Introduction Knowledge graphs, which offer a structured representation of data and its relationships, are revolutionising how we organise and access information. With large amounts of data, it sometimes becomes difficult to draw insights from it. This blog article examines how to combine Neo4j, a graph database, with OpenAI’s Retrieval-Augmented Generation (RAG) model to build a robust knowledge management system.

Introduction The ability to use cutting-edge tools and frameworks is essential for staying ahead in the ever-changing field of technology. When it comes to natural language processing, search capabilities, and graph databases, LLM (Large Language Models), Typesense , and Neo4j work together as a powerful trio that offers a cohesive and effective solution.

Introduction Many users of chatbots such as ChatGPT, have encountered the problem of receiving inappropriate or incompatible responses. There are several reasons why this might happen. One reason is the lack of appropriate training data, as chatbots are usually trained on large amounts of text and code. If the data is insufficient or of poor quality, the chatbot may misunderstand queries and provide inaccurate responses.

Background Clustering is a fascinating technique used in machine learning, where patterns or data points are grouped based on their similarities. It’s like finding hidden connections among different data points without predefined labels. However, clustering comes with its fair share of challenges that make it more complex than supervised classification. One major difficulty with clustering is the absence of labelled data.