Indexing Millions of Wikipedia Articles With Upstash Vector

Upstash has developed a vector database capable of handling scalable similarity searches across millions of vectors, offering features like namespaces, metadata filtering, and built-in embedding models to support a wide range of applications. As a demonstration of its capabilities, Upstash undertook an ambitious project to create a semantic search engine and RAG chat bot using data from Wikipedia, leveraging the multilingual BGE-M3 model for embedding and indexing over 144 million vectors across eleven popular languages. The database efficiently handles large datasets by embedding paragraphs rather than entire articles and integrating article titles to improve query accuracy. The project also highlights the benefits of using Upstash Vector for approximate nearest neighbor searches, which improves performance by overquerying the index and refining results on the client side. The integration of Upstash RAG Chat SDK further showcases the seamless connection between the vector database and chat applications, facilitated by tools like Upstash Redis for storing chat histories and QStash LLM APIs for LLM integration. The successful indexing and querying of Wikipedia data demonstrate how Upstash Vector's features make it a robust and scalable solution for building reliable semantic search systems.