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
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...
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
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
Knowledge graphs: the missing link in enterprise AI
To gain competitive advantage from gen AI, enterprises need to be able to add their own expertise to off-the-shelf systems. Yet standard enterprise data stores aren't a good fit to train large language models.
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
KnowPath: Knowledge-enhanced Reasoning via LLM-generated Inference Paths over Knowledge Graphs
Breaking LLM Hallucinations in a Smarter Way!
(It’s not about feeding more data)
Large Language Models (LLMs) still struggle with factual inaccuracies, but…
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
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》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.
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》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.
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》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.
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》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
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KET-RAG: Turbocharging AI Agents with 10x Cheaper, Smarter Knowledge Retrieval
🌟 Pathway to Artificial General Intelligence (AGI) 🌟 This is my view on the evolutionary steps toward AGI: 1️⃣ Large Language Models (LLMs): Language models…
KAG: Boosting LLMs in Professional Domains via Knowledge Augmented...
The recently developed retrieval-augmented generation (RAG) technology has enabled the efficient construction of domain-specific applications. However, it also has limitations, including the gap...
What is really Graph RAG? Inspired by "From Local to Global: A Graph RAG Approach to Query-Focused Summarization" paper from Microsoft! How do you combine… | 12 comments on LinkedIn
Knowledge Graphs as a source of trust for LLM-powered enterprise question answering
Knowledge Graphs as a source of trust for LLM-powered enterprise question answering That has been our position from the beginning when we started our research… | 29 comments on LinkedIn
Knowledge Graphs as a source of trust for LLM-powered enterprise question answering
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
Enhancing RAG-based apps by constructing and leveraging knowledge graphs with open-source LLMs
Graph Retrieval Augmented Generation (Graph RAG) is emerging as a powerful addition to traditional vector search retrieval methods. Graphs are great at repre...
The journey towards a knowledge graph for generative AI
While retrieval-augmented generation is effective for simpler queries, advanced reasoning questions require deeper connections between information that exist across documents. They require a knowledge graph.
