This tutorial demonstrates how to use Microsoft GraphRAG to process and index CSV files for people-finding tasks. GraphRAG (Graph Retrieval Augmented Generation) enhances semantic search capabilities by structuring hierarchical knowledge graphs from raw text. Using LanceDB as the vector database, this system efficiently manages large and complex datasets, allowing for precise querying and insightful responses.
GraphRAG enables the generation of knowledge graphs from textual data. It offers several advantages over traditional RAG (Retrieval Augmented Generation) methods by mapping relationships and hierarchies within the data, improving the quality and relevance of the responses generated. With LanceDB integrated as the primary vector database, searching through datasets becomes faster and more accurate.
- Processes CSV files containing complex datasets
- Indexes the data and structures it into a hierarchical knowledge graph
- Utilizes LanceDB for fast, efficient vector similarity searches
- Supports natural language queries to retrieve detailed, context-relevant responses
Google Colab Walkthrough
For a detailed, interactive walkthrough of this implementation, you can go over the Google Colab notebook I've included below.
For a detailed explanation of GraphRAG with csv data for people finding usecase, check out blog link .