Solving multilingual queries using vector database

A computer program product comprising:

• one or more computer-readable storage media;
• and
• program instructions stored on the one or more storage media to perform operations comprising:
• causing a received query to be translated from a first language to a second language;
• causing potential answers to be generated for the translated query;
• causing features to be extracted from the translated query;
• causing the extracted features to be converted into feature vectors;
• causing the feature vectors to be compared against existing vectors in a knowledge base that correspond to past question-answer pairs;
• causing the potential answers to be ranked based at least in part on an outcome of comparing the feature vectors against the existing vectors in the knowledge base;
• causing a subset of the potential answers to be enriched with additional context and/or information by modifying the potential answers in the subset to incorporate how often the respective potential answers have been modified in response to being generated as previous final answers;
• evaluating correctness and context of the ranked potential answers and the enriched potential answers;
• causing a final answer to be generated based at least in part on the ranked potential answers and the enriched potential answers;
• and
• using feedback that has been weighted to re-train an AI based model, wherein the unweighted feedback is received from a user in response to the final answer being shown on a display screen.

A computer system comprising:

• a processor set;
• one or more computer-readable storage media;
• and
• program instructions stored on the one or more storage media to cause the processor set to perform operations comprising:
• causing a received query to be translated from a first language to a second language;
• causing potential answers to be generated for the translated query;
• causing features to be extracted from the translated query;
• causing the extracted features to be converted into feature vectors;
• causing the feature vectors to be compared against existing vectors in a knowledge base that correspond to past question-answer pairs;
• causing the potential answers to be ranked based at least in part on an outcome of comparing the feature vectors against the existing vectors in the knowledge base;
• causing a subset of the potential answers to be enriched by altering the potential answers in the subset based at least in part on how the respective potential answers have been modified in response to being generated as previous final answers;
• evaluating correctness and context of the ranked potential answers and the enriched potential answers;
• causing a final answer to be generated based at least in part on the ranked potential answers and the enriched potential answers;
• and
• using feedback that has been weighted to re-train an AI based model, wherein the unweighted feedback is received from a user in response to the final answer being shown on a display screen.