Retrieval Augmented Generation (RAG) is a widely adopted technique that enhances large language models (LLMs) by retrieving relevant information from a specific dataset before generating a response. While traditional or “Naive RAG” relies on vector (semantic) search to find contextually similar text chunks, it treats each data point as independent and does not capture deeper relationships between entities. This limitation becomes apparent when working with interconnected data, such as contracts, organizational records, or research papers, where understanding relationships is crucial. To address this, Graph RAG has emerged as a powerful extension that leverages knowledge graphs to improve retrieval quality by incorporating structural and relational context.
Graph RAG, particularly Microsoft’s implementation, uses LLMs to extract entities (e.g., people, organizations, locations) and their relationships from raw text in a two-stage process. First, entities and relations are identified and stored in a knowledge graph. Then, these are summarized and organized into communities—clusters of densely connected nodes—using graph algorithms like Leiden. This enables the system to generate high-level, domain-specific summaries of entity groups, providing a more holistic view of complex, fragmented information.
A key advantage of Graph RAG over Naive RAG is its ability to perform entity-centric retrieval. Instead of retrieving isolated text chunks, it navigates the graph to find related entities, their attributes, and community-level insights. This is especially effective for detailed, entity-focused queries, such as “What are the business relationships of Company X?” or “Which individuals are linked to Project Y?”
The blog illustrates this with a hybrid approach combining Weaviate (a vector database) and Neo4j (a graph database). In this setup, a user query first triggers a semantic search in Weaviate to identify relevant entities. Their IDs are then used to traverse the Neo4j knowledge graph, uncovering connections, community summaries, and contextual text chunks. A Cypher query orchestrates this multi-source retrieval, merging entity descriptions, relationships, and source content into a comprehensive response.
For example, querying “Weaviate” returns not just isolated mentions but a synthesized answer detailing its legal status, locations, business activities, and partnerships—information pieced together from multiple contracts and relationships in the graph.
Despite its strengths, Graph RAG has limitations. The preprocessing pipeline is computationally expensive and requires full reindexing to update summaries when new data arrives, unlike Naive RAG, which can incrementally add new chunks. Scalability can also be challenging with highly connected nodes, and generic entities (e.g., “CEO”) may skew results if not filtered.
In summary, while Naive RAG is effective for straightforward, content-based queries, Graph RAG excels in complex, relationship-rich domains. By combining vector and graph-based retrieval in a hybrid system, organizations can achieve deeper insights, leveraging both semantic meaning and structural intelligence. The choice between RAG methods ultimately depends on the nature of the data and the complexity of the questions being asked.
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