🤺 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗜𝘀 𝗖𝗹𝗲𝗮𝗿: 𝗔𝗴𝗲𝗻𝘁𝘀 𝗪𝗶𝗹𝗹 𝗡𝗘𝗘𝗗 𝗚𝗿𝗮𝗽𝗵 𝗥𝗔𝗚 Why? It combines Multi-hop reasoning, Non-Parameterized / Learning-Based Retrieval, Topology-Aware Prompting. ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 🤺 𝗪𝗵𝗮𝘁 𝗜𝘀 𝗚𝗿𝗮𝗽𝗵-𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚)? ✩ LLMs hallucinate. ✩ LLMs forget. ✩ LLMs struggle with complex reasoning. Graphs connect facts. They organize knowledge into neat, structured webs. So when RAG retrieves from a graph, the LLM doesn't just guess — it reasons. It follows the map. ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 🤺 𝗧𝗵𝗲 𝟰-𝗦𝘁𝗲𝗽 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗼𝗳 𝗚𝗿𝗮𝗽𝗵 𝗥𝗔𝗚 1️⃣ — User Query: The user asks a question. ("Tell me how Einstein used Riemannian geometry?") 2️⃣ — Retrieval Module: The system fetches the most structurally relevant knowledge from a graph. (Entities: Einstein, Grossmann, Riemannian Geometry.) 3️⃣ — Prompting Module: Retrieved knowledge is reshaped into a golden prompt — sometimes as structured triples, sometimes as smart text. 4️⃣ — Output Response: LLM generates a fact-rich, logically sound answer. ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 🤺 𝗦𝘁𝗲𝗽 𝟭: 𝗕𝘂𝗶𝗹𝗱 𝗚𝗿𝗮𝗽𝗵-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 ✩ Use Existing Knowledge Graphs like Freebase or Wikidata — structured, reliable, but static. ✩ Or Build New Graphs From Text (OpenIE, instruction-tuned LLMs) — dynamic, adaptable, messy but powerful. 🤺 𝗦𝘁𝗲𝗽 𝟮: 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗮𝗻𝗱 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 ✩ Non-Parameterized Retrieval (Deterministic, Probabilistic, Heuristic) ★ Think Dijkstra's algorithm, PageRank, 1-hop neighbors. Fast but rigid. ✩ Learning-Based Retrieval (GNNs, Attention Models) ★ Think "graph convolution" or "graph attention." Smarter, deeper, but heavier. ✩ Prompting Approaches: ★ Topology-Aware: Preserve graph structure — multi-hop reasoning. ★ Text Prompting: Flatten into readable sentences — easier for vanilla LLMs. 🤺 𝗦𝘁𝗲𝗽 𝟯: 𝗚𝗿𝗮𝗽𝗵-𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 ✩ Sequential Pipelines: Straightforward query ➔ retrieve ➔ prompt ➔ answer. ✩ Loop Pipelines: Iterative refinement until the best evidence is found. ✩ Tree Pipelines: Parallel exploration ➔ multiple knowledge paths at once. 🤺 𝗦𝘁𝗲𝗽 𝟰: 𝗚𝗿𝗮𝗽𝗵-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗧𝗮𝘀𝗸𝘀 ✩ Knowledge Graph QA (KGQA): Answering deep, logical questions with graphs. ✩ Graph Tasks: Node classification, link prediction, graph summarization. ✩ Domain-Specific Applications: Biomedicine, law, scientific discovery, finance. ≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣ Join my 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴. Skip the fluff and build real AI agents — fast. 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂 𝗴𝗲𝘁: ✅ Create Smart Agents + Powerful RAG Pipelines ✅ Master 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗖𝗿𝗲𝘄𝗔𝗜 & 𝗦𝘄𝗮𝗿𝗺 – all in one training ✅ Projects with Text, Audio, Video & Tabular Data 𝟰𝟲𝟬+ engineers already enrolled 𝗘𝗻𝗿𝗼𝗹𝗹 𝗻𝗼𝘄 — 𝟯𝟰% 𝗼𝗳𝗳, 𝗲𝗻𝗱𝘀 𝘀𝗼𝗼𝗻: https://lnkd.in/eGuWr4CH | 35 comments on LinkedIn

How to leverage Large Language Models to create consistent Knowledge Graphs from multiple sources

🔎 Lessons Learned from Evaluating NodeRAG vs Other RAG Systems I recently dug into the NodeRAG paper (https://lnkd.in/gwaJHP94) and it was eye-opening not just for how it performed, but for what it revealed about the evolution of RAG (Retrieval-Augmented Generation) systems. Some key takeaways for me: 👉 NaiveRAG is stronger than you think. Brute-force retrieval using simple vector search sometimes beats graph-based methods, especially when graph structures are too coarse or noisy. 👉 GraphRAG was an important step, but not the final answer. While it introduced knowledge graphs and community-based retrieval, GraphRAG sometimes underperformed NaiveRAG because its communities could be too coarse, leading to irrelevant retrieval. 👉 LightRAG reduced token cost, but at the expense of accuracy. By focusing on retrieving just 1-hop neighbors instead of traversing globally, LightRAG made retrieval cheaper — but often missed important multi-hop reasoning paths, losing precision. 👉 NodeRAG shows what mature RAG looks like. NodeRAG redesigned the graph structure itself: Instead of homogeneous graphs, it uses heterogeneous graphs with fine-grained semantic units, entities, relationships, and high-level summaries — all as nodes. It combines dual search (exact match + semantic search) and shallow Personalized PageRank to precisely retrieve the most relevant context. The result? 🚀 Highest accuracy across multi-hop and open-ended benchmarks 🚀 Lowest token retrieval (i.e., lower inference costs) 🚀 Faster indexing and querying 🧠 Key takeaway: In the RAG world, it’s no longer about retrieving more — it’s about retrieving better. Fine-grained, explainable, efficient retrieval will define the next generation of RAG systems. If you’re working on RAG architectures, NodeRAG’s design principles are well worth studying! Would love to hear how others are thinking about the future of RAG systems. 🚀📚 #RAG #KnowledgeGraphs #AI #LLM #NodeRAG #GraphRAG #LightRAG #MachineLearning #GenAI #KnowledegGraphs

An open-source Python framework for real-time, graph-based knowledge in Neo4j

How LLMs can used in a knowledge extraction and visualization pipeline

We’re thrilled to announce new Text2Cypher models and Google’s MCP Toolbox for Databases from the collaboration between Google Cloud and Neo4j.

Choosing the Right Format: How Knowledge Graph Layouts Impact AI Reasoning ... 👉 Why This Matters Most AI systems blend knowledge graphs (structured data) with large language models (flexible reasoning). But there’s a hidden variable: "how" you translate the graph into text for the AI. Researchers discovered that the formatting choice alone can swing performance by up to "17.5%" on reasoning tasks. Imagine solving 1 in 5 more problems correctly just by adjusting how you present data. 👉 What They Built KG-LLM-Bench is a new benchmark to test how language models reason with knowledge graphs. It includes five tasks: - Triple verification (“Does this fact exist?”) - Shortest path finding (“How are two concepts connected?”) - Aggregation (“How many entities meet X condition?”) - Multi-hop reasoning (“Which entities linked to A also have property B?”) - Global analysis (“Which node is most central?”) The team tested seven models (Claude, GPT-4o, Gemini, Llama, Nova) with five ways to “textualize” graphs, from simple edge lists to structured JSON and semantic web formats like RDF Turtle. 👉 Key Insights 1. Format matters more than assumed:   - Structured JSON and edge lists performed best overall, but results varied by task.   - For example, JSON excels at aggregation tasks (data is grouped by entity), while edge lists help identify central nodes (repeated mentions highlight connections). 2. Models don’t cheat: Replacing real entity names with fake ones (e.g., “France” → “Verdania”) caused only a 0.2% performance drop, proving models rely on context, not memorized knowledge. 3. Token efficiency:   - Edge lists used ~2,600 tokens vs. JSON-LD’s ~13,500. Shorter formats free up context space for complex reasoning.   - But concise ≠ always better: structured formats improved accuracy for tasks requiring grouped data. 4. Models struggle with directionality:   Counting outgoing edges (e.g., “Which countries does France border?”) is easier than incoming ones (“Which countries border France?”), likely due to formatting biases. 👉 Practical Takeaways - Optimize for your task: Use JSON for aggregation, edge lists for centrality. - Test your model: The best format depends on the LLM—Claude thrived with RDF Turtle, while Gemini preferred edge lists. - Don’t fear pseudonyms: Masking real names minimally impacts performance, useful for sensitive data. The benchmark is openly available, inviting researchers to add new tasks, graphs, and models. As AI handles larger knowledge bases, choosing the right “data language” becomes as critical as the reasoning logic itself. Paper: [KG-LLM-Bench: A Scalable Benchmark for Evaluating LLM Reasoning on Textualized Knowledge Graphs] Authors: Elan Markowitz, Krupa Galiya, Greg Ver Steeg, Aram Galstyan

Graph Data Modeling Without Graph Databases: PostgreSQL and Hybrid Approaches for Agentic Systems 🖇️ Organizations implementing AI systems today face a practical challenge: maintaining multiple specialized databases (vector stores, graph databases, relational systems) creates significant operational complexity, increases costs, and introduces synchronization headaches. Companies like Writer (insight from a recent Waseem Alshikh interview with Harrison Chase) have tackled this problem by implementing graph-like structures directly within PostgreSQL, eliminating the need for separate graph databases while maintaining the necessary functionality. This approach dramatically simplifies infrastructure management, reduces the number of systems to monitor, and eliminates error-prone synchronization processes that can cost thousands of dollars in wasted resources. For enterprises focused on delivering business value rather than managing technical complexity, these PostgreSQL-based implementations offer a pragmatic path forward, though with important trade-offs when considering more sophisticated agentic systems. Writer implemented a subject-predicate-object triple structure directly in PostgreSQL tables rather than using dedicated graph databases. This approach maintains the semantic richness of knowledge graphs while leveraging PostgreSQL's maturity and scalability. Writer kept the conceptual structure of triples that underpin knowledge graphs implemented through a relational schema design. Instead of relying on native graph traversals, Writer developed a fusion decoder that reconstructs graph-like relationships at query time. This component serves as the bridge between the storage layer (PostgreSQL with its triple-inspired structure) and the language model, enabling sophisticated information retrieval without requiring a dedicated graph database's traversal capabilities. The approach focuses on query translation and result combination rather than storage structure optimization. Complementing the triple-based approach, PostgreSQL with extensions (PG Vector and PG Vector Scale) can function effectively as a vector database. This challenges the notion that specialized vector databases are necessary, Treating embeddings as derived data leads to a more natural and maintainable architecture. This reframes the database's role from storing independent vector embeddings to managing derived data that automatically synchronizes with its source. But a critical distinction between retrieval systems and agentic systems need to be made. While PostgreSQL-based approaches excel at knowledge retrieval tasks where the focus is on precision and relevance, agentic systems operate in dynamic environments where context evolves over time, previous actions influence future decisions, and contradictions need to be resolved. This distinction drives different architectural requirements and suggests potential complementary roles for different database approaches. | 15 comments on LinkedIn

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...

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

David Hughes presented “Unleashing the Power of Multimodal GraphRAG: Integrating Image Features for Deeper Insights” at Data Day Texas 2025.

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.

🧱Building Knowledge Graphs with LLM Graph Transformer A deep dive into LangChain’s implementation of graph construction with LLMs If you want to try out… | 32 comments on LinkedIn

Watch the first podcast of Paco Nathan's Graph Power Hour. This week's topic - Understanding Graph Rag: Enhancing LLM Applications Through Knowledge Graphs.

A GCP native, fully serverless implementation that you will replicate in minutes