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

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.

✨ Congratulations to FalkorDB, Intel Ignite Tel-Aviv batch 9 startup, on raising $3M in funding! The round was led by Angular Ventures, with participation by K5 Tokyo Black; Aryeh Mergi - Co-Founder M-Systems, XtreamIO, Pliops; Jerry Dischler - President, Cloud Applications at Google; Eldad Farkash & Saar Bitner - Firebolt Co-Founders. Guy Korland, Roi Lipman, and Avi Avni - we are so proud of you and are thrilled to be a part of your journey! FalkorDB is developing a solution that fully automates the conversion of organizational information into graphs, a process that can otherwise be complex and challenging. The combination of FalkorDB with #GraphRAG provides an innovative solution that leverages the ability to manage graph data efficiently and quickly, and the ability to integrate relevant external knowledge to improve decision-making processes and interactions with large language models. Learn more about FalkorDB in CTech by Calcalist’s Boarding Pass: https://lnkd.in/d9VyaBJv #IntelIgnite #IamIntel #DeepTech #LLMs #GenAI #Funding

Scaling LLMs for the enterprise is incredibly complex, with challenges around security, auditing, and integration. That’s why we invested in FalkorDB. The team’s visionary GraphRAG solution bridges LLMs with enterprise-grade knowledge graphs. With this powerful combination, knowledge graphs provide structured, validated data, while LLMs generate natural language text that humans can understand and trust. To read the full story about FalkorDB, check out this week’s newsletter, linked in the comment section below. And don’t forget to subscribe to get exclusive insights into enterprise tech, venture capital, and more each week.

While RAG is acclaimed for efficiently fetching related documents for answering queries at relatively lower costs, it faces several limitations in the retrieval process. Advanced RAG, or GraphRAG, emerges as a solution to overcome these limitations by leveraging ‘semantic’ reasoning and retrieval. Key considerations for effectively utilizing GraphRAG include information extraction techniques to infer and generate connections between chunked data, knowledge indexing for storage and retrieval, and models for generating graph queries, such as the Cypher Generation Model. FalkorDB is a super-fast graph database built for applications that need lightning-quick responses without sacrificing data flexibility. Users trust FalkorDB for its uncompromising performance. It can be easily run using Docker.

At #XrVoyage, we generate #TypeScript #BabylonJs plugins and reconcile iterations in #FalkorDB. Why would you do it any other way? :D

New DAGWorks Inc. post on Hamilton Burr FalkorDB for #GraphRAG! Graph DBs have always been really exciting technology -- does anyone else remember the release of Meta's graph-search? That said, they've had some adoption challenges. Generalizing (obviously), they (1) often require data to be extremely well structured for ingestion (relying on relationships that might not be known in advance or be subjective), and (2) often need a specific query language that was close-to-but-not-quite SQL. With the prevalence of #LLMs, however, I think that's all about to change. AI can help turn unstructured data into more structured data (deriving relationships), and turn natural language queries into graph queries. Generally I'm skeptical (bordering on cynical) of the GenAI craze, but this is something I think can really help move things forward. Stoked that Stefan Krawczyk had the chance to work with Falkor to show a very powerful example of how to do this, using both Hamilton *and* Burr. This post shows a full example of a highly parallelized ingestion ETL, a conversational interface, and use of the cypher query language (generated by an LLM) to build a powerful conversational interface on graph data. https://lnkd.in/g-WcJ5ku