Optimum Cluster for 1M Documents with OpenAI Embedding
TLDR 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.
Sep 01, 2023 (3 months ago)
For vector search, it's just
number of dimensions * 7 bytes
If you’re using a built-in model, then enabling GPU Acceleration will speed up the embedding generation process: https://typesense.helpscoutdocs.com/article/174-gpu-acceleration
Indexed 3011 threads (79% resolved)
Issues with Embeddings on Collection with 80K Documents
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.
Integrating OpenAI Embeddings with DocSearch Scraper
Marcos was looking for how to use OpenAI embeddings with DocSearch. Jason guided with an update to the scraper config, and suggested the GTE built-in model for generic use.
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.
Finding Similar Documents Using JSON and Embeddings
Manish wants to find similar JSON documents and asks for advice. Jason suggests using Sentence-BERT with vector query and provides guidance on working with OpenAI embeddings and Typesense. They discuss upcoming Typesense features and alternative models.
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.