Natural language query processing using large language model-based asset analysis

A computer system comprising:

• one or more memories;
• and
• at least one processor coupled to the one or more memories, wherein the at least one processor is configured to:
• generate, using a large language model (LLM), a first Structured Query Language (SQL) statement based on a user query provided by a user;
• extract a plurality of entities, based on the first SQL statement, comprising one or more columns, filter columns, and categorical filters;
• perform a semantic search on an index of a plurality of databases using the one or more columns, filter columns, and categorical filters to determine a confidence score for each database of the plurality of databases, wherein the confidence score indicates a match for each database with respect to the one or more extracted columns, filter columns, and categorical filters;
• select a database based on the confidence score and extract a schema of the selected database;
• generate, using the LLM, a second SQL statement based on the user query and the schema;
• execute the second SQL statement on the selected database to generate query results;
• and
• present the query results to the user.

A computer program product, the computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by at least one processor to cause the at least one processor to:

• generate, using a large language model (LLM), a first Structured Query Language (SQL) statement based on a user query provided by a user;
• extract a plurality of entities, based on the first SQL statement, comprising one or more columns, filter columns, and categorical filters;
• perform a semantic search on an index of a plurality of databases using the one or more columns, filter columns, and categorical filters to determine a confidence score for each database of the plurality of databases, wherein the confidence score indicates a match for each database with respect to the one or more extracted columns, filter columns, and categorical filters;
• select a database based on the confidence score and extract a schema of the selected database;
• generate, using the LLM, a second SQL statement based on the user query and the schema;
• execute the second SQL statement on the selected database to generate query results;
• and
• present the query results to the user.