Project to download current top news headlines and cluster/categorize them by topic and semantic meaning. I intend to start by downloading news artciles from newsapi. I'll need to get an API KEY. Next will be vectorizing the articles using a pr-trained LLM. I'd like to expolore how I can use ChatGPT or other generative model to absorb and analyze this data for direct question and answer chatbot. Initially, I'll cluster the vectors - kmeans or maybe DBSCAN. I'll summarize the clusters - we'll see what model can absorb the full article context. I've used chatGPT to generate initial code: from newsapi import NewsApiClient from sentence_transformers import SentenceTransformer from sklearn.cluster import KMeans from sklearn.metrics import pairwise_distances_argmin_min Initialize NewsAPI client newsapi = NewsApiClient(api_key='YOUR_API_KEY') ### Fetch today's top headlines from NewsAPI top_headlines = newsapi.get_top_headlines(language='en') ### Extract article content from the top headlines articles = [article['content'] for article in top_headlines['articles'] if article['content']] ### Filter out articles with empty content articles = [article for article in articles if article] ### Initialize SentenceTransformer model model = SentenceTransformer('paraphrase-MiniLM-L6-v2') ### Create embeddings for the articles embeddings = model.encode(articles) ### Cluster the embeddings num_clusters = 5 # Adjust as needed kmeans = KMeans(n_clusters=num_clusters) kmeans.fit(embeddings) clusters = kmeans.labels_ ### Summarize the clusters for cluster_idx in range(num_clusters): cluster_articles = [articles[i] for i, cluster_label in enumerate(clusters) if cluster_label == cluster_idx] centroid_idx = pairwise_distances_argmin_min(kmeans.cluster_centers_[cluster_idx].reshape(1, -1), embeddings)[0][0] centroid_article = articles[centroid_idx] print(f"\nCluster {cluster_idx + 1} - Centroid Article: {centroid_article}") print("Top Articles:") for article in cluster_articles[:3]: print(article)

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