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:
    """Configuration for the RetrievalChatbot."""
    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 = 0.001
    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 = 64
    search_batch_size: int = 64
    max_batch_size: int = 64
    neg_samples: int = 10
    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 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 Pooling layer (Global Average Pooling), Projection layer, Dropout, and Normalization
        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 layers of the pretrained model based on configuration."""
        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'):
                        # Freeze the first transformer block
                        if i < 1:
                            layer.trainable = False
                            logger.info(f"Layer {i} frozen.")
                        else:
                            layer.trainable = True
                            
    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 config of the model."""
        config = super().get_config()
        config.update({
            "config": self.config.to_dict(),
            "name": self.name
        })
        return config
        
class RetrievalChatbot(DeviceAwareModel):
    """Retrieval-based chatbot using pretrained 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, encoder, and memory monitor
        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,
            index_file_path='new_iteration/data_prep_iterative_models/faiss_indices/faiss_index_production.index',
            response_pool=[],
            max_length=self.config.max_context_token_limit,
            query_embeddings_cache={},
            neg_samples=self.config.neg_samples,
            index_type='IndexFlatIP',
            nlist=100, # Not used with IndexFlatIP
            max_retries=self.config.max_retries
        )
        
        # 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="cross-encoder/ms-marco-MiniLM-L-12-v2")

    def _initialize_summarizer(self) -> Summarizer:
        """Initialize the Summarizer."""
        return Summarizer(
            tokenizer=self.tokenizer,
            model_name="t5-small",
            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.index_file_path}...")
            self.data_pipeline.load_faiss_index(self.data_pipeline.index_file_path)
            logger.info("FAISS index loaded successfully.")
            
            # Load response pool associated with the FAISS index
            response_pool_path = self.data_pipeline.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)
        
        # 1) Load config
        with open(load_dir / "config.json", "r") as f:
            config = ChatbotConfig.from_dict(json.load(f))
        
        # 2) Initialize chatbot
        chatbot = cls(config, mode=mode)
        
        # 3) Load DistilBERT from huggingface folder
        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)
        
        # 4) Load tokenizer
        chatbot.tokenizer = AutoTokenizer.from_pretrained(load_dir / "tokenizer")
        logger.info(f"Models and tokenizer loaded from {load_dir}")
        
        
        
        # 5) Load the custom top layers' 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.")
        
        # 6) If in 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:
        """Internal method to 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 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 the HF DistilBERT submodule:
        self.encoder.pretrained.save_pretrained(save_dir / "shared_encoder")
        
        # ALSO save custom top-level layers' weights
        self.encoder.save_weights(save_dir / "encoder_custom_weights.weights.h5")

        # Save tokenizer
        self.tokenizer.save_pretrained(save_dir / "tokenizer")
        
        logger.info(f"Models and tokenizer saved to {save_dir}.")
    
    def sigmoid(self, x: float) -> float:
        return 1 / (1 + np.exp(-x))
    
    def retrieve_responses_cross_encoder(
        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 with optional domain-based boosting 
        and cross-encoder re-ranking.

        Args:
            query: The user's input text.
            top_k: Number of final results to return.
            reranker: CrossEncoderReranker for refined scoring, if available.
            summarizer: Summarizer for long queries, if desired.
            summarize_threshold: Summarize if query wordcount > threshold.

        Returns:
            List of (response_text, final_score).
        """
        # 1) Optional 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)
        #logger.debug(f"Detected domain '{detected_domain}' for query: {query}")

        # Retrieve initial candidates from FAISS
        initial_k = min(top_k * 10, len(self.data_pipeline.response_pool))
        faiss_candidates = self.retrieve_responses_faiss(query, domain=detected_domain, top_k=initial_k)
        
        texts = [item[0] for item in faiss_candidates]
        
        # Re-rank these boosted candidates
        if not reranker:
            reranker = CrossEncoderReranker(model_name="cross-encoder/ms-marco-MiniLM-L-12-v2")
        
        ce_scores = reranker.rerank(query, texts, max_length=256)

        # Combine cross-encoder score with the base FAISS score (simple multiplicative approach)
        final_candidates = []
        for (resp_text, faiss_score), ce_score in zip(faiss_candidates, ce_scores):
            # TODO: dial this in.
            ce_prob = self.sigmoid(ce_score)  # ~ relevance in [0..1]
            faiss_norm = (faiss_score + 1)/2.0
            combined_score = 0.9 * ce_prob + 0.1 * faiss_norm
            # alpha = 0.9
            # print(f'CE SCORE: {ce_score} FAISS SCORE: {faiss_score}')
            # combined_score = alpha * ce_score + (1 - alpha) * faiss_score
            length_adjusted_score = self.length_adjust_score(resp_text, combined_score)
            #combined_score = ce_score * faiss_score
            #final_candidates.append((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]
    
    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'],
    }
    
    def extract_keywords(self, query: str) -> List[str]:
        """
        Return any domain keywords present in the query (lowercased).
        """
        query_lower = query.lower()
        found = set()
        for domain, kw_list in self.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 or numeric lines; mildly reward longer lines.
        Adjust carefully so you don't overshadow cross-encoder signals.
        """
        words = text.split()
        wcount = len(words)
        
        # Penalty if under 3 words
        if wcount < 4:
            return base_score * 0.8
        
        # Bonus for lines > 12 words
        if wcount > 12:
            extra = min(wcount - 12, 8)
            bonus = 0.0005 * extra
            base_score += bonus
        
        return base_score
    
    def detect_domain_from_query(self, query: str) -> str:
        """
        Detect the domain of the query based on keywords.
        """
        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), 
        with optional punctuation like '.' at the end.
        """
        pattern = r'^[\s]*[\d]+([\s.,\d]+)*[\s]*$'
        return bool(re.match(pattern, text.strip()))
    
    def retrieve_responses_faiss(
        self, 
        query: str,
        domain: str = 'other',
        top_k: int = 5,
        boost_factor: float = 1.05
    ) -> 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, optional): The detected domain. Defaults to 'other'.
            top_k (int, optional): Number of top results to return. Defaults to 5.
            boost_factor (float, optional): Factor to boost scores for keyword matches. Defaults to 1.3.

        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
            response = self.data_pipeline.response_pool[idx]
            text = response.get('text', '').strip()
            cand_domain = response.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, apply a small boost
            if query_keywords and any(kw in resp_text.lower() for kw in query_keywords):
                new_score *= boost_factor
                #logger.debug(f"Boosting response: '{resp_text}' by factor {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)
        
        # Print top 10
        for resp, score in boosted[:150]:
            logger.debug(f"Candidate: '{resp}' with score {score}")

        # 8) Return top_k
        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]]:
        """
        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):
            # 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.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.
        This method handles:
        - Checkpoint loading/restoring
        - LR scheduling
        - Epoch/iteration tracking
        - Optional training-history logging
        - Basic early stopping
        """
        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 // 10)  # 10% of the dataset

        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.")

        # Initialize optimizer with dummy step
        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
        )

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

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

        # If you want to log all epoch losses across runs
        if not hasattr(self, 'history'):
            self.history = {'train_loss': [], 'val_loss': [], 'learning_rate': []}

        if latest_checkpoint and not test_mode:
            # Add checkpoint inspection
            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 so we keep counting from that
            checkpoint.epoch.assign(tf.cast(initial_epoch, tf.int32))
            logger.info(f"Resuming from epoch {initial_epoch}")
            
            # If you want to load old 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}")
            
            # Fix for custom weights not being saved in the full model.
            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)

        # Optional: test/debug mode with small subset
        if test_mode:
            subset_size = 150
            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.config.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}...")

            # --- Training Phase ---
            epoch_loss_avg = tf.keras.metrics.Mean(dtype=tf.float32)
            batches_processed = 0

            # Progress bar
            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

            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 Phase ---
            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()

            # (Optional) Save model for quick 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
            # (Create or overwrite the file so we always have the latest.)
            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
            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.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
        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)
        
        # Apply gradients
        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.
        """
        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)  # 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)

        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 = 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 phase
                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 phase
                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
                
                # 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)