"""
Embedding generation utilities using sentence transformers.
"""
from typing import List, Union
from sentence_transformers import SentenceTransformer
import numpy as np


class EmbeddingGenerator:
    """Handles text embedding generation."""
    
    def __init__(self, model_name: str = 'all-MiniLM-L6-v2'):
        """
        Initialize embedding generator.
        
        Args:
            model_name: Name of the sentence transformer model
        """
        self.model_name = model_name
        self.model = SentenceTransformer(model_name)
        print(f"[Embeddings] Loaded model: {model_name}")
    
    def generate_embedding(self, text: str) -> List[float]:
        """
        Generate embedding for a single text.
        
        Args:
            text: Input text
            
        Returns:
            Embedding vector as list of floats
        """
        embedding = self.model.encode([text])[0]
        return embedding.tolist()
    
    def generate_embeddings(self, texts: List[str]) -> List[List[float]]:
        """
        Generate embeddings for multiple texts (batch processing).
        
        Args:
            texts: List of input texts
            
        Returns:
            List of embedding vectors
        """
        if not texts:
            return []
        
        embeddings = self.model.encode(texts, show_progress_bar=len(texts) > 10)
        return embeddings.tolist()
    
    def compute_similarity(
        self,
        embedding1: Union[List[float], np.ndarray],
        embedding2: Union[List[float], np.ndarray]
    ) -> float:
        """
        Compute cosine similarity between two embeddings.
        
        Args:
            embedding1: First embedding vector
            embedding2: Second embedding vector
            
        Returns:
            Cosine similarity score (0-1)
        """
        # Convert to numpy arrays if needed
        emb1 = np.array(embedding1) if isinstance(embedding1, list) else embedding1
        emb2 = np.array(embedding2) if isinstance(embedding2, list) else embedding2
        
        # Compute cosine similarity
        dot_product = np.dot(emb1, emb2)
        norm1 = np.linalg.norm(emb1)
        norm2 = np.linalg.norm(emb2)
        
        if norm1 == 0 or norm2 == 0:
            return 0.0
        
        return float(dot_product / (norm1 * norm2))
    
    def get_model_info(self) -> dict:
        """
        Get information about the loaded model.
        
        Returns:
            Dictionary with model information
        """
        return {
            "model_name": self.model_name,
            "embedding_dimension": self.model.get_sentence_embedding_dimension(),
            "max_seq_length": self.model.max_seq_length
        }


# Global embedding generator instance
embedding_generator = EmbeddingGenerator()