Скачать или смотреть What Is GraphRAG?

In this video, we dive deep into GraphRAG (Retrieval-Augmented Generation), a powerful mechanism that significantly enhances GenAI by leveraging the rich context of knowledge graphs.
Are your Large Language Models (LLMs) prone to hallucinations or lacking recent and relevant information? While basic RAG systems aim to improve reliability by basing LLM answers on existing knowledge sources, they often fall short by relying solely on semantic search in vector databases. This approach retrieves isolated text fragments, failing to capture the crucial context and relationships between data points, leading to incomplete or fragmented answers and a lack of explainability.
GraphRAG is the game-changer! It uses knowledge graphs to represent and connect information, capturing not just more data points but also their vital relationships. This allows graph-based retrievers to provide more accurate, relevant, and trustworthy results by uncovering hidden connections essential for correlating information.
Here's what GraphRAG offers:
• Comprehensive Context: Navigate complex data structures to retrieve information beyond initial text chunks, offering a more complete picture.
• Enhanced Explainability: Capture relationships between retrieved data, making it easier to trace the sources and reasoning behind generated responses.
• Superior Accuracy: Prioritize pertinent information based on user context and task, leading to higher quality outputs.
• Unified Data View: Integrate structured and unstructured data, plus computed signals, for nuanced and informed responses.
We'll explain the three key phases of RAG—Retrieval, Augmentation, and Generation—and demonstrate how GraphRAG specifically improves the retrieval phase using knowledge graphs. You'll see how knowledge graphs, with their flexible node and relationship models, efficiently store and process real-world information, acting as a "flexible memory companion" to LLMs.
The video will showcase a practical Neo4j example of constructing a knowledge graph from biomedical research papers using the open-source neo4j-graphrag package. Discover how Neo4j's GraphRAG retriever, using Cypher query language, finds starting points and follows relevant relationships to gather additional context, far surpassing vector-only RAG in comprehensiveness.
GraphRAG is essential for high-trust applications where reliability and transparency are paramount, including:
• Legal and Compliance
• Investment Research
• Biotech and Drug Discovery
• Fraud Detection
• Supply Chain Management
• Natural Language Search and Chatbots
Join the 100K+ Neo4j experts! Learn how GraphRAG enables enterprise-grade AI apps, ensuring accuracy, reliability, and transparency for better decision-making and improved business outcomes.