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Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models
Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models
LLMs are taking Graph Neural Networks to the next level: While we've been discussing LLMs for natural language, they're quietly changing how we represent…
Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large
·linkedin.com·
Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models
Knowledge graphs for LLM grounding and avoiding hallucination
Knowledge graphs for LLM grounding and avoiding hallucination
This blog post is part of a series that dives into various aspects of SAP’s approach to Generative AI, and its technical underpinnings. In previous blog posts of this series, you learned about how to use large language models (LLMs) for developing AI applications in a trustworthy and reliable manner...
·community.sap.com·
Knowledge graphs for LLM grounding and avoiding hallucination
Multi-Layer Agentic Reasoning: Connecting Complex Data and Dynamic Insights in Graph-Based RAG Systems
Multi-Layer Agentic Reasoning: Connecting Complex Data and Dynamic Insights in Graph-Based RAG Systems
Multi-Layer Agentic Reasoning: Connecting Complex Data and Dynamic Insights in Graph-Based RAG Systems 🛜 At the most fundamental level, all approaches rely… | 11 comments on LinkedIn
Multi-Layer Agentic Reasoning: Connecting Complex Data and Dynamic Insights in Graph-Based RAG Systems
·linkedin.com·
Multi-Layer Agentic Reasoning: Connecting Complex Data and Dynamic Insights in Graph-Based RAG Systems
Build your hybrid-Graph for RAG & GraphRAG applications using the power of NLP | LinkedIn
Build your hybrid-Graph for RAG & GraphRAG applications using the power of NLP | LinkedIn
Build a graph for RAG application for a price of a chocolate bar! What is GraphRAG for you? What is GraphRAG? What does GraphRAG mean from your perspective? What if you could have a standard RAG and a GraphRAG as a combi-package, with just a query switch? The fact is, there is no concrete, universal
·linkedin.com·
Build your hybrid-Graph for RAG & GraphRAG applications using the power of NLP | LinkedIn
Zep packs Temporal Knowledge Graphs + Semantic Entity Extraction + Cypher Queries storage—Outperforming MemGPT with 94.8% Accuracy
Zep packs Temporal Knowledge Graphs + Semantic Entity Extraction + Cypher Queries storage—Outperforming MemGPT with 94.8% Accuracy
🎁⏳ Zep packs Temporal Knowledge Graphs + Semantic Entity Extraction + Cypher Queries storage—Outperforming MemGPT with 94.8% Accuracy. Build Personalized AI… | 46 comments on LinkedIn
Zep packs Temporal Knowledge Graphs + Semantic Entity Extraction + Cypher Queries storage—Outperforming MemGPT with 94.8% Accuracy
·linkedin.com·
Zep packs Temporal Knowledge Graphs + Semantic Entity Extraction + Cypher Queries storage—Outperforming MemGPT with 94.8% Accuracy
Synalinks is an open-source framework designed to streamline the creation, evaluation, training, and deployment of industry-standard Language Models (LMs) applications
Synalinks is an open-source framework designed to streamline the creation, evaluation, training, and deployment of industry-standard Language Models (LMs) applications
🎉 We're thrilled to unveil Synalinks (🧠🔗), an open-source framework designed to streamline the creation, evaluation, training, and deployment of…
Synalinks (🧠🔗), an open-source framework designed to streamline the creation, evaluation, training, and deployment of industry-standard Language Models (LMs) applications
·linkedin.com·
Synalinks is an open-source framework designed to streamline the creation, evaluation, training, and deployment of industry-standard Language Models (LMs) applications
KET-RAG: Turbocharging AI Agents with 10x Cheaper, Smarter Knowledge Retrieval
KET-RAG: Turbocharging AI Agents with 10x Cheaper, Smarter Knowledge Retrieval
KET-RAG: Turbocharging AI Agents with 10x Cheaper, Smarter Knowledge Retrieval This Multi-Granular Graph Framework uses PageRank and Keyword-Chunk Graph to have the Best Cost-Quality Tradeoff ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 》The Problem: Knowledge Graphs Are Expensive (and Clunky) AI agents need context to answer complex questions—like connecting “COVID vaccines” to “myocarditis risks” across research papers. But today’s solutions face two nightmares: ✸ Cost: Building detailed knowledge graphs with LLMs can cost $33,000 for a 5GB legal case. ✸ Quality: Cheap methods (like KNN graphs) miss key relationships, leading to 32% worse answers. ☆ Imagine training an AI doctor that either bankrupts you or misdiagnoses patients. Ouch. ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 》The Fix: KET-RAG’s Two-Layer Brain KET-RAG merges precision (knowledge graphs) and efficiency (keyword-text maps) into one system: ✸ Layer 1: Knowledge Graph Skeleton ☆ Uses PageRank to find core text chunks (like “vaccine side effects” in medical docs). ☆ Builds a sparse graph only on these chunks with LLMs—saving 80% of indexing costs. ✸ Layer 2: Keyword-Chunk Bipartite Graph ☆ Links keywords (e.g., “myocarditis”) to all related text snippets—no LLM needed. ☆ Acts as a “fast lane” for retrieving context without expensive entity extraction. ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 》Results: Beating Microsoft’s Graph-RAG with Pennies On HotpotQA and MuSiQue benchmarks, KET-RAG: ✸ Retrieves 81.6% of critical info vs. Microsoft’s 74.6%—with 10x lower cost. ✸ Boosts answer accuracy (F1 score) by 32.4% while cutting indexing bills by 20%. ✸ Scales to terabytes of data without melting budgets. ☆ Think of it as a Tesla Model 3 outperforming a Lamborghini at 1/10th the price. ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 》Why AI Agents Need This AI agents aren’t just chatbots—they’re problem solvers for medicine, law, and customer service. KET-RAG gives them: ✸ Real-time, multi-hop reasoning: Connecting “drug A → gene B → side effect C” in milliseconds. ✸ Cost-effective scalability: Deploying agents across millions of documents without going broke. ✸ Adaptability: Mixing precise knowledge graphs (for critical data) with keyword maps (for speed). Paper in comments ≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣ 》Build Your Own Supercharged AI Agent? 🔮 Join My 𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 TODAY! and Learn Building AI Agent with Langgraph/Langchain, CrewAI and OpenAI Swarm + RAG Pipelines 𝐄𝐧𝐫𝐨𝐥𝐥 𝐍𝐎𝐖 [34% discount]: 👉 https://lnkd.in/eGuWr4CH | 10 comments on LinkedIn
KET-RAG: Turbocharging AI Agents with 10x Cheaper, Smarter Knowledge Retrieval
·linkedin.com·
KET-RAG: Turbocharging AI Agents with 10x Cheaper, Smarter Knowledge Retrieval
Pathway to Artificial General Intelligence (AGI)
Pathway to Artificial General Intelligence (AGI)
🌟 Pathway to Artificial General Intelligence (AGI) 🌟 This is my view on the evolutionary steps toward AGI: 1️⃣ Large Language Models (LLMs): Language models…
Pathway to Artificial General Intelligence (AGI)
·linkedin.com·
Pathway to Artificial General Intelligence (AGI)
A zero-hallucination AI chatbot that answered over 10000 questions of students at the University of Chicago using GraphRAG
A zero-hallucination AI chatbot that answered over 10000 questions of students at the University of Chicago using GraphRAG
UChicago Genie is now open source! How we built a zero-hallucination AI chatbot that answered over 10000 questions of students at the University of… | 25 comments on LinkedIn
a zero-hallucination AI chatbot that answered over 10000 questions of students at the University of Chicago
·linkedin.com·
A zero-hallucination AI chatbot that answered over 10000 questions of students at the University of Chicago using GraphRAG