US 12,386,799 B1
LLM-Graph-of-Thoughts leveraging unified data access across diverse GraphQL APIs
Murali Krishna Akula, Hyderabad Telangana (IN); Uttam Dey, Charlotte, NC (US); and Durga Prasad Kutthumolu, Hyderabad Telangana (IN)
Assigned to Bank of America Corporation, Charlotte, NC (US)
Filed by Bank of America Corporation, Charlotte, NC (US)
Filed on Jun. 20, 2024, as Appl. No. 18/748,241.
Int. Cl. G06F 16/215 (2019.01); G06F 16/2452 (2019.01)
CPC G06F 16/215 (2019.01) [G06F 16/24522 (2019.01)] 20 Claims
OG exemplary drawing
 
1. One or more non-transitory computer-readable media storing computer-executable instructions which, when executed by a processor on a computer system, perform a method for multi-cloud synergistic GraphQL generative artificial intelligence (“AI”) that eliminates a need for manual editing of queries, the method comprising:
receiving a request from an Open Banking application programming interface (“API”) to identify GraphQL APIs;
retrieving data concerning the GraphQL APIs from a GraphQL API schema hub;
transmitting a response to the Open Banking API concerning an accessibility of the GraphQL APIs;
after the transmitting, receiving a request to generate and build a unified GraphQL API based on the retrieved GraphQL APIs;
in response to receiving the request, building the unified GraphQL API by:
searching the Open Banking API to identify data, stored on the Open Banking API, that concerns GraphQL APIs;
retrieving, from the GraphQL API schema hub, Open Banking and GraphQL API schemas related to the GraphQL APIs;
using relationship semantic analysis to identify key relationships between data elements in the GraphQL APIs;
based on the key relationships, forming a unified GraphQL API prompt by generating a text prompt;
transporting the text prompt via a cache to a large language model and Graph-of-Thoughts (“LLM-GoT”) synergistic processor, the LLM-GOT synergistic processor generating information concerning the GraphQL APIs, wherein the information concerning the GraphQL APIs generated by the LLM-GOT synergistic processor includes LLM thoughts concerning relationships between account information and transaction history, each unit of the information concerning the GraphQL APIs being a vertex in a GoT, and each edge in the GoT being a dependency between two vertices in the GoT;
modeling the information concerning the GraphQL APIs generated by the LLM-GOT synergistic processor as an arbitrary graph of nodes that includes connections between the key relationships;
combining, via the LLM-GoT synergistic processor:
distilling networks of the LLM thoughts;
enhancing the LLM thoughts using feedback loops; and
after the distilling and the enhancing, modeling the LLM thoughts, using the arbitrary graph of nodes, into synergistic outcomes;
generating the unified GraphQL API based on the synergistic outcomes; and
storing the unified GraphQL API in the GraphQL API schema hub;
receiving, from a user interface (“UI”), a query;
feeding the query to the unified GraphQL API;
receiving, from the unified GraphQL API, instructions to edit the query; and
executing the instructions to edit the query, eliminating a need to manually edit the query.