Graph RAG enhances AI by improving data precision and context for more accurate, reliable outputs in complex queries.
Generative AI – a technology wonder of modern times – has revolutionized our ability to create and innovate. It also promises to have a profound impact on every facet of our lives. Beyond the seemingly magical powers of ChatGPT, Bard, MidJourney, and others, the emergence of what’s known as RAG (Retrieval Augmented Generation) has opened the possibility of augmenting Large Language Models (LLMs) with domain-specific enterprise data and knowledge.
RAG and its many variants have emerged as a pivotal technique in the realm of applied generative AI, improving LLM reliability and trustworthiness. Most recently, a technique known as Graph RAG has been getting a lot of attention, as it allows generative AI models to be combined with knowledge graphs to provide context for more accurate outputs. But what are its components and can it live up to the hype?
What is Graph RAG and What’s All the Fuss About?
According to Gartner, Graph RAG is a technique to improve the accuracy, reliability and explainability of retrieval-augmented generation (RAG) systems. The approach uses knowledge graphs (KGs) to improve the recall and precision of retrieval, either directly by pulling facts from a KG or indirectly by optimizing other retrieval methods. The added context refines the search space of results, eliminating irrelevant information.
Graph RAG enhances traditional RAG by integrating KGs to retrieve information and, using ontologies and taxonomies, builds context around entities involved in the user query. This approach leverages the structured nature of graphs to organize data as nodes and relationships, enabling efficient and accurate retrieval of relevant information to LLMs for generating responses.
KGs, which are a collection of interlinked descriptions of concepts, entities, relationships, and events, put data in context via linking and semantic metadata and provide a framework for data integration, unification, analytics and sharing. Here, they act as the source of structured, domain-specific context and information, enabling a nuanced understanding and retrieval of interconnected, heterogeneous information. This enhances the context and depth of the retrieved information, which results in accurate and relevant responses to user queries. This is especially true for complex domain-specific topics that require a deeper, holistic understanding of summarized semantic concepts over large data collections.
Why Graph RAG
Despite its benefits, traditional RAG has multiple limitations, as it often fails to index documents relevant to the query resulting in failure to retrieve them to provide the right context. Additionally, it is not uncommon for the documents that are retrieved to be of minimal relevance as context is often missing. This is especially true when numerous documents are retrieved and consolidated. Another common shortcoming is most RAG approaches retrieve “approximate” and not “exact” values leading to irrelevant information.
Graph RAG aims to overcome these imperfections by infusing graph-based retrieval mechanisms. Leveraging graph technology, LLMs provide more precise, contextually aware, and relevant answers to user questions, especially for complex queries that require a comprehensive understanding of summarized semantic concepts over large data.
KGs store and organize facts, relationships, and semantic information about different domain entities. They also provide domain-specific corpus to support RAG systems so that semantically relevant and contextual data can be retrieved. Graph retrieval-augmented generation connects disparate pieces of information and summarizes semantic concepts within large amounts of information. The interconnected nature of entities in the graph is a crucial step for generating contextually and factually coherent responses, enhancing question-answering and information summarization.
Graph RAG: When to use it/When not to/How it’s being used/Patterns to consider
Organizations across a variety of industries have seen improvements in precision and recall using GraphRAG over traditional retrieval methods. For example, Graph RAG is the most appropriate solution when there is a need for explainability, provenance and knowing the source of the answers
It is quickly becoming the preferred method when an exact or hybrid search approach to improve the ranking process of returned results does not enhance RAG performance. It is also a better approach when the information required to answer a user question is spread across multiple chunks as traditional RAG may offer correct but incomplete answers.
Vanilla RAG, based just on vector databases, can reduce hallucination by connecting the generated response to real-world data, but answering complex, domain-specific questions accurately is still best solved using a G-RAG approach.
Although Graph RAG has significant advantages, it is not advisable to use it if traditional RAG works well for the use case in question. This is because Graph RAG requires a knowledge graph to exist in the organization before it can be utilized, so its results will depend on the quality of the knowledge graph itself.
Some business areas where a Graph RAG approach can yield better outcomes include the following:
- Natural Language Querying (NLQ)
- Customer Support
- Recommendation
- Semantic Search
- Chatbots
- Enhancing question-answering
- Information summarization
- Information Extraction
The table below summarizes the approaches and patterns of implementing Graph RAD based on the use case.
| Graph RAG Types | How it works | Prerequisites | Tools Needed |
| Graph as a Content Store | This uses graphs as a document store. The documents are divided into chunks, which are indexed in a vector database.At query time, relevant document chunks are retrieved from the graph and the LLM is prompted to answer the user’s question using them. | KG containing relevant textual content and semantic metadata | Vector DBFull Text Search |
| Graph as а Subject Matter Expert | Here, the information is modeled in the graph to aid the RAG process. A sub-graph, describing the concepts relevant to the user’s question, is extracted and provided to the LLM as a “semantic context.” | KG with a conceptual model (Ontology) | Entity Linking or and identification of relevant concepts |
| Graph as a Database | This maps the user’s question to a structured database query, which is executed, and the LLM is prompted to summarize the results and answer the question. | Graph that holds relevant information | NLQ-to-Graph Query toolEntity Linking |
Early Days but the future looks bright
Despite the increased focus, these are still the early days for Graph RAG. Data and AI teams are in the exploratory phase with this new technique largely because the effectiveness of this approach depends on the completeness and accuracy of the KGs built either manually or through automation.
With this in mind, it’s always better to start with vanilla RAG and measure the effectiveness of the solution before jumping on Graph RAG. Once done, the next step is to ensure the organization has a KG built with clean, quality data before initiating a Graph RAG. The KG can be built automatically or the business can reuse and augment their existing KG with the latest data.
Graph RAG is a promising new technology with the power to generate results far greater than traditional RAG approaches. Leveraging the structured knowledge provided by the KG helps organizations enhance the context, accuracy and relevancy of the text generated, which in my opinion makes it worthy of the hype.
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