Neo4j

Exciting News! Neo4j has reached an incredible milestone, surpassing $200M in revenue! ?? This achievement reflects Neo4j’s leadership in the growing graph technology market, powered by its commitment to improving generative AI accuracy, transparency, and explainability, as well as the strength of its cloud offering! Read all about it here: https://bit.ly/3OAU4MR #Neo4j #GraphDatabase #GenAI

Have you explored Neo4j's LLM Knowledge Graph Builder? ?? Morgan Senechal wrote this detailed blog to help you build your knowledge graph and ask questions about your data in the blink of an eye ;) Take a look and get started! https://bit.ly/4eSvHES

Speed and flexibility when building an app? Neo4j got you covered! ?? Join our next #Neo4jConnections! -Find out about the seven graphs that exist in every enterprise and the business problems they solve -Get walkthroughs of how to build apps that detect more fraud and provide accurate, explainable GenAI results -Learn how to use a graph database to improve data governance, enhance security, streamline processes, and build breakthrough apps Graph databases are the way to preserve the connections and relationships to uncover new patterns in your connected data and find new relationships, context, and insights - get ready to take your app to the next level! Americas and Europe: https://bit.ly/3CJU1LX Asia Pacific: https://bit.ly/3AZT3dU

Going Meta S02E04 – on Semantics, Knowledge Graphs and All Things AI Season 02 Episode 04 of Going Meta – a Series on Semantics, Knowledge Graphs and All Things AI Topic: Jesús Barrasa Repository: https://lnkd.in/dwxTG8eb Knowledge Graph Book: https://bit.ly/3LaqE6b Previous Episodes: https://lnkd.in/ep33Hk76 #graphdatabase #neo4j #graphrag #knowledgegraphs #ontology #data

ICYM: Accelerate GenAI with Knowledge Graphs with Neo4j's GraphRAG Python Package! We are simplifying the end-to-end process for knowledge creation and retrieval so you can get started building GraphRAG apps quickly. ?? Transform documents into GenAI-ready knowledge graphs. ??Easily implement knowledge graph retrievers that combine graph traversals with vector and full-text search for greater completeness and relevance. ??Build end-to-end GraphRAG workflows with just a few lines of code. Zachary Blumenfeld will walk you through this package in this blog https://bit.ly/3UbuctQ ?? You can also register for the webinar coming up on December 12 to learn all: Americas: https://bit.ly/4925um5 Europe: https://bit.ly/3V82Ll8 Asia Pacific: https://bit.ly/3ZiPbhk #Neo4j #GraphRAG #Python

Use the weekend to read (or re-read) this great blog post and learn what is a #KnowledgeGraph! ??Key Characteristics ??Examples ??and more - in one blog by John Stegeman Take a look: https://bit.ly/4dbibvE

???♀? Fraud detection: learn how graph databases are fundamental for identifying and mitigating fraud. Rajat Kanugo, Neo4j Certified Professional and AWS Certified Developer explains the challenges of fraud today, how fraudsters work, and why Neo4j is the right choice for detecting it. https://bit.ly/4e23rPX #Neo4j #graphdatabase #frauddetection

New course at #GraphAcademy for Java developers ?? "Building Neo4j Applications with Spring Data" In this hands-on course, you will develop an application that can read, write, and run queries against a Neo4j graph database of movies and actors Use the Spring Data Neo4j (SDN) library to take advantage of Neo4j and graph databases! Enroll today: https://bit.ly/3V2hgqW #Java #Neo4j

Makbule Gulcin Ozsoy, Leila Messallem, and Jon Besga introduce one of the fine-tuned Neo4j Text2Cypher(2024) models, which serves as a demonstration of how they can enhance performance on Text2Cypher tasks. Check out their findings and capabilities for the Neo4j Text2Cypher (2024) Model here: https://bit.ly/3OjjG0l #Text2Cypher

Neo4j Live: Cognitive Sciences and Dynamic GraphRAG Explore the intersection of cognitive sciences and dynamic graph-based Retrieval Augmented Generation (RAG)! This livestream delves into how cognitive theories can inspire new approaches in machine learning, optimizing data storage, retrieval, and personalization. Learn about the latest advancements in combining graphs and vector stores to create semantic layers, enhancing the in-context learning pipeline, and shaping the future of AI tooling. Guest: Vasilije Markovic https://www.cognee.ai/ #neo4j #graphdatabase #graphrag #rag #genai #llm #science #cognitivescience