Raw Content Storage and Analysis Using Topic Maps

A method comprising:

• receiving raw text from a user;
• wherein the raw text comprises a first tag and one or more second tags;
• identifying one or more formatting attributes in the raw text;
• removing the one or more formatting attributes, comprising one or more whitespace characters, to generate a normalized input;
• storing the normalized input in one or more target datasets;
• and
• generating one or more topic maps for the one or more target datasets based on the first tag and the one or more second tags;
• wherein the one or more topic maps comprise one or more references to content items within the one or more target datasets;
• wherein the method is performed by at least one device including a hardware processor.

A system comprising:

• one or more hardware processors;
• one or more non-transitory computer-readable media;
• and
• program instructions stored on the one or more non-transitory computer-readable media that, when executed by the one or more hardware processors, cause the system to perform operations comprising:
• receiving raw text from a user;
• wherein the raw text comprises a first tag and one or more second tags;
• identifying one or more formatting attributes in the raw text;
• removing the one or more formatting attributes, comprising one or more whitespace characters, to generate a normalized input;
• storing the normalized input in one or more target datasets;
• and
• generating one or more topic maps for the one or more target datasets based on the first tag and the one or more second tags;
• wherein the one or more topic maps comprise one or more references to content items within the one or more target datasets;
• parsing the normalized input for the first tag and the one or more second tags;
• wherein the first tag corresponds to one or more topics and the one or more second tags represent a computing system associated with corresponding portions of the normalized input;
• analyzing the first tag and the one or more second tags to identify the one or more topics;
• and
• for each topic of the one or more topics:
• generating a topic description based on the first tag, the one or more second tags, and the content items in the one or more target datasets that are relevant to the topic;
• identifying a set of references to the first tag, the one or more second tags, and the content items in the set of one or more target datasets that are relevant to the topic;
• and
• creating the topic map comprising the topic, the topic description, and the set of references;
• wherein the one or more topic maps comprise the topic maps generated for the one or more topics;
• receiving a first query;
• selecting a second tag, from the one or more second tags, that corresponds to the first query;
• identifying a subset of the one or more topic maps corresponding to the first query and the second tag;
• generating a prompt for a generative artificial intelligence agent comprising a language model based on the first query, the identified subset of the one or more topic maps, and the selected second tag;
• transmitting the prompt to the generative artificial intelligence agent;
• receiving one or more results from the generative artificial intelligence agent;
• storing the one or more results for the first query.