Microsoft Developer features Anisha Latchman demonstrating how GraphRAG leverages Azure OpenAI and advanced graph embeddings to transform security data search and analysis in this practical and insightful video.

Exploring GraphRAG: AI-Powered Graph Search for Security Data Analysis

In this episode of Mr. Maeda’s Cozy AI Kitchen, Microsoft Security Intern Anisha Latchman takes viewers through a tour of GraphRAG, an open-source Microsoft project that blends Retrieval-Augmented Generation (RAG) with graph-based search approaches to uncover deep patterns within security datasets.

Highlights

  • Introduction to GraphRAG: Anisha explains how GraphRAG enhances traditional RAG by using graph context, parsing emails, and building weighted graphs from nodes and relationships.
  • Hands-On Demo: Guidance on setting up GraphRAG, feeding input email data, configuring the API, indexing, and interpreting results files.
  • Technical Deep-Dive:
    • Understanding nodes, relationships, and the importance of weighted graphs in capturing context.
    • Entity extraction using prompt engineering.
    • Strategies for chunking information in RAG pipelines.
  • Comparisons: Evaluations between baseline RAG and GraphRAG performance, with focus on semantic search versus graph traversal.
  • Practical Takeaways:
    • Advice for aspiring AI builders interested in graph-based search and prompt engineering.
    • Insights into community search, subgraphs, and transparency in entity extraction.

What You’ll Learn

  • How GraphRAG amplifies traditional RAG with graph-based context.
  • The role of embeddings and nodes in security data analysis.
  • Setting up and operationalizing GraphRAG using open-source Microsoft tools.
  • The distinction between semantic (vector-based) and graph (relational) search methodologies.
  • Prompt and context engineering best practices for AI solutions.

Resources

Speakers

For more episodes, visit the Cozy AI Kitchen Playlist.