Intelligent test data troubleshooting in ERP systems

1 . A computing system for improving real-time test data troubleshooting in an enterprise resource planning (ERP) system, the computing system comprising:

memory;

one or more hardware processors coupled to the memory; and

one or more computer readable storage media storing instructions that, when loaded into the memory, cause the one or more hardware processors to perform operations comprising:

receiving an error message generated by an application of the ERP system when processing a set of test data;

embedding, by an embedding engine using an embedding model, the error message into a message vector;

searching, by a similarity analyzer, a vector database comprising a plurality of object vectors generated from text segments of object documents extracted from a document corpus, wherein the searching identifies, among the plurality of object vectors, a target object vector matching the message vector;

retrieving, from a text segment database, a target text segment corresponding to the target object vector;

identifying a target object document containing the target text segment, wherein the target object document contains text descriptions of the error message; and

generating an answer including instructions to modify the set of test data to resolve one or more errors corresponding to the error message, based on the error message and the target object document, wherein generating the answer comprises:

generating a prompt based on a prompt template including a first placeholder for receiving the error message and a second placeholder for receiving context from the target object document;

sending the prompt to a large language model;

receiving, from the large language model, a reply including step-by-step guidance for troubleshooting the set of test data to resolve the one or more errors; and

presenting the reply on a user interface as the answer.

2 . The computing system of claim 1 , wherein searching the vector database comprises measuring similarities between the message vector and the plurality of object vectors.

3 . The computing system of claim 2 , wherein searching the vector database further comprises determining a maximum of the measured similarities.

4 . The computing system of claim 1 , wherein the operations further comprise creating the vector database based on a group of object documents.

5 . The computing system of claim 4 , wherein creating the vector database comprises dividing the group of object documents into a plurality of text segments and embedding the plurality of text segments into the plurality of object vectors, respectively.

6 . The computing system of claim 4 , wherein creating the vector database comprises filtering a document corpus using a set of rules to identify the group of object documents.

7 . The computing system of claim 4 , wherein the operations further comprise periodically detecting whether there is an update to the group of object documents.

8 . The computing system of claim 7 , wherein the operations further comprise updating the vector database responsive to detecting that the group of object documents have had the update.

9 . A computer-implemented method for improving real-time test data troubleshooting in an enterprise resource planning (ERP) system, the method comprising:

receiving an error message generated by an application of the ERP system when processing a set of test data;

embedding, by an embedding engine using an embedding model the error message into a message vector;

searching, by a similarity analyzer, a vector database comprising a plurality of object vectors generated from text segments of object documents extracted from a document corpus, wherein the searching identifies, among the plurality of object vectors, a target object vector matching the message vector;

retrieving, from a text segment database, a target text segment corresponding to the target object vector;

identifying a target object document containing the target text segment, wherein the target object document contains text descriptions of the error message; and

generating an answer including instructions to modify the set of test data to resolve one or more errors corresponding to the error message, based on the error message and the target object document, wherein generating the answer comprises:

generating a prompt based on a prompt template including a first placeholder for receiving the error message and a second placeholder for receiving context from the target object document;

sending the prompt to a large language model;

receiving, from the large language model, a reply including step-by-step guidance for troubleshooting the set of test data to resolve the one or more errors; and

presenting the reply on a user interface as the answer.

10 . The computer-implemented method of claim 9 , wherein searching the vector database comprises measuring similarities between the message vector and the plurality of object vectors.

11 . The computer-implemented method of claim 10 , wherein searching the vector database further comprises determining a maximum of the measured similarities.

12 . The computer-implemented method of claim 9 , further comprising creating the vector database based on a group of object documents.

13 . The computer-implemented method of claim 12 , wherein creating the vector database comprises dividing the group of object documents into a plurality of text segments and embedding the plurality of text segments into the plurality of object vectors, respectively.

14 . The computer-implemented method of claim 12 , wherein creating the vector database comprises filtering a document corpus using a set of rules to identify the group of object documents.

15 . The computer-implemented method of claim 12 , further comprising periodically detecting whether there is an update to the group of object documents.

16 . The computer-implemented method of claim 15 , further comprising updating the vector database responsive to detecting that the group of object documents have had the update.

17 . One or more non-transitory computer-readable media having encoded thereon computer-executable instructions causing one or more processors to perform a method for improving real-time test data troubleshooting in an enterprise resource planning (ERP) system, the method comprising:

receiving an error message generated by an application of the ERP system when processing a set of test data;

embedding, by an embedding engine using an embedding model, the error message into a message vector;

searching, by a similarity analyzer, a vector database comprising a plurality of object vectors generated from text segments of object documents extracted from a document corpus, wherein the searching identifies, among the plurality of object vectors, a target object vector matching the message vector;

retrieving, from a text segment database, a target text segment corresponding to the target object vector;

identifying a target object document containing the target text segment, wherein the target object document contains text descriptions of the error message; and

generating an answer including instructions to modify the set of test data to resolve one or more errors corresponding to the error message, based on the error message and the target object document, wherein generating the answer comprises:

generating a prompt based on a prompt template including a first placeholder for receiving the error message and a second placeholder for receiving context from the target object document;

sending the prompt to a large language model;

receiving, from the large language model, a reply including step-by-step guidance for troubleshooting the set of test data to resolve the one or more errors; and

presenting the reply on a user interface as the answer.