Building Graph RAG for structured and unstructured data.

RAG (Retriever-Augmented Generation) architecture helps solve the issue of missing contextualization in LLMs (Large Language Models) without the need for fine-tuning. While Vector RAGs offer some contextualization, graph-based RAGs capture more intricate relationships, making them more effective. This post discusses how to build knowledge graphs from both unstructured data (like PDFs) and structured data (like CSVs) using tools such as Langchain and Neo4j. It also outlines steps for extracting text, chunking documents, constructing graphs, and querying the graph databases using LLMs.