Embeddings
Vector embeddings are the foundation of the semantic data model, serving as the machine-interpretable representation of ideas and relationships. Embeddings are mathematical representations of objects as points in a multi-dimensional space. They act as the glue that connects the various semantic pieces of data in an intelligent application. The distance between vectors correlates to semantic similarity. You can use this semantic similarity score to retrieve related information that would otherwise be difficult to connect. This concept holds true regardless of the specific use case, be it RAG, recommendation systems, anomaly detection, or others.
Having an embedding model better tailored to a use case can improve accuracy and performance. Experimenting with different embedding models and fine-tuning them on domain-specific data can help identify the best fit for a particular use case, further enhancing their effectiveness.
