Klarna’s AI journey is rooted in the power of graphs. I agree with Sebastian that the value of Neo4j/knowledge graphs/GraphRAG is in the top line. It’s not about replacing SaaS, but bringing data from the many silos into a graph, and using that for better AI decisions.

Build a knowledge graph agent from scratch 🔥 I'm super excited about this blog post by Tomaz Bratanic from @neo4j - this is probably the most thorough treatment I've seen for building a text-to-cypher powered knowledge graph agent that actually works well. Tomaz walks through an entire list of potential strategies and evaluates them according to a standardized benchmark: 1. Naive text-to-cypher 2. Text-to-cypher with retry and evaluation 3. Iterative planning system by generating a plan of sub-queries before creating the final query. All of these can be stitched together as an event-driven system with @llama_index workflows. Check out the post! llamaindex.ai/blog/building-… If you want to get your hands dirty on workflows check out our guide here: docs.llamaindex.ai/en/latest…

GraphRAG with @MistralAI, @CamelAIOrg, and @neo4j: - Use Mistral Large 2 to extract and structure knowledge graph from a given content source, and store this information in a Neo4j graph database. - A hybrid approach: combining vector retrieval and knowledge graph retrieval, to query and explore the stored knowledge. - This hybrid Graph vector RAG approach increases retrieval accuracy and RAG performance. Thank you @ttokzzzzz @guohao_li for contributing this great example to Mistral cookbook. Link in 🧵:

🔍 Exploring GraphRAG with Neo4j and LangChain 📝 Check out Tomaz Bratanic's deep dive into "From Local to Global" GraphRAG implementation Covers how to extract entities & relationships from text and summarize graph structures into natural language. github.com/tomasonjo/blogs/b…

This weekend, we’re providing a definitive set of tutorials on how to build GraphRAG, step-by-step. First, check out this video by @fahdmirza on implementing the core components of GraphRAG using an in-memory implementation: 1. Extract entities and relationships using LLMs 2. Partition graph into communities and generate community summaries 3. Query all communities and synthesize into a final answer. Then, extend this initial in-memory implementation by storing your property graph in a @neo4j graph database. Big shoutout to @ravithejads for the notebook Video: youtube.com/watch?v=xnoEjczo… V1 Notebook: github.com/run-llama/llama_i… V2 notebook (with @neo4j): github.com/run-llama/llama_i… Source GraphRAG paper by Edge et al. ( image credits): arxiv.org/pdf/2404.16130

Microsoft GraphRAG Alternative and 10x Cheaper? 🚀 Introducing Sciphi/Triplex 💸 10x Cheaper 🤖 AI Knowledge Graph Extraction 🌟 Higher Accuracy 🛠️ Setup with @huggingface 🖥️ Local Run with @ollama 📊 Data Visualisation @neo4j Subscribe: youtube.com/@MervinPraison @sayshrey @ocolegro

If you’re looking for a conversation about GraphRAG to go alongside the GraphRAG Manifesto, look no further than my conversation with Ben Lorica on the Data Exchange podcast: thedataexchange.media/superc…

Love this GraphRAG example by @MervinPraison, using @neo4j & #Groq