Building a graph of entities and their relations from your corpus, then retrieving connected subgraphs (and their linked text) to answer questions that span multiple documents.
GraphRAG Retrieval Process
How it works
To build a graph for retrieval, we start with an extraction pass, typically using a large language model (LLM) like GPT or BERT. This model identifies entities, relationships, and community summaries from the text corpus, creating a structured graph where nodes represent entities and edges represent relationships between them. When a query comes in, the system first identifies the relevant entities in the graph. From these matched entities, it traverses the edges to gather related facts, forming a connected subgraph that encapsulates the necessary context. This structured neighborhood, along with supporting text passages, is then sent to a generator model for crafting a coherent answer. Microsoft's GraphRAG enhances this process by pre-summarizing clusters of related entities, which is especially useful for answering global questions that require a broader context.This method allows for complex queries that span multiple documents, as the graph structure inherently supports multi-hop reasoning. For instance, if a user asks about vendors that passed a security review, the system can navigate through the graph to find not only the vendors but also the related security review documents and approvals. Each traversal step is meaningful, as it connects disparate pieces of information that exist across the corpus. Without this graph structure, the system would struggle to synthesize information that is spread out, making it difficult to provide comprehensive answers.the graph's ability to represent relationships and community summaries means that the model can generate responses that are not just factually accurate but also contextually rich. This is crucial when understanding the interplay between different entities is key to answering a question. For example, a query about regulatory compliance might require insights from various documents that discuss different aspects of the compliance process. The graph facilitates this by providing a clear path through the relevant information, ensuring that the answer is grounded in the actual data rather than relying solely on the model's training.
Extract entities and relationships from corpus.
Build a knowledge graph from extracted data.
Query for matched entities in the graph.
Traverse edges to gather related facts.
Return structured neighborhood with supporting passages.
The software-engineering analogue
The process of building a knowledge graph can be likened to performing a foreign-key JOIN across normalized tables in a relational database. In this analogy, each entity in the graph corresponds to a row in a table, and the edges represent the relationships that would typically be established through foreign keys. However, the key difference lies in how the graph is constructed; it is derived from unstructured prose, meaning that the relationships are inferred rather than explicitly defined by a schema. This results in edges that can be imperfect or incomplete, reflecting the nuances and ambiguities of natural language rather than strict database rules.This distinction is critical because it highlights the challenges of working with knowledge graphs. While a database schema enforces data integrity and relationship consistency, a graph extracted from text may contain inaccuracies or gaps. Therefore, while the JOIN analogy helps illustrate the concept of connecting data points, it breaks down when considering the inherent uncertainties in the graph structure, which can lead to unexpected results during query execution.
Why not the obvious alternative
A newcomer might first consider using a vector-based retrieval approach, like traditional Retrieval-Augmented Generation (RAG), where the system retrieves chunks of text independently based on semantic similarity. This method works well for straightforward queries, but it quickly falls short for complex questions that require synthesizing information from multiple sources. For example, if a user asks, 'Which vendors did we onboard that also failed the security review, and who approved them?', a vector RAG would struggle because the answer is likely distributed across several documents, with no single chunk containing all the necessary information.The root cause of this issue is that vector RAG treats each document chunk in isolation, lacking the ability to understand and traverse the relationships between different pieces of information. In contrast, a knowledge graph makes these relationships first-class citizens, allowing the system to perform multi-hop reasoning. While the initial investment in building and maintaining a graph may be high, it ultimately enables richer, more comprehensive answers that a flat RAG structure cannot provide.
When to use which
Choosing between a knowledge graph approach and a vector RAG largely depends on the complexity of the queries you expect to handle. If your domain involves connected entities and multi-hop questions—such as compliance investigations or organizational knowledge—a knowledge graph is worth the investment. It allows for the synthesis of relationships across different documents, providing a more holistic view that a simple vector RAG cannot achieve. However, for straightforward lookup-style Q&A, where the information is largely self-contained and does not require deep contextual understanding, a vector RAG is often cheaper and nearly as effective.In practice, consider the trade-offs: while knowledge graphs offer powerful capabilities for complex queries, they also require significant resources for extraction, maintenance, and updates. If your use case involves frequent changes in the underlying data or requires rapid iteration, the overhead of maintaining a graph might outweigh its benefits. In contrast, vector RAG systems can be quicker to deploy and easier to manage, making them suitable for applications where speed and simplicity are paramount.
Understand the Extraction Cost The graph extraction process is resource-intensive; ensure the use case justifies the investment.
In production: when it breaks
The scenario Graph retrieval accuracy dropped to 65%, down from 85%.
Debugging Graph Retrieval Issues
Why it happens. The low accuracy often stems from incomplete or incorrectly linked entities in the graph. If the relationships between entities are not well-defined or if key entities are missing, the retrieval process will yield suboptimal results, leading to irrelevant or incorrect answers.
Check the entity completeness in the graph.
Review the relationship definitions between entities.
Analyze query logs to identify common failure points.
The call To fix the issue, enhance the entity linking process and ensure comprehensive coverage of the corpus. This may lead to increased processing time during graph updates. Monitor the accuracy of query results and the frequency of entity updates going forward.
What it delivers
Ultimately, a knowledge graph for retrieval delivers answers that require synthesizing relationships across a corpus, backed by an explainable traversal path. This means that users receive not only accurate answers but also insights into how those answers were derived, enhancing trust and understanding of the information provided.
