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

✨ As we close this great year, we wanted to thank our 6,000 attendees, partners, #Neo4j experts, and customers like Sky, AstraZeneca, Klarna, Cencora, and ANZ for bringing graphs to 17 cities around the world with our #GraphSummit series – all of you could not inspire us more! Missed them? Don't worry! You can watch many of the sessions on demand: https://bit.ly/3DoNu9I ➡️ They feature some of the biggest trailblazers using graph technology to uncover hidden patterns and relationships and solve the most pressing business problems in ways other technologies simply can’t. The GraphSummit 2025 agenda will be released soon...stay tuned! #Graphdatabase #GraphRAG

Let's revisit this great livestream with Mike Morley, Director of AI/ML Technology at Arcurve Inc., and let's explore how to build a Personal Knowledge Vault using Neo4j's GraphRAG pattern. ➡️ Discover, with a live demo, how to transform website URLs into structured graph documents, enabling advanced Retrieval Augmented Generation (RAG) Full session: https://bit.ly/3Bt1Khc Enjoy! #Neo4j #GraphRAG Alexander Erdl Arcurve

Text embeddings are great at encoding unstructured text, but they aren’t very good at dealing with structured information and operations such as filtering, sorting, or aggregations. What do you need? Other tools, such as #KnowledgeGraphs :) Take a look at Tomaz Bratanic's great post: https://bit.ly/3XMWrjC #Neo4j #llms

Neo4j Live: Entity Architecture for Efficient RAG on Graphs Join us live as we dive into the Entity Architecture for efficient Retrieval-Augmented Generation (RAG) on Knowledge Graphs. The architecture organizes RAG workflows into three distinct layers: an input layer for data ingestion, a middle layer for knowledge graph representation and reasoning, and an output layer for generating contextually accurate AI-driven results. Learn how this innovative approach leverages fixed entities to enhance data retrieval, improve contextual understanding and boost AI performance. Guest: Irina Adamchic, PhD Blog: https://lnkd.in/dp7ttKBM #genai #graphrag #entity #knowledgegraph #neo4j

Curious about the Neo4j GraphQL Library Roadmap for 2024–2025? Here it is! Psst... will launch a managed GraphQL service into Neo4j Aura in 2025🤩 and this will bring many new features... Take a look: https://bit.ly/3YGzrmV #Neo4j #graphQL Darrell Warde

A great webinar where we introduced the ⭐ GraphRAG Python package⭐, supported by #Neo4j, now available on-demand! ☑️ Quickly build knowledge graphs from unstructured text documents ☑️Easily implement knowledge graph retrievers combining graph traversals and vector and full-text search ☑️Develop end-to-end GraphRAG workflows to boost RAG quality and effectiveness in knowledge assistants and other GenAI applications. Watch it now! with Zachary Blumenfeld and Estelle Scifo https://bit.ly/49DDzsV

Neo4j Live: Demystifying the Mahabharata Chatbot with GraphRAG This session explores the intersection of rich cultural heritage and cutting-edge technology. We'll delve into the creation of a unique chatbot that unlocks the complexities of the Mahabharata epic. This chatbot leverages GraphRAG, a powerful technique that utilizes knowledge graphs to empower Large Language Models (LLMs). Discover how GraphRAG empowers the chatbot to navigate the intricate web of characters, events, and relationships within the Mahabharata. We'll showcase the chatbot's capabilities, demonstrating how it fosters interactive learning and exploration of this timeless tale. Guest: Siddhant Agarwal

Livestream SOON! We’ll use GraphRAG to navigate the complexities of one of India’s greatest epics with Siddhant Agarwal. We untangle intricate relationships and foster a deeper understanding of the Mahabharata #graphrag #chatbot https://lnkd.in/dmvQaYtD

🚀 pip install langchain-neo4j and build #GenAI apps on top of unstructured & structured data using GraphRAG. Supported & maintained by Neo4j, the official LangChain & #Neo4j partner package enables: 🔷 Unified retrieval of structured & unstructured data using Neo4j’s leading graph database: representing vectors, docs, tables, and more as graphs of nodes & relationships 🔷 Complete tooling for vector search & text-to-query generation (via Cypher query language) 🔷Seamless chat memory storage & management to maintain context. Read more here: https://bit.ly/41G4Bhh #Langchain #GraphRAG #graphdatabase Zachary Blumenfeld Andreas Kollegger