Microsoft recently open sourced a graph-based retrieval enhancement generation system - GraphRAG on its official website. This system significantly enhances the capabilities of large models in search, question answering, summarization and reasoning by building entity knowledge graphs. Different from the limitations of traditional RAG systems that rely too much on local text fragment retrieval, GraphRAG can capture complex connections and interactions in data sets to achieve global retrieval, and is especially good at processing large-scale data sets. Its core lies in the two steps of building an entity knowledge graph and generating community summaries. It can efficiently extract key information through community summaries and generate more comprehensive and accurate answers. What’s more noteworthy is that GraphRAG’s demand for tokens is extremely low, which is undoubtedly a huge cost advantage for developers.
Project entrance: https://top.aibase.com/tool/graphrag
When dealing with external data sources, traditional RAG systems rely too much on retrieval of local text fragments and fail to capture the full picture of the entire data set. GraphRAG helps large models better capture complex connections and interactions in text by building entity knowledge graphs, thereby achieving global retrieval capabilities.
The core of GraphRAG consists of two steps: building an entity knowledge graph and generating community summaries. Through community summarization, GraphRAG is able to extract relevant information from the entire dataset to generate more comprehensive and accurate answers. In addition, GraphRAG has very low demand for tokens, which means it can help developers save a lot of costs.
Microsoft conducted a comprehensive test on GraphRAG on a data set with 1 million tokens and an ultra-complex structure. The results showed that GraphRAG surpassed methods such as Naive RAG in comprehensiveness and diversity testing, and was more effective in podcast transcription and news article data sets. All of them have shown extremely high standards and are currently one of the best RAG methods.
Highlights:
- GraphRAG enhances the search, question answering, summarization, reasoning and other capabilities of large models by building entity knowledge graphs, and is particularly good at processing large-scale data sets.
- The core of GraphRAG includes two steps: building an entity knowledge graph and generating community summaries. Community summaries are used to extract relevant information in the data set to generate more comprehensive and accurate answers.
- GraphRAG has very low demand for tokens and can help developers save costs. It performs well in comprehensive tests and is one of the best RAG methods currently.
In summary, GraphRAG has brought new breakthroughs to the field of retrieval enhancement generation with its excellent performance in processing large-scale data sets and significant cost advantages, which deserves attention and further research. Its open source also provides developers with valuable resources and tools.