Visualising AI Embeddings in APEX

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" To deal with hyper-planes in a 14-dimensional space, visualize a 3-D space and say 'fourteen' to yourself very loudly. Everyone does it."     - Geoffrey Hinton , 2018 Turing Award winner. Within the wonderful world of  Generative AI , one concept that is all the rage is RAG, or  Retrieval Augmented Generation , which is an  AI framework that combines the strengths of traditional information retrieval systems (such as databases) with the capabilities of generative large language models (LLM s) . RAG's goal is to improve the accuracy, relevance, and timeliness of information  generation  - such as  documents, text and images -  by optimizing LLM output.  When creating a RAG system, it’s essential to store information in a format that a LLM can retrieve. This is where  data is converted into embeddings through a pro...

Oracle Locator and Spatial

If you already use Oracle Spatial or Locator then you probably already know Simon Greeners SpatialDB Advisor blog, but if not then I can recommend it as an invaluable source of Spatial nuggets.  Whether you are just starting out with Spatial or have some level of experience with it you will find something there which will knock your socks off!

When I was helping a client with creating a geo-spatial database I found it confusing with regard to what is included as part of the Locator licence ('free' with Enterprise Edition) instead of the additional fee option of Spatial.  One of Simons posts really makes it clear what is and isn't included with Locator, and thus I was able to ascertain that the client only really needed the functionality available within Locator, resulting in a considerable saving on licence fees for the Spatial extension.

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