FalkorDB

🚨 Attention Developers and Data Enthusiasts, The End of Life (EOL) deadline for RedisGraph is fast approaching! Now is the time to transition to its robust and innovative successor, FalkorDB. By migrating to FalkorDB, you can ensure seamless transitions, enhanced performance, and continued support for your graph database needs. Don’t wait until the last minute—make the switch today and stay ahead of the curve. For more details on how to migrate and the benefits of FalkorDB, feel free to reach out or check the official documentation. Links for more data are in the first comment. #RedisGraph #FalkorDB #DataMigration #TechUpdate #GraphDatabase #DataManagement #Innovation #TechCommunity Best regards, FalkorDB Team

I’m thrilled to announce that I’ll be presenting at Intel Innovation in San Jose (September 24-25)! 🚀 Join me for my talk: "AI Myopia? GraphRAG Sees the Bigger Picture (vs. Vector RAG)" where I’ll be showcasing how GraphRAG soar beyond the standard Vector RAG. Discover FalkorDB's advancements in real-time data retrieval are pushing the boundaries of AI, graph technology and Knowledge retrieval. Looking forward to connecting with fellow tech enthusiasts and sharing insights on how GraphRAG outperforms traditional Vector RAG approaches. Stay tuned for updates! https://lnkd.in/dbdvtnN7 Special thanks to Intel Ignite! #IntelInnovation #FalkorDB #GraphRAG #AI #GraphDatabase #RAG #LLM

📈 LangGraph excels at creating custom processing chains, empowered by the LangChain Expression Language. Intriguingly, a common pattern emerges in complex LLM applications: the incorporation of cycles during execution. These cycles leverage the LLM's reasoning capabilities to determine the next step. This ability to reason within a loop is fundamental to unlocking the full potential of LLMs, essentially creating "agents." The power of this cyclic, "agentic behavior" is evident in Retrieval Augmented Generation (#RAG) applications. Traditionally, RAG applications retrieve documents and feed them to an #LLM for response generation. While effective, this approach falters when the initial retrieval fails to yield relevant results. A multi-agent approach can be significantly enhanced by leveraging #GraphRAG. This framework allows each agent to specialize by working with a domain-specific knowledge graph, leading to improved performance. FalkorDB is a cutting-edge graph database designed to power large language models (LLMs) with the fastest and most efficient knowledge infrastructure.

🚀 Celebrating the First Year of FalkorDB! 🎉 We are thrilled to unveil the new branding for FalkorDB as part of our one-year celebration! Our refreshed look embodies our commitment to innovation, performance, and reliability in the world of graph databases. A key focus of our rebranding is #GraphRAG – our cutting-edge approach to integrating Graph Relational Augmented Generation, enabling unparalleled data processing and analysis capabilities. This rebranding is more than just a new logo and color scheme; it's a reflection of our evolution and our dedication to providing state-of-the-art solutions for complex data challenges. 🌐 Stay tuned for more updates and exciting developments as we continue to push the boundaries of what's possible with graph technology. Thank you for being a part of our journey! Thanks to Awesome - a Deloitte studio and Group 107 for making it all happen! And to Intel Ignite for making the connection. #FalkorDB #GraphDatabases #GraphRAG #Innovation #Branding #TechNews #Anniversary

More Exciting News from Gartner! 🚀 The latest Gartner Radar: **Generative AI and Knowledge Graphs are happening now!** 🌐✨ #GraphRAG #KnowledgeGraph #LLM

Ever wonder how Netflix knows exactly what you want to watch next? It's not magic, it's movie recommendation systems! In this blog, we'll build a basic movie recommender system using OpenAI and FalkorDB, giving you a taste of how it works! These clever algorithms track your viewing habits, from what you browse to what you rate, to suggest shows and movies you'll love. Think of them as a mix of tech, psychology, and even a bit of art. They're designed to keep you glued to your screen by understanding your tastes. These systems consider everything from genre and language to actors and ratings to create the perfect watchlist. FalkorDB is a high-performance graph #database designed for low-latency retrieval and scalable management of interconnected data. Its speed and reliability make it ideal for applications demanding real-time #access to complex relationships. You are more than welcome to check the full guide 👇 https://lnkd.in/d-Wn7vuj

HybridAGI: A Programmable Graph-based Open Source Framework for Neuro-Symbolic AI HybridAGI is an AgentOS framework designed for creating explainable and deterministic agent systems suitable for real-world applications. It is a programmable LM-based Agent that enables defining behavior using a graph-based prompt programming approach. The metaphor its creators use to describe it that if DSPy is the PyTorch of LMs Applications, HybridAGI is the equivalent of Keras for LMs Agents systems.  It's a memory-centric system which centralizes knowledge, documents, programs and traces into a hybrid vector/graph database. HybridAGI is designed for data scientists, prompt engineers, researchers, and AI enthusiasts who love to experiment with AI.  It is a "Build Yourself" product that focuses on human creativity rather than AI autonomy. It's open source, GitHub link available in the comments. Has anyone used it? Let us know what you think. #KnowledgeGraph #AI #LLM #DataScience #Python #OpenSource #GenAI #EmergingTech 

Knowledge graphs (KGs) are a specific type of #data structure designed to represent entities and the connections between them. They move beyond simply storing information and provide a framework for machines to reason about that information. Here's a breakdown of the key functionalities: 𝗚𝗿𝗮𝗽𝗵 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 - KGs leverage a network structure. Entities (data points) are nodes, and connections (relationships) are labeled edges, providing context (e.g., "works at," "located in"). This enables efficient retrieval based on relationships. For example, a KG can find entities connected to "Alice" by the "works at" label. 𝗚𝗿𝗮𝗽𝗵 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 - KGs extend beyond retrieval by incorporating reasoning. Machines can infer new knowledge based on relationships. Reasoning rules define how to traverse the graph to answer questions. For instance, a rule might state: "A working at B located in City X implies A associated with City X." This allows answering implicit questions, like "Alice's work city?". In essence, #knowledgegraphs act as a semantic network, enabling machines to not just store data but also understand the relationships and meaning within that data. FalkorDB is a high-performance graph #database designed for low-latency and high-throughput applications. It leverages GraphBLAS for efficient graph operations using sparse linear algebra.

Learn the key differences, pros, and cons of Knowledge Graphs vs Vector Databases to choose the best solution for your needs. https://lnkd.in/eKiyFHen #GraphRAG #RAG #LLM #KnowledgeGraph #VectorDatabase

Meta's recent experiment with AI-generated comment summaries on Facebook is raising eyebrows. These summaries aim to capture the essence of discussions, but their accuracy in often-heated comment sections is questionable. Additionally, the use of user data to train the AI has privacy advocates concerned. Meta claims users can opt-out, but the process is opaque and some requests have been denied. This is a situation worth watching, with implications for both content moderation and user privacy on the platform. Designed for high-performance applications, FalkorDB is a cutting-edge graph database that offers unmatched speed and adaptable data modeling capabilities.