Building Advanced RAG Pipelines with Neo4j and LangChain: A Complete Guide to Knowledge Graph-Powered AI

Author(s): GenAI Lab

Originally published on Towards AI.

Learn how to combine Neo4j knowledge graphs with LangChain to build accurate, explainable, and production-ready Retrieval-Augmented Generation (RAG) systems.

Retrieval-Augmented Generation (RAG) has quickly become the go-to architecture for making Large Language Models (LLMs) useful in production. Instead of relying solely on the LLM’s internal memory, RAG connects it with external knowledge sources.

This article discusses the integration of Neo4j with LangChain to create efficient and explainable Retrieval-Augmented Generation (RAG) systems. It covers the motivation for using knowledge graphs, the setup process, and concrete steps for connecting to Neo4j, ingesting data, and implementing hybrid retrieval mechanisms. The pieces highlight the workings of LangChain with Cypher queries, as well as the benefits of leveraging both vector databases and graph-based models to create production-ready AI systems that provide reliable and interpretable outcomes.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI