Exploring Graph-Based Search: A New Era in LLMs & Gen

Unlocking the power of Graph-RAG: How graph-based search technology is revolutionizing AI-powered applications, from customer service bots to knowledge management systems.

* Speaker is a music industry professional who has focused on using data and relationships to improve application development.
* The evolution of search was driven by the need to handle large amounts of data and provide relevant results.
* Early web search engines, such as AltaVista, used keyword-based text search with inverted index technology (bm25).
* The "AltaVista effect" occurred when users were overwhelmed with too many irrelevant search results.
* Google solved this problem by introducing PageRank, a graph algorithm that delivers relevant results early in the search.
* In 2012, Google introduced the Knowledge Graph, which stores and organizes concepts and relationships within documents.
* The Knowledge Graph era has been marked by Google's dominance in web search, providing more accurate and structured results.
* Recently, Google I/O introduced a new era of web search: the graph rag era, where knowledge graphs are combined with language models (LMs).
* Graph rag is a retrieval path that uses a Knowledge Graph to enhance search results, often in combination with other technologies like vector search.
* The benefits of graph rag include higher accuracy, easier development, and increased explainability/governance for businesses.
* To get started with graph rag, you must create a knowledge graph by processing different types of data: structured, unstructured, or mixed (semi-structured).
* There are two main types of graphs: lexical graphs and domain graphs, which are relevant when creating knowledge graphs.
* A new tool, the Knowledge Graph Builder, has been developed to help create knowledge graphs by extracting data from various sources (PDFs, web pages, etc.).
* The Knowledge Graph Builder can process documents and extract logical concept elements, building a graph of related information.
* This tool also includes a chat feature for introspecting results, and offers a QR code for easy access to its capabilities.
* Graph rag is being applied in various industries, such as fintech, where it improves application performance and debugging.
* The use of graph rag leads to more accurate answers, easier development, and increased explainability/governance for businesses.
* The speaker encourages the audience to try the Knowledge Graph Builder and explore its potential for their own projects.

Source: AI Engineer via YouTube

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