Wikipedia Semantic Search with Weaviate — 11.3M Articles

AI Impact Summary

This project establishes a semantic search solution leveraging Wikipedia data within Weaviate, utilizing a vector database and SentenceBERT models for efficient querying. The implementation involves importing a massive 11.348 million Wikipedia articles into Weaviate, employing a schema with Article and Paragraph classes and vectorizing paragraph content with the ‘text2vec-transformers’ module. This setup enables complex queries, including natural language questions, concept searches, and graph-based relationship exploration, demonstrating a production-ready semantic search system.