text'].strip()
                positive = next_turn['text'].strip()
                pairs.append((query, positive))
                
        return pairs

    def estimate_total_pairs(self, dialogues: List[dict]) -> int:
        """Estimate total number of training pairs including hard negatives."""
        base_pairs = sum(
            len([
                1 for i in range(len(d.get('turns', [])) - 1)
                if (d['turns'][i].get('speaker') == 'user' and
                    d['turns'][i+1].get('speaker') == 'assistant')
            ])
            for d in dialogues
        )
        # Account for hard negatives
        return base_pairs * (1 + self.neg_samples)
    
    def _find_hard_negatives_batch(self, queries: List[str], positives: List[str]) -> List[List[str]]:
        """Find hard negatives for a batch of queries with error handling and retries."""
        retry_count = 0
        total_responses = len(self.response_pool)

        while retry_count < self.max_retries:
            try:
                query_embeddings = np.vstack([
                    self.query_embeddings_cache[q] for q in queries
                ]).astype(np.float32)

                query_embeddings = np.ascontiguousarray(query_embeddings)
                faiss.normalize_L2(query_embeddings)

                k = 1  # TODO: try higher k for better results
                #logger.debug(f"Searching with k={k} among {total_responses} responses")

                distances, indices = self.index.search(query_embeddings, k)

                all_negatives = []
                for query_indices, query, positive in zip(indices, queries, positives):
                    negatives = []
                    positive_strip = positive.strip()
                    seen = {positive_strip}

                    for idx in query_indices:
                        if idx >= 0 and idx < total_responses:
                            candidate = self.response_pool[idx].strip()
                            if candidate and candidate not in seen:
                                seen.add(candidate)
                                negatives.append(candidate)
                                if len(negatives) >= self.neg_samples:
                                    break

                    # Pad with a special empty negative if necessary
                    while len(negatives) < self.neg_samples:
                        negatives.append("<EMPTY_NEGATIVE>")  # Use a special token

                    all_negatives.append(negatives)

                return all_negatives

            except Exception as e:
                retry_count += 1
                logger.warning(f"Hard negative search attempt {retry_count} failed: {e}")
                if retry_count == self.max_retries:
                    logger.error("Max retries reached for hard negative search")
                    return [["<EMPTY_NEGATIVE>"] * self.neg_samples for _ in queries]  # Return empty negatives for all queries
                gc.collect()
                if tf.config.list_physical_devices('GPU'):
                    tf.keras.backend.clear_session()

    def _tokenize_negatives_tf(self, negatives):
        """Tokenizes negatives using tf.py_function."""
        # Handle the case where negatives is an empty tensor
        if tf.size(negatives) == 0:
            return tf.zeros([0, self.neg_samples, self.max_length], dtype=tf.int32)

        # Convert EagerTensor to a list of strings
        negatives_list = []
        for neg_list in negatives.numpy():
            decoded_negs = [neg.decode("utf-8") for neg in neg_list if neg]  # Filter out empty strings
            negatives_list.append(decoded_negs)

        # Flatten the list of lists
        flattened_negatives = [neg for sublist in negatives_list for neg in sublist]

        # Tokenize the flattened negatives
        if flattened_negatives:
            n_tokens = self.tokenizer(
                flattened_negatives,
                padding='max_length',
                truncation=True,
                max_length=self.max_length,
                return_tensors='tf'
            )
            # Reshape the tokens
            n_tokens_reshaped = tf.reshape(n_tokens['input_ids'], [-1, self.neg_samples, self.max_length])
            return n_tokens_reshaped
        else:
            return tf.zeros([0, self.neg_samples, self.max_length], dtype=tf.int32)
        
    def _compute_embeddings(self, queries: List[str]) -> None:
        """Computes and caches embeddings for new queries."""
        new_queries = [q for q in queries if q not in self.query_embeddings_cache]
        if not new_queries:
            return  # All queries already cached

        new_embeddings = []
        for i in range(0, len(new_queries), self.embedding_batch_size):
            batch_queries = new_queries[i:i + self.embedding_batch_size]
            
            encoded = self.tokenizer(
                batch_queries,
                padding=True,
                truncation=True,
                max_length=self.max_length,
                return_tensors='tf'
            )

            # Compute embeddings on CPU
            with tf.device('/CPU:0'):
                batch_embeddings = self.encoder(encoded['input_ids'], training=False).numpy()

            new_embeddings.extend(batch_embeddings)

        # Update cache with new embeddings
        for query, emb in zip(new_queries, new_embeddings):
            self.query_embeddings_cache[query] = emb

    def data_generator(self, dialogues: List[dict]) -> Generator[Tuple[str, str, List[str]], None, None]:
        """
        Generates training examples: (query, positive, hard_negatives).
        Wrapped the outer loop with tqdm for progress tracking.
        """
        total_dialogues = len(dialogues)
        logger.debug(f"Total dialogues to process: {total_dialogues}")

        # Initialize tqdm progress bar
        with tqdm(total=total_dialogues, desc="Processing Dialogues", unit="dialogue") as pbar:
            for dialogue in dialogues:
                pairs = self._extract_pairs_from_dialogue(dialogue)
                for query, positive in pairs:
                    # Ensure embeddings are computed, find hard negatives, etc.
                    self._compute_embeddings([query])
                    hard_negatives = self._find_hard_negatives_batch([query], [positive])[0]
                    yield (query, positive, hard_negatives)
                pbar.update(1)
    
    def _prepare_batch(self, queries: tf.Tensor, positives: tf.Tensor, negatives: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
        """Prepares a batch of data for training."""

        # Convert EagerTensors to lists of strings
        queries_list = [query.decode("utf-8") for query in queries.numpy()]
        positives_list = [pos.decode("utf-8") for pos in positives.numpy()]

        # Tokenize queries and positives
        q_tokens = self.tokenizer(queries_list, padding='max_length', truncation=True, max_length=self.max_length, return_tensors='tf')
        p_tokens = self.tokenizer(positives_list, padding='max_length', truncation=True, max_length=self.max_length, return_tensors='tf')

        # Decode negatives and ensure they are lists of strings
        negatives_list = []
        for neg_list in negatives.numpy():
            decoded_negs = [neg.decode("utf-8") for neg in neg_list if neg]  # Filter out empty strings
            negatives_list.append(decoded_negs)

        # Flatten negatives for tokenization if there are any valid negatives
        flattened_negatives = [neg for sublist in negatives_list for neg in sublist if neg]

        # Tokenize negatives if there are any
        n_tokens_reshaped = None
        if flattened_negatives:
            n_tokens = self.tokenizer(flattened_negatives, padding='max_length', truncation=True, max_length=self.max_length, return_tensors='tf')

            # Reshape n_tokens to match the expected shape based on the number of negatives per query
            # This part may need adjustment if the number of negatives varies per query
            n_tokens_reshaped = tf.reshape(n_tokens['input_ids'], [len(queries_list), -1, self.max_length])
        else:
            # Create a placeholder tensor for the case where there are no negatives
            n_tokens_reshaped = tf.zeros([len(queries_list), 0, self.max_length], dtype=tf.int32)

        # Ensure n_tokens_reshaped has a consistent shape even when there are no negatives
        # Adjust shape to [batch_size, num_neg_samples, max_length]
        if n_tokens_reshaped.shape[1] != self.neg_samples:
            # Pad or truncate the second dimension to match neg_samples
            padding = tf.zeros([len(queries_list), tf.maximum(0, self.neg_samples - n_tokens_reshaped.shape[1]), self.max_length], dtype=tf.int32)
            n_tokens_reshaped = tf.concat([n_tokens_reshaped, padding], axis=1)
            n_tokens_reshaped = n_tokens_reshaped[:, :self.neg_samples, :]

        # Concatenate the positive and negative examples along the 'neg_samples' dimension
        combined_p_n_tokens = tf.concat([tf.expand_dims(p_tokens['input_ids'], axis=1), n_tokens_reshaped], axis=1)

        return q_tokens['input_ids'], combined_p_n_tokens

    def get_tf_dataset(self, dialogues: List[dict], batch_size: int) -> tf.data.Dataset:
        """
        Creates a tf.data.Dataset for streaming training that yields
        (input_ids_query, input_ids_positive, input_ids_negatives).
        """
        # 1) Start with a generator dataset
        dataset = tf.data.Dataset.from_generator(
            lambda: self.data_generator(dialogues),
            output_signature=(
                tf.TensorSpec(shape=(), dtype=tf.string),        # Query (single string)
                tf.TensorSpec(shape=(), dtype=tf.string),        # Positive (single string)
                tf.TensorSpec(shape=(None,), dtype=tf.string)    # Hard Negatives (list of strings)
            )
        )
        
        # 2) Batch the raw strings
        dataset = dataset.batch(batch_size)

        # 3) Now map them through a tokenize step (via py_function)
        dataset = dataset.map(
            lambda q, p, n: self._tokenize_triple(q, p, n),
            num_parallel_calls=1 #tf.data.AUTOTUNE
        )

        dataset = dataset.prefetch(tf.data.AUTOTUNE)
        return dataset
    
    def _tokenize_triple(
        self, 
        q: tf.Tensor, 
        p: tf.Tensor, 
        n: tf.Tensor
    ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
        """
        Wraps a Python function via tf.py_function to convert tf.Tensors of strings 
        -> Python lists of strings -> HF tokenizer -> Tensors of IDs.
        
        q is shape [batch_size], p is shape [batch_size], 
        n is shape [batch_size, neg_samples] (i.e., each row is a list of negatives).
        """
        # Use tf.py_function with limited parallelism
        q_ids, p_ids, n_ids = tf.py_function(
            func=self._tokenize_triple_py,
            inp=[q, p, n, tf.constant(self.max_length), tf.constant(self.neg_samples)],
            Tout=[tf.int32, tf.int32, tf.int32]
        )

        # Manually set shape information
        q_ids.set_shape([None, self.max_length])                # [batch_size, max_length]
        p_ids.set_shape([None, self.max_length])                # [batch_size, max_length]
        n_ids.set_shape([None, self.neg_samples, self.max_length])  # [batch_size, neg_samples, max_length]

        return q_ids, p_ids, n_ids
    # def _tokenize_triple(
    #     self, 
    #     q: tf.Tensor, 
    #     p: tf.Tensor, 
    #     n: tf.Tensor
    # ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
    #     """
    #     Wraps a Python function via tf.py_function to convert tf.Tensors of strings 
    #     -> Python lists of strings -> HF tokenizer -> Tensors of IDs.
        
    #     q is shape [batch_size], p is shape [batch_size], 
    #     n is shape [batch_size, None] (i.e. each row is a variable number of negatives).
    #     """
    #     # Use tf.py_function
    #     # We pass in self.max_length as well, so we can do it in one shot.
    #     q_ids, p_ids, n_ids = tf.py_function(
    #         func=self._tokenize_triple_py,
    #         inp=[q, p, n, tf.constant(self.max_length), tf.constant(self.neg_samples)],
    #         Tout=[tf.int32, tf.int32, tf.int32]
    #     )

    #     # We must manually set shape information so that TF data pipeline knows the dimensions
    #     q_ids.set_shape([None, self.max_length])                # [batch_size, max_length]
    #     p_ids.set_shape([None, self.max_length])                # [batch_size, max_length]
    #     n_ids.set_shape([None, self.neg_samples, self.max_length])  
    #     # The negative dimension is set to `self.neg_samples` for consistency.

    #     return q_ids, p_ids, n_ids

    def _tokenize_triple_py(
        self, 
        q: tf.Tensor, 
        p: tf.Tensor, 
        n: tf.Tensor,
        max_len: tf.Tensor,
        neg_samples: tf.Tensor
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """
        Python function that:
        - Decodes each tf.string Tensor to a Python list of strings
        - Calls the HF tokenizer
        - Reshapes negatives
        - Returns np.array of int32s for (q_ids, p_ids, n_ids).
        
        q: shape [batch_size], p: shape [batch_size]
        n: shape [batch_size, neg_samples]
        max_len: scalar int
        neg_samples: scalar int
        """
        max_len = int(max_len.numpy())       # Convert to Python int
        neg_samples = int(neg_samples.numpy())

        # 1) Convert Tensors -> Python lists of strings
        q_list = [q_i.decode("utf-8") for q_i in q.numpy()]  # shape [batch_size]
        p_list = [p_i.decode("utf-8") for p_i in p.numpy()]  # shape [batch_size]

        # shape [batch_size, neg_samples], decode each row
        n_list = []
        for row in n.numpy():
            # row is shape [neg_samples], each is a tf.string
            decoded = [neg.decode("utf-8") for neg in row]
            n_list.append(decoded)

        # 2) Tokenize queries & positives
        q_enc = self.tokenizer(
            q_list,
            padding="max_length",
            truncation=True,
            max_length=max_len,
            return_tensors="np"
        )
        p_enc = self.tokenizer(
            p_list,
            padding="max_length",
            truncation=True,
            max_length=max_len,
            return_tensors="np"
        )

        # 3) Tokenize negatives
        # Flatten [batch_size, neg_samples] -> single list
        flattened_negatives = [neg for row in n_list for neg in row]
        if len(flattened_negatives) == 0:
            # No negatives at all: return a zero array
            n_ids = np.zeros((len(q_list), neg_samples, max_len), dtype=np.int32)
        else:
            n_enc = self.tokenizer(
                flattened_negatives,
                padding="max_length",
                truncation=True,
                max_length=max_len,
                return_tensors="np"
            )
            # shape [batch_size * neg_samples, max_len]
            n_input_ids = n_enc["input_ids"]

            # We want to reshape to [batch_size, neg_samples, max_len]
            # Handle cases where there might be fewer negatives
            batch_size = len(q_list)
            n_ids_list = []
            for i in range(batch_size):
                start_idx = i * neg_samples
                end_idx = start_idx + neg_samples
                row_negs = n_input_ids[start_idx:end_idx]

                # If fewer negatives, pad with zeros
                if row_negs.shape[0] < neg_samples:
                    deficit = neg_samples - row_negs.shape[0]
                    pad_arr = np.zeros((deficit, max_len), dtype=np.int32)
                    row_negs = np.concatenate([row_negs, pad_arr], axis=0)

                n_ids_list.append(row_negs)

            # stack them -> shape [batch_size, neg_samples, max_len]
            n_ids = np.stack(n_ids_list, axis=0)

        # 4) Return as np.int32 arrays
        q_ids = q_enc["input_ids"].astype(np.int32)  # shape [batch_size, max_len]
        p_ids = p_enc["input_ids"].astype(np.int32)  # shape [batch_size, max_len]
        n_ids = n_ids.astype(np.int32)               # shape [batch_size, neg_samples, max_len]

        return q_ids, p_ids, n_ids
    # def _tokenize_triple_py(
    #     self, 
    #     q: tf.Tensor, 
    #     p: tf.Tensor, 
    #     n: tf.Tensor,
    #     max_len: tf.Tensor,
    #     neg_samples: tf.Tensor
    # ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
    #     """
    #     Python function that:
    #      - Decodes each tf.string Tensor to a Python list of strings
    #      - Calls the HF tokenizer
    #      - Reshapes negatives
    #      - Returns np.array of int32s for (q_ids, p_ids, n_ids).
        
    #     q: shape [batch_size], p: shape [batch_size]
    #     n: shape [batch_size, None]
    #     max_len: scalar int
    #     neg_samples: scalar int
    #     """
    #     max_len = int(max_len.numpy())       # convert to python int
    #     neg_samples = int(neg_samples.numpy())

    #     # 1) Convert Tensors -> Python lists of strings
    #     q_list = [q_i.decode("utf-8") for q_i in q.numpy()]  # shape [batch_size]
    #     p_list = [p_i.decode("utf-8") for p_i in p.numpy()]  # shape [batch_size]

    #     # shape [batch_size, variable_negatives], decode each row
    #     n_list = []
    #     for row in n.numpy():
    #         # row is shape [N], each is a tf.string
    #         decoded = [neg.decode("utf-8") for neg in row]
    #         n_list.append(decoded)

    #     # 2) Tokenize queries & positives
    #     q_enc = self.tokenizer(
    #         q_list,
    #         padding="max_length",
    #         truncation=True,
    #         max_length=max_len,
    #         return_tensors="np"  # you can do return_tensors="tf", but "np" is often simpler here
    #     )
    #     p_enc = self.tokenizer(
    #         p_list,
    #         padding="max_length",
    #         truncation=True,
    #         max_length=max_len,
    #         return_tensors="np"
    #     )

    #     # 3) Tokenize negatives
    #     # Flatten [batch_size, variable_negatives] -> single list
    #     flattened_negatives = [neg for row in n_list for neg in row]
    #     if len(flattened_negatives) == 0:
    #         # No negatives at all: return a zero array
    #         n_ids = np.zeros((len(q_list), neg_samples, max_len), dtype=np.int32)
    #     else:
    #         n_enc = self.tokenizer(
    #             flattened_negatives,
    #             padding="max_length",
    #             truncation=True,
    #             max_length=max_len,
    #             return_tensors="np"
    #         )
    #         # shape [batch_size * total_negatives, max_len]
    #         n_input_ids = n_enc["input_ids"]

    #         # We want to reshape to [batch_size, neg_samples, max_len].
    #         # If each row truly has exactly `neg_samples` (or fewer), we can do:
    #         #   n_input_ids = n_input_ids.reshape(len(q_list), neg_samples, max_len)
    #         # But if the rows have variable # of negatives, we must clamp or pad.
    #         # For simplicity, let's just "take first neg_samples" per row
    #         # and pad if fewer.
            
    #         # We'll do it row by row:
    #         batch_size = len(q_list)
    #         row_offsets = 0
    #         n_ids_list = []
    #         for row_idx in range(batch_size):
    #             row_negs = n_list[row_idx]
    #             row_count = len(row_negs)
                
    #             # slice from the flattened array
    #             row_slice = n_input_ids[row_offsets:row_offsets + row_count]
    #             row_offsets += row_count

    #             # Now pick out up to neg_samples
    #             row_slice = row_slice[:neg_samples]

    #             # If fewer than neg_samples, pad
    #             if row_slice.shape[0] < neg_samples:
    #                 deficit = neg_samples - row_slice.shape[0]
    #                 pad_arr = np.zeros((deficit, max_len), dtype=np.int32)
    #                 row_slice = np.concatenate([row_slice, pad_arr], axis=0)

    #             # row_slice is now shape [neg_samples, max_len]
    #             n_ids_list.append(row_slice)

    #         # stack them -> shape [batch_size, neg_samples, max_len]
    #         n_ids = np.stack(n_ids_list, axis=0)

    #     # 4) Return as np.int32 arrays (tokenizer should already return int32, 
    #     #    but we can cast to be sure)
    #     q_ids = q_enc["input_ids"].astype(np.int32)  # shape [batch_size, max_len]
    #     p_ids = p_enc["input_ids"].astype(np.int32)  # shape [batch_size, max_len]
    #     n_ids = n_ids.astype(np.int32)               # shape [batch_size, neg_samples, max_len]

    #     return q_ids, p_ids, n_ids