Machine learning-driven system architecture automation

A method, comprising:

• receiving, with a machine learning platform, a query from a user device;
• generating, with the machine learning platform, a query embedding based on the query;
• selecting, with the machine learning platform, artifact embeddings from a set of artifact embeddings based on a closeness between each artifact embedding and the query embedding, the query embedding and the set of artifact embeddings being in a same feature space;
• retrieving, with the machine learning platform, a knowledge graph from a data store, the knowledge graph including nodes representing artifacts corresponding to the selected artifacts and edges representing semantic relationships between the artifacts;
• providing, with the machine learning platform, an input including a representation of the query, the selected artifact embeddings, and the knowledge graph to a generative model to generate an output;
• performing, with the machine learning platform, a postprocessing operation on the output by comparing the output with the selected artifact embeddings to determine whether the output aligns with the selected artifact embeddings;
• transmitting, with the machine learning platform, the output to a user device.

A non-transitory computer-readable storage medium comprising instructions that, when executed by an electronic processor, cause the electronic processor to:

• receive a query from a user device;
• generate a query embedding based on the query;
• select artifact embeddings from a set of artifact embeddings based on a closeness between each artifact embedding and the query embedding, the query embedding and the set of artifact embeddings being in a same feature space;
• retrieve a knowledge graph from a data store, the knowledge graph including nodes representing artifacts corresponding to the selected artifact embeddings and edges representing semantic relationships between the artifacts;
• provide an input including a representation of the query, the selected artifact embeddings, and the knowledge graph to a generative model to generate an output;
• perform a postprocessing operation on the output by comparing the output with the selected artifact embeddings to determine whether the output aligns with the selected artifact embeddings;
• and
• in response to determining that the output aligns with the selected artifact embeddings, transmit the output to the user device.