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 (1 month 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 2779 threads (79% resolved)
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.
Discussing Indexing and Embedding Performance in Typesense
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.
Issues with Cluster Upgrade and Embedding Field
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.