Unlocking GenAI’s Potential with Knowledge Graphs

The Hype (and Struggle) with GenAI in Business

McKinsey says 63% of business leaders are all-in on Generative AI (GenAI), but here’s the kicker—91% of them admit they’re nowhere near ready to handle the tech responsibly. That’s a lot of enthusiasm with a side of panic. The risks, like inaccurate data outputs and cybersecurity issues, are huge hurdles they can’t ignore (McKinsey & Company).

On top of that, McKinsey predicts GenAI could pump trillions into the global economy. Think lead generation, marketing that actually works, and personalized customer outreach at scale. The potential is enormous, but there’s a catch: none of this matters unless companies can tap into and centralize their data. Right now, most of that data is stuck in silos (McKinsey & Company).

This is where knowledge graphs shine.

Why Knowledge Graphs Matter for GenAI

Knowledge graphs are the key to making sense of messy data. They don’t just connect the dots between disparate datasets—they build relationships between data points, enabling GenAI to provide actionable insights instead of random guesses. This is especially important in applications where you need accuracy, context, and the most relevant answers, and fast.

When you combine this with GraphRAG—ArangoDB’s unique take on Retrieval Augmented Generation—the power multiplies. GraphRAG integrates knowledge graphs and LLMs, allowing for dynamic AQL (Arango Query Language) generation using ArangoDB’s LangChain Integration Pack. This lets users pull out meaningful insights through natural language queries without needing to handcraft every query or know AQL. It’s like having an intuitive data assistant that understands your business context. And best of all, it leverages your PRIVATE and internal data stored in your ArangoDB knowledge grap Knowledge graphs are the key to making sense of messy data. They don’t just connect the dots between disparate datasets—they build relationships between data points, enabling GenAI to provide action able insights instead of random guesses. This is especially important in applications where you need accuracy, context, and the most relevant answers, and fast.

What’s a Knowledge Graph, Really?

A knowledge graph is a bit like a web of relationships between entities (nodes) and the connections (edges) between them. These entities can be anything — people, places, products — and the relationships show how everything is interconnected. It’s this interconnectedness that allows GenAI to infer deeper — and far more accurate — insights.

Knowledge graphs don’t just hold raw data; they infer meaning, uncover hidden connections, and provide context. This is where they beat vector-only databases — graphs can “fill in the blanks,” helping you not just store but understand data at a deeper level.

GenAI Meets Knowledge Graphs: A Perfect Pair

Even before GenAI, knowledge graphs were shaking things up in fields like finance, healthcare, energy, and logistics. With GenAI in the mix, the benefits skyrocket.

Here’s how:

1. Smarter Queries- GraphRAG enables companies to ask nuanced, inference-driven questions that go beyond simple searches. Imagine asking, “What products in my inventory could be delayed due to a supply chain disruption in Asia?” Asking this type of question against a knowledge graph will yield EXTREMELY precise responses, so there are far fewer AI “hallucinations”. More on that later.
2. Accessible Data for All- With dynamic AQL generation via the LangChain Integration Pack, business users and analysts don’t need to know how to write complex queries or use specialized analytic tools. They can use natural language to interact with the data, making insights available to everyone, not just data scientists. Set up the LangChain integration once, and then let business users loose on the knowledge graph without needing any further technical intervention.

“According to a recent survey by McKinsey, 63% of business leaders have prioritized the implementation of Generative AI (GenAI) within their organizations. Despite this high interest, 91% of these leaders do not feel adequately prepared to manage the technology responsibly.”

Excerpt from ‘The state of AI in early 2024: Gen AI adoption spikes and starts to generate value’ survey by McKinsey & Co., May 2024

What is interesting about this MicKinsey observation is that Graph-Powered GenAI with knowledge graphs can dramatically speed the time to market of GenAI projects, and make them far more accessible across the organization. Talk about preparedness!

A Word on GraphRAG vs. Other Approaches

So, what makes GraphRAG different from other RAG approaches or even traditional databases like MongoDB?

• Context is King: MongoDB or traditional RAG systems mainly rely on unstructured text retrieval. Sure, they can give you snippets of data, but they can’t connect that data to meaningful context. A graph database, on the other hand, inherently understands relationships. When you ask a question, it doesn’t just give you pieces of an answer— it gives you the full picture, with all the connections between nodes clearly mapped out.
• Dynamic Querying: GraphRAG with ArangoDB lets you dynamically generate AQL based on your question. MongoDB and other NoSQL databases might require more manual setup to get the right insights, but with GraphRAG, the system intuitively knows how to retrieve the relevant relationships from your knowledge graph. This is crucial when you’re working with complex, interconnected data and especially important for real-time decision-making.
• Data Integrity: While vector databases are good at matching patterns, they often fail to explain why two data pieces are related. Knowledge graphs provide that “why,” offering context and making the data more trustworthy. This is especially useful in industries like healthcare, where regulatory compliance and data traceability are critical.

So what about a RAG system that can blend GraphRAG with traditional RAG solutions such as those built on top of vector databases? This approach – often called HybridRAG – can yield very accurate results. A recent, collaborative study between NVIDIA and BlackRock titled, “HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction” (Bhaskarjit, Pasquali, Mehta, Rao, Hall, Patel) delves into this realm in a very compelling and thought-provoking fashion. HybridRAG contemplates a “best of both worlds” combination of GraphRAG and VectorRAG.

The study shows that a combination of knowledge graphs and vector-based retrieval methods increases the effectiveness of information extraction, especially in complex, domain-specific applications.

Public vs. Private LLMs: A Crucial Decision

One of the significant challenges with LLMs is data privacy. Many organizations are reluctant to send their proprietary data to public LLMs, where they risk data leaks or exposure to competitors. With ArangoDB’s LangChain Integration Pack, you have two options: use a public LLM or host your own private LLM in a secure environment.

If you’re worried about protecting sensitive info, you can deploy a private LLM in a private cloud, keeping everything under lock and key. This option provides the same powerful integration between GenAI and knowledge graphs but ensures that your data stays private, secure, and within your control. It’s ideal for industries like finance or healthcare, where privacy isn’t just important—it’s legally required.

Practical Applications Across Industries

This integration is making a real-world impact, especially in industries where data accuracy, compliance, and real-time insights are mission-critical:

• Healthcare: Say you need to track down the entire history of a patient’s medical records to ensure compliance with HIPAA regulations. A knowledge graph can link everything together—diagnoses, treatments, medication histories—so you get a complete, accurate picture in seconds. Care providers can then use natural language – even speech-enabled – to access this information via GraphRAG- generated AQL queries.
• Finance: When doing financial audits, tracing data lineage is crucial for avoiding compliance issues. With a knowledge graph, you don’t just know the numbers— you know where they came from, how they’re connected, and why they matter. Auditors can easily ask natural language interfaces to trace lineage fast and without technical intervention.
• Energy: In a crisis, like a pipeline disruption, a knowledge graph allows energy companies to see how that disruption impacts everything—from supplier contracts to end-user delivery—allowing them to react quickly and accurately. Imagine field workers using mobile devices to access repair information and service disruption status by just speaking.

Lowering the Risk of Hallucinations

An Arizona State University study on knowledge graph-enhanced language models demonstrates that integrating Generative AI with knowledge graphs, as in GraphRAG, can significantly reduce hallucinations by allowing models to retrieve and verify factual data from structured sources.

Frequently, hallucinations happen exactly because the connectedness of data is not properly or cleanly represented within the storage mechanism. Knowledge graphs provide the most structured way of representing entities and their relationships, offering the ability to trace data back to its source with extreme precision. This approach improves the reasoning capabilities of AI systems and enhances the factual accuracy of their outputs, thereby lowering the risk of generating incorrect or misleading information in the form of hallucinations.

The GraphRAG framework combines with knowledge graphs to give models access to structured, verified data during the generation process. This helps mitigate hallucinations by ensuring the generated content aligns with real-world facts. This enhancement of reasoning and data accuracy is a key benefit of incorporating knowledge graphs into AI systems like GenAI. In highly regulated and sensitive business cases, there is no room for incorrect or “hallucinatory” responses. Only the most accurate means of AI-driven data retrieval can be trusted.

Strategies to Embark on GenAI and Knowledge Graphs

If you’re ready to dive into GenAI and knowledge graphs, here’s a simplified plan:

• Pick a Problem or Application: Find a business issue or application that could benefit not just from more intelligent insights, but from a natural language interface that complements traditional interfaces.
• Test the Waters: Build a natural language chatbot-style interface outside of your application or business service and test it out with users before you fully integrate it into the next version of your application.
• Start Small: Don’t try to revolutionize the entire company at once. Build a focused knowledge graph in ArangoDB with some test data and prove the concept. From there, you can scale up.
• Build the Right Team: Get people on board who understand both GenAI and ArangoDB. The right combination of skills will ensure you implement solutions that actually deliver value. ArangoDB is easy to use, and you can learn the ropes at the ArangoDB University.

Data on the Move: From Lonely Rows to Connected Graphs

You might ask, “But what if my data is in a traditional RDBMS or NoSQL database?” Don’t worry, it’s easy to get all your data into ArangoDB in 3 easy steps:

• Export your data: First, extract your data into standard formats like TSV (Tab- Separated Values), Excel (XLS/XLSX), Custom Export via API, SQL Dumps, or ETL (Extract, Transform, Load) Tools.
• Import into ArangoDB: Load your data using ArangoDB’s import tools or REST API. Documents will go into collections, just like tables in SQL or JSON in NoSQL.
• Easily define relationships: Relationships (edges) aren’t inferred automatically, but ArangoDB makes it simple. Just follow a few straightforward rules: identify the connected documents, then create edges between them. You can batch create relationships or define them on the fly using ArangoDB’s AQL. You can even use LLMs to automatically do this!

ArangoDB simplifies this process with clear, step-by-step guidance, helping you turn your old data into graph-powered insights quickly!

Why ArangoDB?

ArangoDB’s multi-model approach (graph, document, full-text search, geospatial, and key/value) makes it uniquely flexible. You can build robust knowledge graphs that interact with different types of data.

Unmatched in the market, ArangoDB’s horizontal scalability means you can effortlessly scale out without needing to worry about where data lives within the cluster. This avoids manual data striping and constant re-partitioning, which is the bane of other legacy graph database systems.

With the ArangoDB LangChain Integration Pack, you get the power of GraphRAG—dynamic AQL generation, private LLM hosting options, and the ability to query your graph using natural language. This makes it one of the most powerful tools available for businesses looking to harness the full potential of GenAI while keeping control over their data.

In short, if you’re looking to make GenAI work for your business, ArangoDB provides the perfect foundation. You get the ability to deploy private LLMs, dynamically generate queries, and leverage the insights hidden in your data, all while maintaining security and scalability.

Getting Started with ArangoDB, LLMs, and GraphRAG: A Step-by-Step Technical Guide

To help you integrate ArangoDB with LLMs and GraphRAG, here’s a practical guide outlining the necessary steps, along with links to relevant ArangoDB resources and documentation.

References

1. McKinsey & Company. (2024). The state of AI in early 2024: Gen AI adoption spikes and starts to generate value. Retrieved from McKinsey.
2. McKinsey & Company. (2024). Leading Off, March 2024. Retrieved from McKinsey.
3. Gartner. (2024). Artificial Intelligence requires an extended governance framework. Retrieved from Gartner.
4. Arizona State University. (2024). Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey. Retrieved from Arxiv.
5. Advanced Intelligent Computing Technology and Applications. (2024). Enhancing Retrieval-Augmented Generation Models with Knowledge Graphs: Innovative Practices Through a Dual-Pathway Approach. Retrieved from SpringerLink.
6. International Journal of Data Science and Analytics. (2024). Leveraging Knowledge Graphs for Enhanced Predictive Accuracy in AI.
7. ArangoDB. (2023). Bridging Knowledge and Language: ArangoDB Empowers Large Language Models for Real-World Applications. Retrieved from ArangoDB.
8. ArangoDB. (2023). Enterprise Knowledge Graphs with ArangoDB’s Multi-Model Approach. Retrieved from ArangoDB.
9. ArangoDB Developer Hub. (2023). Developer Resources and Community Support. Retrieved from ArangoDB.
10. ArangoDB LangChain Integration Pack. Retrieved from ArangoDB.