Build a knowledge graph from structured & unstructured data [Code Tutorial]

Looking into building knowledge graphs? Check out this code tutorial on how we built a knowledge graph of the latest 'La Liga' standings! ⚽️ 👩‍💻 Google Collab for the demo: https://colab.research.google.com/drive/1HbDPKlsz9tYfRGeWHn60vsWeGhFIsqyF?usp=sharing -- Your RAG systems may miss critical relationships between entities. Vector search retrieves documents but fails when queries require multi-hop reasoning. Production applications return incomplete answers because similarity matching cannot traverse logical connections between data points. Teams assume vector embeddings would solve retrieval problems. This approach works for semantic similarity but breaks down when applications need to understand how entities connect and interact across your data sources. Graph-native storage solves relationship queries through structural traversal. Instead of relying on embedding proximity, graph databases encode actual connections between entities, enabling accurate multi-hop reasoning.