Talking with a GraphDB leveraging generative AI

Have you ever wondered if it's possible to navigate a graph database using the power of generative AI? The answer is a resounding "yes," and it's an exciting fusion of cutting-edge technologies.

?? Graph DB: The State of the Art

At the heart of this innovation lies the Graph Database (Graph DB). A graph database (GDB) is a database that employs graph structures for semantic queries. It uses nodes, edges, and properties to represent and store data. The central concept is the "graph" or "edge," which connects data items through nodes and edges, representing their relationships. These relationships enable efficient linkage and retrieval of data, and they are consistently stored in the database, making querying relationships fast. Graph databases prioritize preserving and visualizing relationships, making them ideal for handling highly interconnected data.

Using the RDF (Resource Description Framework) language, we can define a graph, indicating nodes (entities) and edges (properties) and constraints.

?? The Power of Inference

But it doesn't stop at mere representation. Enter the realm of inference. You can feed a set of information into your graph and watch it work its magic through an inference engine. What you get in return is not just information but a super set of it! It's like having a digital oracle at your fingertips.

?? Navigating the Graph

To truly explore your graph, you can use the SPARQL language. It's your map through this vast knowledge landscape. Moreover, you can visualize the RDF-defined graph with handy visual tools. For instance, GraphDB Semantic Graph, even available for free, offers a user-friendly interface to display and navigate your graph. Until here, you need to write SPARQL queries to extract useful information from your graph.

?? The Arrival of Generative AI

The real game-changer here is Generative AI. It opens the doors to navigating a knowledge graph using natural language. Imagine having a conversation with your data, asking questions, and receiving insightful responses.

Large language models (LLMs), a subset of foundations models diffused with generative AI "virus", are able to analyse complex text and to identify relevant information starting from a user request in natural language (prompt).

?? A Simple Experiment

In a simple experiment, you can include your graph's definition as RDF triples in a prompt and pose a question. For instance, you can ask, "Tell me about the historical events related to the Eiffel Tower.", passing as input the RDF triples representing your graph.The AI responds with information derived from your graph.

?? Real-World Scenarios

In more complex scenarios, include all your graph DB in a prompt will not be possible. The key is to identify a manner to indexing your graph DB data in a smarter way, using, for instance, the populars Vector DB.

A structured approach to do that is represented below:

1. Consider all your RDF definition of your graph
2. Extract RDF data and divide it into manageable chunks
3. Create an embeddings for each chunk. An embedding is a numerical representation of a text, where similar text data are positioned closest together in a n-dimensional space . These embeddings enable us to understand the textual information in a mathematical context.
4. Implement an intermediary layer between the end user and the system: it takes user requests, identifies the closest vector based on the user's input, extracts RDF data related to this vector, and then formulates prompts to ask the user questions based on the RDF triples.

?Conclusion

This simple method allows us to navigate a graph database in natural language, leveraging the capabilities of large language models (LLMs).

The fusion of Graph DBs and Generative AI is a game-changer in the realm of data exploration. It's like having a personal tour guide in a vast, interconnected data universe. The possibilities are boundless, and the journey is just beginning. ??

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