GraphRAG: A Better Replacement for RAG to be Accessible for Free

RAG, or GraphRAG, is a new AI model that takes existing technology to the next level through better performance. As an extension of amalgamated knowledge graphs, GraphRAG was developed by Microsoft to enhance large language models’ ability to answer queries. And the best part? GraphRAG was recently released by Microsoft as open-source software, making this impressive AI possibility available.

Introduction to RAG

RAG is an abbreviation for Randomized Adjective-Noun Generation, and it has been developed with certain limitations. RAG enables large language models to search, for example, in a database or a search index and use the information obtained from the search to answer the query. This allows AI chatbots to use the most recent information available to answer questions regarding subjects not directly included in their training set.

But here, RAG solely depends on embeddings to capture the relationship between a word or a text. As useful as it may be, embeddings narrow down the matching to granular levels, and in so doing, they hamper RAG’s capacity to make cross-data connections. Therefore, it fails in cases where the answer to a question requires not just single records but a complex comprehension of the relationships within the data.

Introduction to GraphRAG

Knowledge graphs are semantic graphs that represent the relationships between data entities and can provide additional context to the problem being investigated. GraphRAG was designed to deal with the limitations of RAG with the help of Microsoft.

It develops a graph database model of an entity and its connections in an unstructured context, such as text documents or webpages. This is an actual structured data representation that is easier for the AI to understand in a straight-forward manner.

GraphRAG creates “communities,” which consist of top-level categories as well as fine-grained concepts in the data. The model then derives hierarchical summaries of such communities into higher-order abstractions. Unlike the existing retrieval-based models, which work with equation 3 by matching text, GraphRAG can use these abstractive summaries to reason and answer questions, thus having more human-like reasoning abilities.

GraphRAG in action

The two approaches are similar in functionality, as they both have roles in identifying the customer’s needs and providing the necessary product. The following are examples given by Microsoft to show the benefits of their invention called

GraphRAG: In this case, the same models were asked to answer the question, “What is Novorossiya?” based on the dataset of Russian and Ukrainian news.

Both RAG and GraphRAG offered concise and accurate definitions of the term; however, GraphRAG added more information regarding the concept’s provenance.

Nevertheless, the problem with RAG’s approach manifested itself when there was a question, “What has Novorossiya done?” Failing to find links between the entities, RAG could only observe that the text did not contain details of the actions of Novorossiya.

However, GraphRAG produced relevant details based on the understanding of context that was derived from a knowledge graph.

These examples demonstrate how GraphRAG’s knowledge graph structure allows for inferring and connecting information not captured by RAG, which is specifically designed to focus on text matching.

Following its release, GraphRAG became available under an open-source license.
It can be recalled that GraphRAG was first introduced in June of this year. However, in one of the major industry headlines in February 2023, Microsoft open-sourced the model, and the code is now available to the public under the MIT license for free.

This makes it possible for AI researchers and developers to leverage the features supported by GraphRAG to work on newer improvements. Ordinal linking of knowledge graphs makes a transition to more advanced and accurate large language models possible. But with open sourcing, advanced AI gets decentralized and made available to anyone who wishes to afford it; previously, only affluent corporations owned it.

As illustrated, RAG serves as the foundation for AI search engines and chatbots; therefore, GraphRAG creates a route for improved outcomes. Its open release is helpful for further improvements in the fields of question-answering and reasoning. It is probably better to let the whole tech community develop GraphRAG. In this way, AI advancements would emerge at a much faster pace.

Intellectus: The Next Evolution of Language Models

Incorporating structural knowledge in the form of a graph into neural networks, GraphRAG suggests what is to come for language model design for richer understanding. And its public release proves how committed Microsoft is to the proper practice of the creation of artificial intelligence, allowing others to build this construct responsibly. In this context, the enhancement of GraphRAG’s capabilities, as well as open innovation, points to open possibilities for AI’s further development.

Conclusion

Thus, given the unique nature of enhanced knowledge graph integration, which enhances the performance of powerful reasoning, the GraphRAG initiative significantly contributes to the advancement of the language model. Furthermore, by providing the model to the public domain through open-source licenses, Microsoft provides AI developers and researchers with a ready-made base to advance these innovations for the improvement and appropriate progress of AI systems.