Aravind-Suresh commented on issue #10919: URL: https://github.com/apache/pinot/issues/10919#issuecomment-1593475379
Thanks for the inputs @siddharthteotia @jasperjiaguo - yes, given the high dimensionality of the embeddings (OpenAI-davinci embeddings are >12k in dimensions), it's practical to use approximate algorithms. In addition to recommendation systems and vector-search based prompts, there are also applications in semantic searches, clustering (grouping of related issues, text) as well. We recently tried powering automated Q&A via vector-search (using vector search based prompts) and it achieves good precision on unstructured data input as well (we used langchain here - https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/chroma.html) Given that new features are being powered via embeddings (Glean's AI powered enterprise search is one recent example - https://www.glean.com/blog/unlocking-the-power-of-vector-search-in-enterprise), it would be good to evaluate how Pinot can support this in a real-time setup. Looking forward to the collaboration here! -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: commits-unsubscr...@pinot.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@pinot.apache.org For additional commands, e-mail: commits-h...@pinot.apache.org
