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Bridging Knowledge and Language: ArangoDB Empowers Large Language Models for Real-World Applications

Estimated reading time: 5 minutes

Understanding Large Language Models (LLMs) and Knowledge Graphs

Today, two very different technology concepts have become prominent in data analysis and predictive analytics: Knowledge Graphs and Large Language Models (LLMs). These domains each have their unique benefits, and influence the ways that we engage with and derive meaningful insights from constantly expanding and complex datasets.  They are like the Odd Couple – better together than on their own!

Their distinctiveness aside, let’s not forget that these approaches share a commonality—they both change our view of information analysis, interpretation, and application. Both are important to how we make sense of and harness data.

Large Language Models (LLMs): Unveiling Language Potential

Large Language Models, for example OpenAI’s ChatGPT, have become powerful language transformers. These models, with the help of advanced neural networks, possess the uncanny ability to understand, generate, and engage in contextually-aware conversations. LLMs craft coherent responses, generate insightful outputs, and perform many different text-based tasks. They excel at natural language understanding and generation capabilities; they navigate and interpret complex textual inputs almost too easily.  And, how they do this so fast is almost beyond belief.

Knowledge Graphs: Revealing Information Interconnections

On the other hand, Knowledge Graphs contain carefully structured data and are designed to capture intricate relationships among discrete and seemingly unrelated information. These graph-based structures organize data hierarchically while interconnecting data points and relationships. Knowledge Graphs are great at contextual insights, allowing users to explore and comprehend associations and dependencies among data fragments. They are very good at structured queries that show hidden yet insightful connections.

Examples of Knowledge Graphs: Powering Insights

Google has a pretty cool Knowledge graph, known for enhancing search results with contextual insights. Amazon’s Product Graph refines e-commerce recommendations through structured data, while Facebook’s Graph API improves social interactions. DBpedia extracts structured data from Wikipedia, aiding research. Google’s Knowledge Graph stands out as a leading example, revolutionizing search with semantic understanding and very complete results. These Knowledge Graphs uplevel data understanding and relevance across industries.

Synergistic Potential and Limitations

Both LLMs and Knowledge Graphs have their own strengths and limitations. LLMs are really good at capturing language nuances but frankly suck at interpreting complex, structured data. Conversely, while Knowledge Graphs master structured data organization, it’s not as easy for them to understand the nuances and idiosyncrasies of human language. This is why these two domains are “better together”.

Knowledge graph for Large Language Models
Unifying Large Language Models and Knowledge Graphs: A Roadmap; Journal of Latex Class Files, Volume 14, No. 8 August 2021

ArangoDB: Bridging the Gap Between Knowledge Graphs and LLMs

Here, ArangoDB assumes a central role; it’s the bridge that spans LLMs and Knowledge Graphs. As the most complete and scalable graph database, ArangoDB has the added advantage of having “model flexibility”.  This means it can adeptly accommodate LLM capabilities together with the structured insights of more holistic Knowledge Graphs in a very unique and special way.  Why?  ArangoDB is the only graph database available on the market that can incorporate data of various formats (Graph, Document, Full-Text Search, and Key/Value) within a unified platform and supported by a unified query language.

This flexible yet powerful integration of Knowledge Graphs + LLM addresses the limitations of each domain while harnessing their collective strengths, resulting in a comprehensive and intelligent solution. Imagine having to separately query and then integrate disparate data types from multiple database vendors before you can even pair up the data with an LLM.  The LLM is blazingly fast while you have to wait around for the Knowledge Graph to deliver on its half of the bargain!  

Real-World Use Cases for Knowledge Graphs + LLM

To really bring out the power of this marriage, let’s consider some real-world examples and use cases of how this all works in a practical sense.

  • Enhancing Healthcare Diagnosis: In the healthcare sector, think about a scenario where a medical Knowledge Graph contains patient records, research data, and treatment information. By integrating LLMs, practitioners can pose complex queries about patient symptoms. LLMs understand these queries and process them to provide contextually-aware insights, aiding in more accurate diagnoses.  Without an LLM, the usefulness of the Knowledge Graph, while powerful in its own right, cannot come close to reaching its full potential.
  • Refining E-Commerce Recommendations: E-commerce Knowledge Graphs include data about products, categories, user preferences, reviews, and much more. Adding LLMs into the mix can make customer interactions more meaningful and tailored. Analysts can present queries or preferences and the LLMs comprehend and process the inputs.  This makes possible more personalized and sensible recommendations based on the structured data contained in the Knowledge Graph.
  • Uncovering Financial Market Insights: In the financial sector, combining Knowledge Graphs with LLMs enables a better understanding of market trends. By posing questions or analyzing historical data, LLMs interpret queries and rapidly extract insights from the structured financial Knowledge Graph. This lets traders make more informed and timely decisions.  In Financial Markets, especially, acting fast can make all the difference.

Getting Started: ArangoDB & Langchain 

ArangoDB’s commitment to LLMs begins with its integration with LangChain, the de facto Python framework for building LLM applications through composability.

Our integration with LangChain provides ArangoDB users the ability to analyze data seamlessly via natural language, eliminating the need for query language design. By using LLM chat models such as OpenAI’s ChatGPT, Anthropic’s Claude, or Google’s PaLM, users can speak to their data instead of querying it.

As we commit to building the LLM ecosystem for our ArangoDB users, we invite you to take a look at our first steps with LangChain. 

Conclusion: Empowering Holistic Data Understanding

In today’s uncertain and ever-changing technology landscape – especially as it relates to data analytics – ArangoDB emerges as the natural choice in uniting the potential of LLMs and Knowledge Graphs. This integration is key in leveraging the structural depth of Knowledge Graphs alongside the linguistic power of LLMs.  As we move forward, this convergence of capabilities promises to create new opportunities to create innovative applications that address multiple use cases. Just like Arnold Schwarzenegger in the movie True Lies, who would’ve thought data could lead such a double life?

Don’t miss ArangoDB CTO Jörg Schad’s webinar Unifying Minds: Unleashing the Synergy Between LLMs and Knowledge Graphs on Tuesday, August 29th. Register HERE

As always, don’t hesitate to reach out to us at any time. 


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