Let Your Graph Do the Talking: Encoding Structured Data for LLMs
𝗟𝗲𝘁 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝗽𝗲𝗮𝗸! Inject structured data directly with GraphTokens and supercharge your LLM's reasoning abilities. Our exciting research is… | 16 comments on LinkedIn
Ontologies and Knowledge Graphs offer a way to connect embedding vectors to structured knowledge
Ontologies and Knowledge Graphs offer a way to connect embedding vectors to structured knowledge, enhancing their meaning and explainability. Let's delve into… | 25 comments on LinkedIn
Ontologies and Knowledge Graphs offer a way to connect embedding vectors to structured knowledge,
Building A Graph+LLM Powered RAG Application from PDF Documents
A Step-by-step Walkthrough with GenAI-Stack and OpenAI
Advanced RAG and the 3 types of Recursive Retrieval
We explore the 3 types of recursive retrieval — Page-Based, Information-Centric, and Concept-Centric retrieval
From RAGs to Riches: Helping AI Connect the Dots with a Knowledge Vector Graph
There is no intelligence without knowledge. Despite the incredible power of Large Language Models (LLM), they still significantly struggle…
Ontologies are the backbone of the Semantic Web bridging the gap between human and machine understanding
Ontologies are the backbone of the Semantic Web bridging the gap between human and machine understanding. They define the concepts and relationships that… | 29 comments on LinkedIn
Ontologies are the backbone of the Semantic Web bridging the gap between human and machine understanding
Graph neural networks in TensorFlow
How to Combine RAG, Reinforcement Learning, and Knowledge Graphs for More Robust AI Agents
How to Combine RAG, Reinforcement Learning, and Knowledge Graphs for More Robust AI Agents ⭕ Recent months have seen rapid progress in developing large…
How to Combine RAG, Reinforcement Learning, and Knowledge Graphs for More Robust AI Agents
Harry Potter and the Self-Learning Knowledge Graph RAG
With this demo, we wanted to demonstrate 3 key powerful applications of knowledge graph in RAG, and demonstrate how it can improve RAG…
Leveraging Large Language Models to Assemble Knowledge Graphs Grounded in Reality
Leveraging Large Language Models to Assemble Knowledge Graphs Grounded in Reality 🔺 The exponential growth of data has brought both vast opportunities and… | 17 comments on LinkedIn
Leveraging Large Language Models to Assemble Knowledge Graphs Grounded in Reality
LLMs for Knowledge Graph 3: Challenges and Opportunities for GPT in KGs | GraphAware
Several scientists in the first half of the 20th century made enormous contributions to science and technology in different fields. We can mention sever...
Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment
Entity alignment, which is a prerequisite for creating a more comprehensive Knowledge Graph (KG), involves pinpointing equivalent entities across disparate KGs. Contemporary methods for entity alignment have predominantly utilized knowledge embedding models to procure entity embeddings that encapsulate various similarities-structural, relational, and attributive. These embeddings are then integrated through attention-based information fusion mechanisms. Despite this progress, effectively harnessing multifaceted information remains challenging due to inherent heterogeneity. Moreover, while Large Language Models (LLMs) have exhibited exceptional performance across diverse downstream tasks by implicitly capturing entity semantics, this implicit knowledge has yet to be exploited for entity alignment. In this study, we propose a Large Language Model-enhanced Entity Alignment framework (LLMEA), integrating structural knowledge from KGs with semantic knowledge from LLMs to enhance entity alignment. Specifically, LLMEA identifies candidate alignments for a given entity by considering both embedding similarities between entities across KGs and edit distances to a virtual equivalent entity. It then engages an LLM iteratively, posing multiple multi-choice questions to draw upon the LLM's inference capability. The final prediction of the equivalent entity is derived from the LLM's output. Experiments conducted on three public datasets reveal that LLMEA surpasses leading baseline models. Additional ablation studies underscore the efficacy of our proposed framework.
The Intersection of Graphs and Language Models
The Intersection of Graphs and Language Models 🔲 ⚫ Large language models (LLMs) have rapidly advanced, displaying impressive abilities in comprehending… | 27 comments on LinkedIn
The Intersection of Graphs and Language Models
LangGraph: Multi-Agent Workflows
Links
* Python Examples
* JS Examples
* YouTube
Last week we highlighted LangGraph - a new package (available in both Python and JS) to better enable creation of LLM workflows containing cycles, which are a critical component of most agent runtimes. As a part of the launch, we highlighted two simple runtimes:
a second set of use cases for langgraph - multi-agent workflows. In this blog we will cover:What does "multi-agent" mean?Why are "multi-agent" workflows interesting?Three concrete examples of using LangGraph for multi-agent workflowsTwo examples of third-party applications built on top of LangGraph using multi-agent workflows (GPT-Newspaper and CrewAI)Comparison to other frameworks (Autogen and CrewAI)
🦜🕸️LangGraph | 🦜️🔗 Langchain
⚡ Building language agents as graphs ⚡
🦜🕸️LangGraph⚡ Building language agents as graphs ⚡OverviewLangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain.
It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner.
It is inspired by Pregel and Apache Beam.
The current interface exposed is one inspired by NetworkX.The main use is for adding cycles to your LLM application.
Crucially, this is NOT a DAG framework.
If you want to build a DAG, you should use just use LangChain Expression Language.Cycles are important for agent-like behaviors, where you call an LLM in a loop, asking it what action to take next.
Harry Potter and the Self-Learning Knowledge Graph RAG
WhyHow.AI's self-learning RAG with knowledge graphs to bring accuracy and rules to Vertical AI - demonstrating recursive retrieval, memory, automated context-aware knowledge graph construction.
Knowledge Graphs Achieve Superior Reasoning versus Vector Search alone for Retrieval Augmentation
Knowledge Graphs Achieve Superior Reasoning versus Vector Search alone for Retrieval Augmentation 🔗 As artificial intelligence permeates business… | 29 comments on LinkedIn
Knowledge Graphs Achieve Superior Reasoning versus Vector Search alone for Retrieval Augmentation
Leveraging Graph Algorithms to Enable Responsible AI Reasoning
Leveraging Graph Algorithms to Enable Responsible AI Reasoning 💡 Large language models have shown immense promise in their ability to generate remarkably…
Leveraging Graph Algorithms to Enable Responsible AI Reasoning
Neuralizing Retrieval: Infusing Symbolic Reasoning into Language Models through Algorithmic Alignment
Neuralizing Retrieval: Infusing Symbolic Reasoning into Language Models through Algorithmic Alignment ↗ Retrieval-augmented generation (RAG) has emerged as…
Graph & Geometric ML in 2024: Where We Are and What’s Next (Part II — Applications)
Trends and recent advancements in Graph and Geometric Deep Learning
Graph & Geometric ML in 2024: Where We Are and What’s Next (Part I — Theory & Architectures)
Trends and recent advancements in Graph and Geometric Deep Learning
Following the tradition from previous years, we interviewed a cohort of distinguished and prolific academic and industrial experts in an attempt to summarise the highlights of the past year and predict what is in store for 2024. Past 2023 was so ripe with results that we had to break this post into two parts. This is Part I focusing on theory & new architectures,
Leveraging Graphs to Advance Chain-of-Thought Reasoning
Leveraging Graphs to Advance Chain-of-Thought Reasoning ⛓ Chain-of-thought (CoT) prompting has rapidly emerged as a technique to substantially improve the…
Leveraging Graphs to Advance Chain-of-Thought Reasoning
Text-to-Graph via LLM: pre-training, prompting, or tuning?
Knowledge Graphs are, IMHO, the best way to structure data for any subsequent analysis. The problem is that the majority of data that is…
RAG Using Unstructured Data & Role of Knowledge Graphs | Kùzu
In my previous post,
Reasoning with Knowledge Graph Clustering in Retrieval-Augmented Generation Systems
Reasoning with Knowledge Graph Clustering in Retrieval-Augmented Generation Systems 🔲 ⚫ Retrieval-augmented generation (RAG) systems have gained immense… | 35 comments on LinkedIn
Reasoning with Knowledge Graph Clustering in Retrieval-Augmented Generation Systems
The secret of AI: it’s all about building your Knowledge Graphs
The secret of AI: it’s all about building your Knowledge Graphs. This open secret deserves more spotlight: a LLM, at its core, is fundamentally a database, a… | 69 comments on LinkedIn
The secret of AI: it’s all about building your Knowledge Graphs
The WebConf Workshop on Graph Foundation Models
The WebConf Workshop on Graph Foundation Models
Injecting Knowledge Graphs in different RAG stages
Injecting KGs in RAG in pre-processing, post-processing, chunk extraction, document and contextual hierarchies, with a concrete example.
pacoid (Paco Xander Nathan)
Python open source projects; natural language meets graph technologies; graph topological transformations; graph levels of detail (abstraction layers)
Understand and Exploit GenAI With Gartner’s New Impact Radar
Use Gartner’s impact radar for generative AI to plan investments and strategy with four key themes in mind: ☑️Model-related innovations ☑️Model performance and AI safety ☑️Model build and data-related ☑️AI-enabled applications Explore all 25 technologies and trends: https://www.gartner.com/en/articles/understand-and-exploit-gen-ai-with-gartner-s-new-impact-radar