Knowledge Graph-Driven Retrieval-Augmented Generation(RAG)

Implementation of Retrieval-Augmented Generation (RAG) Systems with Neo4j Knowledge Graphs

Knowledge Graph- Neo4j

Prerequisites:

Understanding of

• Retrieval Augmented Generation(RAG) is a framework that empowers the LLM(Large Language Models) to extend their knowledge beyond their training data by augmenting it with data from external sources or private datasets. It does this by combining document retrieval and text generation using vector similarity.
• GraphRAG generates knowledge graphs and summaries from private unstructured knowledge datasets using large language models(LLMs). It uses the knowledge graph at query time to improve retrieval-augmented generation(RAG) for more accurate and complete answers for knowledge spread across datasets.

This article demonstrates how to build a Knowledge graph in Neo4j using langchain_experimental library and extracting the data from a PDF document. Once the knowledge graph is built, query it using an LLM to deliver accurate, context-aware answers.

Ensure you have installed these libraries and refer to the correct versions, as the version compatibility changes as new features are released.

#!pip install…