MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage
šš£MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage.
Achieving that by Semantic-Aware Heterogeneous Graphā¦
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, revealingā¦ | 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
šš£MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage.
Achieving that by Semantic-Aware Heterogeneous Graphā¦
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!
(Itās not about feeding more data)
Large Language Models (LLMs) still struggle with factual inaccuracies, butā¦
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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ćThe Problem: Knowledge Graphs Are Expensive (and Clunky)
AI agents needĀ contextĀ to answer complex questionsālike connecting āCOVID vaccinesā to āmyocarditis risksā across research papers. But todayās solutions face two nightmares:
āøĀ Cost: Building detailed knowledge graphs with LLMs can costĀ $33,000 for a 5GB legal case.
āøĀ Quality: Cheap methods (like KNN graphs) miss key relationships, leading toĀ 32% worse answers.
āĀ Imagine training an AI doctor that either bankrupts you or misdiagnoses patients. Ouch.
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ćThe Fix: KET-RAGās Two-Layer Brain
KET-RAG mergesĀ precisionĀ (knowledge graphs) andĀ efficiencyĀ (keyword-text maps) into one system:
āøĀ Layer 1: Knowledge Graph Skeleton
ā Uses PageRank to findĀ core text chunksĀ (like āvaccine side effectsā in medical docs).
ā Builds a sparse graphĀ onlyĀ on these chunks with LLMsāsaving 80% of indexing costs.
āøĀ Layer 2: Keyword-Chunk Bipartite Graph
ā Links keywords (e.g., āmyocarditisā) to all related text snippetsāno LLM needed.
ā Acts as a āfast laneā for retrieving context without expensive entity extraction.
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ćResults: Beating Microsoftās Graph-RAG with Pennies
On HotpotQA and MuSiQue benchmarks, KET-RAG:
āøĀ Retrieves 81.6%Ā of critical info vs. Microsoftās 74.6%āwith 10x lower cost.
āø Boosts answer accuracy (F1 score) byĀ 32.4%Ā while cutting indexing bills byĀ 20%.
āø Scales to terabytes of data without melting budgets.
āĀ Think of it as a Tesla Model 3 outperforming a Lamborghini at 1/10th the price.
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ćWhy AI Agents Need This
AI agents arenāt just chatbotsātheyāreĀ problem solversĀ for medicine, law, and customer service. KET-RAG gives them:
āøĀ Real-time, multi-hop reasoning: Connecting ādrug A ā gene B ā side effect Cā in milliseconds.
āøĀ Cost-effective scalability: Deploying agents across millions of documents without going broke.
āøĀ 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
š Pathway to Artificial General Intelligence (AGI) š This is my view on the evolutionary steps toward AGI: 1ļøā£ Large Language Models (LLMs): Language modelsā¦
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 toā¦ | 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 combineā¦ | 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 researchā¦ | 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 ofā¦ | 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...
OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large...
This paper presents OG-RAG, an Ontology-Grounded Retrieval Augmented Generation method designed to enhance LLM-generated responses by anchoring retrieval processes in domain-specific ontologies....
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.
Large Language Models, Knowledge Graphs and Search Engines: A...
Much has been discussed about how Large Language Models, Knowledge Graphs and Search Engines can be combined in a synergistic manner. A dimension largely absent from current academic discourse is...
Improving Retrieval Augmented Generation accuracy with GraphRAG | Amazon Web Services
Lettria, an AWS Partner, demonstrated that integrating graph-based structures into RAG workflows improves answer precision by up to 35% compared to vector-only retrieval methods. In this post, we explore why GraphRAG is more comprehensive and explainable than vector RAG alone, and how you can use this approach using AWS services and Lettria.
