Reranking Search Results from Different Sources
TLDR Viktor seeks advice on reranking search results. Kishore Nallan suggests hybrid search and custom ranking algorithms. John recommends Metarank as a potential solution.
Apr 20, 2023 (7 months ago)
Kishore Nallan10:07 AM
Kishore Nallan10:10 AM
Kishore Nallan12:29 PM
Indexed 3005 threads (79% resolved)
Improving Search Relevance with Typesense
Viktor asks how Typesense calculates relevance and Jason suggests using vector search, specifically S-BERT embeddings, to better match low information queries to relevant documents.
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.
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.
Enhancing Typesense Search for Multiple Indexes in CRM Data
William was facing issues with Typesense's search performance on CRM data. JinW and Kishore Nallan suggested strategies, such as adjusting typesense tokens and creating a "concatenated" field for better search results.
Discussion on Performance and Scalability for Multiple Term Search
Bill asks the best way for multi-term searches in a recommendation system they developed. Kishore Nallan suggested using embeddings and remote embedder or storing and averaging vectors. Despite testing several suggested solutions, Bill continued to face performance issues, leading to unresolved discussions about scalability and recommendation system performance.