Ask questions with Natural Language (GraphRAG)
How to use GraphRAG to ask questions with natural language.
What is GraphRAG?
GraphRAG (Graph-Retrieval Augmented Generation) is a system that lets you ask natural-language questions about your data — and get accurate answers directly from your custom knowledge graph.
Instead of manually writing query language or browsing complex graph structures, GraphRAG uses an AI agent to:
Understand your question — It interprets what you’re asking in plain English (e.g., “Which genes are upregulated in ALS?”).
Translate it into a graph query — The AI automatically generates the correct Cypher query based on BioBox’s custom ontology and data model.
Retrieve and summarize results — The query runs against your knowledge graph, and GraphRAG summarizes the findings into clear, readable text alongside the supporting evidence.
This approach combines the reasoning ability of large language models with the accuracy and structure of a knowledge graph, allowing scientists to explore complex biological relationships without needing to know query syntax.
Asking Questions with Natural Language
Ensure that your knowledge engine is toggled to "Natural Language". Type your question into the search bar and hit enter.
When you submit a question, the question and the agent’s interpretation are pinned to the top of the page for easy reference.
The agent then begins transforming your natural-language question into a graph query, which you can observe in real time under the Prompts tab. Once the answer has been generated, the interface automatically switches to the Results tab.
The results section is composed of several specialized agents, each providing a different perspective on the retrieved data (e.g., Biological Concept Agent, Graph Paths Agent, Text-Based Agent).
For example, the question “Which genes are upregulated in ALS?” is converted into the following graph query:
(d:Disease {displayName = "familial amyotrophic lateral sclerosis"})<- studies condition<- (d2:DiffExpDataset)-> upregulates-> (g:Gene)Results Summary Agent
The agent provides a text-based summary of all of the data returned in the search. Select "View More" next to each agent summary to view a longer, comprehensive summary.
Text-Based Agent
The Text-Based Agent retrieves relevant electronic laboratory notebooks (ELNs) that contain information related to your question.
If ELNs are integrated into your knowledge graph, this agent will automatically display all notebooks containing supporting data. A summary of the findings appears above the list of ELNs, and selecting a specific notebook opens a side panel where you can view its detailed content.
Biological Concept Agent
The Biological Concept Agent retrieves all biological concepts and their related data objects that help answer your question. The concepts and data returned are specific to your custom knowledge graph.
For example, the question “What genes are upregulated in ALS?” is automatically transformed into a graph query involving the biological concepts Genes, Disease, and Differential Expression Datasets.
You can explore the results by selecting specific concepts from the left-hand panel. The corresponding data objects for each concept will appear in the table view on the right.
Graph Paths Agent
The Graph Paths Agent retrieves the individual graph paths that connect concepts and provide evidence for your question. This view offers additional flexibility by allowing you to customize which data properties are displayed and how they are ordered.
Use Edit columns to choose which data properties to show, and Reorder columns to drag and arrange them in your preferred order.
For instance, in the question “Which genes are upregulated in ALS?”, a graph path may include the specific dataset, gene symbol, log₂ fold change, and adjusted p-value associated with each result.
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