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The Power of Graph RAG Unleashed | GraphRAG End-to-End Implementation With @Microsoft Azure OpenAI
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#RAG #ai #generativeai #openai #azureopenai #datascience
Retrieval-augmented generation (RAG) is a technique to search for information based on a user query and provide the results as reference for an AI answer to be generated. This technique is an important part of most LLM-based tools and the majority of RAG approaches use vector similarity as the search technique. GraphRAG uses LLM-generated knowledge graphs to provide substantial improvements in question-and-answer performance when conducting document analysis of complex information.
By combining LLM-generated knowledge graphs and graph machine learning, GraphRAG enables us to answer important classes of questions that we cannot attempt with baseline RAG alone. We have seen promising results after applying this technology to a variety of scenarios, including social media, news articles, workplace productivity, and chemistry. Looking forward, we plan to work closely with customers on a variety of new domains as we continue to apply this technology while working on metrics and robust evaluation.
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Time stamps:
00:01:30 Introduction
00:02:00 What is Graph RAG and how is it different from traditional RAG
00:20:03 Coding Implementation Of GraphRAG
Retrieval-augmented generation (RAG) is a technique to search for information based on a user query and provide the results as reference for an AI answer to be generated. This technique is an important part of most LLM-based tools and the majority of RAG approaches use vector similarity as the search technique. GraphRAG uses LLM-generated knowledge graphs to provide substantial improvements in question-and-answer performance when conducting document analysis of complex information.
By combining LLM-generated knowledge graphs and graph machine learning, GraphRAG enables us to answer important classes of questions that we cannot attempt with baseline RAG alone. We have seen promising results after applying this technology to a variety of scenarios, including social media, news articles, workplace productivity, and chemistry. Looking forward, we plan to work closely with customers on a variety of new domains as we continue to apply this technology while working on metrics and robust evaluation.
do mail here
Do Support the channel friends.
And also Guys follow me on social media links are available below.
Time stamps:
00:01:30 Introduction
00:02:00 What is Graph RAG and how is it different from traditional RAG
00:20:03 Coding Implementation Of GraphRAG
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