Abstract: The present disclosure provides a system for providing virtual assistant for generating customized query for retrieving data from a database. The system comprises a processor and a memory storing program instructions, which, when executed by the processor, causes the processor to provide one or more index of categories of the data, for efficient retrieval of the data from the database based on a customer's query. The processor is also configured to provide one or more few-shot prompts for use by the customer for generating an output in a predetermined format corresponding to the customer's query. The processor is configured to receive a query from the one or more customer, wherein said query is a text and is based on at least one of index of categories and the few-shot prompt. The processor, based on indexes and few-shot prompt processes the query using natural language processing to extract one or more textual content from the query and sends the query to the database for retrieval of the data.

Abstract: The disclosure relates to a system and method for unsupervised validation of Large Language Model (LLM)-generated outputs. The method includes the steps of receiving input data, which may comprise structured or unstructured text; generating an LLM output based on the input data; extracting a first set of topics from the input data, with each topic represented by a set of keywords; converting the first set of topics into human-readable reference data; extracting a second set of topics from the LLM output; and converting the second set of topics into human-readable candidate data. The method further includes comparing the reference data with the candidate data using one or more performance metrics. Finally, the method determines a validation score based on the comparison results. The disclosed method allows for the objective validation of LLM-generated content, enhancing accuracy and efficiency in diverse applications such as customer service, technical documentation, and legal analysis.

Abstract: The disclosure relates to a system and method for unsupervised validation of Large Language Model (LLM)-generated outputs. The method includes the steps of receiving input data, which may comprise structured or unstructured text; generating an LLM output based on the input data; extracting a first set of topics from the input data, with each topic represented by a set of keywords; converting the first set of topics into human-readable reference data; extracting a second set of topics from the LLM output; and converting the second set of topics into human-readable candidate data. The method further includes comparing the reference data with the candidate data using one or more performance metrics. Finally, the method determines a validation score based on the comparison results. The disclosed method allows for the objective validation of LLM-generated content, enhancing accuracy and efficiency in diverse applications such as customer service, technical documentation, and legal analysis.

Abstract: The present disclosure provides approaches for enhanced query resolution. Initially, a user query is processed to determine a contextual data and identifying one or more indexes from a plurality of indexes. The indexes may be searched to respond to the user query. For each identified index, a permissible content length for a summary of data available in the index is calculated based on the number of indexes determined to be searched and the context size of a query resolution model to be used for processing the user query. Summary of the information available in each index is generated using a summarization model. The sum of content lengths of summaries of each of the indexes does not exceed the context size of the query resolution model. User query and the generated summaries are then provided to the query resolution model for generating a response to the user query.

Abstract: The present disclosure provides approaches for enhanced query resolution. Initially, a user query is processed to determine a contextual data and identifying one or more indexes from a plurality of indexes. The indexes may be searched to respond to the user query. For each identified index, a permissible content length for a summary of data available in the index is calculated based on the number of indexes determined to be searched and the context size of a query resolution model to be used for processing the user query. Summary of the information available in each index is generated using a summarization model. The sum of content lengths of summaries of each of the indexes does not exceed the context size of the query resolution model. User query and the generated summaries are then provided to the query resolution model for generating a response to the user query.

Abstract: Approaches for generating purpose driven responses to queries are described. According to one example, a query may be processed to determine an application field based on a context of the query and accordingly a set of customized prompts may be generated and then combined before being parsed through a query resolution model along with the query to generate a tailored response. The present invention enables tailored responses by leveraging multiple prompts to provide context and customization before querying the query resolution model.

Abstract: The present disclosure discloses a system and method for handling of JSON objects by a large language model (LLM). The proposed system and method involves receiving a user input requesting an analysis of one or more quality records, and extracting the one or more of quality records, wherein each quality record comprises one or more JSON objects. The system and method further involve identifying an applicable rule for parsing each of the one or more JSON objects, parsing each of the one or more JSON objects by an API corresponding to the identified applicable rule, feeding the parsed JSON objects to the LLM to obtain the analysis of the one or more quality records, and displaying the obtained analysis of the one or more quality records to the user.

Abstract: The present disclosure provides a system for providing virtual assistant for generating customized query for retrieving data from a database. The system comprises a processor and a memory storing program instructions, which, when executed by the processor, causes the processor to provide one or more index of categories of the data, for efficient retrieval of the data from the database based on a customer's query. The processor is also configured to provide one or more few-shot prompts for use by the customer for generating an output in a predetermined format corresponding to the customer's query. The processor is configured to receive a query from the one or more customer, wherein said query is a text and is based on at least one of index of categories and the few-shot prompt. The processor, based on indexes and few-shot prompt processes the query using natural language processing to extract one or more textual content from the query and sends the query to the database for retrieval of the data.

Abstract: Various embodiments described herein relate to managing metadata between organizations within an enterprise. In this regard, a set of code comprising one or more code features is received from a first organization. The one or more code features are tagged with one or more code feature tags to associate each of the one or more code features with the one or more code feature tags. The one or more tagged code features are stored in a repository based on the one or more code feature tags. The repository is at least partially accessible by a second organization. Based on a request received from a requestor for one or more features, it is determined if the one or more requested features are tagged code features. Further, one or more of the tagged code features are provided to the requester based on the requested features having been tagged as tagged code features.

Abstract: Various embodiments described herein relate to managing metadata between organizations within an enterprise. In this regard, a set of code comprising one or more code features is received from a first organization. The one or more code features are tagged with one or more code feature tags to associate each of the one or more code features with the one or more code feature tags. The one or more tagged code features are stored in a repository based on the one or more code feature tags. The repository is at least partially accessible by a second organization. Based on a request received from a requestor for one or more features, it is determined if the one or more requested features are tagged code features. Further, one or more of the tagged code features are provided to the requester based on the requested features having been tagged as tagged code features.