import numpy as np
import faiss
import os


class VectorDB:
    def __init__(self):
        self.index = None
        self.articles = []
        self.dimension = 1024  # Cohere embedding dimension

    def initialize_index(self):
        """Initialize FAISS index"""
        self.index = faiss.IndexFlatL2(self.dimension)

    def add_vectors(self, embeddings, articles):
        """Add vectors to the database"""
        if self.index is None:
            self.initialize_index()

        embeddings_array = np.array(embeddings).astype('float32')
        self.index.add(embeddings_array)
        self.articles.extend(articles)

    def search(self, query_embedding, k=5):
        """Search for similar vectors"""
        if self.index is None or self.index.ntotal == 0:
            return []

        query_array = np.array([query_embedding]).astype('float32')
        distances, indices = self.index.search(query_array, k)

        results = []
        for i, idx in enumerate(indices[0]):
            if idx < len(self.articles):
                result = self.articles[idx].copy()
                result['similarity_score'] = float(distances[0][i])
                results.append(result)

        return results

    def save_index(self, filepath):
        """Save the index to file"""
        if self.index is not None:
            faiss.write_index(self.index, filepath)

    def load_index(self, filepath):
        """Load the index from file"""
        if os.path.exists(filepath):
            self.index = faiss.read_index(filepath)

    def get_article_by_id(self, article_id):
        """Get article by ID/slug"""
        for article in self.articles:
            if (article.get('slug') == article_id or
                article.get('title') == article_id or
                article.get('title', '').lower().replace(' ', '-').replace(',', '').replace('.', '') == article_id):
                return article
        return None