US 12,748,755 B1Granted

Generative AI-based algorithm for improved cache hit performance in text-to-SQL systems

AssigneeOracle International Corporation

InventorLuigi Saetta

Life of this patent

Estimating the term…

Loading patent…

Abstract

Techniques are disclosed herein for optimizing text-to-SQL systems using generative models to identify structurally equivalent requests in a cache to improve natural language conversion to SQL queries. In one aspect, a method includes: receiving a data request from a user. An embedding model embeds the data request generating a request embedding vector that is used to search a cache comprising stored requests. A candidate request is identified that comprises a curated natural language utterance that is structurally equivalent to the natural language utterance in the data request. The data request and the candidate request are transmitted to a generative model, that determines if the two requests are structurally equivalent and generates an SQL instruction corresponding to the data request using the SQL instruction associated with the candidate request as a template. The SQL instruction, a query result, or both are provided to the user.

Claims20

Dependent claims

A computer implemented method comprising:

receiving a data request from a user, wherein the data request comprises a natural language utterance;
embedding, using an embedding model, the data request to generate a request embedding vector;
searching, using the request embedding vector, a cache, wherein the cache comprises stored requests, and each of the stored requests comprises:
a curated natural language utterance, an SQL instruction associated with the curated natural language utterance, and an embedding vector for the curated natural language utterance;
identifying, based on the searching, a candidate request from the stored requests that comprises a curated natural language utterance that is structurally equivalent to the natural language utterance in the data request;
transmitting, using a prompt, the data request, and the candidate request to a generative model;
determining, using a structural verification process executed by the generative model, that the natural language utterance in the data request and the curated natural language utterance in the candidate request are structurally equivalent;
generating, by the generative model using the SQL instruction associated with the curated natural language utterance of the candidate request as a template, an SQL instruction corresponding to the data request;
and
providing the SQL instruction, a query result, or both to the user.

7 more elements

A system comprising:

one or more processors;
and
one or more computer-readable media storing instructions which, when executed by the one or more processors, cause the system to perform operations comprising:
receiving a data request from a user, wherein the data request comprises a natural language utterance;
embedding, using an embedding model, the data request to generate a request embedding vector;
searching, using the request embedding vector, a cache, wherein the cache comprises stored requests, and each of the stored requests comprises:
a curated natural language utterance, an SQL instruction associated with the curated natural language utterance, and an embedding vector for the curated natural language utterance;
identifying, based on the searching, a candidate request from the stored requests that comprises a curated natural language utterance that is structurally equivalent to the natural language utterance in the data request;
transmitting, using a prompt, the data request and the candidate request to a generative model;
determining, using a structural verification process executed by the generative model, that the natural language utterance in the data request and the curated natural language utterance in the candidate request are structurally equivalent;
generating, by the generative model using the SQL instruction associated with the curated natural language utterance of the candidate request as a template, an SQL instruction corresponding to the data request;
and
providing the SQL instruction, a query result, or both to the user.

10 more elements

One or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:

receiving a data request from a user, wherein the data request comprises a natural language utterance;
embedding, using an embedding model, the data request to generate a request embedding vector;
searching, using the request embedding vector, a cache, wherein the cache comprises stored requests, and each of the stored requests comprises:
a curated natural language utterance, an SQL instruction associated with the curated natural language utterance, and an embedding vector for the curated natural language utterance;
identifying, based on the searching, a candidate request from the stored requests that comprises a curated natural language utterance that is structurally equivalent to the natural language utterance in the data request;
transmitting, using a prompt, the data request and the candidate request to a generative model;
determining, using a structural verification process executed by the generative model, that the natural language utterance in the data request and the curated natural language utterance in the candidate request are structurally equivalent;
generating, by the generative model using the SQL instruction associated with the curated natural language utterance of the candidate request as a template, an SQL instruction corresponding to the data request;
and
providing the SQL instruction, a query result, or both to the user.

7 more elements

Dependencies are inferred from the claim text.