Retrieval-Augmented Generation (RAG) has become a mainstream approach for working with large language models (LLMs) since its introduction in early research. At its core, RAG
Highlights Retrieval-augmented generation (RAG) has emerged as a powerful technique to address key limitations of large language models (LLMs). By augmenting LLM prompts with relevant
What is LLM and Knowledge Graph Integration? In today’s AI landscape, there are two key technologies that are transforming machine understanding, reasoning, and natural language
Large Language Models (LLMs) are powerful Generative AI models that can learn statistical relationships between words, which enables them to generate human-like text, translate languages,
If you are working with data, you might be familiar with the concepts of rows and columns, which are the basic building blocks of most
The seminal paper “Unifying Large Language Models and Knowledge Graphs: A Roadmap” published on June 14, 2023, presents a comprehensive framework for integrating the emergent
We’re excited to announce that FalkorDB 4.0 Beta is now available for download and testing. FalkorDB is a graph database that builds on the legacy of RedisGraph,
We are thrilled to announce the release of FalkorDB version 4.0.0-a1, a major update that brings two exciting features to our graph database platform. Check the
In this blog post, I will explain what is RAG, why it is useful, and how to build it using Vector Database and Knowledge Graph as a leading option
If you are looking for a simple way to build a Q&A system based on your knowledge graph, you should check out LangChain. Langchain allows you to