Build reliable AI with Aura Agents
Highlights: -Knowledge Graph Fundamentals: Explanation of how nodes (people, places, things) and relationships (interactions) create interconnected data. -The Problem with Standard RAG: Demonstrates how a standard agentic stack fails to accurately count specific talent or analyze distribution from PDF resumes. -Aura Agent Workflow: Shows how to automate the extraction of entities from resumes into a Neo4j graph model to ground the agent with connected facts. -Deployment and Tools: Features a low-code UI for drafting, testing, and deploying agents to secure REST and MCP endpoints. -Graph vs. Relational Databases: A comparison highlighting the performance advantages of multi-hop querying and the flexibility to easily extend schemas without complex join tables 0:07 - What is a knowledge graph? 0:33 - Introduction to Aura Agent capabilities 1:34 - HR talent agent use case walkthrough 2:40 - Demonstration of failed results using the standard vector RAG 3:04 - Fixing AI hallucinations using Neo4j and Aura Agent 3:39 - Achieving accurate results with graph-grounded agent tools 4:10 - Advanced multihop pattern matching and extending data models 4:42 - Comparison: Knowledge graphs vs. relational databases 6:53 - Resources for getting started with Aura Agent Get started: https://neo4j.com/developer/genai-eco...
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