Issue with Inbuilt Model for Creating Embeddings

Samuel experienced issues when enabling embeddings on a large collection, leading to an unhealthy cluster. Kishore Nallan suggested rolling back to a previous snapshot, advised on memory calculations for OpenAI embeddings, and confirmed that creating a new cluster should solve the problem.

Gustavo had issues upgrading their cluster and their embedding field wasn't being filled. Jason helped to solve the upgrade issue and advised re-indexing the documents to solve the embedding field issue. Both problems were successfully resolved.

Dima had queries about indexing with embedding in Typesense. Kishore Nallan and Jason provided solutions, including reducing documents sent in an API call and running embeddings on a GPU. They facilitated Dima with the latest RC.

Bill sought clarification on using vector search with multiple word embeddings in Typesense and using them instead of OpenAI's embedding. Kishore Nallan and Jason informed him that their development version 0.25 supports open source embedding models. They also resolved Bill's concerns regarding search performance, language support, and limitations in the search parameters.

Denny inquired about the ideal cluster configuration for handling 1M documents with openAI embedding. Jason recommended a specific configuration, explained record size calculation, and clarified embedding generation speed factors and the conditions that trigger openAI.