Andrew Ng’s DeepLearning.AI recently a free short course called Knowledge Graphs for RAG, collaborating with Neo4j, the king of graph databases. The course provides an intermediate-level exploration of knowledge graphs specifically tailored for Retrieval Augmented Generation (RAG). 

The one-hour course, led by instructor Andreas Kollegger, who handles developer relations for Generative AI at Neo4j, is free for a limited time.

What will you learn? 

Participants will learn to use Neo4j’s Cypher query language to manage and retrieve data stored within knowledge graphs. 

Moreover, they will learn the fundamentals of knowledge graph data storage, employing nodes to signify entities and edges to denote interrelations. Practical exercises entail using Cypher to extract data from a sample movie and actor graph. Additionally, attendees will learn to integrate vector indexing for unstructured text data, enabling efficient text retrieval based on similarity metrics.

Through practical exercises, they will develop skills in crafting queries that manipulate text data to furnish more pertinent context for LLMs in RAG applications. 

They will also build a question-answering system employing Neo4j and LangChain, enabling interaction with a knowledge graph comprising structured text documents.

This course is recommended for individuals seeking to comprehend knowledge graph mechanics, construct RAG applications, and amplify their data analytics capabilities. It will be an added advantage if you are already familiar with LangChain or have completed the course “LangChain: Chat with Your Data”.

The curriculum throws light on the importance of knowledge graphs in organising intricate data relationships, facilitating intelligent search capabilities, and empowering AI applications to reason across diverse data formats. 

Unlike conventional databases, knowledge graphs adeptly capture contextual nuances, enabling the uncovering of intricate insights and connections.

Databases or data structure servers form the backbone of generative AI, powering its capabilities. Amidst this revolution, Neo4j takes a strategic approach, playing the long game – i.e. building trust in generative AI.  “We provide fuel for generative AI companies in the form of high-quality, structured graph data,” said Dr Jim Webber, chief data scientist, Neo4j, told AIM at their Annual Graph Summit 2023. 

Neo4j said it provides graph data on which large language models can be trained. Over time, knowledge graphs have become vital for organising and accessing enterprise data across industries. Today, Neo4j is pivotal in helping enterprises integrate LLMs to enhance data handling. They’re focusing on two use cases: developing a natural language interface for knowledge graphs and creating knowledge graphs from unstructured data.