Abstract: Disclosed is system (100) for generating visualizations from structured data (SD) queried through natural language.

Abstract: Disclosed is a method comprising receiving user query (UQ) indicative of task (202) associated with retrieval of data (204) from database (206) comprising structured and unstructured data sources (SAUDS) (208) and generating relevant response (RR) (210) for UQ; generating task plan (220) for determining embedding (224) for task, performing retrieval task (226) for identifying relevant SAUDS (228), generating relevance scores (230), and when relevance scores indicate unstructured pipeline (232): triggering unstructured data query engine (UDQE) (234, 306) for retrieving data, and generating RR, or when relevance scores indicate structured pipeline (236): triggering structured data query engine (SDQE) (238, 308) for retrieving data, and generating RR, or when relevance scores indicate hybrid pipeline (240): triggering SDQE for retrieving first portion of data (FPD) (242), transforming received UQ and FPD, triggering UDQE for retrieving second portion of data (SPD) (246), and combining FPD and SPD for generating

Abstract: The present disclosure provides a method for recommendation of suitable assets using Large Language Model (LLM), method comprising receiving descriptors for each asset from amongst plurality of assets; generating embeddings for each asset from amongst plurality of assets, by employing LLM; creating database of assets by storing generated embeddings for each asset from amongst plurality of assets in database; receiving user query pertaining to request for recommendation to solve enterprise problem; generating cosine similarity scores, wherein each cosine similarity score is generated between user query and corresponding generated embedding for given asset stored in database of assets; identifying predefined number of similar assets from amongst database of assets, based on highest cosine similarity scores; constructing optimal prompt based on identified predefined number of similar assets and user query; and prompting LLM, using constructed optimal prompt for generating response as recommendation of identified