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
MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage
🏆🚣MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage.
Achieving that by Semantic-Aware Heterogeneous Graph…
MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage
Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks
I love Markus J. Buehler's work, and his latest paper "Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks" does not disappoint, revealing… | 19 comments on LinkedIn
Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks
MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage
🏆🚣MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage.
Achieving that by Semantic-Aware Heterogeneous Graph…
MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage
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...
Terminology Augmented Generation (TAG)? Recently some fellow terminologists have proposed the new term "Terminology-Augmented Generation (TAG)" to refer to… | 29 comments on LinkedIn
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...
