Mike Dillinger, PhD的动态

Exciting news for AI Developers! LlamaIndex has introduced the Property Graph Index, a new feature enhancing knowledge graph construction with LLMs. This innovation promises more flexible and robust graph management capabilities. Explore the full announcement for an in-depth look at its features. https://lnkd.in/gMVsQj-H #LlamaIndex #KnowledgeGraphs #AI #DataScience #MachineLearning #TechInnovation

Ready to share your graph story with the world, but doubtful about submitting your talk? Yolande Poirier has 7 key tips for preparing your #NODES2024 talk. https://bit.ly/3VnOEJ4 You can choose between these formats: ?? A 30-minute talk with Q&A ?? A 10-minute lightning talk ?? A two-hour hands-on workshop. Join the biggest graph community gathering! The call for papers ends June 15, 2024! #Neo4j #AI #graphdatabase #visualization #graphdatascience #knowledgegraphs

In the rapidly evolving field of AI, the Retrieval-Augmented Generation (RAG) pattern has become a vital approach to boost the accuracy and relevance of large language models (LLMs). However, as we push the boundaries of innovation, Graph RAG is emerging as a key differentiator, providing superior context management and reasoning capabilities for even more precise answers. The integration of Knowledge Graphs with LLMs is transformative. By leveraging the structured relationships of graph databases like Neo4j, Graph RAG goes beyond simple document retrieval, enabling models to "think" through connected data points and deliver insights with greater context and depth. Having worked extensively with graph technologies, including Neo4j and its application in knowledge-driven AI, I’m particularly excited about the potential this offers for industries needing highly contextualized and accurate AI outputs—from fraud detection to advanced knowledge management. If you're exploring the next level of LLM capabilities or curious about how Graph RAG can enhance your AI models, I highly recommend checking out this excellent guide: Graph RAG Field Guide https://x.gd/3zGOo Let's connect and discuss how these emerging techniques can shape the future of intelligent systems! ??

https://lnkd.in/gbr7si8d So, what exactly is #GraphRAG? For us, it’s a set of #RAG patterns that leverage a graph structure for retrieval. Each pattern demands a unique data structure, or graph pattern, to function effectively. Intrigued? In this post, we’ll dive into the GraphRAG pattern details, breaking down each pattern’s attributes and strategies. #neo4j #graphdatabases #generativeAI #ai #llms #largelanguagemodels

??#GraphRAG?from Microsoft Research is now #OpenSource! GraphRAG is revolutionizing Retrieval-Augmented Generation (RAG) by using knowledge graphs to significantly enhance LLM performance on complex, private datasets! ???? #AI #MachineLearning #Innovation #MicrosoftResearch #OSS #LLM

As LLMs become VLMs, RAG becomes Multimodal RAG. Day 63 of #100DaysofAI - Introduction to Multimodal RAG. Multimodal RAG approach allows models to retrieve relevant information not just from text but from various data sources. This is especially useful for PDFs with multiple modes of data such as tables, images, and text. There are three ways to implement Multimodal RAG: 1. Use OpenCLIP to generate and store image embeddings. Send the images that are similar to the query as context to a VLM. 2. Get text summaries of images from a VLM like GPT-4o, store the summary embeddings in a vector database and send the most similar summaries as context to a LLM along with the query. 3. Get image summaries from a VLM, store the summary embeddings along with the images and then send the summaries, the raw image and the query to a VLM to get the best possible answer. Read more here: https://lnkd.in/eRTMuQys Options 1 and 2 are the most economical ones but Option 3 is the most accurate. Which one do you like the best? Let me know in the comments. On Day 64 of #100DaysofAI, we will see the practical implementation of the 3 methods of Multimodal RAG. Follow me for more such updates on AI. Would you like to create your own custom dataset? Check out DataCreator AI here: https://datacreatorai.com/ #ai #tech #generativeai #datascience #deeplearning #machinelearning #data #computervision #multimodal #rag #retrievalaugmentedgeneration

LLMs are powerful but limited—they can’t pull in real-time information. That’s where Retrieval-Augmented Generation (RAG) steps up. By connecting LLMs to live data sources, RAG unlocks a whole new level of possibilities. Function-based RAG takes it further by letting LLMs interact with systems in real-time, like making database queries or pulling the latest info. It’s like turning a search engine into a hands-on assistant. Curious about how this works? Gentoro CTO patrick chan breaks it all down in this blog post: https://lnkd.in/gezkgVi9 #AI #LLM #RAG

ICYMI ?? TiDB Vector Search is now in open public beta! This game-changing feature brings powerful capabilities for high-dimensional data, enhancing search precision and performance. Dive into the latest blog to see how #TiDB is pushing the boundaries of database technology. #TechInnovation #Database #MachineLearning #VectorSearch #AI ?? Read more: https://lnkd.in/gn5NsCmc

Unlock the power of #GenAI with Oracle Database 23ai AI Vector Search! Generate, store, index, and query vector embeddings along with your business data, using the full power of SQL. Discover how to bring AI to your data: https://lnkd.in/gB-FjSzH