Utilizing Vector Search Feature
TLDR em1nos had questions about using the vector search feature. Jason provided clarification and how to use it correctly, and noted that version 0.24.0.rcn60 or 58 is recommended.
Jan 26, 2023 (8 months ago)
Just to make sure: I can only vector_search by giving it vectors, right?
So for example to find similar products to "Samsung Galaxy S22 Ultra" I would take the vectors of that product, the 384 items array, and give that to
Indexed 2764 threads (79% resolved)
Understanding Vector Search with Typesense
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.
Utilizing Vector Search and Word Embeddings for Comprehensive Search in Typesense
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.
Integrating Semantic Search with Typesense
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.
Announcement: General Availability of Typesense v0.25.0
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.
Optimizing Dataset of Podcast Feeds for a Searchable Database
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.