chatbot_model.py

import os
import numpy as np
from transformers import TFAutoModel, AutoTokenizer
import tensorflow as tf
from typing import List, Tuple, Dict, Optional, Union, Any
import math
from dataclasses import dataclass
import json
from pathlib import Path
import datetime       
import faiss
import gc
import re
from tf_data_pipeline import TFDataPipeline
from response_quality_checker import ResponseQualityChecker
from cross_encoder_reranker import CrossEncoderReranker
from conversation_summarizer import DeviceAwareModel, Summarizer
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:
    """RetrievalChatbot Config"""
    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
    learning_rate: float = 0.0005
    min_text_length: int = 3
    max_context_turns: int = 20
    warmup_steps: int = 200
    pretrained_model: str = 'distilbert-base-uncased'
    cross_encoder_model: str = 'cross-encoder/ms-marco-MiniLM-L-12-v2'
    summarizer_model: str = 't5-small'
    dtype: str = 'float32'
    freeze_embeddings: bool = False
    embedding_batch_size: int = 64
    search_batch_size: int = 64
    max_batch_size: int = 64
    max_retries: int = 3

    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 DistilBERT embeddings."""
    def __init__(
        self,
        config: ChatbotConfig,
        name: str = "encoder",
        **kwargs
    ):
        super().__init__(name=name, **kwargs)
        self.config = config

        # Load pretrained model and freeze layers based on config
        self.pretrained = TFAutoModel.from_pretrained(config.pretrained_model)
        self._freeze_layers()
        
        # Add Global Average Pooling, Projection, Dropout, and Normalization layers
        self.pooler = tf.keras.layers.GlobalAveragePooling1D()
        self.projection = tf.keras.layers.Dense(
            config.embedding_dim,
            activation='tanh',
            name="projection",
            dtype=tf.float32 
        )
        self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
        self.normalize = tf.keras.layers.Lambda(
            lambda x: tf.nn.l2_normalize(x, axis=1),
            name="l2_normalize"
        )

    def _freeze_layers(self):
        """Freeze n layers of the pretrained model"""
        if self.config.freeze_embeddings:
            self.pretrained.trainable = False
            logger.info("All pretrained layers frozen.")
        else:
            # Freeze only the first 'n' transformer layers
            for i, layer in enumerate(self.pretrained.layers):
                if isinstance(layer, tf.keras.layers.Layer):
                    if hasattr(layer, 'trainable'):
                        if i < 1:
                            layer.trainable = False
                            logger.info(f"Layer {i} frozen.")
                        else:
                            layer.trainable = True
                            logger.info(f"Layer {i} trainable.")
                            
    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)
        x = self.normalize(x)                       # Shape: [batch_size, 768]

        return x

    def get_config(self) -> dict:
        """Return the model config"""
        config = super().get_config()
        config.update({
            "config": self.config.to_dict(),
            "name": self.name
        })
        return config
        
class RetrievalChatbot(DeviceAwareModel):
    """
    Retrieval-based learning chatbot model.
    Uses trained embeddings and FAISS for similarity search.
    """
    def __init__(
        self,
        config: ChatbotConfig,
        device: str = None,
        strategy=None,
        reranker: Optional[CrossEncoderReranker] = None,
        summarizer: Optional[Summarizer] = None,
        mode: str = 'training' 
    ):
        super().__init__()
        self.config = config
        self.strategy = strategy
        self.device = device or self._setup_default_device()
        self.mode = mode.lower()
        
        # Initialize reranker, summarizer, tokenizer, and encoder
        self.reranker = reranker or self._initialize_reranker()
        self.tokenizer = self._initialize_tokenizer()
        self.encoder = self._initialize_encoder()
        self.summarizer = summarizer or self._initialize_summarizer()
        
        # Initialize data pipeline
        logger.info("Initializing TFDataPipeline.")
        self.data_pipeline = TFDataPipeline(
            config=self.config,
            tokenizer=self.tokenizer,
            encoder=self.encoder,
            response_pool=[],
            max_length=self.config.max_context_token_limit,
            query_embeddings_cache={},
        )
        
        # Collect unique responses from dialogues
        if self.mode == 'inference':
            logger.info("Mode set to 'inference'. Loading FAISS index and response pool.")
            self._load_faiss_index_and_responses()
        elif self.mode != 'training':
            logger.error(f"Unsupported mode in RetrievalChatbot init: {self.mode}")
            raise ValueError(f"Unsupported mode in RetrievalChatbot init: {self.mode}")
            
        # Initialize training history
        self.history = {
            "train_loss": [],
            "val_loss": [],
            "train_metrics": {},
            "val_metrics": {}
        }

    
    def _setup_default_device(self) -> str:
        """Set up default device if none is provided."""
        if tf.config.list_physical_devices('GPU'):
            return 'GPU'
        else:
            return 'CPU'

    def _initialize_reranker(self) -> CrossEncoderReranker:
        """Initialize the CrossEncoderReranker."""
        logger.info("Initializing default CrossEncoderReranker...")
        return CrossEncoderReranker(model_name=self.config.cross_encoder_model)

    def _initialize_summarizer(self) -> Summarizer:
        """Initialize the Summarizer."""
        return Summarizer(
            tokenizer=self.tokenizer,
            model_name=self.config.summarizer_model,
            max_summary_length=self.config.max_context_token_limit // 4,
            device=self.device,
            max_summary_rounds=2
        )

    def _initialize_tokenizer(self) -> AutoTokenizer:
        """Initialize the tokenizer and add special tokens."""
        logger.info("Initializing tokenizer and adding special tokens...")
        tokenizer = AutoTokenizer.from_pretrained(self.config.pretrained_model)
        special_tokens = {
            "user": "<USER>",
            "assistant": "<ASSISTANT>",
            "context": "<CONTEXT>",
            "sep": "<SEP>"
        }
        tokenizer.add_special_tokens(
            {'additional_special_tokens': list(special_tokens.values())}
        )
        return tokenizer
    
    def _initialize_encoder(self) -> EncoderModel:
        """Initialize the EncoderModel and resize token embeddings."""
        logger.info("Initializing encoder model...")
        encoder = EncoderModel(
            self.config,
            name="shared_encoder",
        )
        
        new_vocab_size = len(self.tokenizer)
        encoder.pretrained.resize_token_embeddings(new_vocab_size)
        logger.info(f"Token embeddings resized to: {new_vocab_size}")
        
        return encoder
    
    def _load_faiss_index_and_responses(self) -> None:
        """Load FAISS index and response pool for inference."""
        try:
            logger.info(f"Loading FAISS index from {self.data_pipeline.faiss_index_file_path}...")
            self.data_pipeline.load_faiss_index(self.data_pipeline.faiss_index_file_path)
            logger.info("FAISS index loaded successfully.")
            
            # Load response pool
            response_pool_path = self.data_pipeline.faiss_index_file_path.replace('.index', '_responses.json')
            if os.path.exists(response_pool_path):
                with open(response_pool_path, 'r', encoding='utf-8') as f:
                    self.data_pipeline.response_pool = json.load(f)
                logger.info(f"Loaded {len(self.data_pipeline.response_pool)} responses from {response_pool_path}.")
            else:
                logger.error(f"Response pool file not found at {response_pool_path}.")
                raise FileNotFoundError(f"Response pool file not found at {response_pool_path}.")
                
            # Validate FAISS index and response pool
            self.data_pipeline.validate_faiss_index()
            logger.info("FAISS index and response pool validated successfully.")
            
        except Exception as e:
            logger.error(f"Failed to load FAISS index and response pool: {e}")
            raise
    
    @classmethod
    def load_model(cls, load_dir: Union[str, Path], mode: str = 'training') -> '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, mode=mode)
        
        # Load DistilBERT 
        chatbot.encoder.pretrained = TFAutoModel.from_pretrained(load_dir / "shared_encoder", config=config)
        
        dummy_input = tf.zeros((1, config.max_context_token_limit), dtype=tf.int32)
        _ = chatbot.encoder(dummy_input, training=False)
        
        # Load tokenizer
        chatbot.tokenizer = AutoTokenizer.from_pretrained(load_dir / "tokenizer")
        logger.info(f"Models and tokenizer loaded from {load_dir}")
        
        # Load the custom weights
        custom_weights_path = load_dir / "encoder_custom_weights.weights.h5"
        if custom_weights_path.exists():
            chatbot.encoder.load_weights(str(custom_weights_path))
            logger.info("Loaded custom encoder weights for projection/dropout/etc.")
        else:
            logger.warning(f"No custom encoder weights found at {custom_weights_path}. The top-level projection layer won't have learned parameters.")
        
        # Handle 'inference' mode: load FAISS, etc.
        if mode == 'inference':
            cls._prepare_model_for_inference(chatbot, load_dir)
        
        return chatbot
    
    @classmethod
    def _prepare_model_for_inference(cls, chatbot: 'RetrievalChatbot', load_dir: Path) -> None:
        """Load inference components."""
        try:
            # Load FAISS index
            faiss_path = load_dir / 'faiss_indices/faiss_index_production.index'
            if faiss_path.exists():
                chatbot.index = faiss.read_index(str(faiss_path))
                logger.info("FAISS index loaded successfully")
            else:
                raise FileNotFoundError(f"FAISS index not found at {faiss_path}")
            
            # Load response pool
            response_pool_path = load_dir / 'faiss_indices/faiss_index_production_responses.json'
            if response_pool_path.exists():
                with open(response_pool_path, 'r') as f:
                    chatbot.response_pool = json.load(f)
                logger.info(f"Loaded {len(chatbot.response_pool)} responses")
            else:
                raise FileNotFoundError(f"Response pool not found at {response_pool_path}")
            
            # Verify dimensions match
            if chatbot.index.d != chatbot.config.embedding_dim:
                raise ValueError(
                    f"FAISS index dimension {chatbot.index.d} doesn't match "
                    f"model dimension {chatbot.config.embedding_dim}"
                )
                
        except Exception as e:
            logger.error(f"Error loading inference components: {e}")
            raise
    
    def save_models(self, save_dir: Union[str, Path]):
        """Save model and config"""
        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 the HF DistilBERT submodule, custom top-level layers, and tokenizer
        self.encoder.pretrained.save_pretrained(save_dir / "shared_encoder")
        self.encoder.save_weights(save_dir / "encoder_custom_weights.weights.h5")
        self.tokenizer.save_pretrained(save_dir / "tokenizer")
        logger.info(f"Models and tokenizer saved to {save_dir}.")
    
    def retrieve_responses(
        self,
        query: str,
        top_k: int = 10,
        reranker: Optional[CrossEncoderReranker] = None,
        summarizer: Optional[Summarizer] = None,
        summarize_threshold: int = 512
    ) -> List[Tuple[str, float]]:
        """
        Retrieve top-k responses using FAISS and cross-encoder re-ranking.
        Args:
            query: The user's input text.
            top_k: Number of FAISS results to return
            reranker: CrossEncoderReranker for refined scoring
            summarizer: Summarizer for long queries
            summarize_threshold: Summarize if conversation tokens > threshold.
        Returns:
            List of (response_text, final_score).
        """
        def sigmoid(x: float) -> float:
            return 1 / (1 + np.exp(-x))
        
        # Query summarization
        if summarizer and len(query.split()) > summarize_threshold:
            logger.info(f"Query is long ({len(query.split())} words). Summarizing.")
            query = summarizer.summarize_text(query)
            logger.info(f"Summarized Query: {query}")
        
        detected_domain = self.detect_domain_from_query(query)
        
        # Retrieve initial candidates from FAISS
        initial_k = min(top_k * 10, len(self.data_pipeline.response_pool))
        faiss_candidates = self.faiss_search(query, domain=detected_domain, top_k=initial_k)
        
        if not faiss_candidates:
            return []
        
        texts = [item[0] for item in faiss_candidates]
        
        if not reranker:
            reranker = CrossEncoderReranker(model_name=self.config.cross_encoder_model)
        
        # Re-rank the texts (candidates) from FAISS search using the cross-encoder
        ce_logits = reranker.rerank(query, texts, max_length=256)
        
        # Combine scores from FAISS and cross-encoder
        final_candidates = []
        for (resp_text, faiss_score), logit in zip(faiss_candidates, ce_logits):
            ce_prob = sigmoid(logit)            # now in range [0...1]
            faiss_norm = (faiss_score + 1)/2.0  # now in range [0...1]
            combined_score = 0.85 * ce_prob + 0.15 * faiss_norm
            length_adjusted_score = self.length_adjust_score(resp_text, combined_score)
            
            final_candidates.append((resp_text, length_adjusted_score))
            
        # Sort descending by combined score
        final_candidates.sort(key=lambda x: x[1], reverse=True)

        # Return top_k
        return final_candidates[:top_k]
    
    def extract_keywords(self, query: str) -> List[str]:
        """
        Return any domain keywords present in the query (lowercased).
        """
        domain_keywords = {
            'restaurant': ['restaurant', 'dining', 'food', 'dine', 'reservation', 'table', 'menu', 'cuisine', 'eat', 'place to eat', 'hungry', 'chef', 'dish', 'meal', 'brunch', 'bistro', 'buffet', 'catering', 'gourmet', 'fast food', 'fine dining', 'takeaway', 'delivery', 'restaurant booking'],
            'movie': ['movie', 'cinema', 'film', 'ticket', 'showtime', 'showing', 'theater', 'flick', 'screening', 'film ticket', 'film show', 'blockbuster', 'premiere', 'trailer', 'director', 'actor', 'actress', 'plot', 'genre', 'screen', 'sequel', 'animation', 'documentary'],
            'ride_share': ['ride', 'taxi', 'uber', 'lyft', 'car service', 'pickup', 'dropoff', 'driver', 'cab', 'hailing', 'rideshare', 'ride hailing', 'carpool', 'chauffeur', 'transit', 'transportation', 'hail ride'],
            'coffee': ['coffee', 'café', 'cafe', 'starbucks', 'espresso', 'latte', 'mocha', 'americano', 'barista', 'brew', 'cappuccino', 'macchiato', 'iced coffee', 'cold brew', 'espresso machine', 'coffee shop', 'tea', 'chai', 'java', 'bean', 'roast', 'decaf'],
            'pizza': ['pizza', 'delivery', 'order food', 'pepperoni', 'topping', 'pizzeria', 'slice', 'pie', 'margherita', 'deep dish', 'thin crust', 'cheese', 'oven', 'tossed', 'sauce', 'garlic bread', 'calzone'],
            'auto': ['car', 'vehicle', 'repair', 'maintenance', 'mechanic', 'oil change', 'garage', 'auto shop', 'tire', 'check engine', 'battery', 'transmission', 'brake', 'engine diagnostics', 'carwash', 'detail', 'alignment', 'exhaust', 'spark plug', 'dashboard'],
        }
        
        query_lower = query.lower()
        found = set()
        for domain, kw_list in domain_keywords.items():
            for kw in kw_list:
                if kw in query_lower:
                    found.add(kw)
        return list(found)
    
    def length_adjust_score(self, text: str, base_score: float) -> float:
        """
        Penalize very short lines, reward longer lines.
        """
        words = text.split()
        wcount = len(words)
        
        # Penalty if under 4 words
        if wcount < 4:
            return base_score * 0.8
        
        # Bonus for lines > 15 words
        if wcount > 15:
            bonus = min(0.03, 0.001 * (wcount - 15))
            base_score += bonus
        
        return base_score
    
    def detect_domain_from_query(self, query: str) -> str:
        """
        Detect the domain of the query based on keywords. Used for boosting FAISS search.
        """
        domain_patterns = {
            'restaurant': r'\b(restaurant|restaurants?|dining|food|foods?|dine|reservation|reservations?|table|tables?|menu|menus?|cuisine|cuisines?|eat|eats?|place\s?to\s?eat|places\s?to\s?eat|hungry|chef|chefs?|dish|dishes?|meal|meals?|fork|forks?|knife|knives?|spoon|spoons?|brunch|bistro|buffet|buffets?|catering|caterings?|gourmet|fast\s?food|fine\s?dining|takeaway|takeaways?|delivery|deliveries|restaurant\s?booking)\b',
            'movie': r'\b(movie|movies?|cinema|cinemas?|film|films?|ticket|tickets?|showtime|showtimes?|showing|showings?|theater|theaters?|flick|flicks?|screening|screenings?|film\s?ticket|film\s?tickets?|film\s?show|film\s?shows?|blockbuster|blockbusters?|premiere|premieres?|trailer|trailers?|director|directors?|actor|actors?|actress|actresses?|plot|plots?|genre|genres?|screen|screens?|sequel|sequels?|animation|animations?|documentary|documentaries)\b',
            'ride_share': r'\b(ride|rides?|taxi|taxis?|uber|lyft|car\s?service|car\s?services?|pickup|pickups?|dropoff|dropoffs?|driver|drivers?|cab|cabs?|hailing|hailings?|rideshare|rideshares?|ride\s?hailing|ride\s?hailings?|carpool|carpools?|chauffeur|chauffeurs?|transit|transits?|transportation|transportations?|hail\s?ride|hail\s?rides?)\b',
            'coffee': r'\b(coffee|coffees?|café|cafés?|cafe|cafes?|starbucks|espresso|espressos?|latte|lattes?|mocha|mochas?|americano|americanos?|barista|baristas?|brew|brews?|cappuccino|cappuccinos?|macchiato|macchiatos?|iced\s?coffee|iced\s?coffees?|cold\s?brew|cold\s?brews?|espresso\s?machine|espresso\s?machines?|coffee\s?shop|coffee\s?shops?|tea|teas?|chai|chais?|java|javas?|bean|beans?|roast|roasts?|decaf)\b',
            'pizza': r'\b(pizza|pizzas?|delivery|deliveries|order\s?food|order\s?foods?|pepperoni|pepperonis?|topping|toppings?|pizzeria|pizzerias?|slice|slices?|pie|pies?|margherita|margheritas?|deep\s?dish|deep\s?dishes?|thin\s?crust|thin\s?crusts?|cheese|cheeses?|oven|ovens?|tossed|tosses?|sauce|sauces?|garlic\s?bread|garlic\s?breads?|calzone|calzones?)\b',
            'auto': r'\b(car|cars?|vehicle|vehicles?|repair|repairs?|maintenance|maintenances?|mechanic|mechanics?|oil\s?change|oil\s?changes?|garage|garages?|auto\s?shop|auto\s?shops?|tire|tires?|check\s?engine|check\s?engines?|battery|batteries?|transmission|transmissions?|brake|brakes?|engine\s?diagnostics|engine\s?diagnostic|carwash|carwashes?|detail|details?|alignment|alignments?|exhaust|exhausts?|spark\s?plug|spark\s?plugs?|dashboard|dashboards?)\b',
        }

        # Check for matches
        for domain, pattern in domain_patterns.items():
            if re.search(pattern, query.lower()):
                return domain

        return 'other'
    
    def is_numeric_response(self, text: str) -> bool:
        """
        Return True if `text` is purely digits and/or spaces.
        """
        pattern = r'^[\s]*[\d]+([\s.,\d]+)*[\s]*$'
        return bool(re.match(pattern, text.strip()))
    
    def faiss_search(
        self, 
        query: str,
        domain: str = 'other',
        top_k: int = 10,
        boost_factor: float = 1.15
    ) -> List[Tuple[str, float]]:
        """
        Retrieve top-k responses from the FAISS index (IndexFlatIP) given a user query.
        Args:
            query (str): The user input text.
            domain (str): The detected domain from possible domains: ['restaurant', 'movie', 'ride_share', 'coffee', 'pizza', 'auto', 'other']
            top_k (int): Number of top results to return.
            boost_factor (float, optional): Factor to boost scores for keyword matches.
        Returns:
            List[Tuple[str, float]]: List of (response_text, similarity) sorted by descending similarity.
        """
        # Encode the query
        q_emb = self.data_pipeline.encode_query(query)
        q_emb_np = q_emb.reshape(1, -1).astype('float32')

        # Search the index
        distances, indices = self.data_pipeline.index.search(q_emb_np, top_k * 10)

        # IndexFlatIP: 'distances' are inner products (cosine similarities for normalized vectors).
        candidates = []
        for rank, idx in enumerate(indices[0]):
            if idx < 0:
                continue
            text_dict = self.data_pipeline.response_pool[idx]
            text = text_dict.get('text', '').strip()
            cand_domain = text_dict.get('domain', 'other')
            score = distances[0][rank]

            # Skip purely numeric or extremely short text (fewer than 3 words):
            words = text.split()
            if len(words) < 4:
                continue
            if self.is_numeric_response(text):
                continue
            
            candidates.append((text, cand_domain, score))
        
        if not candidates:
            logger.warning("No valid candidates found after initial numeric/length filtering.")
            return []

        # Sort candidates by score descending
        candidates.sort(key=lambda x: x[2], reverse=True)

        # Filter in-domain responses
        in_domain = [c for c in candidates if c[1] == domain]
        if not in_domain:
            logger.info(f"No in-domain responses found for '{domain}'. Using all candidates.")
            in_domain = candidates

        # Boost responses containing query keywords
        query_keywords = self.extract_keywords(query)
        boosted = []
        for (resp_text, resp_domain, score) in in_domain:
            new_score = score
            # If the domain is known AND the response text shares any query keywords, boost it
            if query_keywords and any(kw in resp_text.lower() for kw in query_keywords):
                new_score *= boost_factor
                
            # Apply length penalty/bonus
            new_score = self.length_adjust_score(resp_text, new_score)
            
            boosted.append((resp_text, new_score))
        
        # Sort boosted responses
        boosted.sort(key=lambda x: x[1], reverse=True)
        
        # Debug logging (see FAISS responses)
        # for resp, score in boosted[:100]:
        #     logger.debug(f"Candidate: '{resp}' with score {score}")
        
        return boosted[:top_k]
    
    def chat(
        self,
        query: str,
        conversation_history: Optional[List[Tuple[str, str]]] = None,
        quality_checker: Optional['ResponseQualityChecker'] = None,
        top_k: int = 10,
    ) -> Tuple[str, List[Tuple[str, float]], Dict[str, Any]]:
        """
        Live chat with the chatbot. Uses same processing flow as validation, except for context handling and quality checking.
        """
        @self.run_on_device
        def get_response(self_arg, query_arg):
            # Build conversation context string
            conversation_str = self_arg._build_conversation_context(query_arg, conversation_history)
            
            # Retrieve and re-rank
            results = self_arg.retrieve_responses(
                query=conversation_str,
                top_k=top_k,
                reranker=self_arg.reranker,
                summarizer=self_arg.summarizer,
                summarize_threshold=512
            )
            
            # Handle low confidence or empty responses
            if not results:
                return ("I'm sorry, but I couldn't find a relevant response.", [], {})
            
            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 get_response(self, query)

    def _build_conversation_context(
        self, 
        query: str, 
        conversation_history: Optional[List[Tuple[str, str]]]
    ) -> str:
        """
        Build conversation context string from conversation history.
        """
        if not conversation_history:
            return f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<USER>')]} {query}"
            
        conversation_parts = []
        for user_txt, assistant_txt in conversation_history:
            conversation_parts.extend([
                f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<USER>')]} {user_txt}",
                f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<ASSISTANT>')]} {assistant_txt}"
            ])
            
        conversation_parts.append(f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<USER>')]} {query}")
        return "\n".join(conversation_parts)
    
    def train_model(
        self,
        tfrecord_file_path: str,
        epochs: int = 20,
        batch_size: int = 16,
        validation_split: float = 0.2,
        checkpoint_dir: str = "checkpoints/",
        use_lr_schedule: bool = True,
        peak_lr: float = 1e-5,
        warmup_steps_ratio: float = 0.1,
        early_stopping_patience: int = 3,
        min_delta: float = 1e-4,
        test_mode: bool = False,
        initial_epoch: int = 0
    ) -> None:
        """
        Train the retrieval model using a pre-prepared TFRecord dataset.
        - Checkpoint loading/restoring
        - LR scheduling
        - Epoch/iteration tracking
        - Training-history logging
        - Early stopping
        - Custom loss function (Contrastive loss with hard negative sampling))
        """
        logger.info("Starting training with pre-prepared TFRecord dataset...")

        def parse_tfrecord_fn(example_proto, max_length, neg_samples):
            """
            Parses a single TFRecord example.
            """
            feature_description = {
                'query_ids': tf.io.FixedLenFeature([max_length], tf.int64),
                'positive_ids': tf.io.FixedLenFeature([max_length], tf.int64),
                'negative_ids': tf.io.FixedLenFeature([neg_samples * max_length], tf.int64),
            }
            parsed_features = tf.io.parse_single_example(example_proto, feature_description)

            query_ids = tf.cast(parsed_features['query_ids'], tf.int32)
            positive_ids = tf.cast(parsed_features['positive_ids'], tf.int32)
            negative_ids = tf.cast(parsed_features['negative_ids'], tf.int32)
            negative_ids = tf.reshape(negative_ids, [neg_samples, max_length])

            return query_ids, positive_ids, negative_ids

        # Count total records in TFRecord
        raw_dataset = tf.data.TFRecordDataset(tfrecord_file_path)
        total_pairs = sum(1 for _ in raw_dataset)
        logger.info(f"Total pairs in TFRecord: {total_pairs}")

        train_size = int(total_pairs * (1 - validation_split))
        val_size = total_pairs - train_size
        steps_per_epoch = math.ceil(train_size / batch_size)
        val_steps = math.ceil(val_size / batch_size)
        total_steps = steps_per_epoch * epochs
        buffer_size = max(1, total_pairs // 2)  # 50% of the dataset for shuffling

        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 & LR 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=tf.cast(peak_lr, tf.float32),
                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=tf.cast(peak_lr, tf.float32))
            logger.info("Using fixed learning rate.")

        # Dummy step to force initialization
        dummy_input = tf.zeros((1, self.config.max_context_token_limit), dtype=tf.int32)
        with tf.GradientTape() as tape:
            dummy_output = self.encoder(dummy_input)
            dummy_loss = tf.cast(tf.reduce_mean(dummy_output), tf.float32)
        dummy_grads = tape.gradient(dummy_loss, self.encoder.trainable_variables)
        self.optimizer.apply_gradients(zip(dummy_grads, self.encoder.trainable_variables))

        # Create checkpoint and manager
        checkpoint = tf.train.Checkpoint(
            epoch=tf.Variable(0, dtype=tf.int32),
            optimizer=self.optimizer,
            model=self.encoder
        )

        # Create a CheckpointManager
        manager = tf.train.CheckpointManager(
            checkpoint,
            directory=checkpoint_dir,
            max_to_keep=3,
            checkpoint_name='ckpt'
        )

        # Restore from existing checkpoint if one is provided
        latest_checkpoint = manager.latest_checkpoint
        history_path = Path(checkpoint_dir) / 'training_history.json'

        # Log epoch losses across runs, including restore from checkpoint
        if not hasattr(self, 'history'):
            self.history = {'train_loss': [], 'val_loss': [], 'learning_rate': []}

        if latest_checkpoint and not test_mode:
            # Debug checkpoint loading
            # logger.info(f"\nTrying to load checkpoint from: {latest_checkpoint}")
            # reader = tf.train.load_checkpoint(latest_checkpoint)
            # shape_from_key = reader.get_variable_to_shape_map()
            # dtype_from_key = reader.get_variable_to_dtype_map()
            # logger.info("\nCheckpoint Variables:")
            # for key in shape_from_key:
            #     logger.info(f"{key}: dtype={dtype_from_key[key]} - Shape: {shape_from_key[key]}")
                
            status = checkpoint.restore(latest_checkpoint)
            status.assert_consumed()
            logger.info(f"Restored from checkpoint: {latest_checkpoint}")
            logger.info(f"Optimizer iterations after restore: {self.optimizer.iterations.numpy()}")
            
            # Verify learning rate after restore
            if use_lr_schedule:
                current_lr = float(lr_schedule(self.optimizer.iterations))
            else:
                current_lr = float(self.optimizer.learning_rate.numpy())
            logger.info(f"Current learning rate after restore: {current_lr:.2e}")

            # Derive initial_epoch from checkpoint name if not passed in
            ckpt_number = int(latest_checkpoint.split('ckpt-')[-1])
            if initial_epoch == 0:
                initial_epoch = ckpt_number

            # Assign to checkpoint.epoch for counting
            checkpoint.epoch.assign(tf.cast(initial_epoch, tf.int32))
            logger.info(f"Resuming from epoch {initial_epoch}")
            
            # Load history from file:
            if history_path.exists():
                try:
                    with open(history_path, 'r') as f:
                        self.history = json.load(f)
                    logger.info(f"Loaded previous training history from {history_path}")
                except Exception as e:
                    logger.warning(f"Could not load history, starting fresh: {e}")
            
            # Save custom weights not being saved in the full model.
            # This was a bugfix to extract weights from a checkpoint without retraining.
            # Before updating save_models, only Distilbert weights were being saved (custom layers were missed).
            # Not needed, also not harmful.
            self.save_models(Path(checkpoint_dir) / "pretrained_full_model")
            logger.info(f"Manually saved custom weights after restore.")
        else:
            logger.info("Starting training from scratch")
            checkpoint.epoch.assign(tf.cast(0, tf.int32))
            initial_epoch = 0

        # Set up 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}")
        
        # Parse dataset
        dataset = tf.data.TFRecordDataset(tfrecord_file_path)
        
        # Debug mode uses small subset. Useful for CPU debugging.
        if test_mode:
            subset_size = 200
            dataset = dataset.take(subset_size)
            logger.info(f"TEST MODE: Using only {subset_size} examples")
            # Recompute sizes, steps, epochs, etc., as needed
            total_pairs = subset_size
            train_size = int(total_pairs * (1 - validation_split))
            val_size = total_pairs - train_size
            batch_size = min(batch_size, val_size)
            steps_per_epoch = math.ceil(train_size / batch_size)
            val_steps = math.ceil(val_size / batch_size)
            total_steps = steps_per_epoch * epochs
            buffer_size = max(1, total_pairs // 10)
            epochs = min(epochs, 5)  # For quick debug
            early_stopping_patience = 2
            logger.info(f"New training pairs: {train_size}")
            logger.info(f"New validation pairs: {val_size}")
            
        dataset = dataset.map(
            lambda x: parse_tfrecord_fn(x, self.config.max_context_token_limit, self.data_pipeline.neg_samples),
            num_parallel_calls=tf.data.AUTOTUNE
        )
        
        # Train/val split
        train_dataset = dataset.take(train_size)
        val_dataset = dataset.skip(train_size).take(val_size)
        
        # Shuffle and batch
        train_dataset = train_dataset.shuffle(buffer_size=buffer_size)
        train_dataset = train_dataset.batch(batch_size, drop_remainder=True)
        train_dataset = train_dataset.prefetch(tf.data.AUTOTUNE)
        
        val_dataset = val_dataset.batch(batch_size, drop_remainder=False)
        val_dataset = val_dataset.prefetch(tf.data.AUTOTUNE)
        val_dataset = val_dataset.cache()
        
        # Training loop
        best_val_loss = float("inf")
        epochs_no_improve = 0
        
        for epoch in range(int(checkpoint.epoch.numpy()) + 1, epochs + 1):
            checkpoint.epoch.assign(epoch)
            logger.info(f"Starting Epoch {epoch}...")
            
            epoch_loss_avg = tf.keras.metrics.Mean(dtype=tf.float32)
            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
                
            # --- Training ---
            for q_batch, p_batch, n_batch in train_dataset:
                loss, grad_norm, post_clip_norm = 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():
                    step = (epoch - 1) * steps_per_epoch + batches_processed
                    tf.summary.scalar("loss", tf.cast(loss, tf.float32), step=step)
                    tf.summary.scalar("gradient_norm_pre_clip", tf.cast(grad_norm, tf.float32), step=step)
                    tf.summary.scalar("gradient_norm_post_clip", tf.cast(post_clip_norm, tf.float32), step=step)

                # 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}",
                        "pre_clip": f"{grad_norm.numpy():.2e}",
                        "post_clip": f"{post_clip_norm.numpy():.2e}",
                        "lr": f"{current_lr:.2e}",
                        "batches": f"{batches_processed}/{steps_per_epoch}"
                    })
                
                gc.collect()
                
                # End the epoch early if we've processed all steps
                if batches_processed >= steps_per_epoch:
                    break
            
            if is_tqdm_train and train_pbar:
                train_pbar.close()
            
            # --- Validation ---
            val_loss_avg = tf.keras.metrics.Mean(dtype=tf.float32)
            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
            
            last_valid_val_loss = None
            valid_batches = False
            
            for q_batch, p_batch, n_batch in val_dataset:
                # If batch is too small, skip
                if tf.shape(q_batch)[0] < 2:
                    logger.warning(f"Skipping validation batch of size {tf.shape(q_batch)[0]}")
                    continue
                
                valid_batches = True
                val_loss = self.validation_step(q_batch, p_batch, n_batch)
                val_loss_avg(val_loss)
                last_valid_val_loss = 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}"
                    })
                    
                gc.collect()
                
                if val_batches_processed >= val_steps:
                    break
            
            if not valid_batches:
                # If no valid batch is found, fallback
                logger.warning("No valid validation batches in this epoch")
                if last_valid_val_loss is not None:
                    val_loss = last_valid_val_loss
                    val_loss_avg(val_loss)
                else:
                    val_loss = epoch_loss_avg.result()
                    val_loss_avg(val_loss)
            
            if is_tqdm_val and val_pbar:
                val_pbar.close()
            
            # End of epoch: final stats
            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}")
            
            # TensorBoard epoch logs
            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()
            
            # Save model for iterative testing/inference
            model_save_path = Path(checkpoint_dir) / f"model_epoch_{epoch}"
            self.save_models(model_save_path)
            logger.info(f"Saved model for epoch {epoch} at {model_save_path}")
            
            # Update local history
            self.history['train_loss'].append(train_loss)
            self.history['val_loss'].append(val_loss)
            self.history.setdefault('learning_rate', []).append(current_lr)
            
            def convert_to_py_floats(obj):
                if isinstance(obj, dict):
                    return {k: convert_to_py_floats(v) for k, v in obj.items()}
                elif isinstance(obj, list):
                    return [convert_to_py_floats(x) for x in obj]
                elif isinstance(obj, (np.float32, np.float64)):
                    return float(obj)
                elif tf.is_tensor(obj):
                    return float(obj.numpy())
                else:
                    return obj
                
            json_history = convert_to_py_floats(self.history)
            
            # Save training history to file every epoch
            with open(history_path, 'w') as f:
                json.dump(json_history, f)
            logger.info(f"Saved training history to {history_path}")
            
            # Early stopping
            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("Training completed!")
        
    @tf.function
    def train_step(
        self, 
        q_batch: tf.Tensor, 
        p_batch: tf.Tensor, 
        n_batch: tf.Tensor
    ) -> tf.Tensor:
        """
        Single training step using queries, positives, and hard negatives.
        """
        with tf.GradientTape() as tape:
            # Encode queries, positives, and negatives
            q_enc = self.encoder(q_batch, training=True)  # [batch_size, embed_dim]
            p_enc = self.encoder(p_batch, training=True)  # [batch_size, embed_dim]
            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]
            # Col 1 is the pos, subsequent cols are negatives
            combined_p_n = tf.concat([tf.expand_dims(p_enc, axis=1), n_enc], axis=1)  # [bs, (1+neg_samples), embed_dim]

            # Compute scores: dot product of q_enc with each column in combined_p_n. `tf.einsum` handles the batch dimension
            dot_products = tf.cast(tf.einsum('bd,bkd->bk', q_enc, combined_p_n), tf.float32)
            labels = tf.zeros([bs], dtype=tf.int32)  # Keep labels as int32
            loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
                labels=labels,
                logits=dot_products
            )
            loss = tf.cast(tf.reduce_mean(loss), tf.float32)

        # Calculate gradients and clip
        gradients = tape.gradient(loss, self.encoder.trainable_variables)
        gradients_norm = tf.cast(tf.linalg.global_norm(gradients), tf.float32)
        max_grad_norm = tf.constant(1.5, dtype=tf.float32)
        gradients, _ = tf.clip_by_global_norm(gradients, max_grad_norm, gradients_norm)
        post_clip_norm = tf.cast(tf.linalg.global_norm(gradients), tf.float32)
        
        self.optimizer.apply_gradients(zip(gradients, self.encoder.trainable_variables))
        
        return loss, gradients_norm, post_clip_norm

    @tf.function
    def validation_step(
        self, 
        q_batch: tf.Tensor, 
        p_batch: tf.Tensor, 
        n_batch: tf.Tensor
    ) -> tf.Tensor:
        """
        Single validation step using queries, positives, and hard negatives.
        Same idea as train_step, but without gradient updates.
        """
        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.cast(tf.einsum('bd,bkd->bk', q_enc, combined_p_n), tf.float32)
        labels = tf.zeros([bs], dtype=tf.int32)
        
        loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
            labels=labels,
            logits=dot_products
        )
        loss = tf.cast(tf.reduce_mean(loss), tf.float32)

        return loss
    
    def _get_lr_schedule(
        self,
        total_steps: int,
        peak_lr: float,
        warmup_steps: int
    ) -> tf.keras.optimizers.schedules.LearningRateSchedule:
        """
        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)
                
                # warmup_steps 10% 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 constants
                self.initial_lr = tf.cast(self.peak_lr * 0.1, tf.float32)
                self.min_lr = tf.cast(self.peak_lr * 0.01, tf.float32)
                
                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
                warmup_factor = tf.cast(tf.minimum(1.0, step / self.warmup_steps), tf.float32)
                warmup_lr = self.initial_lr + (self.peak_lr - self.initial_lr) * warmup_factor
                
                # Decay
                decay_steps = tf.cast(tf.maximum(1.0, self.total_steps - self.warmup_steps), tf.float32)
                decay_factor = tf.cast((step - self.warmup_steps) / decay_steps, tf.float32)
                decay_factor = tf.cast(tf.minimum(tf.maximum(0.0, decay_factor), 1.0), tf.float32)
                cosine_decay = tf.cast(0.5 * (1.0 + tf.cos(tf.constant(math.pi, dtype=tf.float32) * decay_factor)), tf.float32)
                decay_lr = self.min_lr + (self.peak_lr - self.min_lr) * cosine_decay
                
                final_lr = tf.where(step < self.warmup_steps, warmup_lr, decay_lr)
                
                # Ensure valid lr
                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)