Utilizing Vector Search Feature

In a chat with em1nos and Andrew, Kishore Nallan explained how Vector Search works. He clarified that it can be useful for recommendations and personalization, but it requires machine learning to convert data into vectors before searching.

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.

Krish wants to integrate a semantic search functionality with typesense but struggles with the limitations. Kishore Nallan provides resources, clarifications and workarounds to the raised issues.

Jason announces release of Typesense v0.25.0, listing new features. Users express excitement and ask pertinent questions. Gorkem, Manuel, and Daniel commend the team for the new functionalities. Manish and Tugay share their positive experiences with Typesense. Jason and Kishore Nallan answer questions and thank users for their feedback.

Alexander seeks advice on optimizing a podcast database for search. Kishore Nallan suggests data size and stopwords impact RAM usage, and that benchmarking on 1M records would be useful. satish raises the potential need for vector searching. Both recommend feeding user activity data into ML models for relevancy ranking. Collaboration was suggested.