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Project GraphRAG

LLM-Derived Knowledge Graphs

뉴스 & 기능

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Microsoft Research 블로그

| Dasha Metropolitansky

While large language models (LLMs) are capable of synthesizing vast amounts of information, they sometimes produce inaccurate or unsubstantiated content. To mitigate this risk, tools like Azure AI’s Groundedness Detection (opens in new tab) can be used to verify LLM…

LazyGraphRAG blog hero
Microsoft Research 블로그

| Darren Edge, Ha Trinh, 그리고 Jonathan Larson

Introducing a new approach to graph-enabled RAG. LazyGraphRAG needs no prior summarization of source data, avoiding prohibitive up-front indexing costs. It’s inherently scalable in cost and quality across multiple methods and search mechanisms.

The image features three white icons on a gradient background transitioning from blue on the left to green on the right. The first icon, located on the left, depicts a hierarchical structure resembling a workflow with connected squares. The middle icon represents GraphRAG (interconnected nodes and lines). The third icon, on the right, shows a globe with a magnifying glass overlaying it, symbolizing global search.
Microsoft Research 블로그

| Bryan Li, Ha Trinh, Darren Edge, 그리고 Jonathan Larson

Retrieval-augmented generation (RAG) helps AI systems provide more information to a large language model (LLM) when generating responses to user queries. A new method for conducting “global” queries can optimize the performance of global search in GraphRAG.

Three icons that represent (left to right) search, GraphRAG, and the globe.
Microsoft Research 블로그

| Julian Whiting, Zachary Hills, Alonso Guevara Fernández, Ha Trinh, Adam Bradley, 그리고 Jonathan Larson

GraphRAG leverages semantic structuring of data to generate responses to complex user queries. A collaboration with Uncharted expands the frontiers of this technology, developing a new approach to processing local queries: DRIFT search.

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Microsoft Research 블로그

| Alonso Guevara Fernández, Katy Smith, Joshua Bradley, Darren Edge, Ha Trinh, Sarah Smith, Ben Cutler, Steven Truitt, 그리고 Jonathan Larson

GraphRAG uses LLM-generated knowledge graphs to substantially improve complex Q&A over retrieval-augmented generation (RAG). Discover automatic tuning of GraphRAG for new datasets, making it more accurate and relevant.

뉴스에서 | Fierce Network

Grounding AI models is like giving them a map to navigate the world, but what happens when that map isn’t up to date or misses crucial details? That’s the problem hyperscalers and AI leaders are now trying to solve. As…

뉴스에서 | The Stack

Redmond opens up to discuss its new tool, which can extract data from unstructured text using large language models. Microsoft’s GraphRAG is a new approach to Retrieval-Augmented Generation (RAG) that Redmond has described a “significant advance in enhancing the capability…

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Microsoft Research 블로그

| Darren Edge, Ha Trinh, 그리고 Dayenne Souza

Intelligence Toolkit was built to help fight human trafficking and is applicable to a broad range of societal challenges. Learn how Microsoft researchers worked with global experts to develop generative AI tools that could help tackle urgent issues at scale.

GraphRAG blog hero
Microsoft Research 블로그

| Darren Edge, Ha Trinh, Steven Truitt, 그리고 Jonathan Larson

GraphRAG, a graph-based approach to retrieval-augmented generation (RAG) that significantly improves question-answering over private or previously unseen datasets, is now available on GitHub.