#Graphrag

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Трендовые Reels

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#Graphrag Reel by @dr_satya_mallick - LightRAG! An innovative alternative to GraphRAG, designed for fast, precise, and efficient document analysis. Legal topics are often complex and fill
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@dr_satya_mallick
LightRAG! An innovative alternative to GraphRAG, designed for fast, precise, and efficient document analysis. Legal topics are often complex and filled with jargon that’s challenging to navigate. But what if legal insights were at your fingertips, simplified by LightRAG's advanced knowledge graphs? HKU’s LightRAG integrates graph structures with embeddings and uses dual-level retrieval for both local entity and global relationship queries, making it a strong contender for advanced legal and financial document analysis. In our recent article, we explore how LightRAG addresses the limitations of GraphRAG and Naive RAG, testing it locally on Ollama with a real legal document use case to answer complex queries with actionable insights. LINK IN BIO #lightrag #graphrag #legal_doc_analysis #lightrag_legal #ollama #univeristy_of_hongkong
#Graphrag Reel by @martinschaer - Inferring attributes with LLM and Pydantic for GenAI Graph RAG featuring @surrealdb . #genai #graphrag #rag #python #surrealdb #coding #ai #engineerin
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@martinschaer
Inferring attributes with LLM and Pydantic for GenAI Graph RAG featuring @surrealdb . #genai #graphrag #rag #python #surrealdb #coding #ai #engineering
#Graphrag Reel by @chase.h.ai (verified account) - comment "RAG" for this free template If you are dealing with any level of complexity within your RAG ecosystem, then you need to take a look at graph
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@chase.h.ai
comment "RAG" for this free template If you are dealing with any level of complexity within your RAG ecosystem, then you need to take a look at graphRAG setups. Here's how i got it working with LightRAG
#Graphrag Reel by @jganesh.ai (verified account) - If your RAG keeps pulling shallow or half-correct answers, the issue isn't the LLM - it's the retriever. Speaking from personal experience. Here's how
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@jganesh.ai
If your RAG keeps pulling shallow or half-correct answers, the issue isn’t the LLM — it’s the retriever. Speaking from personal experience. Here’s how GraphRAG is one option to solve this problem👇 ⸻ 1️⃣ Plain RAG has a blind spot Vector search only finds similar text, not connected facts. So if the answer lives across multiple documents → RAG misses it. ⸻ 2️⃣ GraphRAG fixes this with a knowledge graph You can use llm to build graph for you once you describe the schema based on your usecase. It builds a graph where: • nodes = entities • edges = relationships Now the retriever can follow relationships → multi-hop reasoning. This captures how information links instead of just “what looks similar.” ⸻ 3️⃣ Why accuracy improves (real reason) GraphRAG retrieves context from: ✔ multiple related chunks ✔ multi-document evidence ✔ cross-referenced facts Instead of top-k similar text, you get connected context. This reduces hallucinations and increases answer completeness. ⸻ 4️⃣ When YOU should use it Use GraphRAG when your data has: ✔ references across docs ✔ entities & relationships ✔ long PDFs ✔ enterprise knowledge bases For simple Q&A → plain vector RAG is enough. ⸻ ⭐ TL;DR Vector RAG = “What looks similar?” GraphRAG = “What’s actually connected?” → more accurate answers. ⸻ 🔖 Tags #GraphRAG #RAG #LLM #genai #machinelearning #mlengineering #techreels #aiengineering #ai #datascience #ml #trend #engineering #llm #mlsystemdesign
#Graphrag Reel by @edhonour (verified account) - GraphRAG has two issues that make it difficult to justify using in a large environment. It is incredibly expensive and needs to be retrained (recreat
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@edhonour
GraphRAG has two issues that make it difficult to justify using in a large environment. It is incredibly expensive and needs to be retrained (recreated) anytime your data changes.
#Graphrag Reel by @didof.dev - Evolve your RAG to the next step. GRAPH RAG allows your system to spot relationships between concepts of any text. The same relationships that a Vanil
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@didof.dev
Evolve your RAG to the next step. GRAPH RAG allows your system to spot relationships between concepts of any text. The same relationships that a Vanilla RAG may not capture. It's pretty easy to setup with neo4j, and the gains are huge! #rag #ai #graphrag #knowledge #machinelearning #coding
#Graphrag Reel by @felix_ved - MiroFish got $4M funding within 24 hours. 33k GitHub stars. Powered by OASIS from CAMEL-AI, GraphRAG, and Zep Cloud. The offline fork runs fully local
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@felix_ved
MiroFish got $4M funding within 24 hours. 33k GitHub stars. Powered by OASIS from CAMEL-AI, GraphRAG, and Zep Cloud. The offline fork runs fully local with Neo4j + Ollama. #opensource #aiagents #aiengineering #aitools
#Graphrag Reel by @infra.peter - Discover how this innovative approach transforms unstructured data into powerful knowledge graphs, significantly enhancing Large Language Models (LLMs
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@infra.peter
Discover how this innovative approach transforms unstructured data into powerful knowledge graphs, significantly enhancing Large Language Models (LLMs) accuracy and contextual understanding. Learn about the intricate process from data ingestion and entity extraction to advanced hybrid retrieval mechanisms. GraphRAG excels at complex, multi-hop queries, providing more precise and explainable AI responses. While powerful, it's a resource-intensive process. Explore the future of AI with GraphRAG! #GraphRAG #AI #LLM #KnowledgeGraphs #DataScience #MachineLearning #AITech #RAG #ArtificialIntelligence #TechExplained
#Graphrag Reel by @mindstoneapp - Your chatbot doesn't know what's new. GraphRAG combines retrieval with a knowledge graph to ground answers, keep citations, and stay current as data c
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@mindstoneapp
Your chatbot doesn’t know what’s new. GraphRAG combines retrieval with a knowledge graph to ground answers, keep citations, and stay current as data changes. Watch it in action (from our Bristol Meetup) https://community.mindstone.com/events/mindstone-bristol-october-ai-meetup #PracticalAI #FutureOfWork #KnowledgeManagement
#Graphrag Reel by @inesthetechie (verified account) - Ever felt like your data is too complex for AI to understand? RAG retrieves similar text chunks - but it doesn't actually know how things connect. E
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@inesthetechie
Ever felt like your data is too complex for AI to understand? RAG retrieves similar text chunks — but it doesn’t actually know how things connect. Enter GraphRAG 🧠 It builds a knowledge graph, letting the AI follow relationships and reason better. Perfect when your data is messy, interconnected, or spread across multiple sources. Think of it like giving your AI a map instead of a pile of papers. 🗺️ #AIexplained #GraphRAG #KnowledgeGraph #AItools #GenAI #RAGvsGraphRAG #aiapps #llm
#Graphrag Reel by @abjeetenge - #ArtificialIntelligence #GenerativeAI #RAG #AgenticAI #GraphRAG LLM AIArchitecture MachineLearning AIEngineering DataEngineering AIInfrastructure Know
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@abjeetenge
#ArtificialIntelligence #GenerativeAI #RAG #AgenticAI #GraphRAG LLM AIArchitecture MachineLearning AIEngineering DataEngineering AIInfrastructure KnowledgeGraphs MLOps AIAgents TechInnovation DeepLearning FutureOfAI TechCommunity
#Graphrag Reel by @baniascodes (verified account) - Vector DBs are not a silver bullet. You need GraphRAG. 1️⃣ The Vector Limitation 👉 Vector search finds "similar" text, but it doesn't understand rel
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@baniascodes
Vector DBs are not a silver bullet. You need GraphRAG. 1️⃣ The Vector Limitation 👉 Vector search finds “similar” text, but it doesn’t understand relationships. If Doc A says “Steve works at Apple” and Doc B says “Apple created the iPhone”, a vector search might fail to answer “Did Steve help create the iPhone?” 2️⃣ Knowledge Graphs 👉 Instead of just chunking text, extract entities (Nodes) and relationships (Edges) to build a Knowledge Graph. This structures your data as: (Steve Jobs) —[FOUNDED]—> (Apple) 3️⃣ Graph Traversal 👉 When a complex question comes in, you don’t just search for keywords. You “traverse” the graph to follow the relationships, allowing the LLM to hop from one fact to another logically. 4️⃣ Hybrid Search 👉 The best production systems use both. Use Vectors for unstructured semantic search (“fuzzy” concepts) and Knowledge Graphs for structured, relational facts.

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#Graphrag — один из самых популярных трендов в Instagram прямо сейчас. С более чем thousands of публикаций в этой категории, создатели вроде @jganesh.ai, @infra.peter and @baniascodes лидируют со своим вирусным контентом. Просматривайте эти популярные видео анонимно на Pictame.

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