GraphNews

Your agents, multi-agent systems and LMs apps are still failing with basic logic? We got you covered. Today we're excited to announce Synalinks 0.3 our Keras-based neuro-symbolic framework that bridges the gap between neural networks and symbolic reasoning. Our latest release focuses entirely on the Knowledge Graph layer, delivering production-ready solutions for real-world applications: - Fully constrained KG extraction powered by Pydantic: ensuring that relations connect to the correct entity types. - Seamless integration with our Agents/Chain-of-Thought and Self-Critique modules. - Automatic entity alignment with HSWN. - KG extraction and retrieval optimizable with OPRO and RandomFewShot algorithms. - 100% reliable Cypher query generation through logic-enhanced hybrid triplet retrieval (works with local models too!). - We took extra care to avoid Cypher injection vulnerabilities (yes, we're looking at you, LangGraph 👀) - The retriever don't need the graph schema, as it is included in the way we constrain the generation, avoiding context pollution (hence better accuracy). - We also fixed Synalinks CLI for Windows users along with some minor bug fixes. Our technology combine constrained structured output with in-context reinforcement learning, making enterprise-grade reasoning both highly efficient and cost-effective. Currently supporting Neo4j with plans to expand to other graph databases. Built this initially for a client project, but the results were too good not to share with the community. Want to add support for your preferred graph database? It's just one file to implement! Drop a comment and let's make it happen! #AI #MachineLearning #KnowledgeGraphs #NeuralNetworks #Keras #Neo4j #AIAgents #TechInnovation #OpenSource | 10 comments on LinkedIn

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

Want to explore the Anthropic Transformer-Circuit's as a queryable graph? Wrote a script to import the graph json into Neo4j - code in Gist. https://lnkd.in/eT4NjQgY https://lnkd.in/e38TfQpF Next step - write directly from the circuit-tracer library to the graph db. https://lnkd.in/eVU_t6mS

A serious knowledge graph effort is much more than a bit of Github, but customers and adventurous minds keep asking me if there is an easy to use (read: POC click-and-go solution) graph RAG open source stack they can use to generate knowledge graphs. So, here is my list of projects I keep an eye on. Mind, there is nothing simple if you venture into graphs, despite all the claims and marketing. Things like graph machine learning, graph layout and distributed graph analytics is more than a bit of pip install. The best solutions are hidden inside multi-nationals, custom made. Equity firms and investors sometimes ask me to evaluate innovations. It's amazing what talented people develop and never shows up in the news, or on Github. TrustGraph - The Knowledge Platform for AI https://trustgraph.ai/ The only one with a distributed architecture and made for enterprise KG. itext2kg - https://lnkd.in/e-eQbwV5 Clean and plain. Wrapped prompts done right. Fast GraphRAG - https://lnkd.in/e7jZ9GZH Popular and with some basic visualization. ZEP - https://lnkd.in/epxtKtCU Geared towards agentic memory. Triplex - https://lnkd.in/eGV8FR56 LLM to extract triples. GraphRAG Local with UI - https://lnkd.in/ePGeqqQE Another starting point for small KG efforts. Or to convince your investors. GraphRAG visualizer - https://lnkd.in/ePuMmfkR Makes pretty pictures but not for drill-downs. Neo4j's GraphRAG - https://lnkd.in/ex_A52RU A python package with a focus on getting data into Neo4j. OpenSPG - https://lnkd.in/er4qUFJv Has a different take and more academic. Microsoft GraphRAG - https://lnkd.in/e_a-mPum A classic but I don't think anyone is using this beyond experimentation. yWorks - https://www.yworks.com If you are serious about interactive graph layout. Ogma - https://lnkd.in/evwnJCBK If you are serious about graph data viz. Orbifold Consulting - https://lnkd.in/e-Dqg4Zx If you are serious about your KG journey. #GraphRAG #GraphViz #GraphMachineLearning #KnowledgeGraphs

Integrating Neo4j, DuckDB, and Kestra for Metadata-Driven Graph Design

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

How LLMs can used in a knowledge extraction and visualization pipeline

UChicago Genie is now open source! How we built a zero-hallucination AI chatbot that answered over 10000 questions of students at the University of… | 25 comments on LinkedIn

Graph Retrieval Augmented Generation (Graph RAG) is emerging as a powerful addition to traditional vector search retrieval methods. Graphs are great at repre...

Authors: Makbule Gulcin Ozsoy, Leila Messallem, Jon Besga

We're excited to publicly release the Diffbot GraphRAG LLM! With larger and larger frontier LLMs, we realized that they would eventually hit a limit in terms… | 48 comments on LinkedIn

Build a model-native app using knowledge graphs and the open source Modus API framework

🧱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

SimGRAG is a novel method for knowledge graph driven RAG, transforms queries into graph patterns and aligns them with candidate subgraphs using a graph…

How we used Kùzu in combination with LLMs in multiple stages of the Graph RAG pipeline to build a QA chatbot for the Connected Data London Knowledge Graph Challenge

A deep dive into LangChain’s implementation of graph construction with LLMs

✨ Attention Information Extraction Enthusiasts ✨ I am excited to announce the release of our latest paper and model family, ReLiK, a cutting-edge… | 33 comments on LinkedIn

Many people have asked about loading Microsoft Research #GraphRAG data into Neo4j. I wrote a quick notebook last night to import Documents, Chunks (TextUnit)… | 27 comments on LinkedIn

This notebook converts CSV data into a Neo4j Graph Database. All you do is describe your data. Have you wanted to see what your data looked like as a graph…