Intelligent datastore search using live embedding

A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

• receiving a search query in a natural language from a device associated with a user;
• converting the search query to a first artificial intelligence (AI) prompt comprising a command that calls an embedding function within an AI model, wherein the AI prompt specifies natural language text to be embedded, and wherein embedding natural language text converts the natural language text to a multi-dimensional vector;
• sending the AI prompt to an AI model;
• receiving, from the AI model, a query embedding representing the search query;
• performing a similarity search between the query embedding and a database of embeddings to identify one or more candidate results, wherein the database of embeddings comprises a plurality of entries, each entry comprising a metadata object describing available data stored at a data source, and a previously generated embedding representing the available data;
• selecting, from the one or more candidate results, a search result;
• and
• sending the search result to the device associated with the user, wherein the search results comprise a link to the available data stored at the data source.

A computer-implemented system, comprising:

• one or more computers;
• and
• one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising:
• receiving a search query in a natural language from a device associated with a user;
• converting the search query to a first artificial intelligence (Al) prompt comprising a command that calls an embedding function within an AI model, wherein the AI prompt specifies natural language text to be embedded, and wherein embedding natural language text converts the natural language text to a multi-dimensional vector;
• sending the AI prompt to an AI model;
• receiving, from the AI model, a query embedding representing the search query;
• performing a similarity search between the query embedding and a database of embeddings to identify one or more candidate results, wherein the database of embeddings comprises a plurality of entries, each entry comprising a metadata object describing available data stored at a data source, and a previously generated embedding representing the available data;
• selecting, from the one or more candidate results, a search result;
• and
• sending the search result to the device associated with the user, wherein the search results comprise a link to the available data stored at the data source.