Embed and Search Text at Scale with Modal and Weaviate

The text discusses how a full application that discovers analogies between Wikipedia articles was built by combining serverless infrastructure from Modal with the search and storage capabilities of Weaviate. Modal is used for computing vector embeddings, while Weaviate provides both a scalable, managed deployment and all the knobs needed to configure indexing to maximize speed. The application uses async indexing, product quantization, vector index configuration, text search configuration, and batch imports to improve performance. The author recommends using Modal and Weaviate together for data- and compute-intensive applications of generative models and artificial intelligence.

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