In today's digital age, efficient management and organization of complex, distributed data is crucial. Analyzing and inferring complete and contextually accurate insights from large, distributed data poses a significant challenge, especially in digital ecosystem.
Graph Agents for IDP processes unstructured documents by extracting its information and storing it as knowledge in form of entities and relationships in a graph database, forming a Knowledge Graph(KG). By integrating KG into Retrieval Augmented Generation (RAG) based Large Language Models (LLM), the factual knowledge, contextual understanding and reasoning capabilities of the model are enhanced, aiding in accurate and contextual response to complex user queries and improved decision making, thereby enhancing overall quality and trust of the LLM generated response.
It stands out by connecting siloed data for coherent inferential processing by creating Agents for IDP using Graph RAG based LLM approach. It offers auto transformation and processing of documents, key entity extraction, graphical representation of data and response generation using multiple role-based agents.
