Vector Search in Memgraph: Turn Unstructured Text into Queryable Knowledge

Join Josip Mrden, Head of Solutions at Memgraph, to explore how unstructured text can be turned into a searchable knowledge graph with vector search in Memgraph.

You'll see how to build a simple Q&A interface using embeddings and graph queries, and how to automatically generate quiz questions from stored content. This approach is especially useful for educational tools, interactive study apps, and knowledge-driven learning platforms.

The session includes a hands-on demo and a look at how graph databases and LLMs can work together for smarter information retrieval.

🔗 GitHub 👉 https://github.com/memgraph/ai-demos/...

🔗 Presentation 👉 https://memgraph.io/images/events/mem...

About Memgraph:
Memgraph offers a light and powerful graph platform comprising the Memgraph Graph Database, MAGE Library, and Memgraph Lab Visualization. Memgraph is a dynamic, lightweight graph database optimized for analyzing data, relationships, and dependencies quickly and efficiently.

It comes with a rich suite of pre-built deep path traversal algorithms and a library of traditional, dynamic, and ML algorithms tailored for advanced graph analysis, making Memgraph an excellent choice in critical decision-making scenarios such as risk assessment (fraud detection, cybersecurity threat analysis, & criminal risk assessment), 360-degree data and network exploration [Identity and Access Management (IAM), Master Data Management (MDM), & Bill of Materials (BOM)], and logistics and network optimization.

Website: https://www.memgraph.com
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