Performing a semantic search on a digital media database

A method comprising:

• obtaining, from a database, multiple digital media and multiple metadata associated with the multiple digital media,
• wherein a digital medium among the multiple digital media includes a metadata among the multiple metadata,
• wherein the metadata among the multiple metadata includes multiple tags generated by a large language model to describe the digital medium and a transcript associated with the digital medium;
• enabling a semantic search of the multiple digital media to obtain multiple multidimensional vectors by:
• combining and converting the multiple tags and the transcript associated with the digital medium into a first multidimensional vector in a multidimensional space,
• wherein the first multidimensional vector encodes information included in the multiple tags and the transcript associated with the digital medium as a numerical representation in the multidimensional space,
• wherein a distance between the first multidimensional vector and a second multidimensional vector in the multidimensional space indicates similarity between the metadata associated with the first multidimensional vector and a second metadata represented by the second multidimensional vector;
• obtaining a natural language query associated with the database;
• converting the natural language query into a third multidimensional vector in the multidimensional space;
• perform the semantic search of the multiple digital media by:
• determining whether the first multidimensional vector satisfies a distance threshold to the third multidimensional vector;
• and
• upon determining that the first multidimensional vector satisfies the distance threshold, presenting the digital medium as a result to the natural language query.

A system comprising:

• at least one hardware processor;
• and
• at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:
• obtain, from a database, multiple digital media and multiple metadata associated with the multiple digital media,
• wherein a digital medium among the multiple digital media includes a metadata among the multiple metadata,
• wherein the metadata among the multiple metadata includes multiple tags generated by a large language model to describe the digital medium and a transcript associated with the digital medium;
• enable a semantic search of the multiple digital media to obtain multiple multidimensional vectors by:
• combining and converting the multiple tags and the transcript associated with the digital medium into a first multidimensional vector in a multidimensional space,
• wherein the first multidimensional vector encodes information included in the multiple tags and the transcript associated with the digital medium as a numerical representation in the multidimensional space,
• wherein a distance between the first multidimensional vector and a second multidimensional vector in the multidimensional space indicates similarity between the metadata associated with the first multidimensional vector and a second metadata represented by the second multidimensional vector;
• obtain a natural language query associated with the database;
• convert the natural language query into a third multidimensional vector in the multidimensional space;
• perform the semantic search of the multiple digital media by:
• determining whether the first multidimensional vector satisfies a distance threshold to the third multidimensional vector;
• and
• upon determining that the first multidimensional vector satisfies the distance threshold, presenting the digital medium as a result to the natural language query.