Agentic GraphRAG

Wed, 29 Oct 2025 00:00:00 +0000

Project Background

To address the problem of temporal discrepancies between model training data and real-world data, Retrieval-Augmented Generation (RAG) technology has emerged. RAG, as the name suggests, is a technique that retrieves relevant data from external data sources (Retrieval) to augment (Argument) the quality of the answers generated (Generation) by large language models.

The earliest RAG employed a simple Retrieval-Generation architecture. We take the user’s question, perform some pre-processing (keyword extraction, etc.), obtain the pre-processed question, and then use an Embedding Model to grab relevant information from a vast amount of data as a Prompt, which is then fed to the large language model to enhance the quality of its responses.

RAG on Apache HugeGraph

Agentic GraphRAG

Project Background